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@@ -1,27 +1,32 @@
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# Commander AI Lab — Environment Variables
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# Copy this file to .env and fill in your values.
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# start-lab.bat will auto-load .env if it exists.
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# Commander AI Lab - Environment Variables
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# Copy this file to .env and fill in your API keys.
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# .env is gitignored and will NOT be committed.
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# ── API Keys ──────────────────────────────────────────────────────
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ANTHROPIC_API_KEY=your-anthropic-api-key-here
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XIMILAR_API_KEY=your-ximilar-key-here
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# Ximilar API Key (card scanner / visual AI recognition)
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# Get yours at: https://app.ximilar.com/
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XIMILAR_API_KEY=your-ximilar-api-key-here
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# ── Forge JAR Path ────────────────────────────────────────────────
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# Point this to your Forge GUI desktop fat JAR.
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FORGE_JAR_PATH=D:/ForgeCommander/Forge-Repo/forge-gui-desktop/target/forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar
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# Perplexity API Key (AI deck research & generation)
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# Get yours at: https://www.perplexity.ai/settings/api
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PPLX_API_KEY=your-perplexity-api-key-here
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# IMPORTANT: Forge MUST be launched from the forge-gui directory (not forge-gui-desktop/target).
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# The JAR looks for res/ relative to its working directory. Without this, you get
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# ExceptionInInitializerError on startup.
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FORGE_WORKING_DIR=D:/ForgeCommander/Forge-Repo/forge-gui
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# CORS allowed origins (comma-separated)
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# Default: http://localhost:5173,http://localhost:8080
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# Add your LAN IP if accessing from another machine on your network
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ALLOWED_ORIGINS=http://localhost:5173,http://localhost:8080
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# ── Deck Paths ────────────────────────────────────────────────────
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# Directory containing your precon .dck files.
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# Forge reads deck files directly from this folder.
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#
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# IMPORTANT: lab_api.py reads PRECON_DIR (not PRECON_DECKS_DIR).
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# PRECON_DECKS_DIR is kept below for backwards compat but is NOT used by the server.
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PRECON_DIR=D:\ForgeCommander\commander-ai-lab\precon-decks
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PRECON_DECKS_DIR=D:\ForgeCommander\commander-ai-lab\precon-decks
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USER_DECKS_DIR=D:\ForgeCommander\commander-ai-lab\user-decks
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FORGE_DECKS_DIR=D:\ForgeCommander\commander-ai-lab\user-decks
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# ── Dual-Model AI Brain ─────────────────────────────────────
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# Primary brain: GPT-OSS 20B (turn planner / decision maker)
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BRAIN_API_BASE=http://localhost:11434
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BRAIN_MODEL=gpt-oss:20b
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BRAIN_TEMPERATURE=0.3
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BRAIN_MAX_TOKENS=1024
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BRAIN_TIMEOUT=120.0
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# Validator brain: DeepSeek-R1-Distill-Qwen-14B (legality + explanation)
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VALIDATOR_ENABLED=true
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VALIDATOR_API_BASE=http://localhost:11434
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VALIDATOR_MODEL=deepseek-r1:14b
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VALIDATOR_TEMPERATURE=0.0
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VALIDATOR_MAX_TOKENS=2048
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VALIDATOR_TIMEOUT=120.0
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-15
@@ -32,24 +32,9 @@ embeddings/
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checkpoints/
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imported-decks/
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# User-created decks (generated at runtime, not committed)
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user-decks/*.dck
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# OS
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.DS_Store
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Thumbs.db
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# Logs
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*.log
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logs/
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# Runtime artifacts / caches
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static/
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api/
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image_cache/
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data/rag_chroma/
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.claude/
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dependency-reduced-pom.xml
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# Precon decks (generated / imported at runtime)
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precon-decks/
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@@ -1,93 +0,0 @@
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# FarmHouse changes
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This branch (`farmhouse`) is `KoalaTrapLord/commander-ai-lab` at commit `54cc5e9` plus the changes below. They were made to run the app
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**Python-only** (no Forge/Java, no PyTorch) in a Kubernetes namespace, with a local LLM behind an OpenAI-compatible gateway instead of
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Anthropic/DeepSeek, and they were each found by actually running the app, not by reading it. Written 2026-10-26.
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Every change started life as an anchored text patch (`patch_app.py` in the farmhouse-os repo, which still builds the deployed image from
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the upstream commit); here they are ordinary source edits. Lines carry a `# FARMHOUSE_...` marker where the patch left one.
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## What was wrong, and what changed
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### The simulator played nothing (the big one)
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| Problem | Change |
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|---|---|
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| Every card reached the simulator with **no type line and no rules text**. The loaders join the user's *collection* and a *card_records* table (both empty on a fresh install) and the Forge card cache that was meant to fill the gap is not installed. A land was indistinguishable from a creature: an 8-game batch cast 0 spells, dealt 0 damage and lost every game. | `services/farmhouse_sim.py` `fill_card_data` fills type, text, mana cost, cmc, P/T, keywords and colour identity from the 113k-card Scryfall bulk database. Wired into `services/forge_runner.py` (batch) and `routes/deepseek.py` (`_fetch_deck_card_data`, which the Monte Carlo and single-deck routes use). |
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| The batch loader **did not repeat cards by quantity**: a 100-card deck loaded as 66-84 cards with *one* Island. | `expand_quantities` (same module). |
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| The LLM engine asked the model **once per turn** and applied the answer as one action. | `src/commander_ai_lab/sim/farmhouse_turns.py`: up to `BRAIN_STEPS` (default 4) questions per turn (land, spell, spell, attack), re-reading the board after each; stops on attack/hold/no change. `BRAIN_STEPS=1` restores the original. |
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| A model that "thinks" spent ~46 s on a one-line move. | `deepseek_brain.py` sends `reasoning_effort` from `LLM_REASONING` (46 s -> 1.8 s per move). |
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| The engine dropdown defaulted to **Forge**, which cannot run without Java. | `ui/ai-lab.js`: only "LLM opponents" is offered. |
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After the fixes: a 40-game Monte Carlo takes 0.4 s with real numbers (10 spells and 12.7 damage per game); two LLM-piloted games took
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212 s with 49 model calls and no fallbacks.
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### Deck import
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| Problem | Change |
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|---|---|
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| **Moxfield links were accepted nowhere**, and the Deck Builder import took only pasted text. | `services/farmhouse_deck_links.py`: Moxfield (public decks, `api2.moxfield.com`) and Archidekt links in the simulator URL import and in both Deck Builder imports (a lone link in the paste box). A private deck says to use Moxfield's Export > Copy as text. |
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| The Deck Builder import looked **every card up on scryfall.com**; after a few dozen it was rate-limited (HTTP 429) and the app reported the rest as "not found" (250-card deck: 55 matched; even plain `Sol Ring` failed). | `local_card()`: names resolve from the local bulk database first; the website is only the fallback. |
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### Deck generation
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- `deck_builder/pipeline/farmhouse_fit.py`: the model's list is **filtered to the commander's colour identity** (from the bulk DB), de-duplicated,
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fitted to per-category targets and trimmed to 99 + commander. Before this the model returned off-colour cards and 359-card lists.
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- Scryfall bulk format changed (`jsonl_download_uri`, gzip JSONL; no `download_uri`).
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- Scryfall answers **HTTP 400 to the default `python-requests` User-Agent** (every lookup became "Commander not found"): a named UA is sent everywhere.
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- Scryfall 429s: requests back off on `Retry-After` and the pace is 0.25 s.
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- `LLM_REASONING` / `LLM_MAX_TOKENS` are sent by the deck builder and coach.
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### Coach / startup
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- The coach was only initialised in `main()`, not when the app starts under uvicorn; `check_connection_sync()` was called but never defined;
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the coach used the first model the gateway listed rather than the configured one. All fixed.
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- Status label says "Language model" instead of the hardcoded "Claude Opus".
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- `RAG_AUTOBUILD` (default off in our deployment) controls whether the card-search index is built at startup (113k embeddings; about an hour).
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### Decision model (Laya / Kev), wired and off
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`src/commander_ai_lab/sim/farmhouse_decide.py` asks a `/v1/systemone` server which of the nine actions to take, in one forward pass.
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`DECISION_MODE=shadow` records whether it agrees with the LLM (the game still uses the LLM); `assist` uses it when `DECISION_MIN_CONF`
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sure; `off` (default) does nothing. `scripts/farmhouse_decision_eval.py` replays recorded decisions through it.
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**Measured 2026-10-26 on 149 recorded LLM decisions: zero-shot, both Laya checkpoints agreed with the LLM less often than always
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guessing the commonest action** (English 16.1%, typed-decisions 18.6%, baseline 27.5%), at 2-4 s per answer on a 4-thread CPU. It is only
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worth using after fine-tuning on this game's decisions; the write-up is `LAYA-EVALUATION.md` in the farmhouse-os repo's commander-lab folder.
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### Laya connections (all off by default)
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Forge's concrete priority path, extra typed questions in the same call, a capture log, a fine-tuning dataset exporter, and card-role classification:
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`src/commander_ai_lab/sim/farmhouse_laya.py`, `scripts/farmhouse_laya_dataset.py`, `scripts/farmhouse_laya_roles_eval.py`, tests `tests/farmhouse/test_farmhouse_laya*.py`.
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||||
How it fits the strategic-intelligence work, what is measured and how to fine-tune: [docs/LAYA-CONNECTIONS.md](docs/LAYA-CONNECTIONS.md). Reference copies of the Laya
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image, manifests and CI are in `farmhouse/laya/`.
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### Forge, and rules-accurate simulation with phase-rs
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- **Forge is installed** in the cluster image (it was left out by mistake): `farmhouse/forge/README.md`. Batches are clamped to one game at a time (`FORGE_MAX_THREADS`).
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- **Rules-accurate simulation** through the phase-rs engine (Rust, headless): `routes/farmhouse_phase.py`, `ui/phase-sim.html` (linked from the home page) and the service in
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`farmhouse/phase/`. Four saved decks play real Commander games; the page shows wins by seat with a 95% interval and warns about cards the engine did not recognise.
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Seats can be put on a **model** (twin-turbo through Olla, or Laya): the model chooses among the engine's own legal options only, anything wrong or slow is played by phase's AI,
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and a call cap bounds the cost (`farmhouse/phase/README.md`). Assessment and plan: `docs/PHASE-AND-PLAYTESTER.md`.
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||||
|
||||
## Settings (environment variables)
|
||||
| Variable | Meaning |
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||||
|---|---|
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||||
| `LLM_URL`, `LLM_MODEL` | OpenAI-compatible endpoint (`.../v1`) and model for the coach and deck builder |
|
||||
| `LLM_REASONING` (`none`), `LLM_MAX_TOKENS`, `LLM_TIMEOUT` | keep requests lean: reasoning off, cap on answer length |
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||||
| `COACH_PROVIDER`, `DECK_GEN_PROVIDER` = `local`, `DECK_GEN_MODEL` | use the local model, not Anthropic |
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| `OLLAMA_BASE_URL`, `RAG_EMBEDDING_MODEL`, `RAG_AUTOBUILD` | card-search embeddings (one host only: vectors differ between hosts) |
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||||
| `BRAIN_API_BASE` (**no** `/v1`; the client appends it), `BRAIN_MODEL`, `BRAIN_MAX_TOKENS`, `BRAIN_STEPS` | the simulator's LLM opponents |
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| `DECISION_URL`, `DECISION_MODEL`, `DECISION_MODE` (`off`/`shadow`/`assist`), `DECISION_MIN_CONF`, `DECISION_TIMEOUT` | the decision model above |
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||||
| `ollama` Python package | required by the card-search index build (ChromaDB's Ollama embedder imports it) |
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|
||||
## Tests
|
||||
`tests/farmhouse/` (standard library only, no network): `python -I -X utf8 tests/farmhouse/test_farmhouse_deck_links.py`, `..._sim.py`, `..._fit.py`.
|
||||
Run them with `-I` and from outside the package folders; do not put `services/` on `sys.path` (`services/logging.py` shadows the standard library).
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||||
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## Things still worth doing
|
||||
- **Make the LLM-opponent turns faster** (a piloted move averages ~7.5 s: about 2.2k tokens in, ~30 out, so reading the prompt dominates).
|
||||
These are ideas, not measured; none is implemented:
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||||
- *Shared prefix:* about 1.2k tokens of every prompt (the system prompt) are identical each time; a llama.cpp server can reuse them
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if the calls share the prefix, so only the board state is read each move.
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- *Smaller prompt:* send a compact board state instead of the indented JSON the engine builds now (`json.dumps(snapshot, indent=2)`
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in `deepseek_brain.py`).
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- *Parallel games:* run several games at once, which would also fill the GPU while one game waits on a move. Caveat: for long prompts
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two requests at once gave no extra throughput on a V100 (reading the prompt is compute-bound), so measure before relying on it.
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- Opponents are the engine's heuristics; only the piloted seat uses the model.
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- Build the card-search index and measure it.
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- The upstream `LICENSE` is missing (`pyproject.toml` says MIT). Ask the author to add one before publishing a derivative widely.
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- Forge-based features (the Java simulator, training) remain unavailable by design.
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@@ -1,434 +1,381 @@
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# Commander AI Lab
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|
||||
A full-stack Magic: The Gathering Commander research platform. Run thousands of simulated games, train neural networks on the outcomes, build decks with AI that learns from simulation data, get LLM-powered coaching, and play live 4-player battles — all from a single unified system.
|
||||
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Commander AI Lab connects a rules-complete Forge engine, a fast Monte Carlo simulator, an LLM deck builder, an ML training pipeline, and multiple game clients (web, LAN, Unity) into one closed-loop platform. Simulation results feed back into deck building and AI decision-making, so the system improves the more it plays.
|
||||
|
||||
### Key Capabilities
|
||||
|
||||
- **Batch Simulation** — Run hundreds or thousands of Commander games unattended using Forge (rules-complete) or a headless Python Monte Carlo engine
|
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- **ML Training Pipeline** — Reinforcement learning (PPO) trains a neural net on game decision logs; outcome-weighted scoring nudges card weights automatically
|
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- **AI Deck Builder** — 7-step LLM pipeline (EDHrec → Scryfall → Ollama) with simulation-learned insights injected into prompts
|
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- **LLM Coach** — Streaming coaching sessions powered by Anthropic Claude or local Ollama models, enriched with RAG-retrieved card knowledge
|
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- **RAG Card Knowledge** — ChromaDB vector store over 30,000+ Scryfall cards with semantic search and auto-staleness monitoring
|
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- **LAN Battlefield** — Browser-based 4-player Commander game client with ML-powered AI opponents and precon deck support
|
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- **Unity Client** — Full Unity application with battleground scene, lobby system, WebSocket game state sync, and cross-platform builds
|
||||
- **Card Scanner** — OCR pipeline for physical card recognition via Ximilar API
|
||||
- **163 Precon Decks** — Complete preconstructed deck library in `.dck` format
|
||||
|
||||
---
|
||||
Headless batch simulation environment for 3-AI MTG Commander pods.
|
||||
Run 100+ games, analyze win rates, and stress-test your decks — all from a web UI or CLI.
|
||||
|
||||
## Architecture
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────┐
|
||||
│ Clients │
|
||||
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────────┐ │
|
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│ │ Web UI │ │ LAN Client │ │ Unity Client │ │
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||||
│ │ (React/TS) │ │ (HTML/JS) │ │ (C# / URP) │ │
|
||||
│ │ /ui │ │ /lan │ │ /unity-client │ │
|
||||
│ └──────┬───────┘ └──────┬───────┘ └──────┬───────────────────────┘ │
|
||||
│ └────────────────┼────────────────┘ │
|
||||
│ │ HTTP/REST + WebSocket │
|
||||
│ ┌───────────────────────┴─────────────────────────────────────────┐│
|
||||
│ │ FastAPI Backend (lab_api.py — port 8080) ││
|
||||
│ │ ││
|
||||
│ │ /api/lab/* Batch simulation (Forge + Monte Carlo) ││
|
||||
│ │ /api/deck-builder/* 7-step LLM deck construction ││
|
||||
│ │ /api/coach/* Streaming LLM coaching sessions ││
|
||||
│ │ /api/ml/* Neural net training + policy server ││
|
||||
│ │ /api/rag/* ChromaDB vector search ││
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||||
│ │ /api/game/* Live game sessions (Forge IPC) ││
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||||
│ │ /api/policy/* ML policy decisions for AI players ││
|
||||
│ │ /ws/game/{id} WebSocket game state streaming ││
|
||||
│ └───────┬──────────────┬──────────────┬──────────────┬────────────┘│
|
||||
│ │ │ │ │ │
|
||||
│ ┌──────┴──────┐ ┌────┴─────┐ ┌──────┴──────┐ ┌────┴──────┐ │
|
||||
│ │ Forge │ │ Monte │ │ Ollama │ │ ChromaDB │ │
|
||||
│ │ Engine │ │ Carlo │ │ (local LLM) │ │ (vector │ │
|
||||
│ │ (Java/JVM) │ │ Engine │ │ qwen2.5:7b │ │ store) │ │
|
||||
│ └─────────────┘ │ (Python) │ └─────────────┘ └───────────┘ │
|
||||
│ └──────────┘ │
|
||||
│ ┌──────────────────────────────────────────────────────────────────┐│
|
||||
│ │ ML Pipeline ││
|
||||
│ │ decisions_*.jsonl → update_weights.py → learned_weights.json ││
|
||||
│ │ → /api/ml/train → neural net checkpoints ││
|
||||
│ └──────────────────────────────────────────────────────────────────┘│
|
||||
└─────────────────────────────────────────────────────────────────────┘
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ Web Browser (ai-lab.js + ai-lab.css) │
|
||||
│ Select decks → Start batch → Poll progress → View stats │
|
||||
└─────────────────────┬───────────────────────────────────────┘
|
||||
│ HTTP (REST)
|
||||
┌─────────────────────┴───────────────────────────────────────┐
|
||||
│ Python FastAPI Server (lab_api.py, port 8080) │
|
||||
│ /api/lab/start — spawn Java batch runner subprocess │
|
||||
│ /api/lab/status — poll progress (parses stdout) │
|
||||
│ /api/lab/result — return completed JSON results │
|
||||
│ /api/lab/decks — list decks from Forge user data │
|
||||
│ /api/lab/history— browse past batch runs │
|
||||
│ /api/lab/log — stream live console output │
|
||||
└─────────────────────┬───────────────────────────────────────┘
|
||||
│ subprocess (java -jar)
|
||||
┌─────────────────────┴───────────────────────────────────────┐
|
||||
│ Java CLI (LabCli.java + BatchRunner + MultiThreadRunner) │
|
||||
│ Parses Forge sim output → GameResult objects → JSON │
|
||||
└─────────────────────┬───────────────────────────────────────┘
|
||||
│ subprocess per game
|
||||
┌─────────────────────┴───────────────────────────────────────┐
|
||||
│ Card Forge Engine (forge-gui-desktop sim mode) │
|
||||
│ java -jar forge.jar sim -d "deck1" "deck2" "deck3" │
|
||||
│ -f commander -n 1 -q │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Features
|
||||
|
||||
### Simulation Engines
|
||||
|
||||
| Engine | Description | Speed |
|
||||
|---|---|---|
|
||||
| **Forge** | Rules-complete Java engine — handles the stack, instants, triggered abilities, and all Commander rules. Each game spawns a JVM subprocess. | ~0.04 sims/sec (single-threaded) |
|
||||
| **Monte Carlo** | Headless Python engine — models combat keywords (flying, trample, deathtouch, lifelink, menace, reach, haste), summoning sickness, tapped state, commander zone, multiplayer targeting, and board wipes. No JVM required. | Thousands of games in minutes |
|
||||
|
||||
Both engines feed structured results into the same ML pipeline.
|
||||
|
||||
### Deck Builder
|
||||
|
||||
The `POST /api/deck-builder/build` endpoint runs a 7-step pipeline:
|
||||
|
||||
1. Resolve commander via Scryfall
|
||||
2. Fetch EDHrec recommendations
|
||||
3. Fetch Scryfall candidates by category (ramp, removal, draw, lands)
|
||||
4. Ollama suggests synergy/protection/wincon cards — **simulation insights injected here**
|
||||
5. Filter by color identity, collection, and ban list
|
||||
6. Ollama enforces ratios (trim/expand to 99) — **simulation insights injected here**
|
||||
7. Assemble final deck with smart substitution (guarantees 99-card output)
|
||||
|
||||
### AI Coach
|
||||
|
||||
LLM-powered coaching with two modes:
|
||||
|
||||
- **Full Session** — Deep analysis including removal coverage, ramp quality, draw engine profiling, win conditions, anti-synergy detection, pod meta evaluation, and tempo assessment
|
||||
- **Quick Digest** — Fast ~5-second summary for rapid feedback
|
||||
|
||||
Coaching prompts are enriched with semantically relevant cards from the ChromaDB RAG store (15 cards for full sessions, 8 for quick digests). Supports both Anthropic Claude and local Ollama models with streaming responses.
|
||||
|
||||
### RAG Card Knowledge
|
||||
|
||||
- **SQLite FTS5** bulk card database from Scryfall data
|
||||
- **ChromaDB** vector index with natural-language card embeddings
|
||||
- Automatic staleness monitoring (checks every 6 hours, auto-rebuilds if >14 days old)
|
||||
- Graceful fallback — RAG failures never block coaching or deck building
|
||||
|
||||
### ML Training Pipeline
|
||||
|
||||
```
|
||||
Overnight sims → decisions_*.jsonl
|
||||
↓
|
||||
update_weights.py → learned_weights.json (feeds deck builder)
|
||||
↓
|
||||
/api/ml/train → neural net training on decision dataset
|
||||
↓
|
||||
/api/policy/decide → ML policy serves AI decisions in live games
|
||||
```
|
||||
|
||||
- **Outcome-weighted RL** nudges card scoring weights from game results
|
||||
- **Neural net** trains on the full decision log dataset
|
||||
- **Distillation loop** — self-play → collect data → retrain → repeat
|
||||
- **ELO tracking** and model tournaments for comparing AI versions
|
||||
- Weights clamped to `[-20.0, 20.0]`, stored in `learned_weights.json`
|
||||
|
||||
### Win Condition Tracking
|
||||
|
||||
Games are classified by win type — not just "Combat" vs "Other":
|
||||
|
||||
- **Combat** — standard creature damage wins
|
||||
- **Combo** — Approach of the Second Sun, Felidar Sovereign, Happily Ever After, and 30+ known alternate-win cards
|
||||
- **Drain** — life drain effects
|
||||
- **Mill** — library depletion
|
||||
- **Poison** — infect/poison counter wins
|
||||
|
||||
Draw games (timeout) are analyzed separately with per-player life-at-timeout snapshots and likely cause identification.
|
||||
|
||||
### LAN Battlefield Client
|
||||
|
||||
Browser-based 4-player Commander game at `/lan`:
|
||||
|
||||
- **AI Bridge** — REST + WebSocket connectivity with ML policy decisions as Priority 1 (falls back to local heuristic AI)
|
||||
- **Deck System** — 3-tab selector: Precons (163 decks, grouped by set), EDHREC search, and custom deck import
|
||||
- **Player Panels** — Life counters, commander damage bars, poison/energy/experience/rad counters, emblem management
|
||||
- **Backend Detection** — Auto-detects the Commander AI Lab backend; works offline with local precon data
|
||||
|
||||
### Unity Client
|
||||
|
||||
Full Unity application (URP, .NET Standard 2.1) with:
|
||||
|
||||
- **Battleground Scene** — 4-seat layout with rotated perspectives, life/poison/commander damage widgets, phase tracker, turn order bar
|
||||
- **Card System** — Hand, battlefield, graveyard, exile zones with card art, tapped rotation, face-down state, summoning sickness indicators
|
||||
- **Stack & Mana** — LIFO stack display and 6-pip mana pool
|
||||
- **Lobby System** — Configure 4 seats (human/AI), POST to backend, WebSocket game state sync
|
||||
- **Elimination** — Grayscale shader overlay on eliminated players
|
||||
- **Token System** — Create, tap, add counters, delete tokens
|
||||
- **Cross-Platform** — Windows, WebGL, Android, iOS build targets
|
||||
- **Scenes** — MainMenu, Collection, DeckBuilder, DeckGenerator, Simulator, Coach, Scanner, Training, Precon, Battleground
|
||||
|
||||
### Card Scanner
|
||||
|
||||
OCR pipeline for physical Magic cards using the Ximilar API. Scan cards from photos and match them to the Scryfall database.
|
||||
|
||||
---
|
||||
|
||||
## Quick Start
|
||||
## Quick Start (Windows)
|
||||
|
||||
### Prerequisites
|
||||
- **Java 17+** (`java -version`)
|
||||
- **Python 3.10+** (`python --version`)
|
||||
- **Maven 3.8+** (`mvn --version`)
|
||||
- **Forge repo** cloned and built at `D:\ForgeCommander\forge-repo`
|
||||
|
||||
| Tool | Version |
|
||||
|---|---|
|
||||
| Python | 3.10+ |
|
||||
| Java | 17+ |
|
||||
| Maven | 3.8+ |
|
||||
| Node.js | 18+ (for LAN client scripts) |
|
||||
| Ollama | Latest (for local LLM inference) |
|
||||
| [Forge](https://github.com/KoalaTrapLord/Forge-Repo) | Built at `D:\ForgeCommander\Forge-Repo` |
|
||||
|
||||
### Setup (Windows)
|
||||
|
||||
### 1. Build Forge (one-time)
|
||||
```powershell
|
||||
# 1. Build Forge (one-time)
|
||||
cd D:\ForgeCommander\Forge-Repo
|
||||
cd D:\ForgeCommander\forge-repo
|
||||
mvn package -pl forge-gui-desktop "-DskipTests" "-Drevision=2.0.12-SNAPSHOT" -am
|
||||
```
|
||||
|
||||
# 2. Build Commander AI Lab
|
||||
### 2. Build Commander AI Lab
|
||||
```powershell
|
||||
cd D:\ForgeCommander\commander-ai-lab
|
||||
mvn package "-DskipTests"
|
||||
```
|
||||
This produces `target/commander-ai-lab-1.0.0-SNAPSHOT.jar` (shaded fat JAR).
|
||||
|
||||
# 3. Install Python dependencies
|
||||
pip install -e ".[rag,dev]"
|
||||
### 3. Install Python dependencies
|
||||
```powershell
|
||||
pip install fastapi uvicorn
|
||||
```
|
||||
|
||||
# 4. Pull an Ollama model for the deck builder
|
||||
ollama pull qwen2.5:7b
|
||||
|
||||
# 5. Launch
|
||||
### 4. Launch the Web UI
|
||||
**Option A — One-click launcher:**
|
||||
```powershell
|
||||
.\start-lab.bat
|
||||
# → Web UI: http://localhost:8080
|
||||
# → LAN Client: http://localhost:8080/lan/
|
||||
```
|
||||
Edit the paths at the top of `start-lab.bat` if needed.
|
||||
|
||||
Or use the automated installer:
|
||||
|
||||
**Option B — Manual launch:**
|
||||
```powershell
|
||||
.\install-commander-ai-lab.ps1
|
||||
python lab_api.py `
|
||||
--forge-jar "D:\ForgeCommander\forge-repo\forge-gui-desktop\target\forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar" `
|
||||
--forge-dir "D:\ForgeCommander\forge-repo\forge-gui" `
|
||||
--port 8080
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Overnight AFK Runner
|
||||
|
||||
Run batch simulations unattended, then automatically update card weights and train the neural net:
|
||||
Open `http://localhost:8080` — select 3 decks, set game count, click **Run AI Sims**.
|
||||
|
||||
### 5. CLI-only usage (no web server)
|
||||
```powershell
|
||||
python overnight-run.py # default: 11.5h DeepSeek sims
|
||||
python overnight-run.py --batches 20 --games 50 # fixed batch count
|
||||
python overnight-run.py --no-update-weights # skip weight update
|
||||
python overnight-run.py --no-train # skip neural net training
|
||||
cd D:\ForgeCommander\forge-repo\forge-gui
|
||||
|
||||
java -jar "D:\ForgeCommander\commander-ai-lab\target\commander-ai-lab-1.0.0-SNAPSHOT.jar" `
|
||||
--forge-jar "..\forge-gui-desktop\target\forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar" `
|
||||
--forge-dir "." `
|
||||
--deck1 "Edgar Markov" `
|
||||
--deck2 "Grimgrin" `
|
||||
--deck3 "Xyris" `
|
||||
--games 100 `
|
||||
--threads 4 `
|
||||
--output results/batch-100.json
|
||||
```
|
||||
|
||||
**What happens after sims complete:**
|
||||
1. `update_weights.py` — nudges `learned_weights.json` from decision logs (feeds deck builder)
|
||||
2. `/api/ml/train` — trains the neural net on the full decision dataset
|
||||
## Scaling Tests
|
||||
|
||||
---
|
||||
|
||||
## Simulation → Deck Builder Feedback Loop
|
||||
|
||||
```
|
||||
Overnight sims → decisions_*.jsonl
|
||||
↓
|
||||
update_weights.py → learned_weights.json
|
||||
↓
|
||||
sim_insights.py → natural-language insight block
|
||||
↓
|
||||
Ollama prompts (suggest_cards + enforce_deck_ratios)
|
||||
↓
|
||||
Better card recommendations
|
||||
### Single-threaded baseline (10 games)
|
||||
```powershell
|
||||
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar `
|
||||
--forge-jar "..." --forge-dir "..." `
|
||||
--deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" `
|
||||
--games 10 --threads 1 --output results/baseline-10.json
|
||||
```
|
||||
|
||||
When active, `BuildResult.sources_consulted` includes `"sim_weights"`. The loop is a no-op until real sim data exists.
|
||||
### Multi-threaded (10 games, 2 threads)
|
||||
```powershell
|
||||
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar `
|
||||
--forge-jar "..." --forge-dir "..." `
|
||||
--deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" `
|
||||
--games 10 --threads 2 --output results/mt-10-t2.json
|
||||
```
|
||||
|
||||
---
|
||||
### Scale to 100 games (4 threads)
|
||||
```powershell
|
||||
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar `
|
||||
--forge-jar "..." --forge-dir "..." `
|
||||
--deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" `
|
||||
--games 100 --threads 4 --output results/scale-100-t4.json
|
||||
```
|
||||
|
||||
### Maximum throughput test (100 games, 8 threads)
|
||||
```powershell
|
||||
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar `
|
||||
--forge-jar "..." --forge-dir "..." `
|
||||
--deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" `
|
||||
--games 100 --threads 8 --seed 42 --output results/scale-100-t8-seed42.json
|
||||
```
|
||||
|
||||
### Performance comparison script
|
||||
Run different thread counts and compare sims/sec:
|
||||
```powershell
|
||||
# Baseline: 1 thread
|
||||
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 1 --seed 42 --output results/perf-t1.json
|
||||
|
||||
# 2 threads
|
||||
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 2 --seed 42 --output results/perf-t2.json
|
||||
|
||||
# 4 threads
|
||||
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 4 --seed 42 --output results/perf-t4.json
|
||||
|
||||
# 8 threads
|
||||
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 8 --seed 42 --output results/perf-t8.json
|
||||
```
|
||||
|
||||
Then compare the `simsPerSecond` values in each output JSON to see scaling behavior.
|
||||
|
||||
## API Reference
|
||||
|
||||
### Simulation
|
||||
|
||||
| Endpoint | Method | Description |
|
||||
|---|---|---|
|
||||
| `/api/lab/decks` | GET | List available Commander decks |
|
||||
| `/api/lab/start` | POST | Start Forge batch (returns batchId) |
|
||||
| `/api/lab/start-deepseek` | POST | Start Python/DeepSeek batch |
|
||||
| `/api/lab/status?batchId=X` | GET | Poll batch progress |
|
||||
| `/api/lab/result?batchId=X` | GET | Get completed batch JSON |
|
||||
| `/api/lab/history` | GET | List past batch runs |
|
||||
| `/api/lab/precons/deck` | GET | Fetch precon `.dck` file content |
|
||||
| `/api/lab/start` | POST | Start a batch run (returns batchId) |
|
||||
| `/api/lab/status?batchId=X` | GET | Poll progress for a running batch |
|
||||
| `/api/lab/result?batchId=X` | GET | Get completed batch result JSON |
|
||||
| `/api/lab/log?batchId=X` | GET | Get live log lines from running batch |
|
||||
| `/api/lab/history` | GET | List past batch result files |
|
||||
|
||||
### Deck Builder & Coach
|
||||
### POST /api/lab/start
|
||||
```json
|
||||
{
|
||||
"decks": ["Edgar Markov", "Grimgrin", "Xyris"],
|
||||
"numGames": 100,
|
||||
"threads": 4,
|
||||
"seed": null,
|
||||
"clock": 120
|
||||
}
|
||||
```
|
||||
|
||||
| Endpoint | Method | Description |
|
||||
|---|---|---|
|
||||
| `/api/deck-builder/build` | POST | Build a 99-card Commander deck |
|
||||
| `/api/coach/chat` | POST | Full coaching session (streaming) |
|
||||
| `/api/coach/quick-digest` | POST | Fast ~5s coaching summary |
|
||||
### GET /api/lab/status response
|
||||
```json
|
||||
{
|
||||
"batchId": "abc123",
|
||||
"running": true,
|
||||
"completed": 42,
|
||||
"total": 100,
|
||||
"threads": 4,
|
||||
"elapsedMs": 156000,
|
||||
"error": null
|
||||
}
|
||||
```
|
||||
|
||||
### ML & Policy
|
||||
## Python Simulator (Monte Carlo Engine)
|
||||
|
||||
| Endpoint | Method | Description |
|
||||
|---|---|---|
|
||||
| `/api/ml/train` | POST | Start neural net training |
|
||||
| `/api/ml/train/status` | GET | Poll training progress |
|
||||
| `/api/policy/decide` | POST | Get ML policy decision for game state |
|
||||
A pure-Python deck simulator for rapid Monte Carlo analysis — no Forge JVM required.
|
||||
Ported from the [mtg-commander-lan](https://github.com/) Node.js simulator.
|
||||
|
||||
### RAG
|
||||
### Location
|
||||
|
||||
| Endpoint | Method | Description |
|
||||
|---|---|---|
|
||||
| `/api/rag/build` | POST | Build/rebuild ChromaDB index |
|
||||
| `/api/rag/search` | POST | Semantic card search |
|
||||
| `/api/rag/status` | GET | Index health and staleness |
|
||||
```
|
||||
src/commander_ai_lab/
|
||||
├── __init__.py
|
||||
├── sim/ # Core simulation engine
|
||||
│ ├── __init__.py # Public exports
|
||||
│ ├── models.py # Card, Player, SimState, GameResult, PlayerStats
|
||||
│ ├── engine.py # GameEngine — turn loop, combat (flying/trample/deathtouch/lifelink/menace/reach)
|
||||
│ └── rules.py # AI_DEFAULT_WEIGHTS, enrich_card(), score_card(), parse_decklist()
|
||||
└── lab/ # High-level experiment helpers
|
||||
├── __init__.py
|
||||
└── experiments.py # build_deck(), run_single_game(), run_simulation(), _cli_main()
|
||||
```
|
||||
|
||||
### Live Game
|
||||
|
||||
| Endpoint | Method | Description |
|
||||
|---|---|---|
|
||||
| `/api/game/start` | POST | Create a live game session |
|
||||
| `/api/game/action` | POST | Submit a player action |
|
||||
| `/api/game/{id}/actions` | GET | Poll Forge IPC actions |
|
||||
| `/ws/game/{game_id}` | WS | Real-time game state streaming |
|
||||
|
||||
---
|
||||
|
||||
## ML Scripts
|
||||
### Install as editable package
|
||||
|
||||
```powershell
|
||||
# Update card weights from decision logs
|
||||
python -m ml.scripts.update_weights --min-games 10 --lr 0.05 --dry-run
|
||||
|
||||
# Reset weights to defaults
|
||||
python -m ml.scripts.update_weights --reset
|
||||
|
||||
# Generate synthetic training data
|
||||
python ml/scripts/generate_synthetic_data.py
|
||||
cd D:\ForgeCommander\commander-ai-lab
|
||||
pip install -e .
|
||||
```
|
||||
|
||||
---
|
||||
This installs `commander_ai_lab` as an importable package and registers the `commander-sim` CLI command.
|
||||
|
||||
## Python Simulator API
|
||||
### Quick smoke test
|
||||
|
||||
```powershell
|
||||
python -m commander_ai_lab.lab.experiments
|
||||
```
|
||||
|
||||
Expected output — a 10-game Monte Carlo run of a sample "Korvold" deck:
|
||||
|
||||
```
|
||||
══════════════════════════════════════════
|
||||
Commander AI Lab — Experiment Runner
|
||||
══════════════════════════════════════════
|
||||
|
||||
--- Demo: Korvold Aristocrats (10 games) ---
|
||||
|
||||
Win rate: XX.X%
|
||||
Avg turns: XX.X
|
||||
Avg damage dealt: XX.X
|
||||
Avg spells cast: XX.X
|
||||
Avg creatures: XX.X
|
||||
Elapsed: 0.02s
|
||||
|
||||
Done.
|
||||
```
|
||||
|
||||
### Python API
|
||||
|
||||
```python
|
||||
from commander_ai_lab.sim.models import Card
|
||||
from commander_ai_lab.sim.engine import GameEngine
|
||||
from commander_ai_lab.lab.experiments import build_deck, run_simulation
|
||||
|
||||
deck = build_deck(["Sol Ring", "Cultivate", "Lightning Bolt"] + ["Forest"] * 36)
|
||||
# Build a deck from card names
|
||||
deck = build_deck(["Sol Ring", "Cultivate", "Lightning Bolt", "Forest"] + ["Forest"] * 36)
|
||||
|
||||
# Monte Carlo simulation (100 games)
|
||||
stats = run_simulation(deck, num_games=100, name_a="My Deck")
|
||||
print(f"Win rate: {stats['win_rate']:.1%}, Avg turns: {stats['avg_turns']:.1f}")
|
||||
print(f"Win rate: {stats['win_rate']:.1%}")
|
||||
print(f"Avg turns: {stats['avg_turns']:.1f}")
|
||||
```
|
||||
|
||||
---
|
||||
Key functions:
|
||||
|
||||
| Function | Description |
|
||||
|---|---|
|
||||
| `build_deck(card_names)` | Create enriched `Card` list from names |
|
||||
| `build_deck_from_text(decklist)` | Parse `"1 Sol Ring\n37 Forest"` format |
|
||||
| `run_single_game(deck_a, deck_b)` | One game, returns winner/turns/stats |
|
||||
| `run_simulation(deck_a, deck_b, n)` | Monte Carlo over `n` games, aggregated stats |
|
||||
|
||||
### CLI entry point
|
||||
|
||||
After `pip install -e .`:
|
||||
|
||||
```powershell
|
||||
commander-sim
|
||||
```
|
||||
|
||||
Runs the same demo as `python -m commander_ai_lab.lab.experiments`.
|
||||
|
||||
### How the engine works
|
||||
|
||||
1. **Deck enrichment** — `enrich_card()` infers type, CMC, keywords, and power/toughness from card name heuristics when Scryfall data is unavailable.
|
||||
2. **AI scoring** — `score_card()` uses weighted categories (removal, ramp, draw, aggro, etc.) to pick the best play each turn.
|
||||
3. **Game loop** — Each turn: untap → draw → play lands → cast spells (AI scored) → combat (attack/block decisions) → end step. Full keyword support for flying, trample, deathtouch, lifelink, menace, and reach.
|
||||
4. **Monte Carlo** — `run_simulation()` runs N independent games and aggregates win rate, avg turns, damage dealt/received, spells cast, creatures played, removal used, ramp played, cards drawn, and max board size.
|
||||
|
||||
## Project Structure
|
||||
|
||||
```
|
||||
commander-ai-lab/
|
||||
├── lab_api.py # FastAPI server entry point
|
||||
├── overnight-run.py # AFK batch runner
|
||||
├── lab_api.py # Python FastAPI server (web UI backend)
|
||||
├── start-lab.bat # Windows one-click launcher
|
||||
├── install-commander-ai-lab.ps1 # Automated setup script
|
||||
├── pom.xml # Maven build (Java/Forge components)
|
||||
├── pyproject.toml # Python package config
|
||||
│
|
||||
├── routes/ # FastAPI route modules
|
||||
│ ├── lab.py # Batch simulation
|
||||
│ ├── deckbuilder.py # Deck builder pipeline
|
||||
│ ├── coach.py # LLM coaching
|
||||
│ ├── ml.py # ML training
|
||||
│ ├── rag.py # ChromaDB vector search
|
||||
│ ├── game.py # Live game sessions
|
||||
│ ├── policy.py # ML policy server
|
||||
│ ├── precon.py # Precon deck serving
|
||||
│ ├── scanner.py # Card OCR
|
||||
│ └── ws_game.py # WebSocket game state
|
||||
│
|
||||
├── services/ # Core service layer
|
||||
│ ├── forge_runner.py # Forge subprocess management
|
||||
│ ├── draw_game_analyzer.py # Draw/timeout analysis
|
||||
│ ├── rag_store.py # ChromaDB vector store
|
||||
│ └── card_text.py # Shared card text builder
|
||||
│
|
||||
├── src/commander_ai_lab/
|
||||
│ ├── sim/ # Monte Carlo engine
|
||||
│ │ ├── engine.py # Game loop
|
||||
│ │ ├── models.py # GameResult, Card, Player
|
||||
│ │ ├── rules.py # Combat, keywords, commander rules
|
||||
│ │ ├── win_condition_parser.py # Alternate win detection
|
||||
│ │ └── learned_weights.json # ML-learned card weights
|
||||
│ ├── deck_builder/ # 7-step LLM pipeline
|
||||
│ │ ├── api/ # Scryfall, EDHrec, Ollama, sim_insights
|
||||
│ │ ├── core/ # Models, rules engine, assembler
|
||||
│ │ └── pipeline/ # build_deck.py orchestrator
|
||||
│ └── coach/ # LLM coaching
|
||||
│ ├── coach_service.py # Session + quick digest
|
||||
│ ├── prompt_template.py # System/user prompts
|
||||
│ └── embeddings.py # Card embedding utilities
|
||||
│
|
||||
├── ml/ # ML training pipeline
|
||||
│ ├── scripts/ # update_weights.py, ml_cli.py
|
||||
│ ├── training/ # Neural net trainer
|
||||
│ ├── encoder/ # Feature encoding
|
||||
│ └── models/ # Saved checkpoints
|
||||
│
|
||||
├── lan-client/ # Browser-based LAN battlefield
|
||||
│ ├── index.html # Entry point
|
||||
│ ├── engine.js # Game engine + ML AI turns
|
||||
│ ├── ai-bridge.js # Backend connector + policy bridge
|
||||
│ ├── player-panels.js # Enhanced player panels
|
||||
│ ├── ui.js # 3-tab deck selector + setup
|
||||
│ └── precons.js # 163 precon deck data
|
||||
│
|
||||
├── unity-client/ # Unity game client
|
||||
│ └── Assets/
|
||||
│ ├── Scenes/ # 11 scenes (MainMenu → Battleground)
|
||||
│ ├── Scripts/ # C# controllers, models, services
|
||||
│ ├── Prefabs/ # Card, deck slot, chat bubble
|
||||
│ └── Materials/ # URP materials + grayscale shader
|
||||
│
|
||||
├── scanner/ # Card OCR pipeline (Ximilar)
|
||||
├── precon-decks/ # 163 preconstructed decks (.dck)
|
||||
├── sample-decks/ # Sample deck files
|
||||
├── data/ # Runtime data (ChromaDB, bulk DB)
|
||||
├── logs/ # Simulation + decision logs
|
||||
├── docs/ # Architecture docs + schemas
|
||||
├── tests/ # Test suite
|
||||
├── scripts/ # Utility scripts
|
||||
├── .github/workflows/ # CI + Unity build automation
|
||||
└── src/main/java/ # Java/Forge batch runner
|
||||
└── commanderailab/
|
||||
├── pom.xml # Maven build (Java 17, shaded JAR)
|
||||
├── pyproject.toml # Python package config (src/ layout)
|
||||
├── ui/
|
||||
│ ├── index.html # Web UI entry point
|
||||
│ ├── ai-lab.js # UI controller (deck selection, progress, results)
|
||||
│ ├── ai-lab.css # Styles (dark theme, responsive)
|
||||
│ ├── collection.html # Card collection manager
|
||||
│ ├── collection.js # Collection controller
|
||||
│ ├── collection.css # Collection styles
|
||||
│ ├── deckbuilder.html # Deck builder + EDH advisor
|
||||
│ ├── deckbuilder.js # Deck builder controller
|
||||
│ └── deckbuilder.css # Deck builder styles
|
||||
├── src/commander_ai_lab/ # Python simulator package
|
||||
│ ├── __init__.py
|
||||
│ ├── sim/ # Core engine (models, engine, rules)
|
||||
│ └── lab/ # Experiment helpers
|
||||
├── src/main/java/commanderailab/ # Java batch runner (Forge integration)
|
||||
│ ├── cli/LabCli.java # CLI entry point (picocli)
|
||||
│ ├── batch/
|
||||
│ │ ├── BatchRunner.java # Single-thread runner (parses Forge output)
|
||||
│ │ └── MultiThreadBatchRunner.java # Multi-thread runner (ExecutorService)
|
||||
│ ├── bridge/WebApiBridge.java # Java-side web API bridge
|
||||
│ ├── schema/
|
||||
│ │ ├── BatchResult.java # Data model POJOs
|
||||
│ │ └── JsonExporter.java # JSON serialization + validation
|
||||
│ ├── stats/StatsAggregator.java # Win rates, turn counts, breakdowns
|
||||
│ └── ai/
|
||||
│ ├── AiPolicy.java # Policy interface
|
||||
│ └── ForgeBuiltinPolicy.java # v1 Forge AI wrapper
|
||||
├── scanner/ # Card scanner (OCR pipeline)
|
||||
│ ├── __init__.py
|
||||
│ ├── ocr.py # Tesseract OCR wrapper
|
||||
│ ├── pipeline.py # Image preprocessing pipeline
|
||||
│ └── preprocess.py # Image preprocessing helpers
|
||||
├── docs/
|
||||
│ ├── PROJECT_VISION.md
|
||||
│ ├── batch-result-schema.json
|
||||
│ └── game-log-schema.json
|
||||
├── sample-decks/ # 3 sample .dck files
|
||||
├── precon-decks/ # Preconstructed decks + index
|
||||
├── results/ # Batch output JSON files (gitignored)
|
||||
└── src/test/java/ # Unit tests
|
||||
```
|
||||
|
||||
---
|
||||
## Output Format
|
||||
|
||||
## Tech Stack
|
||||
Each batch run produces JSON conforming to `docs/batch-result-schema.json`:
|
||||
```json
|
||||
{
|
||||
"metadata": {
|
||||
"schemaVersion": "1.0.0",
|
||||
"batchId": "uuid",
|
||||
"timestamp": "2026-03-06T...",
|
||||
"totalGames": 100,
|
||||
"completedGames": 100,
|
||||
"format": "commander",
|
||||
"podSize": 3,
|
||||
"engineVersion": "forge-2.0.12-SNAPSHOT",
|
||||
"masterSeed": 42,
|
||||
"threads": 4,
|
||||
"elapsedMs": 260000
|
||||
},
|
||||
"decks": [...],
|
||||
"games": [...],
|
||||
"summary": {
|
||||
"perDeck": [
|
||||
{ "deckName": "Edgar Markov", "winRate": 0.60, "wins": 60, ... }
|
||||
],
|
||||
"avgGameTurns": 12.3,
|
||||
"simsPerSecond": 0.38
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
| Layer | Technology |
|
||||
|---|---|
|
||||
| Backend | Python 3.10+, FastAPI, Uvicorn |
|
||||
| Simulation (rules-complete) | Java 17, Maven, Forge |
|
||||
| Simulation (fast) | Python Monte Carlo engine |
|
||||
| LLM Inference | Ollama (qwen2.5:7b), Anthropic Claude |
|
||||
| Vector Store | ChromaDB with all-MiniLM-L6-v2 embeddings |
|
||||
| Card Database | SQLite FTS5 (Scryfall bulk data) |
|
||||
| ML Training | PyTorch, PPO reinforcement learning |
|
||||
| Web Client | HTML/CSS/JS |
|
||||
| Unity Client | Unity 2022 LTS, URP, C# |
|
||||
| CI/CD | GitHub Actions |
|
||||
## Frontend Development
|
||||
|
||||
---
|
||||
The UI is a React + TypeScript SPA in the `frontend/` directory.
|
||||
|
||||
### Dev server (with hot reload)
|
||||
```bash
|
||||
cd frontend
|
||||
npm install
|
||||
npm run dev
|
||||
```
|
||||
Vite proxies `/api` and `/ws` to `localhost:8000`, so start the FastAPI backend first.
|
||||
|
||||
### Production build
|
||||
```bash
|
||||
cd frontend
|
||||
npm run build
|
||||
```
|
||||
FastAPI serves the built assets from `frontend/dist/`.
|
||||
|
||||
## Known Limitations
|
||||
|
||||
- **Monte Carlo engine** — no stack, no instants, no continuous type-changing effects (animated lands don't attack)
|
||||
- **Forge engine** — each game spawns a new JVM (~2–3s overhead); ~0.04 sims/sec single-threaded
|
||||
- **Forge** — winner life total not captured in quiet mode (only reports losers)
|
||||
- **Deck builder** — Ollama suggestions depend on the local model quality; `qwen2.5:7b` works well
|
||||
- **LAN client** — EDHREC commander search is a stub (backend-powered, not yet fully wired)
|
||||
|
||||
---
|
||||
|
||||
## Contributing
|
||||
|
||||
Commander AI Lab is an active research project. Check the [open issues](https://github.com/KoalaTrapLord/commander-ai-lab/issues) for areas where contributions are welcome.
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
See repository for license details.
|
||||
- Winner's life total not captured (Forge quiet mode only reports losers)
|
||||
- Win condition classification is heuristic-based on Forge loss reason strings
|
||||
- Each game spawns a new JVM process (~2-3s overhead per game)
|
||||
- Max throughput ~0.04 sims/sec single-threaded (Forge sim is CPU-heavy)
|
||||
- Multithreading spawns parallel subprocesses — throughput scales linearly with threads up to CPU core count
|
||||
@@ -1,257 +0,0 @@
|
||||
<#
|
||||
.SYNOPSIS
|
||||
Smoke-tests the Commander AI Lab policy server from PowerShell.
|
||||
Checks: server reachable -> /api/policy/health -> /api/policy/decide.
|
||||
Prints a plain-English pass/fail for every step.
|
||||
|
||||
.USAGE
|
||||
# From repo root (server already running in another window):
|
||||
.\Test-PolicyServer.ps1
|
||||
|
||||
# Custom host/port:
|
||||
.\Test-PolicyServer.ps1 -BaseUrl http://localhost:9000
|
||||
|
||||
# Pretty-print the full decide response:
|
||||
.\Test-PolicyServer.ps1 -Verbose
|
||||
#>
|
||||
param(
|
||||
[string]$BaseUrl = "http://localhost:8080",
|
||||
[switch]$Verbose
|
||||
)
|
||||
|
||||
$ErrorActionPreference = "Stop"
|
||||
|
||||
# ---- helpers ----------------------------------------------------------------
|
||||
|
||||
function Write-Pass { param([string]$msg) Write-Host " [PASS] $msg" -ForegroundColor Green }
|
||||
function Write-Fail { param([string]$msg) Write-Host " [FAIL] $msg" -ForegroundColor Red }
|
||||
function Write-Info { param([string]$msg) Write-Host " [INFO] $msg" -ForegroundColor Cyan }
|
||||
function Write-Warn { param([string]$msg) Write-Host " [WARN] $msg" -ForegroundColor Yellow }
|
||||
function Write-Step { param([string]$msg) Write-Host "`n-- $msg" -ForegroundColor White }
|
||||
|
||||
$allPassed = $true
|
||||
|
||||
function Invoke-Step {
|
||||
param(
|
||||
[string] $Name,
|
||||
[scriptblock] $Block
|
||||
)
|
||||
Write-Step $Name
|
||||
try { & $Block }
|
||||
catch {
|
||||
Write-Fail $_.Exception.Message
|
||||
$script:allPassed = $false
|
||||
}
|
||||
}
|
||||
|
||||
# ---- Step 1: server reachable -----------------------------------------------
|
||||
|
||||
Invoke-Step "Step 1 -- Server reachable at $BaseUrl" {
|
||||
$r1 = $null
|
||||
try { $r1 = Invoke-WebRequest -Uri "$BaseUrl/docs" -UseBasicParsing -TimeoutSec 5 } catch {}
|
||||
if ($r1 -and $r1.StatusCode -eq 200) {
|
||||
Write-Pass "FastAPI server is up (GET /docs -> 200)"
|
||||
return
|
||||
}
|
||||
$r2 = $null
|
||||
try { $r2 = Invoke-WebRequest -Uri "$BaseUrl/" -UseBasicParsing -TimeoutSec 5 } catch {}
|
||||
if ($r2 -and $r2.StatusCode -lt 500) {
|
||||
Write-Pass "FastAPI server is up (GET / -> $($r2.StatusCode))"
|
||||
return
|
||||
}
|
||||
throw "Cannot reach $BaseUrl -- is lab_api.py / uvicorn running?"
|
||||
}
|
||||
|
||||
# ---- Step 2: /api/policy/health ---------------------------------------------
|
||||
|
||||
$modelLoaded = $false
|
||||
|
||||
Invoke-Step "Step 2 -- GET $BaseUrl/api/policy/health" {
|
||||
$resp = Invoke-RestMethod -Uri "$BaseUrl/api/policy/health" -Method Get -TimeoutSec 10
|
||||
if ($Verbose) { $resp | ConvertTo-Json -Depth 5 | Write-Host }
|
||||
|
||||
if ($resp.ready -eq $true) {
|
||||
Write-Pass "Model is loaded and ready (ready=true)"
|
||||
$script:modelLoaded = $true
|
||||
} elseif ($resp.status -eq "degraded") {
|
||||
Write-Fail "Health returned status=degraded / ready=false"
|
||||
Write-Warn " -> Policy model is NOT loaded."
|
||||
Write-Warn " -> Start the server with --load-policy, or call POST /api/ml/load_model first."
|
||||
Write-Warn " -> Confirm models/ contains a valid .pt checkpoint."
|
||||
$script:allPassed = $false
|
||||
} else {
|
||||
Write-Warn "Unexpected health response -- dumping:"
|
||||
$resp | ConvertTo-Json -Depth 5 | Write-Host
|
||||
$script:allPassed = $false
|
||||
}
|
||||
}
|
||||
|
||||
# ---- Step 3: /api/policy/decide (synthetic snapshot) ------------------------
|
||||
|
||||
Invoke-Step "Step 3 -- POST $BaseUrl/api/policy/decide (synthetic 4-player snapshot)" {
|
||||
|
||||
if (-not $script:modelLoaded) {
|
||||
Write-Warn "Skipping /decide -- model not loaded (Step 2 failed)."
|
||||
Write-Warn "Fix Step 2 first, then re-run."
|
||||
return
|
||||
}
|
||||
|
||||
$body = [ordered]@{
|
||||
game_id = "ps1-smoke-test"
|
||||
turn = 3
|
||||
phase = "MAIN1"
|
||||
active_player = 0
|
||||
playstyle = "midrange"
|
||||
temperature = 1.0
|
||||
greedy = $false
|
||||
players = @(
|
||||
[ordered]@{
|
||||
seat = 0
|
||||
name = "TestPlayer0"
|
||||
life = 40
|
||||
manaAvailable = 3
|
||||
landCount = 3
|
||||
creaturesOnField = 1
|
||||
totalPowerOnBoard = 2
|
||||
hand = @("Cultivate", "Solemn Simulacrum", "Sol Ring")
|
||||
battlefield = @("Forest", "Forest", "Forest", "Birds of Paradise")
|
||||
graveyard = @()
|
||||
commandZone = @("Atraxa, Praetors Voice")
|
||||
},
|
||||
[ordered]@{
|
||||
seat = 1
|
||||
name = "TestPlayer1"
|
||||
life = 38
|
||||
manaAvailable = 2
|
||||
landCount = 2
|
||||
creaturesOnField = 0
|
||||
totalPowerOnBoard = 0
|
||||
handCount = 4
|
||||
hand = @()
|
||||
battlefield = @("Island", "Swamp")
|
||||
graveyard = @()
|
||||
commandZone = @("Ur-Dragon")
|
||||
},
|
||||
[ordered]@{
|
||||
seat = 2
|
||||
name = "TestPlayer2"
|
||||
life = 35
|
||||
manaAvailable = 4
|
||||
landCount = 4
|
||||
creaturesOnField = 2
|
||||
totalPowerOnBoard = 5
|
||||
handCount = 3
|
||||
hand = @()
|
||||
battlefield = @("Mountain", "Mountain", "Mountain", "Mountain", "Goblin Guide", "Monastery Swiftspear")
|
||||
graveyard = @()
|
||||
commandZone = @("Krenko, Mob Boss")
|
||||
},
|
||||
[ordered]@{
|
||||
seat = 3
|
||||
name = "TestPlayer3"
|
||||
life = 40
|
||||
manaAvailable = 0
|
||||
landCount = 0
|
||||
creaturesOnField = 0
|
||||
totalPowerOnBoard = 0
|
||||
handCount = 7
|
||||
hand = @()
|
||||
battlefield = @()
|
||||
graveyard = @()
|
||||
commandZone = @("Prossh, Skyraider of Kher")
|
||||
}
|
||||
)
|
||||
} | ConvertTo-Json -Depth 10
|
||||
|
||||
$resp = Invoke-RestMethod `
|
||||
-Uri "$BaseUrl/api/policy/decide" `
|
||||
-Method Post `
|
||||
-Body $body `
|
||||
-ContentType "application/json" `
|
||||
-TimeoutSec 30
|
||||
|
||||
if ($Verbose) {
|
||||
Write-Host ""
|
||||
Write-Host " Full /decide response:" -ForegroundColor White
|
||||
$resp | ConvertTo-Json -Depth 6 | Write-Host
|
||||
}
|
||||
|
||||
$ok = $true
|
||||
if ([string]::IsNullOrEmpty($resp.action)) {
|
||||
Write-Fail "response.action is empty"
|
||||
$ok = $false
|
||||
}
|
||||
if ($null -eq $resp.confidence) {
|
||||
Write-Fail "response.confidence is missing"
|
||||
$ok = $false
|
||||
}
|
||||
if ($resp.PSObject.Properties["error"] -and -not [string]::IsNullOrEmpty($resp.error)) {
|
||||
Write-Fail "Server returned error: $($resp.error)"
|
||||
$ok = $false
|
||||
}
|
||||
|
||||
if ($ok) {
|
||||
$confPct = [math]::Round($resp.confidence * 100, 1)
|
||||
Write-Pass "Decision received -> action='$($resp.action)' conf=$($confPct)% latency=$($resp.inference_ms)ms src=$($resp.vector_source)"
|
||||
|
||||
if ($resp.probabilities) {
|
||||
$top3 = $resp.probabilities.PSObject.Properties |
|
||||
Sort-Object { $_.Value } -Descending |
|
||||
Select-Object -First 3
|
||||
Write-Info "Top-3 action probabilities:"
|
||||
foreach ($p in $top3) {
|
||||
$pct = [math]::Round($p.Value * 100, 1)
|
||||
Write-Info (" " + $p.Name.PadRight(22) + "$pct%")
|
||||
}
|
||||
}
|
||||
} else {
|
||||
$script:allPassed = $false
|
||||
}
|
||||
}
|
||||
|
||||
# ---- Step 4: log line guidance ----------------------------------------------
|
||||
|
||||
Invoke-Step "Step 4 -- Confirm [routes.policy.decide] log line in server console" {
|
||||
Write-Info "After Step 3 you should see in the uvicorn window:"
|
||||
Write-Info " [decide] turn=3 phase=main_1 -> cast_ramp (conf=0.810) | top5=[...] | src=encoder"
|
||||
Write-Info ""
|
||||
Write-Info "If that line is MISSING:"
|
||||
Write-Info " 1. Confirm uvicorn started with --log-level info (the default)"
|
||||
Write-Info " 2. Confirm lab_api.py calls register_policy_routes(app, policy_svc)"
|
||||
Write-Info " 3. Confirm routes/policy.py is included via routes/__init__.py"
|
||||
Write-Info " 4. Logger name is [routes.policy.decide] -- check no filter silences it"
|
||||
Write-Pass "Guidance printed"
|
||||
}
|
||||
|
||||
# ---- Step 5: why Forge ignores the policy server ----------------------------
|
||||
|
||||
Invoke-Step "Step 5 -- Forge IPC status (informational)" {
|
||||
Write-Warn "Forge does NOT call /api/policy/decide on its own."
|
||||
Write-Warn "routes/game.py POST /api/game/start has a TODO: wire Forge engine launch."
|
||||
Write-Warn ""
|
||||
Write-Warn "Until wired, /decide is only hit by:"
|
||||
Write-Warn " - This script"
|
||||
Write-Warn " - overnight-run.py (batch sim path)"
|
||||
Write-Warn " - Manual Invoke-RestMethod / curl calls"
|
||||
Write-Warn ""
|
||||
Write-Warn "Forge launches its built-in AI and plays lands because it has"
|
||||
Write-Warn "no HTTP client pointed at localhost:8080."
|
||||
Write-Warn ""
|
||||
Write-Warn "Next step: wire GameSession inside POST /api/game/start,"
|
||||
Write-Warn "or add a PolicyClient hook in Forge AiController.java."
|
||||
Write-Pass "Status explained"
|
||||
}
|
||||
|
||||
# ---- Summary ----------------------------------------------------------------
|
||||
|
||||
Write-Host ""
|
||||
if ($allPassed) {
|
||||
Write-Host "==========================================" -ForegroundColor Green
|
||||
Write-Host " ALL STEPS PASSED -- policy server live" -ForegroundColor Green
|
||||
Write-Host "==========================================" -ForegroundColor Green
|
||||
} else {
|
||||
Write-Host "==========================================" -ForegroundColor Red
|
||||
Write-Host " ONE OR MORE STEPS FAILED -- see above" -ForegroundColor Red
|
||||
Write-Host "==========================================" -ForegroundColor Red
|
||||
exit 1
|
||||
}
|
||||
@@ -1 +1 @@
|
||||
# Coach clients — Anthropic Claude, LM Studio
|
||||
# Coach clients — Perplexity, LM Studio
|
||||
@@ -1,401 +0,0 @@
|
||||
"""
|
||||
Commander AI Lab — Anthropic Claude Structured Output Client
|
||||
════════════════════════════════════════════════════════════
|
||||
Uses the anthropic SDK to call Claude's messages endpoint
|
||||
with structured JSON output enforced via system prompt.
|
||||
Supports both sync and native async operation.
|
||||
|
||||
All substantive calls use .messages.stream() to avoid the SDK's
|
||||
10-minute non-streaming timeout restriction on large max_tokens.
|
||||
"""
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
from typing import Optional
|
||||
|
||||
import anthropic
|
||||
|
||||
logger = logging.getLogger("coach.anthropic")
|
||||
|
||||
# ── Anthropic Models ───────────────────────────────────────────
|
||||
CLAUDE_OPUS = "claude-opus-4-0-20250514"
|
||||
CLAUDE_SONNET = "claude-sonnet-4-5"
|
||||
CLAUDE_HAIKU = "claude-haiku-4-5"
|
||||
|
||||
|
||||
class AnthropicResponse:
|
||||
"""Parsed response container from Anthropic API.
|
||||
|
||||
Drop-in replacement for PerplexityResponse — same attribute shape
|
||||
so all existing call-sites work without changes.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
content: str,
|
||||
parsed_json: Optional[dict],
|
||||
prompt_tokens: int,
|
||||
completion_tokens: int,
|
||||
model: str,
|
||||
citations: list[str] = None,
|
||||
):
|
||||
self.content = content
|
||||
self.parsed_json = parsed_json
|
||||
self.prompt_tokens = prompt_tokens
|
||||
self.completion_tokens = completion_tokens
|
||||
self.model = model
|
||||
self.citations = citations or [] # Anthropic has no citations; kept for compat
|
||||
|
||||
@property
|
||||
def ok(self) -> bool:
|
||||
return self.parsed_json is not None
|
||||
|
||||
|
||||
class AnthropicClient:
|
||||
"""
|
||||
Anthropic Claude API client.
|
||||
|
||||
Drop-in replacement for PerplexityClient.
|
||||
All substantive calls use .messages.stream() so the SDK never
|
||||
enforces the 10-minute non-streaming timeout.
|
||||
|
||||
Usage:
|
||||
client = AnthropicClient(api_key="sk-ant-...")
|
||||
resp = client.chat_structured(
|
||||
system_prompt="You are a deck builder.",
|
||||
user_prompt="Build a deck for...",
|
||||
json_schema=deck_schema,
|
||||
schema_name="DeckList",
|
||||
)
|
||||
if resp.ok:
|
||||
deck = resp.parsed_json
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_key: str,
|
||||
model: str = CLAUDE_OPUS,
|
||||
timeout: int = 120,
|
||||
base_url: str = None,
|
||||
):
|
||||
if not api_key:
|
||||
raise ValueError("Anthropic API key is required")
|
||||
self.api_key = api_key
|
||||
self.model = model
|
||||
self.timeout = timeout
|
||||
self._client = anthropic.Anthropic(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
timeout=float(timeout),
|
||||
)
|
||||
self._async_client = anthropic.AsyncAnthropic(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
timeout=float(timeout),
|
||||
)
|
||||
|
||||
def chat_structured(
|
||||
self,
|
||||
system_prompt: str,
|
||||
user_prompt: str,
|
||||
json_schema: dict = None,
|
||||
schema_name: str = "response",
|
||||
model: str = None,
|
||||
temperature: float = 0.2,
|
||||
max_tokens: int = 8192,
|
||||
) -> AnthropicResponse:
|
||||
"""
|
||||
Send a chat request expecting JSON output.
|
||||
Uses streaming to avoid the Anthropic SDK 10-minute timeout restriction.
|
||||
"""
|
||||
use_model = model or self.model
|
||||
effective_system = self._build_system(system_prompt, json_schema, schema_name)
|
||||
|
||||
try:
|
||||
with self._client.messages.stream(
|
||||
model=use_model,
|
||||
system=effective_system,
|
||||
messages=[{"role": "user", "content": user_prompt}],
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
) as stream:
|
||||
content = stream.get_final_text()
|
||||
final_msg = stream.get_final_message()
|
||||
return self._parse_streamed(content, final_msg, use_model)
|
||||
except Exception as e:
|
||||
logger.error("[anthropic] chat_structured failed: %s", e)
|
||||
raise
|
||||
|
||||
def chat_plain(
|
||||
self,
|
||||
system_prompt: str,
|
||||
user_prompt: str,
|
||||
model: str = None,
|
||||
temperature: float = 0.2,
|
||||
max_tokens: int = 4096,
|
||||
) -> AnthropicResponse:
|
||||
"""
|
||||
Send a plain chat request without strict structured output.
|
||||
Uses streaming to avoid the Anthropic SDK 10-minute timeout restriction.
|
||||
"""
|
||||
use_model = model or self.model
|
||||
|
||||
try:
|
||||
with self._client.messages.stream(
|
||||
model=use_model,
|
||||
system=system_prompt,
|
||||
messages=[{"role": "user", "content": user_prompt}],
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
) as stream:
|
||||
content = stream.get_final_text()
|
||||
final_msg = stream.get_final_message()
|
||||
return self._parse_streamed(content, final_msg, use_model)
|
||||
except Exception as e:
|
||||
logger.error("[anthropic] chat_plain failed: %s", e)
|
||||
raise
|
||||
|
||||
async def achat_structured(
|
||||
self,
|
||||
system_prompt: str,
|
||||
user_prompt: str,
|
||||
json_schema: dict = None,
|
||||
schema_name: str = "response",
|
||||
model: str = None,
|
||||
temperature: float = 0.2,
|
||||
max_tokens: int = 8192,
|
||||
) -> AnthropicResponse:
|
||||
"""Native async structured chat using streaming."""
|
||||
use_model = model or self.model
|
||||
effective_system = self._build_system(system_prompt, json_schema, schema_name)
|
||||
|
||||
try:
|
||||
async with self._async_client.messages.stream(
|
||||
model=use_model,
|
||||
system=effective_system,
|
||||
messages=[{"role": "user", "content": user_prompt}],
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
) as stream:
|
||||
content = await stream.get_final_text()
|
||||
final_msg = await stream.get_final_message()
|
||||
return self._parse_streamed(content, final_msg, use_model)
|
||||
except Exception as e:
|
||||
logger.error("[anthropic] achat_structured failed: %s", e)
|
||||
raise
|
||||
|
||||
async def achat_plain(
|
||||
self,
|
||||
system_prompt: str,
|
||||
user_prompt: str,
|
||||
model: str = None,
|
||||
temperature: float = 0.2,
|
||||
max_tokens: int = 4096,
|
||||
) -> AnthropicResponse:
|
||||
"""Native async plain chat using streaming."""
|
||||
use_model = model or self.model
|
||||
|
||||
try:
|
||||
async with self._async_client.messages.stream(
|
||||
model=use_model,
|
||||
system=system_prompt,
|
||||
messages=[{"role": "user", "content": user_prompt}],
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
) as stream:
|
||||
content = await stream.get_final_text()
|
||||
final_msg = await stream.get_final_message()
|
||||
return self._parse_streamed(content, final_msg, use_model)
|
||||
except Exception as e:
|
||||
logger.error("[anthropic] achat_plain failed: %s", e)
|
||||
raise
|
||||
|
||||
def check_status(self) -> dict:
|
||||
"""
|
||||
Quick health check — tiny max_tokens=10 call, safe to use non-streaming
|
||||
since it will never approach the 10-minute threshold.
|
||||
"""
|
||||
try:
|
||||
response = self._client.messages.create(
|
||||
model=self.model,
|
||||
system="Reply with the word OK.",
|
||||
messages=[{"role": "user", "content": "Status check."}],
|
||||
max_tokens=10,
|
||||
)
|
||||
content = response.content[0].text if response.content else ""
|
||||
return {
|
||||
"connected": True,
|
||||
"model": self.model,
|
||||
"response": content,
|
||||
}
|
||||
except Exception as e:
|
||||
return {
|
||||
"connected": False,
|
||||
"model": self.model,
|
||||
"error": str(e),
|
||||
}
|
||||
|
||||
# ── Internal helpers ──────────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def _build_system(system_prompt: str, json_schema: dict, schema_name: str) -> str:
|
||||
"""Append JSON schema hint to system prompt when a schema is provided."""
|
||||
if not json_schema:
|
||||
return system_prompt
|
||||
schema_str = json.dumps(json_schema, indent=2)
|
||||
return (
|
||||
f"{system_prompt}\n\n"
|
||||
f"You MUST respond with valid JSON only — no markdown fences, no prose.\n"
|
||||
f"The response must conform to the following JSON schema ({schema_name}):\n"
|
||||
f"{schema_str}"
|
||||
)
|
||||
|
||||
def _parse_streamed(self, content: str, final_msg, use_model: str) -> AnthropicResponse:
|
||||
"""Build an AnthropicResponse from streamed text + final message metadata."""
|
||||
usage = getattr(final_msg, "usage", None)
|
||||
prompt_tokens = usage.input_tokens if usage else 0
|
||||
completion_tokens = usage.output_tokens if usage else 0
|
||||
|
||||
parsed = None
|
||||
try:
|
||||
parsed = json.loads(content)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
parsed = self._extract_json(content)
|
||||
|
||||
logger.info(
|
||||
"[anthropic] model=%s tokens=(%d in, %d out) parsed=%s",
|
||||
use_model,
|
||||
prompt_tokens,
|
||||
completion_tokens,
|
||||
"yes" if parsed else "no",
|
||||
)
|
||||
|
||||
return AnthropicResponse(
|
||||
content=content,
|
||||
parsed_json=parsed,
|
||||
prompt_tokens=prompt_tokens,
|
||||
completion_tokens=completion_tokens,
|
||||
model=use_model,
|
||||
citations=[],
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _extract_json(text: str) -> Optional[dict]:
|
||||
"""Best-effort JSON extraction from possibly noisy text."""
|
||||
try:
|
||||
return json.loads(text.strip())
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
# Markdown fences
|
||||
fence_match = re.search(r'```(?:json)?\s*([\s\S]*?)```', text)
|
||||
if fence_match:
|
||||
try:
|
||||
return json.loads(fence_match.group(1).strip())
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
# Brace counting
|
||||
start = text.find('{')
|
||||
if start == -1:
|
||||
return None
|
||||
depth = 0
|
||||
in_string = False
|
||||
escape_next = False
|
||||
for i in range(start, len(text)):
|
||||
ch = text[i]
|
||||
if escape_next:
|
||||
escape_next = False
|
||||
continue
|
||||
if ch == '\\' and in_string:
|
||||
escape_next = True
|
||||
continue
|
||||
if ch == '"':
|
||||
in_string = not in_string
|
||||
continue
|
||||
if in_string:
|
||||
continue
|
||||
if ch == '{':
|
||||
depth += 1
|
||||
elif ch == '}':
|
||||
depth -= 1
|
||||
if depth == 0:
|
||||
try:
|
||||
return json.loads(text[start:i + 1])
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
return None
|
||||
|
||||
# Truncated JSON — attempt repair
|
||||
return AnthropicClient._repair_truncated_json(text[start:])
|
||||
|
||||
@staticmethod
|
||||
def _repair_truncated_json(text: str) -> Optional[dict]:
|
||||
"""
|
||||
Attempt to repair truncated JSON from max_tokens cutoff.
|
||||
Strategy: find the last complete nested object, then close
|
||||
all open brackets/braces.
|
||||
"""
|
||||
logger.info("Attempting truncated JSON repair (%d chars)...", len(text))
|
||||
last_good = -1
|
||||
depth = 0
|
||||
in_string = False
|
||||
escape_next = False
|
||||
for i in range(len(text)):
|
||||
ch = text[i]
|
||||
if escape_next:
|
||||
escape_next = False
|
||||
continue
|
||||
if ch == '\\' and in_string:
|
||||
escape_next = True
|
||||
continue
|
||||
if ch == '"':
|
||||
in_string = not in_string
|
||||
continue
|
||||
if in_string:
|
||||
continue
|
||||
if ch == '{':
|
||||
depth += 1
|
||||
elif ch == '}':
|
||||
depth -= 1
|
||||
if depth >= 1:
|
||||
last_good = i
|
||||
|
||||
if last_good == -1:
|
||||
return None
|
||||
|
||||
truncated = text[:last_good + 1]
|
||||
close_depth = 0
|
||||
in_arr = 0
|
||||
in_str = False
|
||||
esc = False
|
||||
for ch in truncated:
|
||||
if esc:
|
||||
esc = False
|
||||
continue
|
||||
if ch == '\\' and in_str:
|
||||
esc = True
|
||||
continue
|
||||
if ch == '"':
|
||||
in_str = not in_str
|
||||
continue
|
||||
if in_str:
|
||||
continue
|
||||
if ch == '{':
|
||||
close_depth += 1
|
||||
elif ch == '}':
|
||||
close_depth -= 1
|
||||
elif ch == '[':
|
||||
in_arr += 1
|
||||
elif ch == ']':
|
||||
in_arr -= 1
|
||||
|
||||
suffix = ']' * in_arr + '}' * close_depth
|
||||
repaired = truncated + suffix
|
||||
try:
|
||||
result = json.loads(repaired)
|
||||
card_count = len(result.get('cards', []))
|
||||
logger.info("JSON repair succeeded: recovered %d cards", card_count)
|
||||
return result
|
||||
except (json.JSONDecodeError, TypeError) as e:
|
||||
logger.warning("JSON repair failed: %s", e)
|
||||
return None
|
||||
+68
-346
@@ -5,11 +5,10 @@ Main orchestrator: loads deck reports, identifies underperformers,
|
||||
finds replacement candidates via embeddings, builds prompts,
|
||||
calls the LLM, parses responses, and persists coaching sessions.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import uuid
|
||||
import os
|
||||
import re
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Optional, List, Dict
|
||||
@@ -17,16 +16,11 @@ from typing import Optional, List, Dict
|
||||
from .config import (
|
||||
DECK_REPORTS_DIR, COACH_SESSIONS_DIR,
|
||||
MAX_UNDERPERFORMERS, MAX_CANDIDATES_PER_UNDERPERFORMER,
|
||||
COACH_PROVIDER, ANTHROPIC_API_KEY, ANTHROPIC_MODEL,
|
||||
UNDERPERFORMER_IMPACT_THRESHOLD, DEFAULT_MAX_TOKENS, ensure_dirs,
|
||||
UNDERPERFORMER_IMPACT_THRESHOLD, ensure_dirs,
|
||||
)
|
||||
from .models import (
|
||||
DeckReport, CoachGoals, CoachSession,
|
||||
SuggestedCut, SuggestedAdd, CoachStatus,
|
||||
UpgradePriorityItem, CommanderDependency, MulliganAnalysis,
|
||||
RemovalCoverage, RampQuality, DrawEngineProfile,
|
||||
WinCondition, AntiSynergyFlag,
|
||||
PodPresence, TempoAssessment, MetaMatchup,
|
||||
)
|
||||
from .llm_client import LLMClient
|
||||
from .embeddings import MTGEmbeddingIndex
|
||||
@@ -34,18 +28,6 @@ from .prompt_template import build_system_prompt, build_user_prompt
|
||||
|
||||
logger = logging.getLogger("coach.service")
|
||||
|
||||
# SQLite persistence for coach sessions
|
||||
try:
|
||||
from services.database import (
|
||||
save_coach_session as _db_save_session,
|
||||
load_coach_session as _db_load_session,
|
||||
list_coach_sessions as _db_list_sessions,
|
||||
delete_coach_session as _db_delete_session,
|
||||
)
|
||||
_DB_AVAILABLE = True
|
||||
except ImportError:
|
||||
_DB_AVAILABLE = False
|
||||
|
||||
|
||||
class CoachService:
|
||||
"""
|
||||
@@ -64,20 +46,25 @@ class CoachService:
|
||||
self.embeddings = embeddings
|
||||
ensure_dirs()
|
||||
|
||||
# ── Deck Report I/O ──────────────────────
|
||||
# ── Deck Report I/O ────────────────────────────────────────
|
||||
|
||||
def load_deck_report(self, deck_id: str) -> Optional[DeckReport]:
|
||||
"""Load a deck report from disk."""
|
||||
# Try exact filename first, then case-insensitive search
|
||||
report_path = DECK_REPORTS_DIR / f"{deck_id}.json"
|
||||
if not report_path.exists():
|
||||
# Try case-insensitive match
|
||||
for f in DECK_REPORTS_DIR.glob("*.json"):
|
||||
if re.sub(r'[^a-z0-9]', '', f.stem.lower()) == re.sub(r'[^a-z0-9]', '', deck_id.lower()):
|
||||
if f.stem.lower() == deck_id.lower():
|
||||
report_path = f
|
||||
break
|
||||
|
||||
if not report_path.exists():
|
||||
logger.warning("Deck report not found: %s", deck_id)
|
||||
return None
|
||||
|
||||
try:
|
||||
with open(report_path, "r", encoding="utf-8") as f:
|
||||
with open(report_path, "r") as f:
|
||||
data = json.load(f)
|
||||
return DeckReport(**data)
|
||||
except Exception as e:
|
||||
@@ -90,64 +77,23 @@ class CoachService:
|
||||
return []
|
||||
return [f.stem for f in DECK_REPORTS_DIR.glob("*.json")]
|
||||
|
||||
# ── Coach Session I/O ──────────────────
|
||||
# ── Coach Session I/O ──────────────────────────────────────
|
||||
|
||||
def save_session(self, session: CoachSession) -> Path:
|
||||
"""Persist a coaching session to disk and SQLite."""
|
||||
"""Persist a coaching session to disk."""
|
||||
COACH_SESSIONS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
path = COACH_SESSIONS_DIR / f"{session.sessionId}.json"
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
with open(path, "w") as f:
|
||||
f.write(session.model_dump_json(indent=2))
|
||||
if _DB_AVAILABLE:
|
||||
try:
|
||||
_db_save_session({
|
||||
"session_id": session.sessionId,
|
||||
"deck_id": session.deckId,
|
||||
"timestamp": session.timestamp,
|
||||
"model_used": session.modelUsed or "",
|
||||
"prompt_tokens": session.promptTokens or 0,
|
||||
"completion_tokens": session.completionTokens or 0,
|
||||
"summary": session.summary or "",
|
||||
"goals_json": json.dumps(session.goals.model_dump() if session.goals else {}),
|
||||
"cuts_json": json.dumps([c.model_dump() for c in session.suggestedCuts]),
|
||||
"adds_json": json.dumps([a.model_dump() for a in session.suggestedAdds]),
|
||||
"heuristic_hints_json": json.dumps(session.heuristicHints or []),
|
||||
"mana_base_advice": session.manaBaseAdvice or "",
|
||||
"raw_text": session.rawTextExplanation or "",
|
||||
})
|
||||
logger.info("Coach session persisted to SQLite: %s", session.sessionId)
|
||||
except Exception as e:
|
||||
logger.warning("Failed to persist session to SQLite: %s", e)
|
||||
return path
|
||||
|
||||
def load_session(self, session_id: str) -> Optional[CoachSession]:
|
||||
"""Load a coaching session from SQLite, falling back to disk."""
|
||||
if _DB_AVAILABLE:
|
||||
try:
|
||||
row = _db_load_session(session_id)
|
||||
if row:
|
||||
data = dict(row)
|
||||
return CoachSession(
|
||||
sessionId=data["session_id"],
|
||||
deckId=data["deck_id"],
|
||||
timestamp=data["timestamp"],
|
||||
modelUsed=data.get("model_used", ""),
|
||||
promptTokens=data.get("prompt_tokens", 0),
|
||||
completionTokens=data.get("completion_tokens", 0),
|
||||
summary=data.get("summary", ""),
|
||||
goals=json.loads(data["goals_json"]) if data.get("goals_json") and data["goals_json"] != "{}" else None,
|
||||
suggestedCuts=[SuggestedCut(**c) for c in json.loads(data.get("cuts_json", "[]"))],
|
||||
suggestedAdds=[SuggestedAdd(**a) for a in json.loads(data.get("adds_json", "[]"))],
|
||||
heuristicHints=json.loads(data.get("heuristic_hints_json", "[]")),
|
||||
manaBaseAdvice=data.get("mana_base_advice", ""),
|
||||
rawTextExplanation=data.get("raw_text", ""),
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning("SQLite load failed for %s: %s", session_id, e)
|
||||
"""Load a coaching session from disk."""
|
||||
path = COACH_SESSIONS_DIR / f"{session_id}.json"
|
||||
if not path.exists():
|
||||
return None
|
||||
try:
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
with open(path, "r") as f:
|
||||
data = json.load(f)
|
||||
return CoachSession(**data)
|
||||
except Exception as e:
|
||||
@@ -155,20 +101,13 @@ class CoachService:
|
||||
return None
|
||||
|
||||
def list_sessions(self, deck_id: str = None) -> List[dict]:
|
||||
"""List coaching sessions from SQLite, falling back to disk."""
|
||||
if _DB_AVAILABLE:
|
||||
try:
|
||||
rows = _db_list_sessions(deck_id)
|
||||
if rows is not None:
|
||||
return rows
|
||||
except Exception as e:
|
||||
logger.warning("SQLite list_sessions failed: %s", e)
|
||||
"""List all coaching sessions, optionally filtered by deck."""
|
||||
if not COACH_SESSIONS_DIR.exists():
|
||||
return []
|
||||
sessions = []
|
||||
for f in sorted(COACH_SESSIONS_DIR.glob("*.json"), reverse=True):
|
||||
try:
|
||||
with open(f, "r", encoding="utf-8") as fh:
|
||||
with open(f, "r") as fh:
|
||||
data = json.load(fh)
|
||||
if deck_id and data.get("deckId", "") != deck_id:
|
||||
continue
|
||||
@@ -184,23 +123,23 @@ class CoachService:
|
||||
continue
|
||||
return sessions
|
||||
|
||||
# ── Main Coaching Pipeline ───────────────────────
|
||||
async def run_coaching_session(
|
||||
self,
|
||||
deck_id: str,
|
||||
goals: Optional[CoachGoals] = None,
|
||||
fallback_report: Optional[DeckReport] = None,
|
||||
) -> CoachSession:
|
||||
# ── Main Coaching Pipeline ─────────────────────────────────
|
||||
|
||||
async def run_coaching_session(self, deck_id: str,
|
||||
goals: Optional[CoachGoals] = None,
|
||||
fallback_report: Optional[DeckReport] = None,
|
||||
) -> CoachSession:
|
||||
"""
|
||||
Full coaching pipeline:
|
||||
1. Load deck report (or use fallback from DB card list)
|
||||
2. Identify underperformers
|
||||
3. Find replacement candidates via embeddings
|
||||
4. Build prompt
|
||||
5. Call LLM (streaming for Anthropic to avoid 10-min SDK timeout)
|
||||
5. Call LLM
|
||||
6. Parse response
|
||||
7. Persist session
|
||||
"""
|
||||
# 0. Auto-load embeddings if not loaded
|
||||
if not self.embeddings.loaded:
|
||||
logger.info("Embeddings not loaded — attempting auto-load...")
|
||||
try:
|
||||
@@ -212,6 +151,7 @@ class CoachService:
|
||||
except Exception as e:
|
||||
logger.warning("Embeddings auto-load failed: %s", e)
|
||||
|
||||
# 1. Load deck report — try simulation report first, then fallback
|
||||
report = self.load_deck_report(deck_id)
|
||||
if report is None and fallback_report is not None:
|
||||
report = fallback_report
|
||||
@@ -219,17 +159,25 @@ class CoachService:
|
||||
if report is None:
|
||||
raise ValueError(f"Deck report not found for: {deck_id}")
|
||||
|
||||
# 2. Get deck card names (for exclusion from candidates)
|
||||
deck_card_names = [c.name for c in report.cards]
|
||||
|
||||
# 3. Find replacement candidates for underperformers
|
||||
candidates: Dict[str, List[dict]] = {}
|
||||
|
||||
underperformers = report.underperformers[:MAX_UNDERPERFORMERS]
|
||||
if not underperformers:
|
||||
# If no explicit underperformers, pick lowest impact cards
|
||||
sorted_cards = sorted(report.cards, key=lambda c: c.impactScore)
|
||||
underperformers = [
|
||||
c.name for c in sorted_cards[:MAX_UNDERPERFORMERS]
|
||||
if c.impactScore < UNDERPERFORMER_IMPACT_THRESHOLD
|
||||
]
|
||||
|
||||
# If still no underperformers and we have cards, pick bottom N by impact
|
||||
if not underperformers and report.cards:
|
||||
sorted_cards = sorted(report.cards, key=lambda c: c.impactScore)
|
||||
# Take the bottom 5 non-land cards as candidates for cuts
|
||||
underperformers = [
|
||||
c.name for c in sorted_cards[:5]
|
||||
if not any(t in (c.tags or []) for t in [])
|
||||
@@ -248,81 +196,17 @@ class CoachService:
|
||||
else:
|
||||
logger.warning("Embeddings not loaded — skipping candidate search")
|
||||
|
||||
# Phase 4a: behavior-aware RAG — primary + replacement-effect secondary query
|
||||
rag_cards: List[dict] = []
|
||||
try:
|
||||
from services.rag_store import query_cards
|
||||
# Primary: commander synergy context
|
||||
base_query = f"{report.commander} {' '.join(report.colorIdentity)} commander deck"
|
||||
rag_cards = query_cards(base_query, n_results=10, color_identity=report.colorIdentity)
|
||||
# Secondary: replacement effects / graveyard hate / protection
|
||||
if len(rag_cards) < 15:
|
||||
repl_cards = query_cards(
|
||||
f"replacement effect graveyard {' '.join(report.colorIdentity)}",
|
||||
n_results=5,
|
||||
color_identity=report.colorIdentity,
|
||||
require_replacement=True,
|
||||
)
|
||||
# Deduplicate by name before merging
|
||||
existing_names = {c["name"] for c in rag_cards}
|
||||
rag_cards += [c for c in repl_cards if c["name"] not in existing_names]
|
||||
logger.info("RAG returned %d cards for '%s'", len(rag_cards), deck_id)
|
||||
except Exception as e:
|
||||
logger.warning("RAG query failed (non-fatal): %s", e)
|
||||
|
||||
# Phase 4b: rules context for grounding the coach prompt
|
||||
rules_context: List[dict] = []
|
||||
try:
|
||||
from services.rag_store import query_rules
|
||||
rules_context = query_rules(
|
||||
f"Commander rules {report.commander} {' '.join(report.colorIdentity)}",
|
||||
n_results=3,
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 4. Build prompts
|
||||
system_prompt = build_system_prompt(report, goals)
|
||||
user_prompt = build_user_prompt(report, candidates, rag_cards=rag_cards, rules_context=rules_context)
|
||||
logger.info("Calling LLM for deck: %s (provider=%s)", deck_id, COACH_PROVIDER)
|
||||
if COACH_PROVIDER == "anthropic":
|
||||
import anthropic as _anthropic
|
||||
anthropic_key = ANTHROPIC_API_KEY
|
||||
if not anthropic_key:
|
||||
raise ConnectionError("Anthropic API key not configured. Set ANTHROPIC_API_KEY env var.")
|
||||
aclient = _anthropic.AsyncAnthropic(api_key=anthropic_key)
|
||||
async with aclient.messages.stream(
|
||||
model=ANTHROPIC_MODEL,
|
||||
system=system_prompt,
|
||||
messages=[{"role": "user", "content": user_prompt}],
|
||||
max_tokens=DEFAULT_MAX_TOKENS,
|
||||
temperature=0.7,
|
||||
) as stream:
|
||||
content = await stream.get_final_text()
|
||||
final_msg = await stream.get_final_message()
|
||||
usage = getattr(final_msg, "usage", None)
|
||||
from types import SimpleNamespace
|
||||
llm_response = SimpleNamespace(
|
||||
content=content,
|
||||
model=final_msg.model,
|
||||
prompt_tokens=usage.input_tokens if usage else 0,
|
||||
completion_tokens=usage.output_tokens if usage else 0,
|
||||
parsed_json=None,
|
||||
)
|
||||
try:
|
||||
cleaned = re.sub(r'^```(?:json)?\s*', '', content.strip())
|
||||
cleaned = re.sub(r'\s*```$', '', cleaned.strip())
|
||||
llm_response.parsed_json = json.loads(cleaned)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
match = re.search(r'\{[\s\S]*\}', content)
|
||||
if match:
|
||||
try:
|
||||
llm_response.parsed_json = json.loads(match.group(0))
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
else:
|
||||
llm_response = await self.llm.chat(system_prompt, user_prompt)
|
||||
user_prompt = build_user_prompt(report, candidates)
|
||||
|
||||
# 5. Call LLM
|
||||
logger.info("Calling LLM for deck: %s", deck_id)
|
||||
llm_response = await self.llm.chat(system_prompt, user_prompt)
|
||||
|
||||
# 6. Parse response into CoachSession
|
||||
session_id = f"sess-{datetime.now(timezone.utc).strftime('%Y%m%d-%H%M%S')}-{uuid.uuid4().hex[:6]}"
|
||||
|
||||
session = CoachSession(
|
||||
sessionId=session_id,
|
||||
deckId=deck_id,
|
||||
@@ -332,109 +216,31 @@ class CoachService:
|
||||
completionTokens=llm_response.completion_tokens,
|
||||
goals=goals,
|
||||
)
|
||||
|
||||
if llm_response.parsed_json:
|
||||
self._populate_session_from_json(session, llm_response.parsed_json)
|
||||
else:
|
||||
# Fallback: store raw text
|
||||
session.rawTextExplanation = llm_response.content
|
||||
session.summary = "LLM returned non-JSON response. See raw explanation."
|
||||
|
||||
# 7. Persist
|
||||
self.save_session(session)
|
||||
logger.info("Coaching session saved: %s", session_id)
|
||||
|
||||
return session
|
||||
|
||||
# ── Quick Digest ──────────────────────────────
|
||||
async def run_quick_digest(
|
||||
self,
|
||||
deck_id: str,
|
||||
goals: Optional[CoachGoals] = None,
|
||||
fallback_report: Optional[DeckReport] = None,
|
||||
) -> CoachSession:
|
||||
"""Fast ~400-600 token call: summary + top 3 upgradePriority + commanderDependency."""
|
||||
report = self.load_deck_report(deck_id)
|
||||
if report is None and fallback_report is not None:
|
||||
report = fallback_report
|
||||
if report is None:
|
||||
raise ValueError(f"Deck report not found for: {deck_id}")
|
||||
|
||||
# Phase 4a: light RAG query for quick digest
|
||||
rag_cards: List[dict] = []
|
||||
try:
|
||||
from services.rag_store import query_cards
|
||||
query_text = f"{report.commander} {' '.join(report.colorIdentity)}"
|
||||
rag_cards = query_cards(query_text, n_results=8, color_identity=report.colorIdentity)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
from .prompt_template import build_quick_system_prompt, build_user_prompt
|
||||
system_prompt = build_quick_system_prompt(report, goals)
|
||||
user_prompt = build_user_prompt(report, {}, rag_cards=rag_cards) # no candidates for speed
|
||||
logger.info("Quick digest for deck: %s (provider=%s)", deck_id, COACH_PROVIDER)
|
||||
|
||||
if COACH_PROVIDER == "anthropic":
|
||||
import anthropic as _anthropic
|
||||
anthropic_key = ANTHROPIC_API_KEY
|
||||
if not anthropic_key:
|
||||
raise ConnectionError("Anthropic API key not configured.")
|
||||
aclient = _anthropic.AsyncAnthropic(api_key=anthropic_key)
|
||||
async with aclient.messages.stream(
|
||||
model=ANTHROPIC_MODEL,
|
||||
system=system_prompt,
|
||||
messages=[{"role": "user", "content": user_prompt}],
|
||||
max_tokens=1024,
|
||||
temperature=0.5,
|
||||
) as stream:
|
||||
content = await stream.get_final_text()
|
||||
final_msg = await stream.get_final_message()
|
||||
usage = getattr(final_msg, "usage", None)
|
||||
from types import SimpleNamespace
|
||||
llm_response = SimpleNamespace(
|
||||
content=content,
|
||||
model=final_msg.model,
|
||||
prompt_tokens=usage.input_tokens if usage else 0,
|
||||
completion_tokens=usage.output_tokens if usage else 0,
|
||||
parsed_json=None,
|
||||
)
|
||||
try:
|
||||
cleaned = re.sub(r'^```(?:json)?\s*', '', content.strip())
|
||||
cleaned = re.sub(r'\s*```$', '', cleaned.strip())
|
||||
llm_response.parsed_json = json.loads(cleaned)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
match = re.search(r'\{[\s\S]*\}', content)
|
||||
if match:
|
||||
try:
|
||||
llm_response.parsed_json = json.loads(match.group(0))
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
else:
|
||||
llm_response = await self.llm.chat(system_prompt, user_prompt)
|
||||
|
||||
session_id = f"quick-{datetime.now(timezone.utc).strftime('%Y%m%d-%H%M%S')}-{uuid.uuid4().hex[:6]}"
|
||||
session = CoachSession(
|
||||
sessionId=session_id,
|
||||
deckId=deck_id,
|
||||
timestamp=datetime.now(timezone.utc).isoformat(),
|
||||
modelUsed=llm_response.model,
|
||||
promptTokens=llm_response.prompt_tokens,
|
||||
completionTokens=llm_response.completion_tokens,
|
||||
goals=goals,
|
||||
isQuickDigest=True,
|
||||
)
|
||||
if llm_response.parsed_json:
|
||||
self._populate_session_from_json(session, llm_response.parsed_json)
|
||||
else:
|
||||
session.rawTextExplanation = llm_response.content
|
||||
session.summary = "Quick digest returned non-JSON. See raw explanation."
|
||||
self.save_session(session)
|
||||
return session
|
||||
|
||||
# ── JSON Parser ───────────────────────────────
|
||||
def _populate_session_from_json(self, session: CoachSession, data: dict):
|
||||
"""Parse LLM JSON response into CoachSession fields."""
|
||||
session.summary = data.get("summary", "")
|
||||
session.rawTextExplanation = data.get("rawTextExplanation", "")
|
||||
session.manaBaseAdvice = data.get("manaBaseAdvice")
|
||||
|
||||
# Heuristic hints
|
||||
hints = data.get("heuristicHints", [])
|
||||
session.heuristicHints = hints if isinstance(hints, list) else [str(hints)]
|
||||
|
||||
# Suggested cuts
|
||||
for cut_data in data.get("suggestedCuts", []):
|
||||
if isinstance(cut_data, dict):
|
||||
session.suggestedCuts.append(SuggestedCut(
|
||||
@@ -442,6 +248,8 @@ class CoachService:
|
||||
reason=cut_data.get("reason", ""),
|
||||
replacementOptions=cut_data.get("replacementOptions", []),
|
||||
))
|
||||
|
||||
# Suggested adds
|
||||
for add_data in data.get("suggestedAdds", []):
|
||||
if isinstance(add_data, dict):
|
||||
session.suggestedAdds.append(SuggestedAdd(
|
||||
@@ -450,112 +258,26 @@ class CoachService:
|
||||
reason=add_data.get("reason", ""),
|
||||
synergyWith=add_data.get("synergyWith", []),
|
||||
))
|
||||
for item in data.get("upgradePriority", []):
|
||||
if isinstance(item, dict):
|
||||
session.upgradePriority.append(UpgradePriorityItem(
|
||||
rank=item.get("rank", 0),
|
||||
cut=item.get("cut", ""),
|
||||
add=item.get("add", ""),
|
||||
reasoning=item.get("reasoning", ""),
|
||||
expectedImpact=item.get("expectedImpact", ""),
|
||||
))
|
||||
cd = data.get("commanderDependency")
|
||||
if isinstance(cd, dict):
|
||||
session.commanderDependency = CommanderDependency(
|
||||
score=cd.get("score", 5),
|
||||
dependentCards=cd.get("dependentCards", []),
|
||||
recoveryPlan=cd.get("recoveryPlan", ""),
|
||||
)
|
||||
ma = data.get("mulliganAnalysis")
|
||||
if isinstance(ma, dict):
|
||||
session.mulliganAnalysis = MulliganAnalysis(
|
||||
estimatedKeepRate=ma.get("estimatedKeepRate", ""),
|
||||
worstOffenders=ma.get("worstOffenders", []),
|
||||
recommendation=ma.get("recommendation", ""),
|
||||
)
|
||||
rc = data.get("removalCoverage")
|
||||
if isinstance(rc, dict):
|
||||
session.removalCoverage = RemovalCoverage(**{
|
||||
k: rc.get(k, "" if k != "gaps" else [])
|
||||
for k in ["creatures", "artifacts", "enchantments", "planeswalkers", "lands", "massRemoval", "gaps"]
|
||||
})
|
||||
rq = data.get("rampQuality")
|
||||
if isinstance(rq, dict):
|
||||
session.rampQuality = RampQuality(**{
|
||||
k: rq.get(k, "") for k in ["manaRocks", "landFetch", "manaDorks", "costReducers", "fragility", "canReachFourByTurnThree"]
|
||||
})
|
||||
dep = data.get("drawEngineProfile")
|
||||
if isinstance(dep, dict):
|
||||
session.drawEngineProfile = DrawEngineProfile(
|
||||
burstDraw=dep.get("burstDraw", []),
|
||||
repeatableEngines=dep.get("repeatableEngines", []),
|
||||
assessment=dep.get("assessment", ""),
|
||||
sustainability=dep.get("sustainability", ""),
|
||||
)
|
||||
for wc in data.get("winConditions", []):
|
||||
if isinstance(wc, dict):
|
||||
session.winConditions.append(WinCondition(
|
||||
name=wc.get("name", ""),
|
||||
cards=wc.get("cards", []),
|
||||
independence=wc.get("independence", ""),
|
||||
description=wc.get("description", ""),
|
||||
))
|
||||
for af in data.get("antiSynergyFlags", []):
|
||||
if isinstance(af, dict):
|
||||
session.antiSynergyFlags.append(AntiSynergyFlag(
|
||||
cards=af.get("cards", []),
|
||||
conflict=af.get("conflict", ""),
|
||||
severity=af.get("severity", ""),
|
||||
))
|
||||
pp = data.get("podPresence")
|
||||
if isinstance(pp, dict):
|
||||
session.podPresence = PodPresence(
|
||||
threatLevel=pp.get("threatLevel", ""),
|
||||
politicalTools=pp.get("politicalTools", []),
|
||||
adversarialRating=pp.get("adversarialRating", ""),
|
||||
recommendation=pp.get("recommendation", ""),
|
||||
)
|
||||
ta = data.get("tempoAssessment")
|
||||
if isinstance(ta, dict):
|
||||
session.tempoAssessment = TempoAssessment(
|
||||
peakTurn=ta.get("peakTurn", 0),
|
||||
profile=ta.get("profile", ""),
|
||||
survivalWindow=ta.get("survivalWindow", ""),
|
||||
assessment=ta.get("assessment", ""),
|
||||
)
|
||||
for mm in data.get("metaMatchups", []):
|
||||
if isinstance(mm, dict):
|
||||
session.metaMatchups.append(MetaMatchup(
|
||||
archetype=mm.get("archetype", ""),
|
||||
assessment=mm.get("assessment", ""),
|
||||
keyThreats=mm.get("keyThreats", []),
|
||||
keyAnswers=mm.get("keyAnswers", []),
|
||||
overallRating=mm.get("overallRating", ""),
|
||||
))
|
||||
|
||||
# ── Status Check ──────────────────────────────
|
||||
# ── Status Check ───────────────────────────────────────────
|
||||
|
||||
def get_status(self) -> CoachStatus:
|
||||
"""Check the health of all coach subsystems."""
|
||||
status = CoachStatus()
|
||||
if COACH_PROVIDER == "anthropic":
|
||||
anthropic_key = ANTHROPIC_API_KEY
|
||||
if anthropic_key:
|
||||
status.llmConnected = True
|
||||
status.llmModel = f"Claude Opus ({ANTHROPIC_MODEL})"
|
||||
status.llmModels = [ANTHROPIC_MODEL]
|
||||
else:
|
||||
status.llmConnected = False
|
||||
status.llmModel = None
|
||||
status.llmModels = []
|
||||
status.error = "Anthropic API key not configured. Set ANTHROPIC_API_KEY env var."
|
||||
else:
|
||||
llm_status = self.llm.check_connection_sync() # FARMHOUSE_COACH_STATUS_V1 (was an un-awaited coroutine)
|
||||
status.llmConnected = llm_status.get("connected", False)
|
||||
status.llmModel = llm_status.get("active_model")
|
||||
status.llmModels = llm_status.get("models", [])
|
||||
if not status.llmConnected:
|
||||
status.error = llm_status.get("error", "Local LLM not reachable")
|
||||
|
||||
# Check LLM
|
||||
llm_status = self.llm.check_connection()
|
||||
status.llmConnected = llm_status.get("connected", False)
|
||||
status.llmModel = llm_status.get("active_model")
|
||||
status.llmModels = llm_status.get("models", [])
|
||||
if not status.llmConnected:
|
||||
status.error = llm_status.get("error", "LM Studio not reachable")
|
||||
|
||||
# Check embeddings
|
||||
status.embeddingsLoaded = self.embeddings.loaded
|
||||
status.embeddingCards = self.embeddings.card_count
|
||||
|
||||
# Check deck reports
|
||||
status.deckReportsAvailable = len(self.list_deck_reports())
|
||||
|
||||
return status
|
||||
+28
-31
@@ -1,6 +1,6 @@
|
||||
"""
|
||||
Commander AI Lab — Coach Configuration
|
||||
────────────────────────────────────────
|
||||
═══════════════════════════════════════
|
||||
Central config for Ollama, embeddings, and coach service paths.
|
||||
All paths are relative to the commander-ai-lab root directory.
|
||||
"""
|
||||
@@ -8,60 +8,57 @@ All paths are relative to the commander-ai-lab root directory.
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# ── Base Paths ───────────────────────────────────────────────────────
|
||||
# ── Base Paths ──────────────────────────────────────────────
|
||||
# Resolve relative to wherever the lab is installed
|
||||
LAB_ROOT = Path(os.environ.get("COMMANDER_LAB_ROOT", Path(__file__).parent.parent))
|
||||
|
||||
# ── Ollama (OpenAI-compatible API) ────────────────────────────
|
||||
LLM_URL = os.environ.get("LLM_URL", "http://localhost:11434/v1")
|
||||
LLM_MODEL = os.environ.get("LLM_MODEL", "claude-opus-4-20250514")
|
||||
# ── Ollama (OpenAI-compatible API) ──────────────────────
|
||||
LLM_URL = os.environ.get("LLM_URL", "http://192.168.0.240:11434/v1")
|
||||
LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-oss:20b")
|
||||
LLM_TIMEOUT = int(os.environ.get("LLM_TIMEOUT", "360")) # seconds
|
||||
LLM_MAX_RETRIES = 3
|
||||
|
||||
# ── Embeddings ───────────────────────────────────────────────────
|
||||
# ── Embeddings ─────────────────────────────────────────────
|
||||
EMBEDDINGS_DIR = LAB_ROOT / "data"
|
||||
EMBEDDINGS_NPZ = EMBEDDINGS_DIR / "mtg-embeddings.npz"
|
||||
EMBEDDINGS_PARQUET = EMBEDDINGS_DIR / "mtg-embeddings.parquet"
|
||||
|
||||
# ── Deck Reports ────────────────────────────────────────────────
|
||||
# ── Deck Reports ───────────────────────────────────────────
|
||||
DECK_REPORTS_DIR = LAB_ROOT / "deck-reports"
|
||||
COACH_SESSIONS_DIR = LAB_ROOT / "coach-sessions"
|
||||
|
||||
# ── Prompt Limits ───────────────────────────────────────────────
|
||||
# ── Prompt Limits ──────────────────────────────────────────
|
||||
MAX_PROMPT_TOKENS = 2048
|
||||
MAX_CANDIDATES_PER_UNDERPERFORMER = 8
|
||||
MAX_UNDERPERFORMERS = 8
|
||||
MAX_CANDIDATES_PER_UNDERPERFORMER = 5
|
||||
MAX_UNDERPERFORMERS = 4
|
||||
UNDERPERFORMER_IMPACT_THRESHOLD = -0.05 # impactScore below this = underperformer
|
||||
|
||||
# ── LLM Generation Settings (Coach — Ollama fallback) ─────────────
|
||||
# ── LLM Generation Settings (Coach — Ollama) ──────────────
|
||||
DEFAULT_TEMPERATURE = 0.7
|
||||
DEFAULT_MAX_TOKENS = 16384
|
||||
# DeepSeek-R1 uses <think> reasoning tokens before the JSON output,
|
||||
# so we need a generous limit to avoid truncation
|
||||
DEFAULT_MAX_TOKENS = 8192
|
||||
|
||||
# ── Anthropic Claude Deck Generation Settings ──────────────────────
|
||||
# Provider for deck generation: "anthropic" (Claude) or "local" (Ollama)
|
||||
DECK_GEN_PROVIDER = os.environ.get("DECK_GEN_PROVIDER", "anthropic")
|
||||
# Model used when DECK_GEN_PROVIDER="anthropic"
|
||||
DECK_GEN_MODEL = os.environ.get("DECK_GEN_MODEL", "claude-opus-4-20250514")
|
||||
# Anthropic API key
|
||||
ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", "")
|
||||
# Anthropic model for deck generation and coaching
|
||||
ANTHROPIC_MODEL = os.environ.get("ANTHROPIC_MODEL", "claude-opus-4-20250514")
|
||||
# Coach chat provider: "anthropic" (Claude API) or "local" (Ollama)
|
||||
COACH_PROVIDER = os.environ.get("COACH_PROVIDER", "anthropic")
|
||||
DECK_GEN_BASE_URL = os.environ.get("DECK_GEN_BASE_URL", "http://localhost:11434/v1")
|
||||
# ── Perplexity Deck Generation Settings ───────────────────────
|
||||
# Provider for deck generation: "perplexity" (V3) or "local" (V2 collection-based)
|
||||
DECK_GEN_PROVIDER = os.environ.get("DECK_GEN_PROVIDER", "local")
|
||||
# Model for deck generation (sonar = fast/$0.004, sonar-pro = deep/$0.04)
|
||||
DECK_GEN_MODEL = os.environ.get("DECK_GEN_MODEL", "gpt-oss:20b")
|
||||
DECK_GEN_BASE_URL = os.environ.get("DECK_GEN_BASE_URL", "http://192.168.0.240:11434/v1")
|
||||
DECK_GEN_TEMPERATURE = float(os.environ.get("DECK_GEN_TEMPERATURE", "0.2"))
|
||||
DECK_GEN_MAX_TOKENS = int(os.environ.get("DECK_GEN_MAX_TOKENS", "16384"))
|
||||
|
||||
# ── Smart Substitution Settings ───────────────────────────────────
|
||||
# Minimum embedding similarity to accept a substitute without Anthropic fallback
|
||||
# ── Smart Substitution Settings ───────────────────────────────
|
||||
# Minimum embedding similarity to accept a substitute without Perplexity fallback
|
||||
SUBSTITUTION_MIN_SIMILARITY = float(os.environ.get("SUBSTITUTION_MIN_SIMILARITY", "0.75"))
|
||||
# Maximum number of alternatives to suggest per missing card
|
||||
SUBSTITUTION_MAX_ALTERNATIVES = int(os.environ.get("SUBSTITUTION_MAX_ALTERNATIVES", "5"))
|
||||
# Model for substitution fallback (claude-haiku-4-5 is fast + cheap for focused queries)
|
||||
SUBSTITUTION_MODEL = os.environ.get("SUBSTITUTION_MODEL", "claude-haiku-4-5")
|
||||
# Enable Anthropic fallback for low-confidence embedding matches
|
||||
SUBSTITUTION_USE_ANTHROPIC_FALLBACK = os.environ.get("SUBSTITUTION_USE_ANTHROPIC_FALLBACK", "true").lower() == "true"
|
||||
# Model for substitution fallback (sonar is fine — small focused queries)
|
||||
SUBSTITUTION_MODEL = os.environ.get("SUBSTITUTION_MODEL", "gpt-oss:20b")
|
||||
# Enable Perplexity fallback for low-confidence embedding matches
|
||||
SUBSTITUTION_USE_PPLX_FALLBACK = os.environ.get("SUBSTITUTION_USE_PPLX_FALLBACK", "true").lower() == "true"
|
||||
|
||||
# ── Ensure directories exist ─────────────────────────────────────
|
||||
# ── Ensure directories exist ───────────────────────────────
|
||||
def ensure_dirs():
|
||||
"""Create required directories if they don't exist."""
|
||||
EMBEDDINGS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
+41
-229
@@ -9,11 +9,6 @@ Data source: https://huggingface.co/datasets/minimaxir/mtg-embeddings
|
||||
Format: Parquet with 768-dim float32 embeddings per card (~32K cards)
|
||||
Model: Alibaba-NLP/gte-modernbert-base
|
||||
|
||||
Unknown cards (e.g. newly printed sets) are fetched from Scryfall,
|
||||
embedded on-the-fly using the same model, and persisted to a local
|
||||
custom cache (embeddings/mtg_embeddings_custom.npz) so they survive
|
||||
restarts without re-hitting Scryfall.
|
||||
|
||||
Uses NumPy brute-force cosine similarity — fast enough for ~32K cards (<100ms).
|
||||
"""
|
||||
|
||||
@@ -24,11 +19,9 @@ import os
|
||||
from pathlib import Path
|
||||
from typing import List, Optional, Tuple
|
||||
from urllib.request import urlopen, Request
|
||||
from urllib.parse import quote
|
||||
|
||||
import numpy as np
|
||||
|
||||
from services.card_text import build_card_text
|
||||
from .config import EMBEDDINGS_DIR, EMBEDDINGS_NPZ, EMBEDDINGS_PARQUET
|
||||
|
||||
logger = logging.getLogger("coach.embeddings")
|
||||
@@ -39,16 +32,8 @@ HF_PARQUET_URL = (
|
||||
"resolve/main/mtg_embeddings.parquet"
|
||||
)
|
||||
|
||||
# ── Scryfall API ───────────────────────────────────────────
|
||||
SCRYFALL_NAMED_URL = "https://api.scryfall.com/cards/named?fuzzy={}"
|
||||
|
||||
# ── Custom cache for on-the-fly embedded cards ─────────────
|
||||
CUSTOM_CACHE_PATH = EMBEDDINGS_DIR / "mtg_embeddings_custom.npz"
|
||||
|
||||
# ── Embedding model (same as minimaxir/mtg-embeddings) ─────
|
||||
EMBEDDING_MODEL = "Alibaba-NLP/gte-modernbert-base"
|
||||
|
||||
# ── Color identity parsing ─────────────────────────────────
|
||||
# Maps mana symbols like {W}, {U}, {B}, {R}, {G} to color letters
|
||||
MANA_SYMBOL_COLORS = {
|
||||
"W": "W", "U": "U", "B": "B", "R": "R", "G": "G"
|
||||
}
|
||||
@@ -61,6 +46,7 @@ def _parse_color_identity(mana_cost: str) -> str:
|
||||
colors = set()
|
||||
for match in re.finditer(r'\{([WUBRG])\}', mana_cost or ""):
|
||||
colors.add(match.group(1))
|
||||
# Sort in WUBRG order
|
||||
order = "WUBRG"
|
||||
return "".join(c for c in order if c in colors)
|
||||
|
||||
@@ -75,11 +61,12 @@ def _parse_mana_value(mana_cost: str) -> float:
|
||||
if symbol in MANA_SYMBOL_COLORS:
|
||||
total += 1.0
|
||||
elif symbol == "X":
|
||||
pass
|
||||
pass # X = 0 for CMC purposes
|
||||
else:
|
||||
try:
|
||||
total += float(symbol)
|
||||
except ValueError:
|
||||
# Hybrid/phyrexian symbols count as 1
|
||||
total += 1.0
|
||||
return total
|
||||
|
||||
@@ -88,35 +75,7 @@ def _is_color_subset(card_colors: str, deck_colors: List[str]) -> bool:
|
||||
"""Check if card's color identity is within deck's color identity."""
|
||||
deck_set = set(deck_colors)
|
||||
card_set = set(card_colors)
|
||||
return card_set.issubset(deck_set) or len(card_set) == 0
|
||||
|
||||
|
||||
def _fetch_scryfall_card(card_name: str) -> Optional[dict]:
|
||||
"""Fetch card data from Scryfall by fuzzy name match.
|
||||
|
||||
Returns a dict with keys: name, mana_cost, type_line, oracle_text,
|
||||
color_identity. Returns None on any error.
|
||||
"""
|
||||
url = SCRYFALL_NAMED_URL.format(quote(card_name))
|
||||
try:
|
||||
req = Request(url, headers={"User-Agent": "commander-ai-lab/1.0"})
|
||||
with urlopen(req, timeout=10) as resp:
|
||||
data = json.loads(resp.read().decode("utf-8"))
|
||||
return {
|
||||
"name": data.get("name", card_name),
|
||||
"mana_cost": data.get("mana_cost", ""),
|
||||
"type_line": data.get("type_line", ""),
|
||||
"oracle_text": data.get("oracle_text", ""),
|
||||
"color_identity": "".join(data.get("color_identity", [])),
|
||||
}
|
||||
except Exception as e:
|
||||
logger.warning("Scryfall lookup failed for '%s': %s", card_name, e)
|
||||
return None
|
||||
|
||||
|
||||
def _build_card_text(card: dict) -> str:
|
||||
"""Delegate to shared build_card_text() -- see services/card_text.py."""
|
||||
return build_card_text(card)
|
||||
return card_set.issubset(deck_set) or len(card_set) == 0 # colorless always allowed
|
||||
|
||||
|
||||
class CardMatch:
|
||||
@@ -155,17 +114,15 @@ class MTGEmbeddingIndex:
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.names: np.ndarray = None
|
||||
self.colors: np.ndarray = None
|
||||
self.mana_values: np.ndarray = None
|
||||
self.types: np.ndarray = None
|
||||
self.mana_costs: np.ndarray = None
|
||||
self.texts: np.ndarray = None
|
||||
self.vectors: np.ndarray = None
|
||||
self.names: np.ndarray = None # (N,) string array
|
||||
self.colors: np.ndarray = None # (N,) string array (e.g., "WU", "BRG")
|
||||
self.mana_values: np.ndarray = None # (N,) float array
|
||||
self.types: np.ndarray = None # (N,) string array
|
||||
self.mana_costs: np.ndarray = None # (N,) string array
|
||||
self.texts: np.ndarray = None # (N,) string array
|
||||
self.vectors: np.ndarray = None # (N, 768) float32, L2-normalized
|
||||
self._name_to_idx: dict = {}
|
||||
self._loaded = False
|
||||
self._sentence_model = None # lazy-loaded only when needed
|
||||
self._custom_dirty = False # tracks whether custom cache needs saving
|
||||
|
||||
@property
|
||||
def loaded(self) -> bool:
|
||||
@@ -195,7 +152,10 @@ class MTGEmbeddingIndex:
|
||||
return EMBEDDINGS_PARQUET
|
||||
|
||||
def convert_parquet_to_npz(self) -> Path:
|
||||
"""Convert the Parquet file to a compact NPZ for fast loading."""
|
||||
"""
|
||||
Convert the Parquet file to a compact NPZ for fast loading.
|
||||
Requires pandas and pyarrow.
|
||||
"""
|
||||
try:
|
||||
import pandas as pd
|
||||
except ImportError:
|
||||
@@ -209,11 +169,14 @@ class MTGEmbeddingIndex:
|
||||
types = df["type"].fillna("").values.astype(str)
|
||||
texts = df["text"].fillna("").values.astype(str)
|
||||
|
||||
# Parse color identities from mana costs
|
||||
colors = np.array([_parse_color_identity(mc) for mc in mana_costs])
|
||||
mana_values = np.array([_parse_mana_value(mc) for mc in mana_costs], dtype=np.float32)
|
||||
|
||||
# Extract embedding vectors
|
||||
embeddings = np.vstack(df["embedding"].to_numpy()).astype(np.float32)
|
||||
|
||||
# L2 normalize for cosine similarity via dot product
|
||||
norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
|
||||
norms[norms == 0] = 1.0
|
||||
embeddings = embeddings / norms
|
||||
@@ -235,27 +198,25 @@ class MTGEmbeddingIndex:
|
||||
return EMBEDDINGS_NPZ
|
||||
|
||||
def load(self, force_download: bool = False) -> bool:
|
||||
"""Load embeddings from NPZ (fast) or fall back to downloading
|
||||
"""
|
||||
Load embeddings from NPZ (fast) or fall back to downloading
|
||||
and converting from Parquet.
|
||||
"""
|
||||
if self._loaded and not force_download:
|
||||
return True
|
||||
|
||||
# Try NPZ first (fast path)
|
||||
if EMBEDDINGS_NPZ.exists() and not force_download:
|
||||
ok = self._load_npz()
|
||||
else:
|
||||
try:
|
||||
self.download_parquet(force=force_download)
|
||||
self.convert_parquet_to_npz()
|
||||
ok = self._load_npz()
|
||||
except Exception as e:
|
||||
logger.error("Failed to load embeddings: %s", e)
|
||||
return False
|
||||
return self._load_npz()
|
||||
|
||||
if ok:
|
||||
self._load_custom_cache()
|
||||
|
||||
return ok
|
||||
# Download and convert
|
||||
try:
|
||||
self.download_parquet(force=force_download)
|
||||
self.convert_parquet_to_npz()
|
||||
return self._load_npz()
|
||||
except Exception as e:
|
||||
logger.error("Failed to load embeddings: %s", e)
|
||||
return False
|
||||
|
||||
def _load_npz(self) -> bool:
|
||||
"""Load from pre-built NPZ file."""
|
||||
@@ -269,6 +230,7 @@ class MTGEmbeddingIndex:
|
||||
self.texts = data.get("texts", np.array([""] * len(self.names)))
|
||||
self.vectors = data["vectors"]
|
||||
|
||||
# Build name lookup
|
||||
self._name_to_idx = {
|
||||
name.lower(): i for i, name in enumerate(self.names)
|
||||
}
|
||||
@@ -279,168 +241,12 @@ class MTGEmbeddingIndex:
|
||||
logger.error("Failed to load NPZ: %s", e)
|
||||
return False
|
||||
|
||||
# ── Custom cache ──────────────────────────────────────────
|
||||
|
||||
def _load_custom_cache(self) -> None:
|
||||
"""Merge on-the-fly embedded cards from the custom cache into the index."""
|
||||
if not CUSTOM_CACHE_PATH.exists():
|
||||
return
|
||||
try:
|
||||
data = np.load(str(CUSTOM_CACHE_PATH), allow_pickle=True)
|
||||
c_names = data["names"]
|
||||
# Only add cards that aren't already in the main index
|
||||
new_mask = np.array(
|
||||
[n.lower() not in self._name_to_idx for n in c_names]
|
||||
)
|
||||
if not new_mask.any():
|
||||
return
|
||||
self._append_cards(
|
||||
c_names[new_mask],
|
||||
data["colors"][new_mask],
|
||||
data["mana_values"][new_mask],
|
||||
data["types"][new_mask],
|
||||
data["mana_costs"][new_mask],
|
||||
data["texts"][new_mask],
|
||||
data["vectors"][new_mask],
|
||||
)
|
||||
logger.info("Loaded %d custom-cached card embeddings", new_mask.sum())
|
||||
except Exception as e:
|
||||
logger.warning("Failed to load custom embedding cache: %s", e)
|
||||
|
||||
def _save_custom_cache(self) -> None:
|
||||
"""Persist the custom cache NPZ (only cards not in the base dataset)."""
|
||||
if not self._custom_dirty:
|
||||
return
|
||||
try:
|
||||
EMBEDDINGS_DIR.mkdir(parents=True, exist_ok=True)
|
||||
# Collect indices that correspond to custom-added cards
|
||||
# We track these by loading the base count at load time — simpler:
|
||||
# just re-save the full custom cache from CUSTOM_CACHE_PATH if it
|
||||
# exists, merging with any new cards we've added this session.
|
||||
existing_names: set = set()
|
||||
existing: dict = {}
|
||||
if CUSTOM_CACHE_PATH.exists():
|
||||
try:
|
||||
d = np.load(str(CUSTOM_CACHE_PATH), allow_pickle=True)
|
||||
for n in d["names"]:
|
||||
existing_names.add(n.lower())
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Find names in our live index that aren't in the base NPZ
|
||||
base_data = np.load(str(EMBEDDINGS_NPZ), allow_pickle=True)
|
||||
base_names = set(n.lower() for n in base_data["names"])
|
||||
custom_mask = np.array(
|
||||
[n.lower() not in base_names for n in self.names]
|
||||
)
|
||||
if not custom_mask.any():
|
||||
return
|
||||
|
||||
np.savez_compressed(
|
||||
str(CUSTOM_CACHE_PATH),
|
||||
names=self.names[custom_mask],
|
||||
colors=self.colors[custom_mask],
|
||||
mana_values=self.mana_values[custom_mask],
|
||||
types=self.types[custom_mask],
|
||||
mana_costs=self.mana_costs[custom_mask],
|
||||
texts=self.texts[custom_mask],
|
||||
vectors=self.vectors[custom_mask],
|
||||
)
|
||||
self._custom_dirty = False
|
||||
logger.info("Saved custom embedding cache: %d cards", custom_mask.sum())
|
||||
except Exception as e:
|
||||
logger.warning("Failed to save custom embedding cache: %s", e)
|
||||
|
||||
def _append_cards(
|
||||
self,
|
||||
names: np.ndarray,
|
||||
colors: np.ndarray,
|
||||
mana_values: np.ndarray,
|
||||
types: np.ndarray,
|
||||
mana_costs: np.ndarray,
|
||||
texts: np.ndarray,
|
||||
vectors: np.ndarray,
|
||||
) -> None:
|
||||
"""Append new card arrays to the live in-memory index."""
|
||||
start_idx = len(self.names)
|
||||
self.names = np.concatenate([self.names, names])
|
||||
self.colors = np.concatenate([self.colors, colors])
|
||||
self.mana_values = np.concatenate([self.mana_values, mana_values])
|
||||
self.types = np.concatenate([self.types, types])
|
||||
self.mana_costs = np.concatenate([self.mana_costs, mana_costs])
|
||||
self.texts = np.concatenate([self.texts, texts])
|
||||
self.vectors = np.concatenate([self.vectors, vectors], axis=0)
|
||||
for i, name in enumerate(names):
|
||||
self._name_to_idx[name.lower()] = start_idx + i
|
||||
|
||||
# ── Sentence model (lazy) ─────────────────────────────────
|
||||
|
||||
def _get_sentence_model(self):
|
||||
"""Lazy-load the sentence-transformers model on first cache miss."""
|
||||
if self._sentence_model is None:
|
||||
try:
|
||||
from sentence_transformers import SentenceTransformer
|
||||
logger.info("Loading embedding model %s (first cache miss)...", EMBEDDING_MODEL)
|
||||
self._sentence_model = SentenceTransformer(EMBEDDING_MODEL)
|
||||
logger.info("Embedding model loaded.")
|
||||
except Exception as e:
|
||||
logger.error("Failed to load SentenceTransformer: %s", e)
|
||||
return None
|
||||
return self._sentence_model
|
||||
|
||||
# ── On-the-fly Scryfall fallback ──────────────────────────
|
||||
|
||||
def _fetch_and_embed_card(self, card_name: str) -> Optional[np.ndarray]:
|
||||
"""Fetch card from Scryfall, embed it, append to index, persist to cache.
|
||||
|
||||
Returns the L2-normalized embedding vector on success, None on failure.
|
||||
"""
|
||||
logger.info("Fetching unknown card from Scryfall: '%s'", card_name)
|
||||
card_data = _fetch_scryfall_card(card_name)
|
||||
if card_data is None:
|
||||
return None
|
||||
|
||||
model = self._get_sentence_model()
|
||||
if model is None:
|
||||
return None
|
||||
|
||||
text = _build_card_text(card_data)
|
||||
vec = model.encode(text, normalize_embeddings=True).astype(np.float32)
|
||||
# Ensure shape (1, 768)
|
||||
vec = vec.reshape(1, -1)
|
||||
|
||||
resolved_name = card_data["name"]
|
||||
mana_cost = card_data.get("mana_cost", "")
|
||||
color_identity = card_data.get("color_identity", "") or _parse_color_identity(mana_cost)
|
||||
|
||||
self._append_cards(
|
||||
names=np.array([resolved_name]),
|
||||
colors=np.array([color_identity]),
|
||||
mana_values=np.array([_parse_mana_value(mana_cost)], dtype=np.float32),
|
||||
types=np.array([card_data.get("type_line", "")]),
|
||||
mana_costs=np.array([mana_cost]),
|
||||
texts=np.array([card_data.get("oracle_text", "")]),
|
||||
vectors=vec,
|
||||
)
|
||||
|
||||
self._custom_dirty = True
|
||||
self._save_custom_cache()
|
||||
logger.info("Embedded and cached new card: '%s'", resolved_name)
|
||||
return self.vectors[self._name_to_idx[resolved_name.lower()]]
|
||||
|
||||
# ── Public API ────────────────────────────────────────────
|
||||
|
||||
def get_card_vector(self, card_name: str) -> Optional[np.ndarray]:
|
||||
"""Get the embedding vector for a card by name (case-insensitive).
|
||||
|
||||
On a cache miss, fetches from Scryfall and embeds on the fly.
|
||||
"""
|
||||
"""Get the embedding vector for a card by name (case-insensitive)."""
|
||||
idx = self._name_to_idx.get(card_name.lower())
|
||||
if idx is not None:
|
||||
return self.vectors[idx]
|
||||
|
||||
# Cache miss — try Scryfall fallback
|
||||
return self._fetch_and_embed_card(card_name)
|
||||
return None
|
||||
|
||||
def search_similar(self, query_card: str,
|
||||
color_filter: List[str] = None,
|
||||
@@ -468,32 +274,38 @@ class MTGEmbeddingIndex:
|
||||
|
||||
query_vec = self.get_card_vector(query_card)
|
||||
if query_vec is None:
|
||||
logger.warning("Card not found in embeddings and Scryfall fallback failed: %s", query_card)
|
||||
logger.warning("Card not found in embeddings: %s", query_card)
|
||||
return []
|
||||
|
||||
# Compute similarities (dot product on normalized vectors = cosine similarity)
|
||||
similarities = self.vectors @ query_vec
|
||||
|
||||
# Build exclusion set
|
||||
exclude_set = set()
|
||||
exclude_set.add(query_card.lower())
|
||||
if exclude_cards:
|
||||
exclude_set.update(name.lower() for name in exclude_cards)
|
||||
|
||||
# Filter and collect results
|
||||
results = []
|
||||
for idx in np.argsort(similarities)[::-1]:
|
||||
name = str(self.names[idx])
|
||||
if name.lower() in exclude_set:
|
||||
continue
|
||||
|
||||
# Color filter
|
||||
if color_filter:
|
||||
card_colors = str(self.colors[idx])
|
||||
if not _is_color_subset(card_colors, color_filter):
|
||||
continue
|
||||
|
||||
# Type filter
|
||||
if type_filter:
|
||||
card_type = str(self.types[idx])
|
||||
if type_filter.lower() not in card_type.lower():
|
||||
continue
|
||||
|
||||
# Mana value range filter
|
||||
if mana_range:
|
||||
mv = float(self.mana_values[idx])
|
||||
if mv < mana_range[0] or mv > mana_range[1]:
|
||||
|
||||
+130
-146
@@ -1,25 +1,22 @@
|
||||
"""
|
||||
Commander AI Lab — Ollama LLM Client
|
||||
Commander AI Lab — Ollama Client
|
||||
════════════════════════════════════
|
||||
Fully async HTTP client targeting Ollama's OpenAI-compatible
|
||||
/v1/chat/completions endpoint. Every network call uses
|
||||
httpx.AsyncClient so it NEVER blocks the FastAPI event loop.
|
||||
Async HTTP client targeting Ollama's OpenAI-compatible
|
||||
/v1/chat/completions endpoint. Includes retry logic for
|
||||
non-JSON responses and configurable timeout.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import asyncio
|
||||
from typing import Optional
|
||||
|
||||
import httpx
|
||||
from urllib.request import urlopen, Request
|
||||
from urllib.error import URLError
|
||||
|
||||
from .config import (
|
||||
LLM_URL,
|
||||
LLM_MODEL,
|
||||
LLM_TIMEOUT,
|
||||
LLM_MAX_RETRIES,
|
||||
DEFAULT_TEMPERATURE,
|
||||
DEFAULT_MAX_TOKENS,
|
||||
LLM_URL, LLM_MODEL,
|
||||
LLM_TIMEOUT, LLM_MAX_RETRIES,
|
||||
DEFAULT_TEMPERATURE, DEFAULT_MAX_TOKENS,
|
||||
)
|
||||
|
||||
logger = logging.getLogger("coach.llm")
|
||||
@@ -27,15 +24,9 @@ logger = logging.getLogger("coach.llm")
|
||||
|
||||
class LLMResponse:
|
||||
"""Parsed LLM response container."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
content: str,
|
||||
parsed_json: Optional[dict],
|
||||
prompt_tokens: int,
|
||||
completion_tokens: int,
|
||||
model: str,
|
||||
):
|
||||
def __init__(self, content: str, parsed_json: Optional[dict],
|
||||
prompt_tokens: int, completion_tokens: int,
|
||||
model: str):
|
||||
self.content = content
|
||||
self.parsed_json = parsed_json
|
||||
self.prompt_tokens = prompt_tokens
|
||||
@@ -45,7 +36,7 @@ class LLMResponse:
|
||||
|
||||
class LLMClient:
|
||||
"""
|
||||
Fully async client for Ollama's OpenAI-compatible API.
|
||||
Client for Ollama's OpenAI-compatible API.
|
||||
|
||||
Usage:
|
||||
client = LLMClient()
|
||||
@@ -54,45 +45,32 @@ class LLMClient:
|
||||
suggestions = response.parsed_json
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
base_url: str = None,
|
||||
model: str = None,
|
||||
timeout: int = None,
|
||||
):
|
||||
def __init__(self, base_url: str = None, model: str = None,
|
||||
timeout: int = None):
|
||||
self.base_url = (base_url or LLM_URL).rstrip("/")
|
||||
self.model = model or LLM_MODEL
|
||||
self.timeout = timeout or LLM_TIMEOUT
|
||||
self._resolved_model: Optional[str] = None
|
||||
self._resolved_model = None # actual model name from /models endpoint
|
||||
|
||||
# ── Model resolution ───────────────────────────────────────
|
||||
|
||||
async def _resolve_model(self) -> str:
|
||||
def _resolve_model(self) -> str:
|
||||
"""Get the actual loaded model name from Ollama /models endpoint."""
|
||||
if self._resolved_model:
|
||||
return self._resolved_model
|
||||
try:
|
||||
status = await self.check_connection()
|
||||
status = self.check_connection()
|
||||
if status.get("connected") and status.get("active_model"):
|
||||
self._resolved_model = self.model if self.model in (status.get("models") or []) else status["active_model"] # FARMHOUSE_MODEL_PIN_V1
|
||||
self._resolved_model = status["active_model"]
|
||||
logger.info("Resolved Ollama model: %s", self._resolved_model)
|
||||
return self._resolved_model
|
||||
except Exception:
|
||||
pass
|
||||
return self.model
|
||||
|
||||
# ── Request building ──────────────────────────────────────
|
||||
|
||||
async def _build_request_body(
|
||||
self,
|
||||
system_prompt: str,
|
||||
user_prompt: str,
|
||||
temperature: float,
|
||||
max_tokens: int,
|
||||
force_json: bool,
|
||||
) -> dict:
|
||||
model_name = await self._resolve_model()
|
||||
body: dict = {
|
||||
def _build_request_body(self, system_prompt: str, user_prompt: str,
|
||||
temperature: float, max_tokens: int,
|
||||
force_json: bool) -> dict:
|
||||
model_name = self._resolve_model()
|
||||
body = {
|
||||
"model": model_name,
|
||||
"messages": [
|
||||
{"role": "system", "content": system_prompt},
|
||||
@@ -101,35 +79,32 @@ class LLMClient:
|
||||
"temperature": temperature,
|
||||
"max_tokens": max_tokens,
|
||||
}
|
||||
# Only request JSON format if using a model that supports it
|
||||
# Some models (esp. smaller ones) don't support response_format
|
||||
if force_json:
|
||||
body["response_format"] = {"type": "json_object"}
|
||||
# FARMHOUSE_LLM_KNOBS_V1 (see patch_app.py)
|
||||
import os as _fh_os
|
||||
_fh_effort = _fh_os.environ.get("LLM_REASONING", "").strip()
|
||||
if _fh_effort:
|
||||
body["reasoning_effort"] = _fh_effort
|
||||
_fh_cap = int(_fh_os.environ.get("LLM_MAX_TOKENS", "0") or 0)
|
||||
if _fh_cap:
|
||||
body["max_tokens"] = min(body["max_tokens"], _fh_cap)
|
||||
return body
|
||||
|
||||
# ── Response parsing ──────────────────────────────────────
|
||||
|
||||
@staticmethod
|
||||
def _strip_think_tags(text: str) -> str:
|
||||
"""Remove DeepSeek-R1 <think>...</think> reasoning blocks."""
|
||||
"""Remove DeepSeek-R1 <think>...</think> reasoning blocks from LLM output."""
|
||||
import re
|
||||
return re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL).strip()
|
||||
# Remove all <think>...</think> blocks (greedy, handles multiline)
|
||||
cleaned = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
|
||||
return cleaned.strip()
|
||||
|
||||
@staticmethod
|
||||
def _extract_json_object(text: str) -> Optional[dict]:
|
||||
"""Find and parse the first complete JSON object via brace-counting."""
|
||||
"""Find and parse the first complete JSON object in text.
|
||||
Uses brace-counting to handle nested objects reliably."""
|
||||
start = text.find('{')
|
||||
if start == -1:
|
||||
return None
|
||||
|
||||
depth = 0
|
||||
in_string = False
|
||||
escape_next = False
|
||||
|
||||
for i in range(start, len(text)):
|
||||
ch = text[i]
|
||||
if escape_next:
|
||||
@@ -149,56 +124,66 @@ class LLMClient:
|
||||
elif ch == '}':
|
||||
depth -= 1
|
||||
if depth == 0:
|
||||
candidate = text[start : i + 1]
|
||||
candidate = text[start:i+1]
|
||||
try:
|
||||
return json.loads(candidate)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
# Malformed — try next opening brace
|
||||
next_start = text.find('{', start + 1)
|
||||
if next_start != -1 and next_start < i:
|
||||
start = next_start
|
||||
depth = 1
|
||||
return None
|
||||
depth = 1 # we're already past this brace
|
||||
return None
|
||||
return None
|
||||
|
||||
def _parse_response(self, raw: dict) -> LLMResponse:
|
||||
"""Extract content and usage from an OpenAI-format response."""
|
||||
"""Extract content and usage from OpenAI-format response.
|
||||
Handles DeepSeek-R1 <think> tags, markdown fences, and raw JSON."""
|
||||
choice = raw.get("choices", [{}])[0]
|
||||
content = choice.get("message", {}).get("content", "")
|
||||
usage = raw.get("usage", {})
|
||||
|
||||
# Step 1: Strip <think>...</think> blocks (DeepSeek-R1)
|
||||
cleaned = self._strip_think_tags(content)
|
||||
parsed: Optional[dict] = None
|
||||
# 1. Direct JSON parse
|
||||
|
||||
# Step 2: Try direct JSON parse on cleaned content
|
||||
parsed = None
|
||||
try:
|
||||
parsed = json.loads(cleaned)
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
# 2. Markdown fences
|
||||
|
||||
# Step 3: Try extracting from markdown fences
|
||||
if parsed is None and "```json" in cleaned:
|
||||
try:
|
||||
s = cleaned.index("```json") + 7
|
||||
e = cleaned.index("```", s)
|
||||
parsed = json.loads(cleaned[s:e].strip())
|
||||
start = cleaned.index("```json") + 7
|
||||
end = cleaned.index("```", start)
|
||||
parsed = json.loads(cleaned[start:end].strip())
|
||||
except (json.JSONDecodeError, ValueError, IndexError):
|
||||
pass
|
||||
if parsed is None and "```" in cleaned:
|
||||
try:
|
||||
s = cleaned.index("```") + 3
|
||||
e = cleaned.index("```", s)
|
||||
parsed = json.loads(cleaned[s:e].strip())
|
||||
start = cleaned.index("```") + 3
|
||||
end = cleaned.index("```", start)
|
||||
parsed = json.loads(cleaned[start:end].strip())
|
||||
except (json.JSONDecodeError, ValueError, IndexError):
|
||||
pass
|
||||
# 3. Brace-counting extraction
|
||||
|
||||
# Step 4: Brute-force — find the first valid JSON object in the text
|
||||
if parsed is None:
|
||||
parsed = self._extract_json_object(cleaned)
|
||||
# 4. Fallback on original content
|
||||
|
||||
# Step 5: If still nothing, try on the original content (in case
|
||||
# think-tag stripping was too aggressive)
|
||||
if parsed is None and content != cleaned:
|
||||
parsed = self._extract_json_object(content)
|
||||
|
||||
if parsed is not None:
|
||||
logger.info("Successfully parsed JSON from LLM response")
|
||||
else:
|
||||
logger.warning(
|
||||
"Could not extract JSON from LLM response (%d chars)", len(content)
|
||||
)
|
||||
logger.warning("Could not extract JSON from LLM response (%d chars)",
|
||||
len(content))
|
||||
|
||||
return LLMResponse(
|
||||
content=content,
|
||||
parsed_json=parsed,
|
||||
@@ -207,95 +192,94 @@ class LLMClient:
|
||||
model=raw.get("model", self.model),
|
||||
)
|
||||
|
||||
# ── Public API ─────────────────────────────────────────────
|
||||
|
||||
async def chat(
|
||||
self,
|
||||
system_prompt: str,
|
||||
user_prompt: str,
|
||||
temperature: float = None,
|
||||
max_tokens: int = None,
|
||||
force_json: bool = True,
|
||||
) -> LLMResponse:
|
||||
def chat_sync(self, system_prompt: str, user_prompt: str,
|
||||
temperature: float = None, max_tokens: int = None,
|
||||
force_json: bool = True) -> LLMResponse:
|
||||
"""
|
||||
Async chat call to Ollama. Retries up to LLM_MAX_RETRIES.
|
||||
Never blocks the event loop — uses httpx.AsyncClient throughout.
|
||||
Synchronous chat call to Ollama.
|
||||
Retries up to LLM_MAX_RETRIES on failure or non-JSON response.
|
||||
"""
|
||||
temp = temperature if temperature is not None else DEFAULT_TEMPERATURE
|
||||
tokens = max_tokens if max_tokens is not None else DEFAULT_MAX_TOKENS
|
||||
body = await self._build_request_body(
|
||||
system_prompt, user_prompt, temp, tokens, force_json
|
||||
)
|
||||
last_error: Optional[Exception] = None
|
||||
body = self._build_request_body(system_prompt, user_prompt,
|
||||
temp, tokens, force_json)
|
||||
|
||||
last_error = None
|
||||
for attempt in range(1, LLM_MAX_RETRIES + 1):
|
||||
try:
|
||||
logger.info("LLM call attempt %d/%d", attempt, LLM_MAX_RETRIES)
|
||||
async with httpx.AsyncClient(
|
||||
timeout=httpx.Timeout(float(self.timeout))
|
||||
) as client:
|
||||
resp = await client.post(
|
||||
f"{self.base_url}/chat/completions",
|
||||
json=body,
|
||||
headers={"Content-Type": "application/json"},
|
||||
)
|
||||
resp.raise_for_status()
|
||||
raw = resp.json()
|
||||
req = Request(
|
||||
f"{self.base_url}/chat/completions",
|
||||
data=json.dumps(body).encode("utf-8"),
|
||||
headers={"Content-Type": "application/json"},
|
||||
method="POST",
|
||||
)
|
||||
with urlopen(req, timeout=self.timeout) as resp:
|
||||
raw = json.loads(resp.read().decode("utf-8"))
|
||||
|
||||
result = self._parse_response(raw)
|
||||
|
||||
# If we need JSON and didn't get it, retry
|
||||
if force_json and result.parsed_json is None:
|
||||
logger.warning(
|
||||
"Attempt %d: LLM returned non-JSON, retrying", attempt
|
||||
)
|
||||
logger.warning("Attempt %d: LLM returned non-JSON, retrying", attempt)
|
||||
last_error = ValueError("Non-JSON response from LLM")
|
||||
continue
|
||||
|
||||
return result
|
||||
except httpx.HTTPStatusError as e:
|
||||
logger.warning("Attempt %d HTTP error %s", attempt, e.response.status_code)
|
||||
last_error = e
|
||||
# 400 with json format → retry without response_format constraint
|
||||
if e.response.status_code == 400 and force_json:
|
||||
logger.warning("Got 400 — retrying without response_format")
|
||||
force_json = False
|
||||
body = await self._build_request_body(
|
||||
system_prompt, user_prompt, temp, tokens, False
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning("Attempt %d failed: %s", attempt, e)
|
||||
last_error = e
|
||||
if attempt < LLM_MAX_RETRIES:
|
||||
import asyncio
|
||||
await asyncio.sleep(2 * attempt)
|
||||
|
||||
# If 400 and we used json response_format, retry without it
|
||||
error_str = str(e)
|
||||
if "400" in error_str and force_json:
|
||||
logger.warning("Got 400 — retrying without response_format constraint")
|
||||
force_json = False
|
||||
body = self._build_request_body(
|
||||
system_prompt, user_prompt, temp, tokens, False)
|
||||
|
||||
if attempt < LLM_MAX_RETRIES:
|
||||
import time
|
||||
time.sleep(2 * attempt) # Simple backoff
|
||||
|
||||
raise ConnectionError(
|
||||
f"LLM call failed after {LLM_MAX_RETRIES} attempts: {last_error}"
|
||||
)
|
||||
|
||||
def check_connection_sync(self) -> dict: # FARMHOUSE_LLM_SYNC_V1
|
||||
"""Blocking version of check_connection (upstream called it and never defined it)."""
|
||||
try:
|
||||
_r = httpx.get(f"{self.base_url}/models", timeout=5.0, headers={"Content-Type": "application/json"})
|
||||
_r.raise_for_status()
|
||||
_ids = [m.get("id", "unknown") for m in _r.json().get("data", [])]
|
||||
return {"connected": True, "models": _ids,
|
||||
"active_model": self.model if self.model in _ids else (_ids[0] if _ids else self.model)}
|
||||
except Exception as _e:
|
||||
return {"connected": False, "error": str(_e), "models": [], "active_model": None}
|
||||
async def chat(self, system_prompt: str, user_prompt: str,
|
||||
temperature: float = None, max_tokens: int = None,
|
||||
force_json: bool = True) -> LLMResponse:
|
||||
"""
|
||||
Async chat call — runs the sync call in a thread executor
|
||||
so it doesn't block the FastAPI event loop.
|
||||
"""
|
||||
loop = asyncio.get_event_loop()
|
||||
return await loop.run_in_executor(
|
||||
None,
|
||||
lambda: self.chat_sync(system_prompt, user_prompt,
|
||||
temperature, max_tokens, force_json)
|
||||
)
|
||||
|
||||
async def check_connection(self) -> dict:
|
||||
"""Check if Ollama is reachable. Returns status dict."""
|
||||
def check_connection(self) -> dict:
|
||||
"""
|
||||
Check if Ollama is reachable. Returns status dict.
|
||||
"""
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=httpx.Timeout(5.0)) as client:
|
||||
resp = await client.get(
|
||||
f"{self.base_url}/models",
|
||||
headers={"Content-Type": "application/json"},
|
||||
)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
models = data.get("data", [])
|
||||
model_ids = [m.get("id", "unknown") for m in models]
|
||||
return {
|
||||
"connected": True,
|
||||
"models": model_ids,
|
||||
"active_model": model_ids[0] if model_ids else self.model,
|
||||
}
|
||||
req = Request(
|
||||
f"{self.base_url}/models",
|
||||
headers={"Content-Type": "application/json"},
|
||||
method="GET",
|
||||
)
|
||||
with urlopen(req, timeout=5) as resp:
|
||||
data = json.loads(resp.read().decode("utf-8"))
|
||||
models = data.get("data", [])
|
||||
model_ids = [m.get("id", "unknown") for m in models]
|
||||
return {
|
||||
"connected": True,
|
||||
"models": model_ids,
|
||||
"active_model": model_ids[0] if model_ids else self.model,
|
||||
}
|
||||
except Exception as e:
|
||||
return {
|
||||
"connected": False,
|
||||
|
||||
+17
-140
@@ -4,9 +4,12 @@ Commander AI Lab — Coach Data Models
|
||||
Pydantic v2 models for deck reports, coach requests/responses,
|
||||
and coaching session persistence.
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
from typing import List, Optional, Dict
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Deck Report Models (mirrors Java DeckReport)
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
@@ -14,16 +17,16 @@ from pydantic import BaseModel, Field
|
||||
class CardPerformance(BaseModel):
|
||||
"""Per-card aggregated stats from simulation data."""
|
||||
name: str
|
||||
drawnRate: float = 0.0
|
||||
castRate: float = 0.0
|
||||
drawnRate: float = 0.0 # Fraction of games where card was drawn
|
||||
castRate: float = 0.0 # Fraction of games where drawn AND cast
|
||||
keptInOpeningHandRate: float = 0.0
|
||||
deadCardRate: float = 0.0
|
||||
impactScore: float = 0.0
|
||||
synergyScore: float = 0.0
|
||||
clunkinessScore: float = 0.0
|
||||
deadCardRate: float = 0.0 # Drawn but never cast/used
|
||||
impactScore: float = 0.0 # (winRateWhenCast - overallWinRate) * castRate
|
||||
synergyScore: float = 0.0 # Co-occurrence uplift with other deck cards
|
||||
clunkinessScore: float = 0.0 # deadCardRate * (1 - castRate)
|
||||
avgTurnCast: Optional[float] = None
|
||||
avgDamageDealt: float = 0.0
|
||||
tags: List[str] = Field(default_factory=list)
|
||||
tags: List[str] = Field(default_factory=list) # ramp, draw, removal, etc.
|
||||
|
||||
|
||||
class MatchupRecord(BaseModel):
|
||||
@@ -45,7 +48,7 @@ class DeckMeta(BaseModel):
|
||||
class DeckStructure(BaseModel):
|
||||
"""Deck composition breakdown."""
|
||||
landCount: int = 0
|
||||
curveBuckets: List[int] = Field(default_factory=lambda: [0]*8)
|
||||
curveBuckets: List[int] = Field(default_factory=lambda: [0]*8) # CMC 0-7+
|
||||
cardTypeCounts: Dict[str, int] = Field(default_factory=dict)
|
||||
functionalCounts: Dict[str, int] = Field(default_factory=dict)
|
||||
|
||||
@@ -54,7 +57,7 @@ class ComboRecord(BaseModel):
|
||||
"""Known combo with performance data."""
|
||||
cardNames: List[str]
|
||||
winRateWhenAssembled: float = 0.0
|
||||
assemblyRate: float = 0.0
|
||||
assemblyRate: float = 0.0 # Fraction of games where all pieces drawn
|
||||
|
||||
|
||||
class DeckReport(BaseModel):
|
||||
@@ -81,10 +84,10 @@ class DeckReport(BaseModel):
|
||||
|
||||
class CoachGoals(BaseModel):
|
||||
"""User-specified coaching goals."""
|
||||
targetPowerLevel: Optional[int] = None
|
||||
metaFocus: Optional[str] = None
|
||||
budget: Optional[str] = None
|
||||
focusAreas: List[str] = Field(default_factory=list)
|
||||
targetPowerLevel: Optional[int] = None # 1-10
|
||||
metaFocus: Optional[str] = None # aggro, control, combo, midrange, stax
|
||||
budget: Optional[str] = None # budget, medium, no-limit
|
||||
focusAreas: List[str] = Field(default_factory=list) # e.g., ["ramp", "card draw"]
|
||||
|
||||
|
||||
class CoachRequest(BaseModel):
|
||||
@@ -104,120 +107,12 @@ class SuggestedCut(BaseModel):
|
||||
class SuggestedAdd(BaseModel):
|
||||
"""A card the coach suggests adding."""
|
||||
cardName: str
|
||||
role: str = ""
|
||||
role: str = "" # ramp, draw, removal, finisher, etc.
|
||||
reason: str = ""
|
||||
synergyWith: List[str] = Field(default_factory=list)
|
||||
estimatedManaValue: Optional[float] = None
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Phase 2: Structured Analysis Models
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
class UpgradePriorityItem(BaseModel):
|
||||
"""A single prioritized cut/add swap ranked by expected impact."""
|
||||
rank: int
|
||||
cut: str
|
||||
add: str
|
||||
reasoning: str = ""
|
||||
expectedImpact: str = ""
|
||||
|
||||
|
||||
class CommanderDependency(BaseModel):
|
||||
"""How reliant the deck is on having the commander in play."""
|
||||
score: int = 5
|
||||
dependentCards: List[str] = Field(default_factory=list)
|
||||
recoveryPlan: str = ""
|
||||
|
||||
|
||||
class MulliganAnalysis(BaseModel):
|
||||
"""Opening hand keepability assessment."""
|
||||
estimatedKeepRate: str = ""
|
||||
worstOffenders: List[str] = Field(default_factory=list)
|
||||
recommendation: str = ""
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Phase 3: Qualitative Analysis Models
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
class RemovalCoverage(BaseModel):
|
||||
"""Categorized removal package by permanent type."""
|
||||
creatures: str = ""
|
||||
artifacts: str = ""
|
||||
enchantments: str = ""
|
||||
planeswalkers: str = ""
|
||||
lands: str = ""
|
||||
massRemoval: str = ""
|
||||
gaps: List[str] = Field(default_factory=list)
|
||||
|
||||
|
||||
class RampQuality(BaseModel):
|
||||
"""Ramp package breakdown by subtype and fragility."""
|
||||
manaRocks: str = ""
|
||||
landFetch: str = ""
|
||||
manaDorks: str = ""
|
||||
costReducers: str = ""
|
||||
fragility: str = ""
|
||||
canReachFourByTurnThree: str = ""
|
||||
|
||||
|
||||
class DrawEngineProfile(BaseModel):
|
||||
"""Card draw sustainability analysis."""
|
||||
burstDraw: List[str] = Field(default_factory=list)
|
||||
repeatableEngines: List[str] = Field(default_factory=list)
|
||||
assessment: str = ""
|
||||
sustainability: str = ""
|
||||
|
||||
|
||||
class WinCondition(BaseModel):
|
||||
"""A named win condition with independence rating."""
|
||||
name: str
|
||||
cards: List[str] = Field(default_factory=list)
|
||||
independence: str = ""
|
||||
description: str = ""
|
||||
|
||||
|
||||
class AntiSynergyFlag(BaseModel):
|
||||
"""An internal conflict between cards in the deck."""
|
||||
cards: List[str] = Field(default_factory=list)
|
||||
conflict: str = ""
|
||||
severity: str = ""
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Phase 5: Pod Politics & Meta Awareness Models
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
class PodPresence(BaseModel):
|
||||
"""Commander pod politics and threat assessment."""
|
||||
threatLevel: str = "" # early/medium/late/low
|
||||
politicalTools: List[str] = Field(default_factory=list)
|
||||
adversarialRating: str = "" # low/medium/high
|
||||
recommendation: str = ""
|
||||
|
||||
|
||||
class TempoAssessment(BaseModel):
|
||||
"""When the deck 'comes online' and whether that's fast enough."""
|
||||
peakTurn: int = 0
|
||||
profile: str = "" # early/mid/late
|
||||
survivalWindow: str = "" # Assessment of whether it survives to peak
|
||||
assessment: str = ""
|
||||
|
||||
|
||||
class MetaMatchup(BaseModel):
|
||||
"""Strategic reasoning about deck vs a meta archetype."""
|
||||
archetype: str # fast-combo, stax, control, aggro, etc.
|
||||
assessment: str = ""
|
||||
keyThreats: List[str] = Field(default_factory=list)
|
||||
keyAnswers: List[str] = Field(default_factory=list)
|
||||
overallRating: str = "" # favored/even/unfavored
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Coach Session (persisted result)
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
class CoachSession(BaseModel):
|
||||
"""
|
||||
Complete coaching session result.
|
||||
@@ -236,24 +131,6 @@ class CoachSession(BaseModel):
|
||||
promptTokens: int = 0
|
||||
completionTokens: int = 0
|
||||
goals: Optional[CoachGoals] = None
|
||||
isQuickDigest: bool = False # True for quick digest, False for full analysis
|
||||
|
||||
# Phase 2: Structured analysis fields
|
||||
upgradePriority: List[UpgradePriorityItem] = Field(default_factory=list)
|
||||
commanderDependency: Optional[CommanderDependency] = None
|
||||
mulliganAnalysis: Optional[MulliganAnalysis] = None
|
||||
|
||||
# Phase 3: Qualitative analysis fields
|
||||
removalCoverage: Optional[RemovalCoverage] = None
|
||||
rampQuality: Optional[RampQuality] = None
|
||||
drawEngineProfile: Optional[DrawEngineProfile] = None
|
||||
winConditions: List[WinCondition] = Field(default_factory=list)
|
||||
antiSynergyFlags: List[AntiSynergyFlag] = Field(default_factory=list)
|
||||
|
||||
# Phase 5: Pod politics & meta
|
||||
podPresence: Optional[PodPresence] = None
|
||||
tempoAssessment: Optional[TempoAssessment] = None
|
||||
metaMatchups: List[MetaMatchup] = Field(default_factory=list)
|
||||
|
||||
|
||||
class ApplyRequest(BaseModel):
|
||||
|
||||
+41
-390
@@ -4,24 +4,18 @@ Commander AI Lab — Coach Prompt Template
|
||||
Constructs system and user prompts for the LLM coach.
|
||||
Injects deck report data, underperformer analysis, and
|
||||
candidate replacement cards into a structured template.
|
||||
|
||||
Phase 1 additions (#108):
|
||||
- Sim Insights section: deck-level opening hand quality, curve velocity,
|
||||
synergy outliers, and archetype weak spots surfaced directly in the
|
||||
user prompt so Claude reasons from numbers, not vibes.
|
||||
- _format_archetypes() annotates weak spots and strengths inline.
|
||||
- _format_top_cards() adds keptInOpeningHandRate per card.
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import List, Dict, Optional
|
||||
|
||||
from .models import DeckReport, CoachGoals, CardPerformance
|
||||
from .config import MAX_CANDIDATES_PER_UNDERPERFORMER, MAX_UNDERPERFORMERS
|
||||
|
||||
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# System Prompt
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
SYSTEM_PROMPT_TEMPLATE = """You are an expert Magic: The Gathering Commander deck coach.
|
||||
You analyze deck performance reports from simulation data and provide specific,
|
||||
@@ -33,197 +27,44 @@ RULES:
|
||||
- Base suggestions on the simulation data provided, not general "goodstuff" advice.
|
||||
- Consider mana curve, role balance (ramp, draw, removal, threats), and synergy.
|
||||
- When suggesting replacements, prefer cards that fill the same functional role.
|
||||
- IMPORTANT: Suggest 5-8 DIFFERENT cards to cut and 5-8 DIFFERENT cards to add.
|
||||
- IMPORTANT: Suggest 3-5 DIFFERENT cards to cut and 3-5 DIFFERENT cards to add.
|
||||
- Each suggested add MUST be a different card — never suggest the same card twice.
|
||||
- Vary your suggestions across different functional roles (ramp, draw, removal, threats, utility).
|
||||
- If the deck is missing a functional role (e.g., no card draw, no removal), prioritize adding cards for that role.
|
||||
- If candidate replacements are provided from embeddings search, prefer those over generic suggestions.
|
||||
- For each suggested cut, provide a DETAILED reason citing specific stats (impact score, cast rate, dead card rate, clunkiness, synergy score).
|
||||
- For each suggested add, explain the SPECIFIC synergy with existing cards in the deck.
|
||||
- When reasoning about cuts, also consider keptInOpeningHandRate (low = bad in opening hands) and avgTurnCast (high = too slow for the curve).
|
||||
- When evaluating synergy, use the synergyScore: cards below 0.1 have weak co-occurrence with the rest of the deck.
|
||||
- Use perArchetypeWinRates to identify which matchup types this deck struggles with and tailor suggestions accordingly.
|
||||
- The Sim Insights section shows pre-computed deck-level signals — use these directly in your reasoning.
|
||||
- Cards flagged as [OPENING HAND TRAP] have keptInOpeningHandRate below the deck average — treat as mulligan liabilities.
|
||||
- Cards flagged as [LATE CASTER] have avgTurnCast > 6 — consider whether they fit the deck's tempo.
|
||||
- Cards flagged as [ISOLATED] have synergyScore in the bottom 3 of the deck — they may not support the gameplan.
|
||||
- Archetypes flagged as [WEAK SPOT] in the Archetype Win Rates section are where this deck loses most — prioritize fixes for those matchups.
|
||||
- Provide at least 5 heuristic hints covering different strategic dimensions.
|
||||
- Assess how dependent the deck is on the commander being in play.
|
||||
- Evaluate opening hand quality and identify cards that cause bad mulligans.
|
||||
- Rank your suggested swaps by priority so the user knows what to change first.
|
||||
- Categorize the removal package by permanent type and flag coverage gaps.
|
||||
- Evaluate the ramp package for fragility (creature/artifact-heavy vs land-based).
|
||||
- Distinguish one-time draw from repeatable draw engines.
|
||||
- Identify the top 3 win conditions and rate each for independence.
|
||||
- Flag internal anti-synergies or card conflicts.
|
||||
- Assess pod threat perception: does this deck draw too much hate too early?
|
||||
- Evaluate when the deck comes online (tempo profile) and whether that's viable.
|
||||
- Reason about matchups vs fast combo, stax/prison, and control archetypes.
|
||||
{goals_section}
|
||||
|
||||
You MUST respond ONLY with valid JSON matching this exact structure:
|
||||
{{
|
||||
"summary": "4-6 sentence detailed overview of the deck's strengths, weaknesses, key synergies, and strategic identity. Reference specific win rates and simulation data.",
|
||||
"summary": "2-3 sentence overview of the deck's strengths and weaknesses",
|
||||
"suggestedCuts": [
|
||||
{{
|
||||
"cardName": "Card To Remove",
|
||||
"reason": "Detailed explanation citing specific simulation stats (impact score, cast rate, dead card rate, clunkiness score, synergy score, avg turn cast) and why this card underperforms in this specific deck context",
|
||||
"replacementOptions": ["Better Card 1", "Better Card 2"],
|
||||
"functionalRole": "ramp|draw|removal|threat|utility|combo|protection",
|
||||
"estimatedImpact": "How cutting this card is expected to improve the deck"
|
||||
"reason": "Why this card underperforms",
|
||||
"replacementOptions": ["Better Card 1", "Better Card 2"]
|
||||
}}
|
||||
],
|
||||
"suggestedAdds": [
|
||||
{{
|
||||
"cardName": "Card To Add",
|
||||
"role": "ramp|draw|removal|threat|utility|combo|protection",
|
||||
"reason": "Detailed explanation of why this card improves the deck, referencing specific weaknesses it addresses",
|
||||
"synergyWith": ["Card In Deck 1", "Card In Deck 2"],
|
||||
"estimatedManaCost": "The mana value of the suggested card",
|
||||
"strategicRationale": "How this card fits into the overall game plan and addresses a specific gap"
|
||||
"reason": "Why this card improves the deck",
|
||||
"synergyWith": ["Card In Deck 1", "Card In Deck 2"]
|
||||
}}
|
||||
],
|
||||
"upgradePriority": [
|
||||
{{
|
||||
"rank": 1,
|
||||
"cut": "Card to remove",
|
||||
"add": "Card to add instead",
|
||||
"reasoning": "Why this is the single highest-impact swap",
|
||||
"expectedImpact": "high|medium|low"
|
||||
}}
|
||||
],
|
||||
"commanderDependency": {{
|
||||
"score": 5,
|
||||
"dependentCards": ["Card that is nearly dead without commander"],
|
||||
"recoveryPlan": "Strategy if the commander gets removed or tucked repeatedly"
|
||||
}},
|
||||
"mulliganAnalysis": {{
|
||||
"estimatedKeepRate": "Percentage estimate of keepable 7-card hands, e.g. ~70%",
|
||||
"worstOffenders": ["Card most responsible for unkeepable hands"],
|
||||
"recommendation": "What to change to improve opening hand quality"
|
||||
}},
|
||||
"manaCurveAnalysis": {{
|
||||
"currentAssessment": "Detailed analysis of the current mana curve distribution and its implications",
|
||||
"recommendations": "Specific suggestions for curve adjustments with reasoning",
|
||||
"idealRange": "What the curve should look like for this deck's strategy"
|
||||
}},
|
||||
"roleBreakdown": {{
|
||||
"ramp": "Assessment of ramp package - count, quality, and suggestions",
|
||||
"draw": "Assessment of card draw package",
|
||||
"removal": "Assessment of removal package",
|
||||
"threats": "Assessment of win conditions and threats",
|
||||
"protection": "Assessment of protection and interaction"
|
||||
}},
|
||||
"synergyAnalysis": [
|
||||
"Description of a key synergy chain in the deck and how well it performed in simulation"
|
||||
],
|
||||
"heuristicHints": [
|
||||
"Detailed strategic tip explaining the reasoning and data behind the suggestion"
|
||||
"Concise strategic tip based on data"
|
||||
],
|
||||
"manaBaseAdvice": "Specific mana base improvement suggestion including land count, color fixing, and utility lands, or null if mana base is solid",
|
||||
"rawTextExplanation": "3-5 paragraph in-depth analysis covering: (1) overall deck strategy assessment and how well the deck executes its game plan based on simulation data, (2) mana curve and resource analysis with specific numbers including avgTurnCast for key cards, (3) card synergy evaluation highlighting the strongest and weakest interactions using synergyScore data, (4) meta considerations and matchup-specific insights referencing perArchetypeWinRates and the [WEAK SPOT] archetypes flagged in Sim Insights, and (5) prioritized list of improvements ranked by expected impact on win rate",
|
||||
"removalCoverage": {{
|
||||
"creatures": "Assessment of creature removal",
|
||||
"artifacts": "Assessment of artifact removal",
|
||||
"enchantments": "Assessment of enchantment removal",
|
||||
"planeswalkers": "Assessment of planeswalker removal",
|
||||
"lands": "Assessment of land destruction",
|
||||
"massRemoval": "Assessment of board wipes",
|
||||
"gaps": ["Permanent types with zero or weak coverage"]
|
||||
}},
|
||||
"rampQuality": {{
|
||||
"manaRocks": "2-mana rocks, signets, etc.",
|
||||
"landFetch": "Cultivate, Kodama's Reach, etc.",
|
||||
"manaDorks": "Llanowar Elves, Birds, etc.",
|
||||
"costReducers": "Medallions, Helm of Awakening, etc.",
|
||||
"fragility": "Overall fragility assessment",
|
||||
"canReachFourByTurnThree": "yes/no with reasoning"
|
||||
}},
|
||||
"drawEngineProfile": {{
|
||||
"burstDraw": ["One-time draw spells"],
|
||||
"repeatableEngines": ["Sustained draw engines"],
|
||||
"assessment": "Overall draw quality",
|
||||
"sustainability": "Whether draw is sustainable or burst-only"
|
||||
}},
|
||||
"winConditions": [
|
||||
{{
|
||||
"name": "Name of win condition",
|
||||
"cards": ["Key cards involved"],
|
||||
"independence": "high|medium|low",
|
||||
"description": "How this win con functions"
|
||||
}}
|
||||
],
|
||||
"antiSynergyFlags": [
|
||||
{{
|
||||
"cards": ["Card A", "Card B"],
|
||||
"conflict": "Description of conflict",
|
||||
"severity": "high|medium|low"
|
||||
}}
|
||||
],
|
||||
"podPresence": {{
|
||||
"threatLevel": "early|medium|late|low",
|
||||
"politicalTools": ["Pillowfort or group effect cards"],
|
||||
"adversarialRating": "low|medium|high",
|
||||
"recommendation": "How to manage threat perception in a pod"
|
||||
}},
|
||||
"tempoAssessment": {{
|
||||
"peakTurn": 5,
|
||||
"profile": "early|mid|late",
|
||||
"survivalWindow": "Assessment of whether the deck survives to its power window",
|
||||
"assessment": "Overall tempo evaluation"
|
||||
}},
|
||||
"metaMatchups": [
|
||||
{{
|
||||
"archetype": "fast-combo|stax|control|aggro|midrange",
|
||||
"assessment": "How the deck fares against this archetype",
|
||||
"keyThreats": ["Cards/strategies that threaten this deck"],
|
||||
"keyAnswers": ["Cards in the deck that answer this archetype"],
|
||||
"overallRating": "favored|even|unfavored"
|
||||
}}
|
||||
]
|
||||
"manaBaseAdvice": "Specific mana base improvement suggestion or null",
|
||||
"rawTextExplanation": "Detailed paragraph explaining the analysis and reasoning"
|
||||
}}
|
||||
|
||||
Do NOT include any text outside the JSON object. No markdown fences, no preamble."""
|
||||
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# Quick Digest Prompt
|
||||
# ════════════════════════════════════════════════════════════
|
||||
|
||||
QUICK_DIGEST_SYSTEM_PROMPT = """You are an expert Magic: The Gathering Commander deck coach.
|
||||
Provide a FAST summary analysis of this deck. Be concise.
|
||||
|
||||
RULES:
|
||||
- NEVER suggest cutting the commander ({commander}).
|
||||
- Only suggest cards within the deck's color identity: {color_identity}.
|
||||
- Cards flagged [OPENING HAND TRAP] have low keptInOpeningHandRate — prime cut candidates.
|
||||
- Cards flagged [WEAK SPOT] in archetype win rates show where the deck loses most.
|
||||
{goals_section}
|
||||
|
||||
You MUST respond ONLY with valid JSON matching this exact structure:
|
||||
{{
|
||||
"summary": "3-sentence deck overview: what it does, main strength, main weakness.",
|
||||
"upgradePriority": [
|
||||
{{
|
||||
"rank": 1,
|
||||
"cut": "Card to remove",
|
||||
"add": "Card to add instead",
|
||||
"reasoning": "Why this is a high-impact swap",
|
||||
"expectedImpact": "high|medium|low"
|
||||
}}
|
||||
],
|
||||
"commanderDependency": {{
|
||||
"score": 5,
|
||||
"dependentCards": ["Card that is nearly dead without commander"],
|
||||
"recoveryPlan": "Brief recovery strategy"
|
||||
}}
|
||||
}}
|
||||
Do NOT include any text outside the JSON object. No markdown fences, no preamble."""
|
||||
|
||||
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# User Prompt Template
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# User Prompt Builder
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
USER_PROMPT_TEMPLATE = """## Deck Report: {deck_name}
|
||||
Commander: {commander}
|
||||
@@ -238,29 +79,19 @@ Avg Game Length: {avg_game_length:.1f} turns
|
||||
- Card Types: {type_summary}
|
||||
- Functional Roles: {role_summary}
|
||||
|
||||
### Sim Insights (pre-computed deck-level signals)
|
||||
{sim_insights_section}
|
||||
|
||||
### Matchup Data
|
||||
{matchup_section}
|
||||
|
||||
### Archetype Win Rates
|
||||
{archetype_section}
|
||||
|
||||
### Top Performing Cards (by Impact Score)
|
||||
{top_cards}
|
||||
|
||||
### Underperforming Cards (candidates for cuts)
|
||||
{underperformer_section}
|
||||
|
||||
|
||||
### RAG-Retrieved Similar Cards (from ChromaDB)
|
||||
{rag_section}
|
||||
### Candidate Replacement Cards (from embeddings search)
|
||||
{candidate_section}
|
||||
|
||||
{combo_section}
|
||||
|
||||
Based on this simulation data, provide your coaching suggestions."""
|
||||
|
||||
|
||||
@@ -268,6 +99,7 @@ def _format_goals_section(goals: Optional[CoachGoals]) -> str:
|
||||
"""Format goals into system prompt rules."""
|
||||
if not goals:
|
||||
return "- Aim for a balanced, well-rounded deck."
|
||||
|
||||
lines = []
|
||||
if goals.targetPowerLevel is not None:
|
||||
lines.append(f"- Target power level: {goals.targetPowerLevel}/10.")
|
||||
@@ -294,148 +126,42 @@ def _format_curve(buckets: List[int]) -> str:
|
||||
|
||||
|
||||
def _format_top_cards(cards: List[CardPerformance], top_n: int = 10) -> str:
|
||||
"""Format the best performing cards with extended stats.
|
||||
|
||||
Phase 1: adds keptInOpeningHandRate so Claude sees hand quality for
|
||||
the good cards too, not just the underperformers.
|
||||
"""
|
||||
"""Format the best performing cards."""
|
||||
sorted_cards = sorted(cards, key=lambda c: c.impactScore, reverse=True)
|
||||
lines = []
|
||||
for c in sorted_cards[:top_n]:
|
||||
tags = f" [{', '.join(c.tags)}]" if c.tags else ""
|
||||
turn_str = f", avgTurnCast={c.avgTurnCast:.1f}" if c.avgTurnCast is not None else ""
|
||||
lines.append(
|
||||
f"- {c.name}: impact={c.impactScore:.3f}, "
|
||||
f"castRate={c.castRate:.1%}, drawnRate={c.drawnRate:.1%}, "
|
||||
f"synergy={c.synergyScore:.3f}, "
|
||||
f"keptInOpeningHand={c.keptInOpeningHandRate:.1%}"
|
||||
f"{turn_str}{tags}"
|
||||
)
|
||||
lines.append(f"- {c.name}: impact={c.impactScore:.3f}, "
|
||||
f"castRate={c.castRate:.1%}, drawnRate={c.drawnRate:.1%}{tags}")
|
||||
return "\n".join(lines) if lines else "No card performance data available."
|
||||
|
||||
|
||||
def _format_underperformers(cards: List[CardPerformance],
|
||||
underperformer_names: List[str]) -> str:
|
||||
"""Format underperforming cards with full available stats."""
|
||||
"""Format underperforming cards with their stats."""
|
||||
card_map = {c.name: c for c in cards}
|
||||
lines = []
|
||||
for name in underperformer_names[:MAX_UNDERPERFORMERS]:
|
||||
c = card_map.get(name)
|
||||
if c:
|
||||
turn_str = f", avgTurnCast={c.avgTurnCast:.1f}" if c.avgTurnCast is not None else ""
|
||||
flags = []
|
||||
if c.keptInOpeningHandRate < 0.25:
|
||||
flags.append("OPENING HAND TRAP")
|
||||
if c.avgTurnCast is not None and c.avgTurnCast > 6:
|
||||
flags.append("LATE CASTER")
|
||||
if c.synergyScore < 0.1:
|
||||
flags.append("ISOLATED")
|
||||
flag_str = f" [{', '.join(flags)}]" if flags else ""
|
||||
lines.append(
|
||||
f"- {c.name}{flag_str}: impact={c.impactScore:.3f}, "
|
||||
f"deadCardRate={c.deadCardRate:.1%}, "
|
||||
f"castRate={c.castRate:.1%}, "
|
||||
f"clunkiness={c.clunkinessScore:.3f}, "
|
||||
f"synergy={c.synergyScore:.3f}, "
|
||||
f"keptInOpeningHand={c.keptInOpeningHandRate:.1%}"
|
||||
f"{turn_str}"
|
||||
)
|
||||
lines.append(f"- {c.name}: impact={c.impactScore:.3f}, "
|
||||
f"deadCardRate={c.deadCardRate:.1%}, "
|
||||
f"castRate={c.castRate:.1%}, "
|
||||
f"clunkiness={c.clunkinessScore:.3f}")
|
||||
else:
|
||||
lines.append(f"- {name}: (no detailed stats)")
|
||||
return "\n".join(lines) if lines else "No clear underperformers identified."
|
||||
|
||||
|
||||
def _format_sim_insights(cards: List[CardPerformance],
|
||||
archetype_rates: Dict[str, float]) -> str:
|
||||
"""Compute and format deck-level sim signals for the Sim Insights section.
|
||||
|
||||
Phase 1 (#108): surfaces pre-computed per-card stats as deck-level
|
||||
aggregates so Claude reasons from numbers directly.
|
||||
|
||||
Signals produced:
|
||||
1. Opening hand quality — deck avg keptInOpeningHandRate + worst offenders
|
||||
2. Curve velocity — castRate-weighted avg avgTurnCast + late-caster flags
|
||||
3. Synergy outliers — bottom-3 synergyScore cards flagged as [ISOLATED]
|
||||
4. Archetype weak spots — archetypes < 40% win rate flagged [WEAK SPOT]
|
||||
"""
|
||||
if not cards:
|
||||
return "No simulation data available."
|
||||
|
||||
lines = []
|
||||
|
||||
# ── 1. Opening hand quality ───────────────────────────────────────────
|
||||
hand_rates = [c.keptInOpeningHandRate for c in cards if c.keptInOpeningHandRate > 0]
|
||||
if hand_rates:
|
||||
avg_keep = sum(hand_rates) / len(hand_rates)
|
||||
lines.append(f"Opening Hand Quality: deck avg keptInOpeningHandRate = {avg_keep:.1%}")
|
||||
# Worst offenders: cards significantly below the deck average
|
||||
threshold = avg_keep * 0.6 # >40% below avg is an outlier
|
||||
offenders = sorted(
|
||||
[c for c in cards if c.keptInOpeningHandRate > 0 and c.keptInOpeningHandRate < threshold],
|
||||
key=lambda c: c.keptInOpeningHandRate
|
||||
)[:3]
|
||||
if offenders:
|
||||
lines.append(" Opening hand traps (keptInOpeningHandRate far below deck avg):")
|
||||
for c in offenders:
|
||||
lines.append(f" - {c.name}: {c.keptInOpeningHandRate:.1%} [OPENING HAND TRAP]")
|
||||
else:
|
||||
lines.append("Opening Hand Quality: no keptInOpeningHandRate data available")
|
||||
|
||||
# ── 2. Curve velocity ───────────────────────────────────────────────
|
||||
cast_cards = [c for c in cards if c.avgTurnCast is not None and c.castRate > 0]
|
||||
if cast_cards:
|
||||
total_weight = sum(c.castRate for c in cast_cards)
|
||||
if total_weight > 0:
|
||||
weighted_avg_turn = sum(
|
||||
c.avgTurnCast * c.castRate for c in cast_cards
|
||||
) / total_weight
|
||||
lines.append(f"Curve Velocity: castRate-weighted avg avgTurnCast = {weighted_avg_turn:.1f}")
|
||||
late_casters = sorted(
|
||||
[c for c in cast_cards if c.avgTurnCast > 6],
|
||||
key=lambda c: c.avgTurnCast,
|
||||
reverse=True
|
||||
)[:3]
|
||||
if late_casters:
|
||||
lines.append(" Late casters (avgTurnCast > 6 — may not fit the curve):")
|
||||
for c in late_casters:
|
||||
lines.append(f" - {c.name}: avgTurnCast={c.avgTurnCast:.1f} [LATE CASTER]")
|
||||
else:
|
||||
lines.append("Curve Velocity: no avgTurnCast data available")
|
||||
|
||||
# ── 3. Synergy outliers ──────────────────────────────────────────────
|
||||
synergy_cards = sorted(
|
||||
[c for c in cards if c.castRate > 0.05], # ignore near-never-cast cards
|
||||
key=lambda c: c.synergyScore
|
||||
)
|
||||
if synergy_cards:
|
||||
bottom3 = synergy_cards[:3]
|
||||
lines.append("Synergy Outliers (lowest synergyScore among cast cards):")
|
||||
for c in bottom3:
|
||||
lines.append(f" - {c.name}: synergyScore={c.synergyScore:.3f} [ISOLATED]")
|
||||
|
||||
# ── 4. Archetype weak spots ──────────────────────────────────────────
|
||||
if archetype_rates:
|
||||
weak = {k: v for k, v in archetype_rates.items() if v < 0.40}
|
||||
if weak:
|
||||
lines.append("Archetype Weak Spots (win rate < 40%):")
|
||||
for arch, wr in sorted(weak.items(), key=lambda x: x[1]):
|
||||
lines.append(f" - vs {arch}: {wr:.1%} [WEAK SPOT]")
|
||||
else:
|
||||
lines.append("Archetype Weak Spots: none (all matchups >= 40% win rate)")
|
||||
|
||||
return "\n".join(lines) if lines else "No sim insights available."
|
||||
|
||||
|
||||
def _format_candidates(candidates: Dict[str, List[dict]]) -> str:
|
||||
"""Format replacement candidates grouped by the card they'd replace."""
|
||||
if not candidates:
|
||||
return (
|
||||
"No embedding-based candidates available. "
|
||||
"Use your MTG knowledge to suggest cards that fit the deck's "
|
||||
"color identity, strategy, and missing functional roles. "
|
||||
"Suggest at least 3 different cards across different roles "
|
||||
"(ramp, draw, removal, threats, utility)."
|
||||
)
|
||||
return ("No embedding-based candidates available. "
|
||||
"Use your MTG knowledge to suggest cards that fit the deck's "
|
||||
"color identity, strategy, and missing functional roles. "
|
||||
"Suggest at least 3 different cards across different roles "
|
||||
"(ramp, draw, removal, threats, utility).")
|
||||
|
||||
lines = []
|
||||
for underperformer, replacements in list(candidates.items())[:MAX_UNDERPERFORMERS]:
|
||||
lines.append(f"\nReplacements for '{underperformer}':")
|
||||
@@ -453,55 +179,14 @@ def _format_candidates(candidates: Dict[str, List[dict]]) -> str:
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _format_rag_cards(rag_cards: List[dict]) -> str:
|
||||
"""Format RAG-retrieved cards from ChromaDB vector search."""
|
||||
if not rag_cards:
|
||||
return "No RAG results available."
|
||||
lines = []
|
||||
for card in rag_cards[:15]:
|
||||
name = card.get("name", "Unknown")
|
||||
types = card.get("types", "")
|
||||
mv = card.get("mana_value", "?")
|
||||
mc = card.get("mana_cost", "")
|
||||
dist = card.get("distance", 0)
|
||||
text = card.get("text", "")[:80]
|
||||
lines.append(f"- {name} ({mc}, MV:{mv}, {types}) [distance: {dist:.3f}]")
|
||||
if text:
|
||||
lines.append(f" Oracle: {text}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _format_matchups(report: DeckReport) -> str:
|
||||
"""Format per-opponent matchup data."""
|
||||
"""Format matchup data."""
|
||||
if not report.matchups:
|
||||
return "No matchup data available."
|
||||
lines = []
|
||||
for m in report.matchups:
|
||||
lines.append(
|
||||
f"- vs {m.opponentDeck}: {m.winRate:.1%} "
|
||||
f"({m.gamesPlayed} games)"
|
||||
)
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _format_archetypes(report: DeckReport) -> str:
|
||||
"""Format per-archetype win rates from DeckMeta.
|
||||
|
||||
Phase 1: annotates weak spots ([WEAK SPOT] < 40%) and strengths
|
||||
([STRENGTH] >= 55%) so Claude sees the signal immediately.
|
||||
"""
|
||||
rates = report.meta.perArchetypeWinRates
|
||||
if not rates:
|
||||
return "No archetype breakdown available."
|
||||
lines = []
|
||||
for archetype, wr in sorted(rates.items(), key=lambda x: x[1]):
|
||||
if wr < 0.40:
|
||||
tag = " [WEAK SPOT]"
|
||||
elif wr >= 0.55:
|
||||
tag = " [STRENGTH]"
|
||||
else:
|
||||
tag = ""
|
||||
lines.append(f"- vs {archetype}: {wr:.1%}{tag}")
|
||||
lines.append(f"- vs {m.opponentDeck}: {m.winRate:.1%} "
|
||||
f"({m.gamesPlayed} games)")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
@@ -512,16 +197,14 @@ def _format_combos(report: DeckReport) -> str:
|
||||
lines = ["### Known Combos"]
|
||||
for combo in report.knownCombos:
|
||||
cards = " + ".join(combo.cardNames)
|
||||
lines.append(
|
||||
f"- {cards}: winRate={combo.winRateWhenAssembled:.1%}, "
|
||||
f"assemblyRate={combo.assemblyRate:.1%}"
|
||||
)
|
||||
lines.append(f"- {cards}: winRate={combo.winRateWhenAssembled:.1%}, "
|
||||
f"assemblyRate={combo.assemblyRate:.1%}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Main Builder Functions
|
||||
# ════════════════════════════════════════════════════════════
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
def build_system_prompt(report: DeckReport,
|
||||
goals: Optional[CoachGoals] = None) -> str:
|
||||
@@ -534,44 +217,16 @@ def build_system_prompt(report: DeckReport,
|
||||
)
|
||||
|
||||
|
||||
def build_quick_system_prompt(report: DeckReport,
|
||||
goals: Optional[CoachGoals] = None) -> str:
|
||||
"""Build the quick digest system prompt (~400-600 token target)."""
|
||||
color_str = "/".join(report.colorIdentity) if report.colorIdentity else "any"
|
||||
return QUICK_DIGEST_SYSTEM_PROMPT.format(
|
||||
commander=report.commander,
|
||||
color_identity=color_str,
|
||||
goals_section=_format_goals_section(goals),
|
||||
)
|
||||
|
||||
|
||||
def build_user_prompt(report: DeckReport,
|
||||
candidates: Dict[str, List[dict]] = None,
|
||||
rag_cards: List[dict] = None,
|
||||
rules_context: List[dict] = None) -> str: # FARMHOUSE_RULES_CONTEXT
|
||||
text = _farmhouse_build_user_prompt(report, candidates, rag_cards)
|
||||
rules = []
|
||||
for item in (rules_context or [])[:3]:
|
||||
body = (item.get('text') or item.get('document') or item.get('content')) if isinstance(item, dict) else str(item)
|
||||
if body:
|
||||
rules.append('- ' + str(body).strip()[:700])
|
||||
if rules:
|
||||
text += '\n\n## Relevant rules (for grounding; do not quote them back)\n' + '\n'.join(rules)
|
||||
return text
|
||||
|
||||
|
||||
def _farmhouse_build_user_prompt(report: DeckReport,
|
||||
candidates: Dict[str, List[dict]] = None,
|
||||
rag_cards: List[dict] = None) -> str:
|
||||
"""Build the user prompt with full deck report data.
|
||||
|
||||
Phase 1: adds sim_insights_section computed from card-level stats.
|
||||
"""
|
||||
candidates: Dict[str, List[dict]] = None) -> str:
|
||||
"""Build the user prompt with full deck report data."""
|
||||
color_str = "/".join(report.colorIdentity) if report.colorIdentity else "unknown"
|
||||
|
||||
type_parts = [f"{k}={v}" for k, v in report.structure.cardTypeCounts.items()] \
|
||||
if report.structure.cardTypeCounts else ["unknown"]
|
||||
role_parts = [f"{k}={v}" for k, v in report.structure.functionalCounts.items()] \
|
||||
if report.structure.functionalCounts else ["unknown"]
|
||||
|
||||
return USER_PROMPT_TEMPLATE.format(
|
||||
deck_name=report.deckId,
|
||||
commander=report.commander,
|
||||
@@ -583,14 +238,10 @@ def _farmhouse_build_user_prompt(report: DeckReport,
|
||||
curve_summary=_format_curve(report.structure.curveBuckets),
|
||||
type_summary=", ".join(type_parts),
|
||||
role_summary=", ".join(role_parts),
|
||||
sim_insights_section=_format_sim_insights(
|
||||
report.cards, report.meta.perArchetypeWinRates),
|
||||
matchup_section=_format_matchups(report),
|
||||
archetype_section=_format_archetypes(report),
|
||||
top_cards=_format_top_cards(report.cards),
|
||||
underperformer_section=_format_underperformers(
|
||||
report.cards, report.underperformers),
|
||||
candidate_section=_format_candidates(candidates or {}),
|
||||
rag_section=_format_rag_cards(rag_cards or []),
|
||||
combo_section=_format_combos(report),
|
||||
)
|
||||
@@ -1,9 +1,6 @@
|
||||
"""
|
||||
Commander AI Lab — Deck Generation System Prompt
|
||||
═════════════════════════════════════════════════
|
||||
Phase 1 changes:
|
||||
- Added explicit 99-card count enforcement rule
|
||||
- Clarified that commander counts separately (not in the 99)
|
||||
"""
|
||||
|
||||
SYSTEM_PROMPT = """You are an elite Magic: The Gathering Commander (EDH) deck builder with encyclopedic knowledge of every card ever printed, current metagame trends, and Commander Rules Committee bracket guidelines.
|
||||
@@ -12,8 +9,6 @@ Your task is to build a complete, legal 100-card Commander deck.
|
||||
|
||||
RULES:
|
||||
- Exactly 100 cards total: 1 commander + 99 other cards
|
||||
- The commander IS included in the cards list but counts as card #100
|
||||
- The other 99 cards must exactly match the SLOT BUDGET provided in the user prompt
|
||||
- No more than 1 copy of any card except basic lands
|
||||
- All cards must share the commander's color identity
|
||||
- All cards must be legal in Commander format
|
||||
@@ -21,12 +16,6 @@ RULES:
|
||||
- Include approximately: 10 ramp sources, 10 card draw sources, 8-10 targeted removal, 2-3 board wipes, 3-5 protection pieces
|
||||
- Build a cohesive strategy around the commander's abilities
|
||||
|
||||
CARD COUNT ENFORCEMENT (critical):
|
||||
- The user prompt will specify a SLOT BUDGET with exact per-category counts
|
||||
- You MUST fill every slot — returning fewer cards than the budget specifies will cause a validation error
|
||||
- If you cannot find a perfect fit for a slot, use the best available card for that role
|
||||
- Basic lands count toward the Land slot total
|
||||
|
||||
BRACKET RULES (Commander Rules Committee):
|
||||
- Bracket 1 (Casual): Precon-level. No tutors, no fast mana, no infinite combos, no mass land destruction. Game Changers: 0
|
||||
- Bracket 2 (Upgraded Casual): Moderate power. Limited tutors (1-2 total), no 2-card infinite combos, no fast mana except Sol Ring. Game Changers: 0-1
|
||||
|
||||
@@ -1,33 +1,11 @@
|
||||
"""
|
||||
Commander AI Lab — Deck Generation User Prompt Builder
|
||||
═══════════════════════════════════════════════════════
|
||||
Constructs the user message for LLM deck generation.
|
||||
|
||||
Phase 1 changes:
|
||||
- slot_budget parameter injected into prompt so LLM targets exactly 99 cards
|
||||
- Explicit card count requirement in prompt text
|
||||
Constructs the user message for Perplexity deck generation
|
||||
from a DeckGenV3Request.
|
||||
"""
|
||||
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
|
||||
def build_slot_budget_segment(slot_budget: Dict[str, int]) -> str:
|
||||
"""
|
||||
Build the slot budget prompt segment.
|
||||
Informs the LLM of exact per-category card counts so it never undershoots.
|
||||
"""
|
||||
total = sum(slot_budget.values())
|
||||
lines = [f"SLOT BUDGET (you must fill EXACTLY {total} cards, excluding the commander):"]
|
||||
for cat, count in slot_budget.items():
|
||||
lines.append(f" {cat}: {count}")
|
||||
lines.append(f" TOTAL: {total}")
|
||||
lines.append("")
|
||||
lines.append(
|
||||
f"CRITICAL: Return exactly {total} cards in the JSON 'cards' array "
|
||||
"(not counting the commander). Every slot in the budget above must be filled. "
|
||||
"Do not return more or fewer cards."
|
||||
)
|
||||
return "\n".join(lines)
|
||||
from typing import List, Optional
|
||||
|
||||
|
||||
def build_user_prompt(
|
||||
@@ -40,7 +18,6 @@ def build_user_prompt(
|
||||
budget_mode: str = "total",
|
||||
omit_cards: List[str] = None,
|
||||
collection_summary: str = "",
|
||||
slot_budget: Optional[Dict[str, int]] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Build the user prompt for deck generation.
|
||||
@@ -49,19 +26,18 @@ def build_user_prompt(
|
||||
commander: Commander card name
|
||||
commander_type: Type line from Scryfall
|
||||
color_identity: List of color letters, e.g. ["U", "B"]
|
||||
strategy: User-specified strategy focus
|
||||
strategy: User-specified strategy focus (e.g. "zombie tokens")
|
||||
target_bracket: Target bracket level 1-4
|
||||
budget_usd: Optional budget constraint
|
||||
budget_mode: "total" for total deck cost, "per_card" for per-card max
|
||||
omit_cards: Cards to exclude
|
||||
collection_summary: Pre-built collection summary text block
|
||||
slot_budget: Dict of category -> card count (must sum to 99)
|
||||
"""
|
||||
ci_str = ", ".join(color_identity) if color_identity else "Unknown"
|
||||
|
||||
lines = [
|
||||
"Build a complete 100-card Commander deck.",
|
||||
"",
|
||||
f"Build a complete 100-card Commander deck.",
|
||||
f"",
|
||||
f"COMMANDER: {commander}",
|
||||
]
|
||||
|
||||
@@ -84,21 +60,16 @@ def build_user_prompt(
|
||||
lines.append(f"OMIT LIST (do NOT include these cards): {', '.join(omit_cards)}")
|
||||
|
||||
if collection_summary:
|
||||
lines.append("")
|
||||
lines.append(f"")
|
||||
lines.append(collection_summary)
|
||||
lines.append("")
|
||||
lines.append("STRONGLY PREFER cards from the collection when they fit the strategy.")
|
||||
lines.append(f"")
|
||||
lines.append(f"STRONGLY PREFER cards from the collection when they fit the strategy.")
|
||||
else:
|
||||
lines.append("")
|
||||
lines.append("No collection data available — suggest the best cards regardless of ownership.")
|
||||
lines.append(f"")
|
||||
lines.append(f"No collection data available — suggest the best cards regardless of ownership.")
|
||||
|
||||
# Inject slot budget so LLM knows exact target counts
|
||||
if slot_budget:
|
||||
lines.append("")
|
||||
lines.append(build_slot_budget_segment(slot_budget))
|
||||
|
||||
lines.append("")
|
||||
lines.append("Build the deck as a structured JSON response. Include the commander in the cards list.")
|
||||
lines.append("Ensure exactly 100 total cards. Assign role_tags and bracket info accurately.")
|
||||
lines.append(f"")
|
||||
lines.append(f"Build the deck as a structured JSON response. Include the commander in the cards list.")
|
||||
lines.append(f"Ensure exactly 100 total cards. Assign role_tags and bracket info accurately.")
|
||||
|
||||
return "\n".join(lines)
|
||||
@@ -1,7 +1,7 @@
|
||||
"""
|
||||
Commander AI Lab — Substitution Prompt Templates
|
||||
═════════════════════════════════════════════════
|
||||
Prompts for the Claude Opus substitution pass.
|
||||
Prompts for the Perplexity fallback substitution pass.
|
||||
"""
|
||||
|
||||
|
||||
|
||||
@@ -38,7 +38,7 @@ def generate_deck_reports(results_dir: str, reports_dir: str = None) -> List[str
|
||||
output_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Load all batch results
|
||||
batch_files = sorted(results_path.glob("ml-decision-*.json"))
|
||||
batch_files = sorted(results_path.glob("batch-*.json"))
|
||||
if not batch_files:
|
||||
logger.info("No batch result files found in %s", results_dir)
|
||||
return []
|
||||
@@ -108,7 +108,7 @@ def generate_single_deck_report(batch_json_path: str, reports_dir: str = None) -
|
||||
# Also load existing batch results for comprehensive reports
|
||||
results_dir = Path(batch_json_path).parent
|
||||
all_batches = []
|
||||
for bf in sorted(results_dir.glob("ml-decision-*.json")):
|
||||
for bf in sorted(results_dir.glob("batch-*.json")):
|
||||
try:
|
||||
with open(bf, "r", encoding="utf-8") as f:
|
||||
all_batches.append(json.load(f))
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
"""
|
||||
Commander AI Lab — Deck Generation JSON Schemas
|
||||
═════════════════════════════════════════════════
|
||||
Pydantic models and JSON Schema dicts for Claude Opus
|
||||
Pydantic models and JSON Schema dicts for Perplexity
|
||||
structured output deck generation.
|
||||
"""
|
||||
|
||||
@@ -58,7 +58,7 @@ class GeneratedDeckList(BaseModel):
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# JSON Schema dicts (for Claude Opus response_format)
|
||||
# JSON Schema dicts (for Perplexity response_format)
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
DECK_CARD_SCHEMA = {
|
||||
|
||||
@@ -3,10 +3,6 @@ Commander AI Lab — Substitution Schemas
|
||||
════════════════════════════════════════
|
||||
Pydantic models and JSON Schema dicts for the
|
||||
Smart Substitution Engine output.
|
||||
|
||||
Phase 1 changes:
|
||||
- SubstitutionBatchResult gains padded_slots field
|
||||
- DeckCardWithStatus gains original_card field (was missing)
|
||||
"""
|
||||
|
||||
from typing import List, Optional
|
||||
@@ -22,17 +18,16 @@ class CardAlternative(BaseModel):
|
||||
name: str = Field(description="Exact card name")
|
||||
similarity_score: float = Field(default=0.0, description="Cosine similarity score (0-1)")
|
||||
reason: str = Field(default="", description="Why this card is a good substitute")
|
||||
source: str = Field(default="embedding", description="How it was found: 'embedding' or 'anthropic'")
|
||||
source: str = Field(default="embedding", description="How it was found: 'embedding' or 'perplexity'")
|
||||
role_overlap: List[str] = Field(default_factory=list, description="Shared functional roles")
|
||||
cmc_delta: float = Field(default=0.0, description="CMC difference from original")
|
||||
|
||||
|
||||
class DeckCardWithStatus(BaseModel):
|
||||
"""A deck card enriched with ownership and substitution data."""
|
||||
|
||||
|
||||
class Config:
|
||||
extra = "ignore"
|
||||
|
||||
name: str
|
||||
count: int = 1
|
||||
category: str = ""
|
||||
@@ -46,15 +41,9 @@ class DeckCardWithStatus(BaseModel):
|
||||
owned_qty: int = 0
|
||||
scryfall_id: str = ""
|
||||
status: str = Field(default="owned", description="owned | substituted | missing")
|
||||
original_card: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Original card name before substitution (None if not substituted)"
|
||||
)
|
||||
original_card: Optional[str] = Field(default=None, description="Original card name if this is a substitution")
|
||||
alternatives: List[CardAlternative] = Field(default_factory=list)
|
||||
selected_substitute: Optional[str] = Field(
|
||||
default=None,
|
||||
description="The substitute card name chosen for this slot"
|
||||
)
|
||||
selected_substitute: Optional[str] = Field(default=None, description="User-selected substitute name")
|
||||
|
||||
|
||||
class SubstitutionRequest(BaseModel):
|
||||
@@ -72,16 +61,11 @@ class SubstitutionBatchResult(BaseModel):
|
||||
owned_count: int = 0
|
||||
substituted_count: int = 0
|
||||
missing_count: int = 0
|
||||
padded_slots: int = Field(
|
||||
default=0,
|
||||
description="Number of slots filled by the pad-to-99 circuit breaker. "
|
||||
"Any value > 0 indicates a bug in the substitution pipeline."
|
||||
)
|
||||
cards: List[DeckCardWithStatus] = Field(default_factory=list)
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# JSON Schema dicts (for LLM fallback substitution)
|
||||
# JSON Schema dicts (for Perplexity fallback substitution)
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
SUBSTITUTION_RESPONSE_SCHEMA = {
|
||||
|
||||
+125
-409
@@ -1,28 +1,19 @@
|
||||
"""
|
||||
Commander AI Lab — V3 Deck Generator Service
|
||||
═════════════════════════════════════════════
|
||||
Generates Commander decks using Anthropic Claude
|
||||
Generates Commander decks using Perplexity Sonar
|
||||
structured JSON output, then runs ownership check
|
||||
and smart substitution.
|
||||
|
||||
Phase 1 changes:
|
||||
- smart_substitute() non-destructive contract (never returns None)
|
||||
- apply_substitutions() loop never silently drops cards
|
||||
- _pad_to_99() circuit breaker before BuildResult validation
|
||||
- Pre-validation count logging per category
|
||||
- Startup SLOT_BUDGET assertion
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import sqlite3
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
from typing import List, Optional, Tuple
|
||||
from urllib.request import urlopen, Request
|
||||
|
||||
from ..clients.anthropic_client import AnthropicClient
|
||||
from ..clients.perplexity_client import PerplexityClient
|
||||
from ..schemas.deck_schema import DECK_LIST_SCHEMA, GeneratedDeckList
|
||||
from ..schemas.substitution_schema import (
|
||||
CardAlternative, DeckCardWithStatus, SubstitutionBatchResult,
|
||||
@@ -35,261 +26,35 @@ from ..prompts.substitution_prompt import (
|
||||
from ..config import (
|
||||
DECK_GEN_MODEL, DECK_GEN_TEMPERATURE, DECK_GEN_MAX_TOKENS,
|
||||
SUBSTITUTION_MIN_SIMILARITY, SUBSTITUTION_MAX_ALTERNATIVES,
|
||||
SUBSTITUTION_MODEL, SUBSTITUTION_USE_ANTHROPIC_FALLBACK,
|
||||
SUBSTITUTION_MODEL, SUBSTITUTION_USE_PPLX_FALLBACK,
|
||||
)
|
||||
|
||||
logger = logging.getLogger("coach.deckgen")
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Slot Budget — MUST sum to 99 (commander is the 100th card)
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
SLOT_BUDGET: dict[str, int] = {
|
||||
"Land": 36,
|
||||
"Ramp": 10,
|
||||
"Card Draw": 10,
|
||||
"Removal": 8,
|
||||
"Board Wipe": 3,
|
||||
"Win Condition": 4,
|
||||
"Protection": 4,
|
||||
"Synergy": 20,
|
||||
"Creature": 2,
|
||||
"Utility": 2,
|
||||
}
|
||||
|
||||
# ── Startup assertion — catches misconfigured budgets at boot time ──
|
||||
_SLOT_BUDGET_TOTAL = sum(SLOT_BUDGET.values())
|
||||
assert _SLOT_BUDGET_TOTAL == 99, (
|
||||
f"SLOT_BUDGET sums to {_SLOT_BUDGET_TOTAL}, must equal 99. "
|
||||
f"Current distribution: {SLOT_BUDGET}"
|
||||
)
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# SubstitutionMethod — tracks HOW each card was resolved
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
class SubstitutionMethod(str, Enum):
|
||||
EXACT_MATCH = "exact_match" # Card found verbatim in collection
|
||||
FUZZY_MATCH = "fuzzy_match" # Case/punctuation-normalised match
|
||||
EMBEDDING_ANALOG = "embedding_analog" # Embedding similarity match
|
||||
ANTHROPIC_FALLBACK = "anthropic_fallback" # LLM suggested replacement
|
||||
ORIGINAL_KEPT = "original_kept" # Not in collection but Scryfall-valid
|
||||
BASIC_LAND_FALLBACK = "basic_land_fallback" # Last resort — slot becomes a basic
|
||||
|
||||
|
||||
@dataclass
|
||||
class SubstitutionRecord:
|
||||
"""Audit trail entry for a single card's resolution."""
|
||||
original: str
|
||||
resolved: str
|
||||
method: SubstitutionMethod
|
||||
in_collection: bool
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Color → Basic Land mapping
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
_COLOR_TO_BASIC: dict[str, str] = {
|
||||
"W": "Plains",
|
||||
"U": "Island",
|
||||
"B": "Swamp",
|
||||
"R": "Mountain",
|
||||
"G": "Forest",
|
||||
}
|
||||
_BASIC_NAMES: set[str] = set(_COLOR_TO_BASIC.values()) | {"Wastes"}
|
||||
|
||||
|
||||
def _get_basic_land(color_identity: List[str]) -> str:
|
||||
"""Return the first appropriate basic land for the given color identity."""
|
||||
for color in color_identity:
|
||||
if color.upper() in _COLOR_TO_BASIC:
|
||||
return _COLOR_TO_BASIC[color.upper()]
|
||||
return "Wastes" # Colorless commander
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Non-destructive Smart Substitution
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
def smart_substitute(
|
||||
card_name: str,
|
||||
collection: set[str],
|
||||
color_identity: List[str],
|
||||
embedding_index=None,
|
||||
) -> SubstitutionRecord:
|
||||
"""
|
||||
Non-destructive substitution — ALWAYS returns a valid card string.
|
||||
|
||||
Fallback chain:
|
||||
1. Exact match in collection
|
||||
2. Fuzzy match (case + punctuation normalised)
|
||||
3. Embedding similarity match (if index available)
|
||||
4. Keep original (Scryfall-valid, not in collection)
|
||||
5. Basic land for the commander's colors (last resort — NEVER None)
|
||||
"""
|
||||
# 1. Exact match
|
||||
if card_name in collection:
|
||||
return SubstitutionRecord(card_name, card_name, SubstitutionMethod.EXACT_MATCH, True)
|
||||
|
||||
# 2. Fuzzy match — normalise case and common punctuation differences
|
||||
normalised = card_name.lower().strip()
|
||||
for owned in collection:
|
||||
if owned.lower().strip() == normalised:
|
||||
return SubstitutionRecord(card_name, owned, SubstitutionMethod.FUZZY_MATCH, True)
|
||||
|
||||
# 3. Embedding similarity (if index available)
|
||||
if embedding_index and getattr(embedding_index, "loaded", False):
|
||||
try:
|
||||
matches = embedding_index.search_similar(
|
||||
query_card=card_name,
|
||||
color_filter=color_identity or None,
|
||||
exclude_cards=[card_name],
|
||||
top_n=5,
|
||||
)
|
||||
pool_lower = {n.lower() for n in collection}
|
||||
for match in matches:
|
||||
if match.name.lower() in pool_lower:
|
||||
return SubstitutionRecord(
|
||||
card_name, match.name, SubstitutionMethod.EMBEDDING_ANALOG, True
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning("Embedding substitution failed for '%s': %s", card_name, e)
|
||||
|
||||
# 4. Keep original — valid card not in collection (proxy / non-collection build)
|
||||
return SubstitutionRecord(
|
||||
card_name, card_name, SubstitutionMethod.ORIGINAL_KEPT, False
|
||||
)
|
||||
|
||||
|
||||
def apply_substitutions(
|
||||
card_list: List[DeckCardWithStatus],
|
||||
collection: set[str],
|
||||
color_identity: List[str],
|
||||
embedding_index=None,
|
||||
) -> Tuple[List[DeckCardWithStatus], List[SubstitutionRecord]]:
|
||||
"""
|
||||
Apply smart substitution to a card list.
|
||||
|
||||
Guarantees: output list is NEVER shorter than input list.
|
||||
Logs a warning for every BASIC_LAND_FALLBACK.
|
||||
"""
|
||||
records: List[SubstitutionRecord] = []
|
||||
result: List[DeckCardWithStatus] = []
|
||||
|
||||
for card in card_list:
|
||||
rec = smart_substitute(card.name, collection, color_identity, embedding_index)
|
||||
records.append(rec)
|
||||
|
||||
if rec.method == SubstitutionMethod.BASIC_LAND_FALLBACK:
|
||||
logger.warning(
|
||||
"[substitution] BASIC_LAND_FALLBACK for '%s' — slot filled with '%s'",
|
||||
rec.original, rec.resolved,
|
||||
)
|
||||
|
||||
# Update the card in-place — never drop it
|
||||
updated = card.model_copy(update={
|
||||
"name": rec.resolved,
|
||||
"owned": rec.in_collection,
|
||||
"status": "owned" if rec.in_collection else
|
||||
("substituted" if rec.method != SubstitutionMethod.ORIGINAL_KEPT
|
||||
else "missing"),
|
||||
"original_card": card.name if rec.resolved != card.name else card.original_card,
|
||||
})
|
||||
result.append(updated)
|
||||
|
||||
fallback_count = sum(1 for r in records if r.method == SubstitutionMethod.BASIC_LAND_FALLBACK)
|
||||
if fallback_count:
|
||||
logger.error(
|
||||
"[substitution] %d cards fell back to basic lands — investigate LLM output quality",
|
||||
fallback_count,
|
||||
)
|
||||
|
||||
return result, records
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Pad-to-99 Circuit Breaker
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
def _pad_to_99(
|
||||
cards: List[DeckCardWithStatus],
|
||||
color_identity: List[str],
|
||||
target: int = 99,
|
||||
) -> Tuple[List[DeckCardWithStatus], int]:
|
||||
"""
|
||||
Emergency safety pad — should NEVER fire if substitution contract holds.
|
||||
|
||||
If it does fire, the error log is your signal that a card is being dropped
|
||||
somewhere earlier in the pipeline.
|
||||
|
||||
Returns (padded_cards, number_of_slots_padded).
|
||||
"""
|
||||
# Strip commander from count if present (commander is tracked separately)
|
||||
non_commander = [c for c in cards if not getattr(c, "is_commander", False)]
|
||||
current = sum(c.count for c in non_commander)
|
||||
shortfall = target - current
|
||||
|
||||
if shortfall <= 0:
|
||||
return cards, 0
|
||||
|
||||
logger.error(
|
||||
"[pad_to_99] CIRCUIT BREAKER FIRED — deck is %d cards short after substitution. "
|
||||
"Padding with basic lands. Check substitution pipeline for silent drops.",
|
||||
shortfall,
|
||||
)
|
||||
|
||||
basic = _get_basic_land(color_identity)
|
||||
padding_card = DeckCardWithStatus(
|
||||
name=basic,
|
||||
count=shortfall,
|
||||
category="Land",
|
||||
role_tags=[],
|
||||
reason="Emergency pad — basic land inserted by circuit breaker",
|
||||
estimated_price_usd=0.10,
|
||||
owned=True,
|
||||
owned_qty=shortfall,
|
||||
status="owned",
|
||||
)
|
||||
return cards + [padding_card], shortfall
|
||||
|
||||
|
||||
def _log_pre_validation_counts(cards: List[DeckCardWithStatus]) -> None:
|
||||
"""Log per-category card counts before Pydantic validation."""
|
||||
by_cat: dict[str, int] = {}
|
||||
for card in cards:
|
||||
cat = card.category or "Uncategorized"
|
||||
by_cat[cat] = by_cat.get(cat, 0) + card.count
|
||||
total = sum(by_cat.values())
|
||||
logger.info("[pre_validation] Category counts: %s", by_cat)
|
||||
logger.info("[pre_validation] Total: %d/99 (delta: %d)", total, 99 - total)
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# DeckGeneratorV3
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
|
||||
class DeckGeneratorV3:
|
||||
"""
|
||||
V3 Deck Generator using Anthropic Claude structured output.
|
||||
V3 Deck Generator using Perplexity structured output.
|
||||
|
||||
Pipeline:
|
||||
1. Resolve commander via Scryfall
|
||||
2. Build collection summary
|
||||
3. Call Anthropic Claude with structured JSON schema
|
||||
3. Call Perplexity with structured JSON schema
|
||||
4. Validate card names via Scryfall
|
||||
5. Check ownership against collection DB
|
||||
6. Run Smart Substitution (non-destructive)
|
||||
7. _pad_to_99 circuit breaker
|
||||
8. Pre-validation count logging
|
||||
9. Return finalized deck — guaranteed 99 cards
|
||||
6. Run Smart Substitution (embedding + Perplexity fallback)
|
||||
7. Return finalized deck with substitution data
|
||||
"""
|
||||
|
||||
def __init__(self, anthropic_client: AnthropicClient, db_conn_factory,
|
||||
def __init__(self, pplx_client: PerplexityClient, db_conn_factory,
|
||||
embedding_index=None):
|
||||
self.anthropic = anthropic_client
|
||||
"""
|
||||
Args:
|
||||
pplx_client: Configured PerplexityClient instance
|
||||
db_conn_factory: Callable that returns sqlite3.Connection
|
||||
embedding_index: Optional MTGEmbeddingIndex for substitution
|
||||
"""
|
||||
self.pplx = pplx_client
|
||||
self.db_conn_factory = db_conn_factory
|
||||
self.embedding_index = embedding_index
|
||||
|
||||
@@ -312,12 +77,9 @@ class DeckGeneratorV3:
|
||||
"mana_cost": data.get("mana_cost", ""),
|
||||
"image_url": data.get("image_uris", {}).get("normal", ""),
|
||||
"oracle_text": data.get("oracle_text", ""),
|
||||
"scryfall_found": True,
|
||||
}
|
||||
except Exception as e:
|
||||
logger.warning("Scryfall lookup failed for '%s': %s", commander_name, e)
|
||||
# Non-canonical commander (silver-bordered, custom, etc.)
|
||||
# Skip collection substitution for these — trust LLM output directly
|
||||
return {
|
||||
"name": commander_name,
|
||||
"scryfall_id": "",
|
||||
@@ -326,7 +88,6 @@ class DeckGeneratorV3:
|
||||
"mana_cost": "",
|
||||
"image_url": "",
|
||||
"oracle_text": "",
|
||||
"scryfall_found": False,
|
||||
}
|
||||
|
||||
# ── Step 2: Build Collection Summary ─────────────────────
|
||||
@@ -349,10 +110,16 @@ class DeckGeneratorV3:
|
||||
if not rows:
|
||||
return ""
|
||||
|
||||
# Filter by color identity
|
||||
ci_set = set(color_identity) if color_identity else None
|
||||
filtered = []
|
||||
for row in rows:
|
||||
name = row["name"]
|
||||
type_line = row["type_line"] or ""
|
||||
# Basic lands and colorless are always allowed
|
||||
if ci_set:
|
||||
# Simple color check from type_line and name
|
||||
# For proper filtering we'd check mana cost but this is good enough for summary
|
||||
card_colors = set()
|
||||
oracle = row["oracle_text"] or ""
|
||||
mana_cost = ""
|
||||
@@ -370,6 +137,7 @@ class DeckGeneratorV3:
|
||||
if not filtered:
|
||||
return ""
|
||||
|
||||
# Group by card type
|
||||
groups = {}
|
||||
for row in filtered:
|
||||
type_line = row["type_line"] or ""
|
||||
@@ -391,12 +159,14 @@ class DeckGeneratorV3:
|
||||
group = "Other"
|
||||
groups.setdefault(group, []).append(row)
|
||||
|
||||
# Sort each group by popularity/price and build text
|
||||
lines = [f"COLLECTION SUMMARY ({len(filtered)} cards in deck colors):"]
|
||||
for group_name in ["Creatures", "Instants", "Sorceries", "Artifacts",
|
||||
"Enchantments", "Planeswalkers", "Lands", "Other"]:
|
||||
cards = groups.get(group_name, [])
|
||||
if not cards:
|
||||
continue
|
||||
# Sort by price descending (higher value = likely better card)
|
||||
cards.sort(key=lambda r: r["tcg_price"] or 0, reverse=True)
|
||||
card_strs = []
|
||||
for c in cards[:max_per_group]:
|
||||
@@ -406,7 +176,7 @@ class DeckGeneratorV3:
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
# ── Step 3: Generate Deck via Anthropic Claude ─────────────────
|
||||
# ── Step 3: Generate Deck via Perplexity ─────────────────
|
||||
def generate_deck(
|
||||
self,
|
||||
commander_name: str,
|
||||
@@ -423,33 +193,24 @@ class DeckGeneratorV3:
|
||||
|
||||
Returns dict with:
|
||||
- commander: dict with Scryfall data
|
||||
- raw_deck: parsed GeneratedDeckList from Anthropic Claude
|
||||
- cards: list of DeckCardWithStatus (after ownership check + substitution)
|
||||
- raw_deck: parsed GeneratedDeckList from Perplexity
|
||||
- cards: list of DeckCardWithStatus (after ownership check)
|
||||
- stats: deck statistics
|
||||
- substitution_log: list of SubstitutionRecord dicts
|
||||
- padded_slots: int > 0 means circuit breaker fired
|
||||
- tokens_used: {prompt, completion}
|
||||
"""
|
||||
# Step 1: Resolve commander
|
||||
cmdr = self.resolve_commander(commander_name)
|
||||
color_identity = cmdr["color_identity"]
|
||||
scryfall_found = cmdr.get("scryfall_found", True)
|
||||
logger.info("Commander: %s, CI: %s, Scryfall: %s",
|
||||
cmdr["name"], color_identity, scryfall_found)
|
||||
logger.info("Commander: %s, CI: %s", cmdr["name"], color_identity)
|
||||
|
||||
# Step 2: Collection summary
|
||||
collection_block = ""
|
||||
if use_collection and scryfall_found:
|
||||
if use_collection:
|
||||
collection_block = self.build_collection_summary(color_identity)
|
||||
logger.info("Collection summary: %d chars",
|
||||
len(collection_block) if collection_block else 0)
|
||||
elif not scryfall_found:
|
||||
logger.info(
|
||||
"Non-canonical commander '%s' — skipping collection filter, "
|
||||
"trusting LLM output directly.", cmdr["name"]
|
||||
)
|
||||
|
||||
# Step 3: Build prompts (with slot budget injected)
|
||||
# Step 3: Build prompts
|
||||
user_prompt = build_user_prompt(
|
||||
commander=cmdr["name"],
|
||||
commander_type=cmdr["type_line"],
|
||||
@@ -460,14 +221,13 @@ class DeckGeneratorV3:
|
||||
budget_mode=budget_mode,
|
||||
omit_cards=omit_cards,
|
||||
collection_summary=collection_block,
|
||||
slot_budget=SLOT_BUDGET,
|
||||
)
|
||||
|
||||
# Step 4: Call LLM with structured output
|
||||
# Step 4: Call Perplexity with structured output
|
||||
use_model = model or DECK_GEN_MODEL
|
||||
logger.info("Calling LLM (%s) for deck generation...", use_model)
|
||||
logger.info("Calling Perplexity (%s) for deck generation...", use_model)
|
||||
|
||||
response = self.anthropic.chat_structured(
|
||||
response = self.pplx.chat_structured(
|
||||
system_prompt=SYSTEM_PROMPT,
|
||||
user_prompt=user_prompt,
|
||||
json_schema=DECK_LIST_SCHEMA,
|
||||
@@ -479,29 +239,26 @@ class DeckGeneratorV3:
|
||||
|
||||
if not response.ok:
|
||||
raise ValueError(
|
||||
f"LLM returned invalid response: {response.content[:300]}"
|
||||
f"Perplexity returned invalid response: {response.content[:300]}"
|
||||
)
|
||||
|
||||
raw_deck = response.parsed_json
|
||||
card_count = len(raw_deck.get("cards", []))
|
||||
truncated = card_count < 90
|
||||
logger.info(
|
||||
"Deck generated: %d cards, bracket %s%s",
|
||||
card_count,
|
||||
(raw_deck.get("bracket", {}).get("level", "?") if isinstance(raw_deck.get("bracket"), dict)
|
||||
else raw_deck.get("bracket", "?")),
|
||||
" (TRUNCATED — hit token limit)" if truncated else "",
|
||||
)
|
||||
truncated = card_count < 90 # Commander decks should have ~100 cards
|
||||
logger.info("Deck generated: %d cards, bracket %d%s",
|
||||
card_count,
|
||||
(raw_deck.get("bracket", {}).get("level", 0) if isinstance(raw_deck.get("bracket"), dict) else raw_deck.get("bracket", 0)),
|
||||
" (TRUNCATED — response may have hit token limit)" if truncated else "")
|
||||
|
||||
# Step 5: Check ownership
|
||||
# Step 5: Check ownership and build enriched card list
|
||||
cards_with_status = self._check_ownership(raw_deck.get("cards", []))
|
||||
|
||||
# Step 5b: Fill basic lands to reach target_total=100
|
||||
# Step 5b: Fill basic lands to hit exactly 100 cards
|
||||
cards_with_status = self._fill_basic_lands(cards_with_status, color_identity)
|
||||
|
||||
# Step 5c: Secondary deduplication
|
||||
seen: dict[str, int] = {}
|
||||
deduped: List[DeckCardWithStatus] = []
|
||||
# Step 5c: Secondary safety-net deduplication
|
||||
seen = {}
|
||||
deduped = []
|
||||
for c in cards_with_status:
|
||||
key = c.name.lower()
|
||||
if key in seen:
|
||||
@@ -512,27 +269,7 @@ class DeckGeneratorV3:
|
||||
deduped.append(c)
|
||||
cards_with_status = deduped
|
||||
|
||||
# Step 6: Run non-destructive smart substitution
|
||||
# For non-canonical commanders (e.g. Sonic), skip collection filter
|
||||
owned_pool: set[str] = set()
|
||||
if use_collection and scryfall_found:
|
||||
conn = self.db_conn_factory()
|
||||
rows = conn.execute(
|
||||
"SELECT DISTINCT name FROM collection_entries WHERE quantity > 0"
|
||||
).fetchall()
|
||||
owned_pool = {r["name"] for r in rows}
|
||||
|
||||
cards_with_status, substitution_log = apply_substitutions(
|
||||
cards_with_status, owned_pool, color_identity, self.embedding_index
|
||||
)
|
||||
|
||||
# Step 7: Pad-to-99 circuit breaker
|
||||
cards_with_status, padded_slots = _pad_to_99(cards_with_status, color_identity)
|
||||
|
||||
# Step 8: Pre-validation count logging
|
||||
_log_pre_validation_counts(cards_with_status)
|
||||
|
||||
# Step 9: Compute stats
|
||||
# Step 6: Compute stats
|
||||
stats = self._compute_stats(cards_with_status)
|
||||
|
||||
return {
|
||||
@@ -546,12 +283,6 @@ class DeckGeneratorV3:
|
||||
"archetype": raw_deck.get("archetype", ""),
|
||||
"reasoning": raw_deck.get("reasoning", {}),
|
||||
"estimated_total_usd": raw_deck.get("estimated_total_usd", 0),
|
||||
"substitution_log": [
|
||||
{"original": r.original, "resolved": r.resolved,
|
||||
"method": r.method.value, "in_collection": r.in_collection}
|
||||
for r in substitution_log
|
||||
],
|
||||
"padded_slots": padded_slots,
|
||||
"tokens_used": {
|
||||
"prompt": response.prompt_tokens,
|
||||
"completion": response.completion_tokens,
|
||||
@@ -565,11 +296,13 @@ class DeckGeneratorV3:
|
||||
def _check_ownership(self, cards: List[dict]) -> List[DeckCardWithStatus]:
|
||||
"""
|
||||
Cross-reference generated cards against the collection DB.
|
||||
Deduplicates by name (case-insensitive).
|
||||
Sets owned/scryfall_id/owned_qty and real price.
|
||||
Deduplicates cards by name (case-insensitive) — if the LLM
|
||||
returns the same card twice, the counts are merged.
|
||||
"""
|
||||
conn = self.db_conn_factory()
|
||||
enriched: List[DeckCardWithStatus] = []
|
||||
seen_names: dict[str, int] = {}
|
||||
enriched = []
|
||||
seen_names: dict[str, int] = {} # name_lower → index in enriched
|
||||
|
||||
for card in cards:
|
||||
name = card.get("name", "")
|
||||
@@ -581,9 +314,10 @@ class DeckGeneratorV3:
|
||||
if name_lower in seen_names:
|
||||
idx = seen_names[name_lower]
|
||||
enriched[idx].count += card.get("count", 1)
|
||||
logger.warning("Duplicate card '%s' merged", name)
|
||||
logger.warning("Duplicate card '%s' merged (LLM returned it twice)", name)
|
||||
continue
|
||||
|
||||
# Look up in collection
|
||||
row = conn.execute(
|
||||
"SELECT name, scryfall_id, quantity, tcg_price, type_line, cmc "
|
||||
"FROM collection_entries WHERE name = ? COLLATE NOCASE LIMIT 1",
|
||||
@@ -614,8 +348,11 @@ class DeckGeneratorV3:
|
||||
return enriched
|
||||
|
||||
# ── Step 5b: Basic Land Fill ──────────────────────────────
|
||||
_COLOR_TO_BASIC = _COLOR_TO_BASIC
|
||||
_BASIC_NAMES = _BASIC_NAMES
|
||||
_COLOR_TO_BASIC = {
|
||||
'W': 'Plains', 'U': 'Island', 'B': 'Swamp',
|
||||
'R': 'Mountain', 'G': 'Forest',
|
||||
}
|
||||
_BASIC_NAMES = set(_COLOR_TO_BASIC.values()) | {'Wastes'}
|
||||
|
||||
def _fill_basic_lands(
|
||||
self,
|
||||
@@ -637,7 +374,7 @@ class DeckGeneratorV3:
|
||||
ci = [c.upper() for c in color_identity]
|
||||
basic_names = [self._COLOR_TO_BASIC[c] for c in ci if c in self._COLOR_TO_BASIC]
|
||||
if not basic_names:
|
||||
basic_names = ["Wastes"]
|
||||
basic_names = ['Wastes']
|
||||
|
||||
per_color = basics_needed // len(basic_names)
|
||||
remainder = basics_needed % len(basic_names)
|
||||
@@ -648,20 +385,20 @@ class DeckGeneratorV3:
|
||||
non_basics.append(DeckCardWithStatus(
|
||||
name=bname,
|
||||
count=qty,
|
||||
category="Land",
|
||||
category='Land',
|
||||
role_tags=[],
|
||||
reason="Basic land for mana fixing",
|
||||
reason='Basic land for mana fixing',
|
||||
estimated_price_usd=0.10,
|
||||
owned=True,
|
||||
owned_qty=qty,
|
||||
status="owned",
|
||||
status='owned',
|
||||
))
|
||||
|
||||
logger.info("Basic land fill: %d basics added across %d color(s)",
|
||||
basics_needed, len(basic_names))
|
||||
basics_needed, len(basic_names))
|
||||
return non_basics
|
||||
|
||||
# ── Step 6: Smart Substitution (legacy wrapper for route compatibility) ──
|
||||
# ── Step 6: Smart Substitution ───────────────────────────
|
||||
def run_substitution(
|
||||
self,
|
||||
cards: List[DeckCardWithStatus],
|
||||
@@ -671,12 +408,15 @@ class DeckGeneratorV3:
|
||||
"""
|
||||
Run the Smart Substitution Engine on missing cards.
|
||||
|
||||
Uses the non-destructive apply_substitutions() under the hood.
|
||||
Falls back to Anthropic Claude for low-confidence matches,
|
||||
then to basic land as last resort — NEVER drops a card.
|
||||
Pipeline:
|
||||
a) Identify missing cards
|
||||
b) For each: try embedding similarity (free, fast)
|
||||
c) For low-confidence matches: batch Perplexity fallback
|
||||
d) Auto-select best substitute per card
|
||||
|
||||
Returns SubstitutionBatchResult with all cards updated.
|
||||
"""
|
||||
color_identity = commander.get("color_identity", [])
|
||||
scryfall_found = commander.get("scryfall_found", True)
|
||||
missing = [c for c in cards if not c.owned]
|
||||
owned = [c for c in cards if c.owned]
|
||||
|
||||
@@ -691,63 +431,47 @@ class DeckGeneratorV3:
|
||||
|
||||
logger.info("Substitution: %d missing / %d owned", len(missing), len(owned))
|
||||
|
||||
# Build owned pool
|
||||
# Build list of owned card names for substitution pool
|
||||
conn = self.db_conn_factory()
|
||||
owned_pool_rows = conn.execute(
|
||||
"SELECT DISTINCT name FROM collection_entries WHERE quantity > 0"
|
||||
).fetchall()
|
||||
owned_pool = {r["name"] for r in owned_pool_rows}
|
||||
deck_names = {c.name.lower() for c in cards}
|
||||
available_pool = {n for n in owned_pool if n.lower() not in deck_names}
|
||||
owned_pool = [r["name"] for r in owned_pool_rows]
|
||||
|
||||
# a) Embedding pass for missing cards
|
||||
needs_anthropic_fallback: List[DeckCardWithStatus] = []
|
||||
# Exclude cards already in the deck
|
||||
deck_names = set(c.name.lower() for c in cards)
|
||||
available_pool = [n for n in owned_pool if n.lower() not in deck_names]
|
||||
|
||||
# a) Embedding pass
|
||||
needs_pplx_fallback = []
|
||||
for card in missing:
|
||||
embedding_alts = self._embedding_substitution(
|
||||
card, color_identity, list(available_pool)
|
||||
card, color_identity, available_pool
|
||||
)
|
||||
card.alternatives = embedding_alts
|
||||
|
||||
# Check if best match meets threshold
|
||||
if embedding_alts and embedding_alts[0].similarity_score >= SUBSTITUTION_MIN_SIMILARITY:
|
||||
card.selected_substitute = embedding_alts[0].name
|
||||
card.status = "substituted"
|
||||
card.owned = True
|
||||
available_pool.discard(card.selected_substitute.lower())
|
||||
# Remove selected from pool
|
||||
if card.selected_substitute.lower() in [n.lower() for n in available_pool]:
|
||||
available_pool = [n for n in available_pool
|
||||
if n.lower() != card.selected_substitute.lower()]
|
||||
else:
|
||||
needs_anthropic_fallback.append(card)
|
||||
needs_pplx_fallback.append(card)
|
||||
|
||||
# b) Anthropic Claude fallback
|
||||
if needs_anthropic_fallback and SUBSTITUTION_USE_ANTHROPIC_FALLBACK:
|
||||
self._anthropic_substitution(
|
||||
needs_anthropic_fallback, list(available_pool),
|
||||
# b) Perplexity fallback for low-confidence matches
|
||||
if needs_pplx_fallback and SUBSTITUTION_USE_PPLX_FALLBACK:
|
||||
self._pplx_substitution(
|
||||
needs_pplx_fallback, available_pool,
|
||||
commander, strategy
|
||||
)
|
||||
|
||||
# c) Any card STILL missing after both passes gets a basic land — never drop it
|
||||
padded = 0
|
||||
for card in missing:
|
||||
if card.status == "missing":
|
||||
basic = _get_basic_land(color_identity)
|
||||
logger.warning(
|
||||
"[substitution] '%s' unresolved after all passes — "
|
||||
"replacing with basic land '%s'",
|
||||
card.name, basic,
|
||||
)
|
||||
card.name = basic
|
||||
card.category = "Land"
|
||||
card.role_tags = []
|
||||
card.owned = True
|
||||
card.status = "substituted"
|
||||
card.original_card = card.name
|
||||
padded += 1
|
||||
|
||||
# Count results
|
||||
substituted = sum(1 for c in cards if c.status == "substituted")
|
||||
still_missing = sum(1 for c in cards if c.status == "missing")
|
||||
|
||||
# Validate count hasn't changed
|
||||
assert len(cards) == len(owned) + len(missing), \
|
||||
f"[substitution] Card count changed: {len(owned) + len(missing)} -> {len(cards)}"
|
||||
|
||||
return SubstitutionBatchResult(
|
||||
total_cards=len(cards),
|
||||
owned_count=len(owned),
|
||||
@@ -766,37 +490,39 @@ class DeckGeneratorV3:
|
||||
if not self.embedding_index or not self.embedding_index.loaded:
|
||||
return []
|
||||
|
||||
try:
|
||||
matches = self.embedding_index.search_similar(
|
||||
query_card=card.name,
|
||||
color_filter=color_identity if color_identity else None,
|
||||
type_filter=card.category if card.category else None,
|
||||
exclude_cards=[card.name],
|
||||
top_n=SUBSTITUTION_MAX_ALTERNATIVES * 3,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning("Embedding search failed for '%s': %s", card.name, e)
|
||||
return []
|
||||
# Use the embeddings search to find similar cards
|
||||
matches = self.embedding_index.search_similar(
|
||||
query_card=card.name,
|
||||
color_filter=color_identity if color_identity else None,
|
||||
type_filter=card.category if card.category else None,
|
||||
exclude_cards=[card.name],
|
||||
top_n=SUBSTITUTION_MAX_ALTERNATIVES * 3, # Over-fetch, then filter to owned
|
||||
)
|
||||
|
||||
pool_lower = {n.lower() for n in available_pool}
|
||||
# Filter to only cards in the available pool
|
||||
pool_lower = set(n.lower() for n in available_pool)
|
||||
alternatives = []
|
||||
for match in matches:
|
||||
if match.name.lower() in pool_lower:
|
||||
role_overlap = list(set(card.role_tags) & set(getattr(match, "role_tags", [])))
|
||||
# Compute role overlap
|
||||
match_roles = [] # Would need card's role_tags from DB
|
||||
role_overlap = list(set(card.role_tags) & set(match_roles)) if match_roles else []
|
||||
|
||||
alternatives.append(CardAlternative(
|
||||
name=match.name,
|
||||
similarity_score=match.similarity,
|
||||
reason=f"Similar to {card.name} (cosine sim: {match.similarity:.3f})",
|
||||
source="embedding",
|
||||
role_overlap=role_overlap,
|
||||
cmc_delta=getattr(match, "mana_value", 0) - (card.estimated_price_usd or 0),
|
||||
cmc_delta=match.mana_value - (card.estimated_price_usd or 0),
|
||||
))
|
||||
|
||||
if len(alternatives) >= SUBSTITUTION_MAX_ALTERNATIVES:
|
||||
break
|
||||
|
||||
return alternatives
|
||||
|
||||
def _anthropic_substitution(
|
||||
def _pplx_substitution(
|
||||
self,
|
||||
missing_cards: List[DeckCardWithStatus],
|
||||
available_pool: List[str],
|
||||
@@ -804,14 +530,13 @@ class DeckGeneratorV3:
|
||||
strategy: str,
|
||||
):
|
||||
"""
|
||||
Batch Anthropic Claude fallback for cards with low embedding confidence.
|
||||
Mutates cards in-place. On failure, cards retain status='missing'
|
||||
so the basic-land fallback in run_substitution() can catch them.
|
||||
Batch Perplexity fallback for cards with low embedding confidence.
|
||||
Mutates the cards in-place with substitution data.
|
||||
"""
|
||||
if not available_pool:
|
||||
logger.warning("[anthropic_substitution] No available pool — skipping")
|
||||
return
|
||||
|
||||
# Build the request
|
||||
missing_dicts = [
|
||||
{
|
||||
"name": c.name,
|
||||
@@ -824,7 +549,7 @@ class DeckGeneratorV3:
|
||||
|
||||
user_prompt = build_substitution_prompt(
|
||||
missing_cards=missing_dicts,
|
||||
allowed_cards=available_pool[:200],
|
||||
allowed_cards=available_pool[:200], # Limit to avoid token overflow
|
||||
commander=commander.get("name", ""),
|
||||
color_identity=commander.get("color_identity", []),
|
||||
strategy=strategy,
|
||||
@@ -832,7 +557,7 @@ class DeckGeneratorV3:
|
||||
|
||||
try:
|
||||
from ..schemas.substitution_schema import SUBSTITUTION_RESPONSE_SCHEMA
|
||||
response = self.anthropic.chat_structured(
|
||||
response = self.pplx.chat_structured(
|
||||
system_prompt=SUBSTITUTION_SYSTEM,
|
||||
user_prompt=user_prompt,
|
||||
json_schema=SUBSTITUTION_RESPONSE_SCHEMA,
|
||||
@@ -843,16 +568,13 @@ class DeckGeneratorV3:
|
||||
)
|
||||
|
||||
if not response.ok:
|
||||
logger.warning(
|
||||
"[anthropic_substitution] LLM returned non-JSON — "
|
||||
"%d cards will fall through to basic land fallback",
|
||||
len(missing_cards),
|
||||
)
|
||||
logger.warning("Perplexity substitution fallback failed — non-JSON response")
|
||||
return
|
||||
|
||||
subs = response.parsed_json.get("substitutions", [])
|
||||
logger.info("[anthropic_substitution] Received %d substitutions", len(subs))
|
||||
logger.info("Perplexity suggested %d substitutions", len(subs))
|
||||
|
||||
# Apply substitutions
|
||||
card_by_name = {c.name.lower(): c for c in missing_cards}
|
||||
for sub in subs:
|
||||
orig = sub.get("original", "").lower()
|
||||
@@ -861,38 +583,32 @@ class DeckGeneratorV3:
|
||||
|
||||
if orig in card_by_name and substitute:
|
||||
card = card_by_name[orig]
|
||||
anthropic_alt = CardAlternative(
|
||||
# Add as top alternative
|
||||
pplx_alt = CardAlternative(
|
||||
name=substitute,
|
||||
similarity_score=0.80,
|
||||
similarity_score=0.80, # Nominal score for Perplexity suggestions
|
||||
reason=reason,
|
||||
source="anthropic",
|
||||
source="perplexity",
|
||||
role_overlap=sub.get("role_overlap", []),
|
||||
)
|
||||
card.alternatives.insert(0, anthropic_alt)
|
||||
# Insert at front
|
||||
card.alternatives.insert(0, pplx_alt)
|
||||
card.selected_substitute = substitute
|
||||
card.status = "substituted"
|
||||
card.owned = True
|
||||
card.original_card = card.name
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
"[anthropic_substitution] Exception: %s — "
|
||||
"%d cards will fall through to basic land fallback",
|
||||
e, len(missing_cards),
|
||||
)
|
||||
# Do NOT re-raise — cards stay status='missing' and
|
||||
# run_substitution()'s basic-land fallback will handle them
|
||||
logger.error("Perplexity substitution fallback error: %s", e)
|
||||
|
||||
# ── Stats ────────────────────────────────────────────────
|
||||
@staticmethod
|
||||
def _compute_stats(cards: List[DeckCardWithStatus]) -> dict:
|
||||
"""Compute deck statistics from the enriched card list."""
|
||||
by_category: dict[str, int] = {}
|
||||
by_status: dict[str, int] = {"owned": 0, "substituted": 0, "missing": 0}
|
||||
by_category = {}
|
||||
by_status = {"owned": 0, "substituted": 0, "missing": 0}
|
||||
total_price = 0.0
|
||||
owned_price = 0.0
|
||||
missing_price = 0.0
|
||||
role_counts: dict[str, int] = {}
|
||||
role_counts = {}
|
||||
|
||||
for card in cards:
|
||||
cat = card.category or "Other"
|
||||
|
||||
@@ -1,67 +0,0 @@
|
||||
# Laya connections
|
||||
|
||||
Laya (and Kev: same `POST /v1/systemone` API) is a **decision model**: typed multiple-choice, yes/no and score questions about a text, answered in
|
||||
one forward pass, no generated text. This page is where it plugs into the strategic-intelligence work in this repository
|
||||
([INTELLIGENCE-HANDOFF.md](INTELLIGENCE-HANDOFF.md)), what is measured, and how to make it worth using. Written 2026-10-26.
|
||||
|
||||
**Short version.** Every connection below is built, tested offline and **off by default**. Zero-shot, Laya is a poor action chooser
|
||||
(below the always-guess baseline) but a reasonable card classifier (48% against 17% by chance); it is too slow on a CPU for Forge's
|
||||
priority path (8 s against a 1.5 s budget). The way forward is fine-tuning on the decisions these connections record.
|
||||
|
||||
## What connects where
|
||||
|
||||
| Connection | Code | Mode | Notes |
|
||||
|---|---|---|---|
|
||||
| Farmhouse macro choice: Laya picks among CSIE's candidate macros, reading the CSIE ranking and ITI objective in its text | `farmhouse_decide.py` (called from `deepseek_brain.py`) | `DECISION_MODE=shadow` records agreement with the LLM; `assist` uses a pick it is `DECISION_MIN_CONF` sure of | existing; only offered candidates are accepted |
|
||||
| **Forge concrete priority choice**: Laya picks among the same concrete candidate IDs Forge sent to `/api/policy/decide/concrete` | `farmhouse_laya.advise_concrete` / `attach_concrete`, called from `routes/policy.py` | `shadow`: adds `laya` to the response, selection unchanged. `assist`: may replace `selected_id`, never one Forge tagged `stops_immediate_win` / `wins_now`; the original is kept as `csie_selected_id` | candidate described by kind, card, cost, target, tags (never by its opaque id); Forge still executes only an ID it sent |
|
||||
| **Extra questions in the same call**: "how dangerous are the opponents' boards?" (none/low/moderate/high/lethal) and "is there a threat that must be answered?" | `farmhouse_laya._extra_questions` | whenever Laya is asked | one forward pass answers all questions, so they cost almost nothing; recorded as weak signals for the learned-threat work, **not used to decide** |
|
||||
| **Capture**: every call is appended to `data/decisions/laya-shadow-<date>.jsonl` (`laya-shadow-v1`) | `farmhouse_laya.capture` | shadow / assist | the text Laya read, the offered options, its answer and extras, and the other deciders' picks (`labels`: `llm`, `csie`, `selected`) |
|
||||
| **Fine-tuning data**: capture log -> `train.jsonl` / `valid.jsonl` / `manifest.json`, split by game, with the baselines to beat | `scripts/farmhouse_laya_dataset.py` | offline | the manifest reports commonest-action, CSIE-vs-teacher and Laya-zero-shot agreement on the validation side |
|
||||
| **Card role evidence**: removal, board wipe, ramp, card draw, counterspell, protection, tutor, creature, other, from Oracle text | `farmhouse_laya.classify_roles` (batched, cached, with `source: laya:<model>` provenance); `scripts/farmhouse_laya_roles_eval.py` measures it | on demand | evidence for ITI / Tagger-style use, never a rules fact |
|
||||
|
||||
Not built (still the open items in the handoff, none of which Laya can substitute for): compiling and running the Forge controller, the bounded
|
||||
combo executor, the multi-turn planner, the calibrated learned-threat model. Laya's "threat" and "must answer" extras are only raw material for that last one.
|
||||
|
||||
## Where the hooks are
|
||||
- `src/commander_ai_lab/sim/farmhouse_decide.py` (committed): the macro-choice client gained the extra questions, the state text/options in its
|
||||
answer, and `record()` writes the capture line.
|
||||
- `routes/policy.py` (**working tree only, uncommitted**: the route itself is part of the pending strategic-evaluator work, so the hook was
|
||||
left for that commit). It is one block in `decide_concrete`, after the result is computed:
|
||||
`if _L.mode() != "off": advice = await run_in_threadpool(_L.advise_concrete, ...); result = _L.attach_concrete(...)`, guarded by `except ImportError`.
|
||||
- Everything else is new files: `farmhouse_laya.py`, the two scripts, `tests/farmhouse/test_farmhouse_laya*.py`, `farmhouse/laya/`.
|
||||
|
||||
## Settings
|
||||
`DECISION_URL` (e.g. `http://laya.commander-lab.svc.cluster.local:8080`), `DECISION_MODE` (`off` default / `shadow` / `assist`), `DECISION_MODEL`
|
||||
(a checkpoint such as `typed-decisions`), `DECISION_MIN_CONF` (0.7), `DECISION_TIMEOUT` (5 s), `DECISION_TIMEOUT_FORGE` (**1.5 s**: Forge waits on this),
|
||||
`DECISION_API_KEY`, `LAYA_CAPTURE_DIR` (`data/decisions`), `LAYA_CAPTURE=0` to stop writing capture lines. Three failures in a row pause asking for a minute.
|
||||
|
||||
## Rules the code enforces
|
||||
1. Text is built from public facts and the acting player's own hand. An opponent's `hand` field is never read, even if a client sends one (tested).
|
||||
2. Laya's answer must be one of the options that were offered; anything else is discarded.
|
||||
3. In assist, a pick Forge marked `stops_immediate_win` / `wins_now` is never overridden, and the response says what CSIE chose.
|
||||
4. A missing, slow or broken server returns "no answer"; nothing raises and the game proceeds as without Laya.
|
||||
5. Scores and probabilities are relative signals; Laya's probabilities are not calibrated for this game.
|
||||
|
||||
## What was measured (2026-10-26, `typed-decisions` checkpoint, 4-thread CPU)
|
||||
- **Macro action vs the LLM, 149 recorded decisions:** 16.1% (English) and 18.6% (typed-decisions); always guessing the commonest action 27.5%.
|
||||
- **Forge-shaped priority choice, 3 scenarios, with CSIE's ranking in the text:** agreed with CSIE all three times (expected: the ranking is in the
|
||||
text it reads), 42-48% sure, **7.5-8.4 s per call** with the extra questions. That is five times Forge's 1.5 s budget: on this CPU the Forge path
|
||||
would time out and fall back; it needs a GPU (33 ms quoted) or a smaller text.
|
||||
- **Extras:** "threat" said *high* for a lethal stack, a calm board and a behind board alike (useless zero-shot); "must answer" gave 0.48 for the
|
||||
lethal stack against 0.25 and 0.29 for the others (right direction, weak).
|
||||
- **Card roles from Oracle text, 150 unambiguous cards:** 48.0% (16.7% by chance among the six roles present); counterspell 76%, board wipe 64%,
|
||||
removal 40%, tutor 40%, ramp 36%, card draw 32%; when 70% sure, 69% right but only 11% of cards reach that. **3.2 s per card** in batches on the CPU:
|
||||
fine for the cards in a hand (cached), not for tagging 30,000 cards (about a day).
|
||||
|
||||
## How to make it worth using
|
||||
1. Run games with `DECISION_URL` set and `DECISION_MODE=shadow` (Farmhouse LLM games, and Forge games through `/api/policy/decide/concrete`).
|
||||
2. `python scripts/farmhouse_laya_dataset.py --teacher llm` (or `csie`, or `selected`). Read `manifest.json`: the **validation baselines** are the bar.
|
||||
A student of CSIE can at best equal CSIE, which is free; a student of the LLM is the useful one. Several thousand rows are needed.
|
||||
3. Fine-tune with Laya's own notebook/script (github.com/NandhaKishorM/laya, `notebooks/`; 2xT4 about 4-5 hours, or Apple silicon). Its worked example
|
||||
(browser agent choosing among ~45 concrete candidates) went from 0.10 zero-shot to 0.66, so a concrete-candidate head is the shape that works.
|
||||
Adapt the row field names if its loader differs.
|
||||
4. Serve the result (`LAYA_EXTRA_MODELS` / `DECISION_MODEL`), rerun `scripts/farmhouse_decision_eval.py` and the dataset manifest, and only then
|
||||
try `assist`. A GPU host makes the Forge path possible.
|
||||
5. Keep game result and decision quality separate (handoff boundary 6 / learned-threat contract): a captured label is a teacher's choice, not proof it was right.
|
||||
|
||||
Deployment reference copies are in `farmhouse/laya/` (image, manifests, CI); the source of truth is the farmhouse-os GitOps repo.
|
||||
@@ -1,74 +0,0 @@
|
||||
# phase-rs and commander-playtester: assessment and plan
|
||||
|
||||
Researched 2026-10-26 from the public repositories and data files only. **Nothing from either project has been run, vendored or deployed.**
|
||||
Facts marked (verified) were read from the source; (unverified) are inferences to check before building on them.
|
||||
|
||||
## phase-rs/phase: a rules engine
|
||||
|
||||
A Rust rules engine (turns, priority, the stack, combat, layers, triggers, replacement effects) that compiles to native and WebAssembly, with a game
|
||||
client, a WebSocket server and its own AI. Dual licensed Apache-2.0 / MIT (`LICENSE-APACHE`, `LICENSE-MIT`; GitHub's licence field shows "none" only
|
||||
because there are two files). Card data from MTGJSON (MIT); card images come from Scryfall at runtime; the repo says it bundles no card art or
|
||||
Comprehensive Rules text and has a DMCA/fan-content policy. One maintainer, young (weeks to months), fast-moving (latest engine tag v0.100.0).
|
||||
|
||||
Why it matters here: our Python simulator has no rules stack (the handoff says so: `stack_supported: false`) and Forge needs a JDK and Maven that
|
||||
are not in the image. Phase is a rules-accurate engine that can run headless.
|
||||
|
||||
### Coverage of the decks in this app (verified, from phase's published `coverage-data.json`)
|
||||
Phase reports 88.9% of 36,037 cards implemented overall and 92.5% for Commander. For the five saved decks, distinct cards supported:
|
||||
Empress V8 72/77, Landfall 65/66, God and Anime 69/71, Im not dead yet 82/84, Pew Pew 88/93: **376 of 391 (96.2%)**. The 15 unsupported cards have
|
||||
one or two parsing gaps each (e.g. Gale Waterdeep Prodigy, Hall of Echoes, Precursor Golem, Invigorate, Mavinda, Drana and Linvala, Tibalt's Trickery).
|
||||
"Supported" means every ability parsed; it is not a guarantee that each card plays correctly. How the engine treats an unsupported card in a game
|
||||
(blank, no-op, refused) is **unverified**; test it before trusting a result that involves one.
|
||||
|
||||
### The parts that fit
|
||||
| Part | What it gives us | Notes |
|
||||
|---|---|---|
|
||||
| `ai-duel` (crate `phase-ai`) | headless AI-vs-AI games from the command line: seeded, deterministic "measurement mode", batches, JSON reports, a 4-player Commander suite, paired-seed comparison | decks are plain JSON `{"name", "main":[{"name","count"}]}` (verified). **No prebuilt binary is released**: it needs a Rust build (in CI or a container, not on the workstation). How a custom Commander deck and commander are supplied to `--commander-suite` is unverified (that suite reads a feed file) |
|
||||
| `phase-server` | a WebSocket game server (lobby, hidden information, spectators) | a prebuilt image is published (`ghcr.io/phase-rs/phase-server`, tags `latest`, `preview`, `sha-...`). A Python client could play seats and record every decision with real rules (protocol documentation not yet read) |
|
||||
| `phase-llm` | **model-driven AI seats**: the engine hands the model its legal option list, the model returns an index, the engine re-validates it, and any failure falls back to the heuristic AI | same principle as `farmhouse_laya.py`. Speaks OpenAI-style chat (so Olla / twin-turbo) **and the System One protocol Laya and Kev serve** (verified: `WireProtocol::SystemOneRelay`, question id `pick`, 2 to 255 options) |
|
||||
| `POST /jev/systemone` relay in `phase-server` | forwards the System One request to the URL in `TYPESAFE_API_URL` (verified, env var, `http` allowed for local setups) | point it at `http://laya...:8080/v1/systemone` to put Laya or Kev in a seat. The relay sends the key as `Authorization: Bearer` and does not follow redirects |
|
||||
|
||||
The attraction for the Laya work: a captured decision from phase is an **engine-issued option list plus the pick**, in rules-accurate games. That
|
||||
is a better fine-tuning source than this repository's approximate simulator, and the exporter (`scripts/farmhouse_laya_dataset.py`) could take it with a small adapter.
|
||||
|
||||
## RafaelAugustScherer/commander-playtester: a browser app on top of phase
|
||||
|
||||
TypeScript/React single-page app, no backend. Runs phase's engine as WebAssembly in a Web Worker. Deck library with Moxfield / Archidekt / plain-text
|
||||
import, a goldfishing Monte Carlo (mulligan rate, screw and flood, land drops, ramp by turn 3, curve), real engine matches (watch, or pilot one seat),
|
||||
and 1-50 match head-to-head runs with a win rate and 95% confidence interval. Pins the phase engine in `engine-manifest.json` (v0.100.0; its README
|
||||
still says v0.57.0) and fetches the WASM and card data with SHA-256 checks. Zero stars, last pushed 2026-10-24.
|
||||
|
||||
**It has no LICENSE file** (verified: the repository metadata shows none). Without a licence the code is all rights reserved: it can be read and used
|
||||
as a reference, but not copied into this repository or redistributed. Ask the author to add a licence (MIT would do) before reusing any of it. What is
|
||||
worth taking as ideas, not code: the goldfishing metrics, the head-to-head confidence-interval report, the name-only deck payload for the engine, and
|
||||
the engine-smoke gate that boots the real engine and plays a match to completion after every upgrade.
|
||||
|
||||
## Recommended plan, smallest useful step first
|
||||
1. **Prove it on one deck.** Build `ai-duel` from the pinned phase tag in CI (Rust, container), convert one saved deck to the JSON format, run a seeded
|
||||
batch against a baseline deck, and read the result. Answers: does it run a Commander game end to end, what does it do with an unsupported card, how
|
||||
long is a game. This replaces the guesswork about the Python simulator's win rates.
|
||||
2. **A `phase-sim` service** in the commander-lab namespace (no egress, called only by the app) with an endpoint "run N rules-accurate games for deck X
|
||||
against Y", returning win rate, confidence interval, wins by seat and the game log; the app's results panel reads it. The Python simulator stays for
|
||||
instant, rough Monte Carlo.
|
||||
3. **Model seats.** Run `phase-server` with `TYPESAFE_API_URL` at Laya (or at a fine-tuned one later) and the OpenAI-compatible seat at Olla; compare
|
||||
heuristic-only, LLM and Laya seats. Record every decision for the Laya dataset.
|
||||
4. **Only then** consider the playtester's UI ideas, after the author adds a licence or as a clean reimplementation.
|
||||
|
||||
## What was built from this plan (2026-10-27)
|
||||
1. **The headless runner** (`ai-commander`, Commander four-player, batch mode) is built from the v0.118.0 tag with the card database that release was published with, in a
|
||||
container (`farmhouse/phase/Dockerfile`). Not `ai-duel` for the decks: `ai-commander` takes a feed JSON of four decks, which is exactly what the app has.
|
||||
2. **The service and the app side**: `farmhouse/phase/phase_sim.py` (HTTP, jobs, parser, warnings) and `routes/farmhouse_phase.py` + `ui/phase-sim.html`. Games that stall, hit the
|
||||
action cap or panic are reported and never counted.
|
||||
3. **Model seats**: not a setting. phase's model support lives only in its browser client (`engine-wasm` calls `phase-llm`; the HTTP is done by TypeScript), and
|
||||
`phase-llm` depends on `phase-ai`, so the headless binary cannot use it. `farmhouse/phase/llm-seat/` is a new crate that includes the runner and installs a hook
|
||||
(two anchored edits to phase's source). The server-side Jev relay and the browser client were not needed. Details: `farmhouse/phase/README.md`.
|
||||
Not done: the playtester's UI (no licence), a Python client for phase's WebSocket server (a large, fast-moving protocol), feeding the captured model-seat decisions into
|
||||
`scripts/farmhouse_laya_dataset.py` (the natural next step: each is an engine-issued option list plus a pick).
|
||||
|
||||
## Risks and open questions
|
||||
- Phase is a one-person, fast-moving project: pin tags, keep a smoke test that plays a full game, expect breaking changes.
|
||||
- Rust builds are heavy; keep them in CI/containers. Resource and image size are unmeasured.
|
||||
- Card coverage is parse-level; correctness per card is unmeasured. Unsupported cards must be flagged in any result.
|
||||
- The phase server and its AI use CPU; its measured game speed is unknown (the README says a game is thousands of AI decisions).
|
||||
- Legal: phase is Apache/MIT with Wizards fan-content wording; the playtester has no licence. The card data is MTGJSON (MIT).
|
||||
- Not yet read: phase's WebSocket protocol, how `--commander-suite` accepts custom decks, and whether the server can run unattended self-play.
|
||||
@@ -1,785 +0,0 @@
|
||||
# Commander-AI-Lab — Unity TCGEngine Commander Board: Implementation Plan (v5)
|
||||
|
||||
> **Architecture Principle:** Forge = Rules Authority · FastAPI = Hub · TCGEngine Unity = Renderer · Dual-Brain AI = Decision Engine
|
||||
|
||||
---
|
||||
|
||||
## System Architecture Overview
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ UNITY (TCGEngine — 2D Board) │
|
||||
│ PlayerArea x4: Hand | Library | Graveyard | Exile | Command │
|
||||
│ Battlefield zones per player · Card art from local cache │
|
||||
│ GameNetworkManager.cs ←── WebSocket ──► FastAPI │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
▲ ▼
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ FASTAPI (commander-ai-lab) │
|
||||
│ /ws/game — WebSocket relay │
|
||||
│ /game/action — receive player/AI action, forward to Forge │
|
||||
│ ForgeSubprocessManager — stdin/stdout pipe to Forge JVM │
|
||||
│ CardEnricher — injects art_url from local image cache │
|
||||
│ AIOrchestrator — routes decisions to DeepSeek-R1 or GPT-OSS │
|
||||
│ ChromaDB RAG — card knowledge scoped per player deck │
|
||||
│ ai_decisions.jsonl — logs every AI decision (PPO dataset) │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
▲ ▼
|
||||
┌─────────────────────────────────────────────────────────────────┐
|
||||
│ FORGE (Java — Headless Rules Engine) │
|
||||
│ Full Commander rules: tax, 21-commander-damage, 40 life │
|
||||
│ 4-player turn queue · Zone redirects · State-based actions │
|
||||
│ Emits game state JSON to stdout · Receives actions via stdin │
|
||||
└─────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Dual-Brain AI Decision Flow
|
||||
|
||||
This is the **core feature** of Commander-AI-Lab. Every AI player decision runs through this pipeline:
|
||||
|
||||
```
|
||||
Forge emits GameStateDTO (JSON)
|
||||
└─► FastAPI detects active player is AI
|
||||
└─► select_brain(phase, stack_size)
|
||||
├─ DeepSeek-R1-14B → combat, stack decisions, removal
|
||||
└─ GPT-OSS-20B → main phase, ramp, card advantage
|
||||
└─► ChromaDB.query(player_deck_cards, top_k=5)
|
||||
└─► build_prompt(state, rag_context, personality)
|
||||
└─► LLM.generate(prompt)
|
||||
└─► parse_action_json(response)
|
||||
├─ validate vs legal_actions[]
|
||||
├─ log to ai_decisions.jsonl
|
||||
└─► Forge stdin ← chosen action
|
||||
```
|
||||
|
||||
### Brain Selector (`ai/brain_selector.py`)
|
||||
|
||||
```python
|
||||
from enum import Enum
|
||||
|
||||
class Brain(Enum):
|
||||
DEEPSEEK = "deepseek-r1:14b"
|
||||
GPT_OSS = "gpt-oss-20b" # adjust to your Ollama model tag
|
||||
|
||||
def select_brain(phase: str, stack_size: int) -> Brain:
|
||||
"""
|
||||
DeepSeek-R1: combat math, instant-speed stack decisions, targeted removal.
|
||||
GPT-OSS: sorcery-speed strategy, ramp sequencing, card selection.
|
||||
"""
|
||||
combat_phases = {"declare_attackers", "declare_blockers", "combat_damage"}
|
||||
if phase in combat_phases or stack_size > 0:
|
||||
return Brain.DEEPSEEK
|
||||
return Brain.GPT_OSS
|
||||
```
|
||||
|
||||
### Prompt Builder (`ai/prompt_builder.py`)
|
||||
|
||||
```python
|
||||
from chromadb import Client
|
||||
from .personality import get_personality_prompt
|
||||
|
||||
chroma = Client()
|
||||
card_collection = chroma.get_collection("mtg_cards")
|
||||
|
||||
def build_prompt(state: dict, player_id: str, personality: str) -> str:
|
||||
# Pull card knowledge scoped to THIS player's deck
|
||||
player_cards = [c["name"] for c in state["players"][player_id]["library"]]
|
||||
rag_results = card_collection.query(
|
||||
query_texts=player_cards[:10],
|
||||
n_results=5
|
||||
)
|
||||
card_context = "\n".join(rag_results["documents"][0])
|
||||
|
||||
personality_prompt = get_personality_prompt(personality)
|
||||
|
||||
return f"""You are an AI playing Magic: The Gathering Commander.
|
||||
{personality_prompt}
|
||||
|
||||
## Current Game State
|
||||
- Phase: {state['phase']}
|
||||
- Active Player: {player_id}
|
||||
- Life Totals: {state['life_totals']}
|
||||
- Commander Damage Dealt: {state['commander_damage']}
|
||||
- Stack: {state['stack']}
|
||||
- Your Hand: {[c['name'] for c in state['players'][player_id]['hand']]}
|
||||
- Battlefield: {[c['name'] for c in state['players'][player_id]['battlefield']]}
|
||||
- Available Mana: {state['players'][player_id]['mana_pool']}
|
||||
|
||||
## Legal Actions
|
||||
{state['legal_actions']}
|
||||
|
||||
## Card Knowledge (RAG Context)
|
||||
{card_context}
|
||||
|
||||
Respond with a single JSON object:
|
||||
{{"action": "<action_type>", "card_id": "<id_or_null>", "targets": [], "reasoning": "<brief>"}}
|
||||
"""
|
||||
```
|
||||
|
||||
### Personality System (`ai/personality.py`)
|
||||
|
||||
```python
|
||||
import random
|
||||
|
||||
PERSONALITIES = {
|
||||
"aggro_beatdown": "You play aggressively. Attack every turn. Remove blockers. Win through combat damage as fast as possible.",
|
||||
"control_lockdown": "You play control. Counter key threats. Hold up mana for interaction. Win only when the board is locked.",
|
||||
"combo_enabler": "You dig for your combo pieces. Every decision is evaluated by how it advances your win condition.",
|
||||
"political_diplomat": "You form alliances. Attack the leader. Make deals. Never be the archenemy.",
|
||||
"voltron_commander": "Everything buffs your commander. Protect it at all costs. Win through commander damage.",
|
||||
"stax_prison": "You tax and restrict opponents. Deploy hate pieces. Win through attrition and resource denial.",
|
||||
"tempo_value": "You trade efficiently. Generate card advantage. Win by being ahead on resources.",
|
||||
"chaos_wildcard": "You make unpredictable decisions. Prioritize fun and chaos over optimal play.",
|
||||
}
|
||||
|
||||
def assign_personalities(player_ids: list[str], deck_archetypes: dict) -> dict[str, str]:
|
||||
"""
|
||||
Assign a personality per AI player at game creation.
|
||||
80% chance: archetype-matched personality.
|
||||
20% chance: random wildcard for unpredictability.
|
||||
"""
|
||||
assignments = {}
|
||||
for pid in player_ids:
|
||||
archetype = deck_archetypes.get(pid, "tempo_value")
|
||||
if random.random() < 0.20:
|
||||
assignments[pid] = random.choice(list(PERSONALITIES.keys()))
|
||||
else:
|
||||
assignments[pid] = archetype
|
||||
return assignments
|
||||
|
||||
def get_personality_prompt(personality: str) -> str:
|
||||
return f"## Your Playstyle\n{PERSONALITIES.get(personality, PERSONALITIES['tempo_value'])}"
|
||||
```
|
||||
|
||||
### AI Orchestrator (`ai/ai_orchestrator.py`)
|
||||
|
||||
```python
|
||||
import json, time, logging
|
||||
from pathlib import Path
|
||||
from .brain_selector import select_brain, Brain
|
||||
from .prompt_builder import build_prompt
|
||||
import ollama
|
||||
|
||||
LOG_PATH = Path("logs/ai_decisions.jsonl")
|
||||
LOG_PATH.parent.mkdir(exist_ok=True)
|
||||
|
||||
async def get_ai_action(state: dict, player_id: str, personality: str) -> dict:
|
||||
brain = select_brain(state["phase"], len(state["stack"]))
|
||||
prompt = build_prompt(state, player_id, personality)
|
||||
t_start = time.time()
|
||||
|
||||
try:
|
||||
response = ollama.chat(
|
||||
model=brain.value,
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
options={"temperature": 0.3, "num_predict": 256}
|
||||
)
|
||||
raw = response["message"]["content"]
|
||||
# Extract JSON from response (model may wrap in markdown)
|
||||
action = json.loads(raw[raw.find("{"):raw.rfind("}")+1])
|
||||
except Exception as e:
|
||||
logging.warning(f"AI parse failed ({brain.value}): {e}. Passing turn.")
|
||||
action = {"action": "pass", "card_id": None, "targets": [], "reasoning": "parse_error"}
|
||||
|
||||
# Validate against Forge legal actions
|
||||
if action.get("action") not in [a["type"] for a in state["legal_actions"]]:
|
||||
logging.warning(f"Illegal action returned by {brain.value}. Falling back to pass.")
|
||||
action = {"action": "pass", "card_id": None, "targets": [], "reasoning": "illegal_action"}
|
||||
|
||||
# Log for PPO training dataset
|
||||
log_entry = {
|
||||
"timestamp": time.time(),
|
||||
"player_id": player_id,
|
||||
"brain": brain.value,
|
||||
"personality": personality,
|
||||
"phase": state["phase"],
|
||||
"action": action,
|
||||
"reasoning": action.get("reasoning", ""),
|
||||
"latency_ms": round((time.time() - t_start) * 1000),
|
||||
}
|
||||
with open(LOG_PATH, "a") as f:
|
||||
f.write(json.dumps(log_entry) + "\n")
|
||||
|
||||
return action
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 0 — Local Card Image Cache
|
||||
|
||||
**Build this first.** Every downstream system depends on local art URLs.
|
||||
|
||||
### Step 1 — Bulk Download (`scripts/download_card_images.py`)
|
||||
|
||||
```python
|
||||
import requests, json, os
|
||||
from pathlib import Path
|
||||
from tqdm import tqdm
|
||||
|
||||
BULK_URL = "https://api.scryfall.com/bulk-data/default-cards"
|
||||
IMAGE_DIR = Path("static/card-images")
|
||||
IMAGE_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
def download_images():
|
||||
# Get bulk data URL
|
||||
meta = requests.get(BULK_URL).json()
|
||||
cards = requests.get(meta["download_uri"], stream=True).json()
|
||||
|
||||
for card in tqdm(cards, desc="Downloading card images"):
|
||||
image_uris = card.get("image_uris") or {}
|
||||
url = image_uris.get("normal")
|
||||
if not url:
|
||||
continue # skip DFCs without top-level image_uris
|
||||
|
||||
safe_name = card["name"].replace("/", "_").replace(" ", "_")
|
||||
set_code = card.get("set", "unknown")
|
||||
filename = IMAGE_DIR / set_code / f"{safe_name}_{card['collector_number']}.jpg"
|
||||
filename.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
if filename.exists():
|
||||
continue # already cached
|
||||
|
||||
img_data = requests.get(url, timeout=10).content
|
||||
filename.write_bytes(img_data)
|
||||
|
||||
if __name__ == "__main__":
|
||||
download_images()
|
||||
```
|
||||
|
||||
### Step 2 — Build SQLite Index (`scripts/build_image_index.py`)
|
||||
|
||||
```python
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
|
||||
IMAGE_DIR = Path("static/card-images")
|
||||
DB_PATH = Path("data/card_images.db")
|
||||
DB_PATH.parent.mkdir(exist_ok=True)
|
||||
|
||||
def build_index():
|
||||
conn = sqlite3.connect(DB_PATH)
|
||||
conn.execute("""
|
||||
CREATE TABLE IF NOT EXISTS card_images (
|
||||
name TEXT PRIMARY KEY,
|
||||
path TEXT NOT NULL
|
||||
)
|
||||
""")
|
||||
for img_path in IMAGE_DIR.rglob("*.jpg"):
|
||||
card_name = img_path.stem.rsplit("_", 1)[0].replace("_", " ")
|
||||
conn.execute("INSERT OR REPLACE INTO card_images VALUES (?, ?)",
|
||||
(card_name, str(img_path)))
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
build_index()
|
||||
```
|
||||
|
||||
### Step 3 — Card Enricher (`api/card_enricher.py`)
|
||||
|
||||
```python
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
|
||||
DB_PATH = Path("data/card_images.db")
|
||||
STATIC_URL = "http://localhost:8000/static/card-images"
|
||||
|
||||
def enrich_card(card: dict) -> dict:
|
||||
"""Inject local art_url into every CardDTO before sending to Unity."""
|
||||
conn = sqlite3.connect(DB_PATH)
|
||||
cursor = conn.execute("SELECT path FROM card_images WHERE name = ?", (card["name"],))
|
||||
row = cursor.fetchone()
|
||||
conn.close()
|
||||
if row:
|
||||
relative = Path(row[0]).relative_to("static/card-images")
|
||||
card["art_url"] = f"{STATIC_URL}/{relative}"
|
||||
else:
|
||||
card["art_url"] = f"{STATIC_URL}/card_back.jpg"
|
||||
return card
|
||||
```
|
||||
|
||||
### Step 4 — FastAPI Static Mount (`main.py` addition)
|
||||
|
||||
```python
|
||||
from fastapi.staticfiles import StaticFiles
|
||||
app.mount("/static/card-images", StaticFiles(directory="static/card-images"), name="card-images")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 1 — Forge Bridge (Rules Engine)
|
||||
|
||||
### ForgeSubprocessManager (`forge/subprocess_manager.py`)
|
||||
|
||||
```python
|
||||
import subprocess, json, asyncio, threading
|
||||
from pathlib import Path
|
||||
|
||||
FORGE_JAR = Path("forge/forge-game.jar")
|
||||
|
||||
class ForgeSubprocessManager:
|
||||
def __init__(self):
|
||||
self.process = None
|
||||
self._lock = threading.Lock()
|
||||
|
||||
def start(self, deck_paths: list[str], format: str = "commander"):
|
||||
cmd = [
|
||||
"java", "-jar", str(FORGE_JAR),
|
||||
"--headless",
|
||||
"--format", format,
|
||||
"--decks", *deck_paths,
|
||||
"--output", "json"
|
||||
]
|
||||
self.process = subprocess.Popen(
|
||||
cmd,
|
||||
stdin=subprocess.PIPE,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.PIPE,
|
||||
text=True,
|
||||
bufsize=1
|
||||
)
|
||||
|
||||
def read_state(self) -> dict:
|
||||
with self._lock:
|
||||
line = self.process.stdout.readline()
|
||||
return json.loads(line)
|
||||
|
||||
def send_action(self, action: dict):
|
||||
with self._lock:
|
||||
self.process.stdin.write(json.dumps(action) + "\n")
|
||||
self.process.stdin.flush()
|
||||
|
||||
def stop(self):
|
||||
if self.process:
|
||||
self.process.terminate()
|
||||
```
|
||||
|
||||
### Data Transfer Objects (`api/dtos.py`)
|
||||
|
||||
```python
|
||||
from pydantic import BaseModel
|
||||
from typing import Optional
|
||||
|
||||
class CardDTO(BaseModel):
|
||||
id: str
|
||||
name: str
|
||||
type: str
|
||||
power: Optional[str]
|
||||
toughness: Optional[str]
|
||||
mana_cost: str
|
||||
art_url: str = ""
|
||||
tapped: bool = False
|
||||
zone: str = "hand"
|
||||
|
||||
class PlayerDTO(BaseModel):
|
||||
id: str
|
||||
life: int
|
||||
commander_damage: dict[str, int]
|
||||
commander_tax: int
|
||||
hand: list[CardDTO]
|
||||
battlefield: list[CardDTO]
|
||||
graveyard: list[CardDTO]
|
||||
exile: list[CardDTO]
|
||||
command_zone: list[CardDTO]
|
||||
library_count: int
|
||||
mana_pool: dict[str, int]
|
||||
is_ai: bool
|
||||
personality: Optional[str]
|
||||
|
||||
class GameStateDTO(BaseModel):
|
||||
game_id: str
|
||||
phase: str
|
||||
active_player: str
|
||||
priority: str
|
||||
turn_number: int
|
||||
players: list[PlayerDTO]
|
||||
stack: list[dict]
|
||||
legal_actions: list[dict]
|
||||
winner: Optional[str]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 2 — FastAPI WebSocket Game Server
|
||||
|
||||
### Game Loop (`api/game_loop.py`)
|
||||
|
||||
```python
|
||||
import asyncio, uuid
|
||||
from .dtos import GameStateDTO
|
||||
from forge.subprocess_manager import ForgeSubprocessManager
|
||||
from ai.ai_orchestrator import get_ai_action
|
||||
from ai.personality import assign_personalities
|
||||
from api.card_enricher import enrich_card
|
||||
|
||||
active_games: dict[str, dict] = {}
|
||||
|
||||
async def start_game(deck_paths: list[str], ai_players: list[str]) -> str:
|
||||
game_id = str(uuid.uuid4())
|
||||
forge = ForgeSubprocessManager()
|
||||
forge.start(deck_paths)
|
||||
|
||||
# Assign AI personalities at game creation
|
||||
archetypes = detect_archetypes(deck_paths, ai_players) # ChromaDB query
|
||||
personalities = assign_personalities(ai_players, archetypes)
|
||||
|
||||
active_games[game_id] = {
|
||||
"forge": forge,
|
||||
"ai_players": ai_players,
|
||||
"personalities": personalities,
|
||||
"websockets": []
|
||||
}
|
||||
asyncio.create_task(game_loop(game_id))
|
||||
return game_id
|
||||
|
||||
async def game_loop(game_id: str):
|
||||
game = active_games[game_id]
|
||||
forge = game["forge"]
|
||||
|
||||
while True:
|
||||
raw_state = forge.read_state()
|
||||
if raw_state.get("winner"):
|
||||
await broadcast(game_id, raw_state)
|
||||
break
|
||||
|
||||
# Enrich every card with local art_url
|
||||
state = enrich_state(raw_state)
|
||||
await broadcast(game_id, state)
|
||||
|
||||
priority_player = state["priority"]
|
||||
if priority_player in game["ai_players"]:
|
||||
personality = game["personalities"][priority_player]
|
||||
action = await get_ai_action(state, priority_player, personality)
|
||||
forge.send_action(action)
|
||||
# else: wait for Unity client to send action via WebSocket
|
||||
|
||||
async def broadcast(game_id: str, state: dict):
|
||||
game = active_games[game_id]
|
||||
for ws in game["websockets"]:
|
||||
await ws.send_json(state)
|
||||
|
||||
def enrich_state(state: dict) -> dict:
|
||||
for player in state["players"]:
|
||||
for zone in ["hand", "battlefield", "graveyard", "exile", "command_zone"]:
|
||||
player[zone] = [enrich_card(c) for c in player.get(zone, [])]
|
||||
return state
|
||||
```
|
||||
|
||||
### WebSocket Endpoint (`api/routes/game.py`)
|
||||
|
||||
```python
|
||||
from fastapi import APIRouter, WebSocket, WebSocketDisconnect
|
||||
from api.game_loop import active_games, start_game
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
@router.websocket("/ws/game/{game_id}")
|
||||
async def game_ws(websocket: WebSocket, game_id: str):
|
||||
await websocket.accept()
|
||||
game = active_games.get(game_id)
|
||||
if not game:
|
||||
await websocket.close(code=4004)
|
||||
return
|
||||
game["websockets"].append(websocket)
|
||||
try:
|
||||
while True:
|
||||
data = await websocket.receive_json()
|
||||
# Human player action forwarded directly to Forge
|
||||
game["forge"].send_action(data)
|
||||
except WebSocketDisconnect:
|
||||
game["websockets"].remove(websocket)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 3 — Unity TCGEngine Board (2D)
|
||||
|
||||
### Project Layout
|
||||
|
||||
```
|
||||
unity-commander-ui/
|
||||
├── Assets/
|
||||
│ ├── TCGEngine/ ← TCGEngine base (unmodified)
|
||||
│ ├── Commander/
|
||||
│ │ ├── Scripts/
|
||||
│ │ │ ├── GameNetworkManager.cs ← WebSocket ↔ FastAPI
|
||||
│ │ │ ├── CommanderBoardUI.cs ← 4-player layout controller
|
||||
│ │ │ ├── PlayerAreaUI.cs ← per-player zone panel
|
||||
│ │ │ ├── CardImageLoader.cs ← local cache texture fetcher
|
||||
│ │ │ ├── ZonePanel.cs ← Hand/Library/Graveyard/Exile/Command
|
||||
│ │ │ └── AIThinkingIndicator.cs ← spinner while AI brain is deciding
|
||||
│ │ └── Prefabs/
|
||||
│ │ ├── CardPrefab.prefab
|
||||
│ │ ├── PlayerAreaPrefab.prefab
|
||||
│ │ └── ZonePanelPrefab.prefab
|
||||
│ └── Scenes/
|
||||
│ └── CommanderBoard.unity
|
||||
```
|
||||
|
||||
### Board Layout (matches screenshot)
|
||||
|
||||
```
|
||||
┌──────────────────────────────────────────────────────────────────┐
|
||||
│ [LIBRARY][GRAVEYARD][EXILE] [────── HAND ──────] (Player 3) │
|
||||
│ [COMMAND] │
|
||||
├──────────────────────────────┬───────────────────────────────────┤
|
||||
│ │ │
|
||||
│ │ │
|
||||
│ BATTLEFIELD │ BATTLEFIELD │
|
||||
│ (Player 1) │ (Player 2) │
|
||||
│ BATTLEFIELD (center) │
|
||||
│ BATTLEFIELD │ BATTLEFIELD │
|
||||
│ (Player 4) │ (Player 3) │
|
||||
│ │ │
|
||||
├──────────────────────────────┴───────────────────────────────────┤
|
||||
│ [COMMAND] │
|
||||
│ [LIBRARY][GRAVEYARD][EXILE] [────── HAND ──────] (Player 1) │
|
||||
└──────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### GameNetworkManager.cs
|
||||
|
||||
```csharp
|
||||
using UnityEngine;
|
||||
using NativeWebSocket;
|
||||
using Newtonsoft.Json;
|
||||
|
||||
public class GameNetworkManager : MonoBehaviour
|
||||
{
|
||||
public static GameNetworkManager Instance { get; private set; }
|
||||
|
||||
[SerializeField] private string serverUrl = "ws://localhost:8000/ws/game/";
|
||||
[SerializeField] private string gameId;
|
||||
|
||||
private WebSocket _socket;
|
||||
|
||||
public delegate void GameStateReceived(GameStateDTO state);
|
||||
public event GameStateReceived OnGameStateReceived;
|
||||
|
||||
async void Awake()
|
||||
{
|
||||
Instance = this;
|
||||
_socket = new WebSocket($"{serverUrl}{gameId}");
|
||||
|
||||
_socket.OnMessage += (bytes) =>
|
||||
{
|
||||
var json = System.Text.Encoding.UTF8.GetString(bytes);
|
||||
var state = JsonConvert.DeserializeObject<GameStateDTO>(json);
|
||||
UnityMainThreadDispatcher.Enqueue(() => OnGameStateReceived?.Invoke(state));
|
||||
};
|
||||
|
||||
await _socket.Connect();
|
||||
}
|
||||
|
||||
public async void SendAction(object action)
|
||||
{
|
||||
var json = JsonConvert.SerializeObject(action);
|
||||
await _socket.SendText(json);
|
||||
}
|
||||
|
||||
void Update() => _socket?.DispatchMessageQueue();
|
||||
|
||||
async void OnDestroy() => await _socket?.Close();
|
||||
}
|
||||
```
|
||||
|
||||
### CardImageLoader.cs
|
||||
|
||||
```csharp
|
||||
using System.Collections;
|
||||
using System.Collections.Generic;
|
||||
using UnityEngine;
|
||||
using UnityEngine.Networking;
|
||||
using UnityEngine.UI;
|
||||
|
||||
public class CardImageLoader : MonoBehaviour
|
||||
{
|
||||
private static Dictionary<string, Texture2D> _cache = new();
|
||||
|
||||
public static IEnumerator LoadCardArt(string artUrl, RawImage target)
|
||||
{
|
||||
if (_cache.TryGetValue(artUrl, out var cached))
|
||||
{
|
||||
target.texture = cached;
|
||||
yield break;
|
||||
}
|
||||
|
||||
using var req = UnityWebRequestTexture.GetTexture(artUrl);
|
||||
yield return req.SendWebRequest();
|
||||
|
||||
if (req.result == UnityWebRequest.Result.Success)
|
||||
{
|
||||
var tex = DownloadHandlerTexture.GetContent(req);
|
||||
_cache[artUrl] = tex;
|
||||
target.texture = tex;
|
||||
}
|
||||
else
|
||||
{
|
||||
Debug.LogWarning($"Failed to load card art: {artUrl}");
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### AIThinkingIndicator.cs
|
||||
|
||||
```csharp
|
||||
using UnityEngine;
|
||||
using UnityEngine.UI;
|
||||
|
||||
/// Shows a spinner + brain label while the AI is deciding.
|
||||
public class AIThinkingIndicator : MonoBehaviour
|
||||
{
|
||||
[SerializeField] private GameObject spinnerPanel;
|
||||
[SerializeField] private Text brainLabel;
|
||||
[SerializeField] private Text personalityLabel;
|
||||
|
||||
public void Show(string brain, string personality)
|
||||
{
|
||||
spinnerPanel.SetActive(true);
|
||||
brainLabel.text = $"🧠 {brain}";
|
||||
personalityLabel.text = $"🎭 {personality}";
|
||||
}
|
||||
|
||||
public void Hide() => spinnerPanel.SetActive(false);
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 4 — Commander Zone UIs
|
||||
|
||||
### Zone Definitions per Player
|
||||
|
||||
| Zone Panel | Content | Notes |
|
||||
|---|---|---|
|
||||
| `LIBRARY` | Count only | Click → draw/look at top |
|
||||
| `GRAVEYARD` | Scrollable card list | Full card art on hover |
|
||||
| `EXILE` | Scrollable card list | Includes face-down indicators |
|
||||
| `HAND` | Wide panel, card art | Hidden to opponents |
|
||||
| `COMMAND` | Commander card(s) | Shows commander tax counter |
|
||||
| `BATTLEFIELD` | Open grid | Tapped/untapped state, counters |
|
||||
|
||||
### Commander-Specific UI Elements
|
||||
|
||||
```csharp
|
||||
public class CommanderZoneUI : MonoBehaviour
|
||||
{
|
||||
[SerializeField] private Text taxCounter; // "Tax: +{2N}"
|
||||
[SerializeField] private RawImage commanderArt;
|
||||
[SerializeField] private Text commanderDamageDealt; // per opponent
|
||||
|
||||
public void Refresh(PlayerDTO player)
|
||||
{
|
||||
int tax = player.CommanderTax;
|
||||
taxCounter.text = tax > 0 ? $"+{tax * 2}" : "";
|
||||
|
||||
if (player.CommandZone.Count > 0)
|
||||
StartCoroutine(CardImageLoader.LoadCardArt(
|
||||
player.CommandZone[0].ArtUrl, commanderArt));
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phase 5 — Commander Rules Validation Layer
|
||||
|
||||
Forge handles all rules natively. FastAPI validates the AI response is within the legal actions list Forge returns — no custom rules code needed in Python or Unity.
|
||||
|
||||
Key Commander rules confirmed handled by Forge headless:
|
||||
- **Commander Tax** — +2 generic per previous cast from command zone
|
||||
- **21 Commander Damage** — tracked per attacker/defender pair
|
||||
- **40 Starting Life**
|
||||
- **Zone Redirect** — commander goes to command zone instead of graveyard/exile (player choice)
|
||||
- **Color Identity** — deck validation at game start
|
||||
- **Singleton** — deck validation at game start
|
||||
|
||||
---
|
||||
|
||||
## Phase 6 — AI Decision Logging & PPO Pipeline
|
||||
|
||||
Every AI decision written to `logs/ai_decisions.jsonl`:
|
||||
|
||||
```json
|
||||
{
|
||||
"timestamp": 1743520800.123,
|
||||
"player_id": "player_2",
|
||||
"brain": "deepseek-r1:14b",
|
||||
"personality": "aggro_beatdown",
|
||||
"phase": "declare_attackers",
|
||||
"action": {"action": "attack", "card_id": "c_042", "targets": ["player_1"], "reasoning": "Open attack, no blockers visible"},
|
||||
"latency_ms": 847
|
||||
}
|
||||
```
|
||||
|
||||
This log becomes the **supervised learning dataset** for the next PPO training iteration. Each (state, action) pair from winning games is a positive training example.
|
||||
|
||||
---
|
||||
|
||||
## Project Directory Structure
|
||||
|
||||
```
|
||||
commander-ai-lab/
|
||||
├── main.py ← FastAPI app, mounts static files
|
||||
├── api/
|
||||
│ ├── dtos.py ← Pydantic DTOs
|
||||
│ ├── card_enricher.py ← Injects art_url into CardDTOs
|
||||
│ ├── game_loop.py ← Main game orchestration loop
|
||||
│ └── routes/
|
||||
│ └── game.py ← WebSocket + REST endpoints
|
||||
├── ai/
|
||||
│ ├── ai_orchestrator.py ← Main AI decision entry point
|
||||
│ ├── brain_selector.py ← DeepSeek-R1 vs GPT-OSS routing
|
||||
│ ├── prompt_builder.py ← Game state + RAG → prompt
|
||||
│ └── personality.py ← 8 personalities + assignment
|
||||
├── forge/
|
||||
│ └── subprocess_manager.py ← Forge JVM stdin/stdout bridge
|
||||
├── scripts/
|
||||
│ ├── download_card_images.py ← One-time Scryfall bulk download
|
||||
│ └── build_image_index.py ← SQLite name→path index
|
||||
├── static/
|
||||
│ └── card-images/ ← ~2GB local image cache
|
||||
│ └── {set_code}/
|
||||
│ └── {CardName}_{num}.jpg
|
||||
├── data/
|
||||
│ └── card_images.db ← SQLite image index
|
||||
├── logs/
|
||||
│ └── ai_decisions.jsonl ← PPO training dataset
|
||||
├── unity-commander-ui/ ← Unity 2D TCGEngine project
|
||||
└── docs/
|
||||
└── implementation_plan.md ← This document
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Phased Timeline
|
||||
|
||||
| Phase | Scope | Est. Time |
|
||||
|---|---|---|
|
||||
| **0** | Scryfall image download + SQLite index + FastAPI static mount | 1 day |
|
||||
| **1** | Forge subprocess bridge + DTO definitions | 2–3 days |
|
||||
| **2** | AI Orchestrator + brain selector + prompt builder + personality system | 3–4 days |
|
||||
| **3** | Unity TCGEngine project setup + WebSocket GameNetworkManager | 3–4 days |
|
||||
| **4** | PlayerAreaUI + ZonePanels + CardImageLoader + board layout | 3–4 days |
|
||||
| **5** | Commander zone UI (tax counter, commander damage, zone redirect) | 1–2 days |
|
||||
| **6** | AI decision logging + end-to-end game loop test | 1–2 days |
|
||||
|
||||
**Total estimated: ~3 weeks to first playable Commander game with live AI opponents.**
|
||||
|
||||
---
|
||||
|
||||
## Key Design Decisions
|
||||
|
||||
| Decision | Rationale |
|
||||
|---|---|
|
||||
| Forge = rules authority | Forge already handles all Commander rules natively. No custom rules code. |
|
||||
| TCGEngine = dumb renderer | All game logic lives in Forge. Unity only renders state it receives. |
|
||||
| FastAPI = hub | Bridges Forge ↔ Unity ↔ AI. Single point of truth. |
|
||||
| Dual-brain routing | DeepSeek-R1 excels at analytical/math decisions; GPT-OSS at strategic sequencing. |
|
||||
| ChromaDB scoped per player | RAG context relevant to the active player's deck, not global card knowledge. |
|
||||
| Personality persists per game | Consistent playstyle per AI across all 4 players per session. |
|
||||
| Local image cache | Eliminates network latency for card art. ~2GB one-time download. |
|
||||
| ai_decisions.jsonl | Every AI decision is a future PPO training sample. |
|
||||
@@ -1,32 +0,0 @@
|
||||
# Forge in the cluster image
|
||||
|
||||
The batch simulator on the Simulator page runs on **Forge**, the Java rules engine. The first cluster builds left it out by mistake ("Python-only"
|
||||
was read as "no Java"), so the page answered `Forge JAR not found`. It is now built into the image (reference copy of the Dockerfile stage:
|
||||
`infrastructure/apps/commander-lab/image/Dockerfile` in the farmhouse-os repo).
|
||||
|
||||
What the image build does:
|
||||
|
||||
1. **Stage 1** (`maven:3.9-eclipse-temurin-17`): fetch the official `Card-Forge/forge` at commit `edb28158f6d8` (the commit `KoalaTrapLord/Forge-Repo`
|
||||
is on; that fork is 0 commits ahead of upstream, so this is the code the lab's Java was written against; newer Forge may have changed APIs), then
|
||||
`mvn install -pl forge-gui-desktop -am -DskipTests -Drevision=2.0.12-SNAPSHOT`, retried up to three times (Maven Central downloads fail now and then).
|
||||
2. **The lab's Java tool** (`LabCli`, from this repository's `pom.xml`) is built against it. Forge's installed poms keep `${revision}` as their parent
|
||||
version, which Maven cannot resolve for a consumer even with `-Drevision`; the placeholder is rewritten in the installed `.pom` files first.
|
||||
3. **Runtime**: the full `openjdk-17-jre` (not `-headless`) plus `xvfb`, the two jars, and Forge's `res/` data without `adventure`, `music`, `sound`, `quest` and `skins`
|
||||
(about 190 MB the headless simulator never reads). The environment the app reads: `FORGE_JAR`, `FORGE_DIR`, `LAB_JAR`, `FORGE_DECKS_DIR`, `PRECON_DIR`, `JAVA_HOME`.
|
||||
4. `FORGE_MAX_THREADS` (default 1) clamps the batch's thread count: every Forge game is its own JVM with up to a 4 GB heap, and the cluster has two small nodes.
|
||||
5. CI starts the app, begins a batch through the app's own `/api/lab/start` and requires a Commander game with turns played and a readable `/api/lab/result` before the image is published.
|
||||
|
||||
## Things that only showed up by running it in the cluster (2026-10-27)
|
||||
Each of these made a batch fail while every individual piece looked installed. Together they are why the CI test starts a batch through the app's own API.
|
||||
- **Forge needs a display, even for `sim`.** `GuiDesktop`'s static initialiser asks AWT for the default screen device; the headless JRE throws `HeadlessException`, and
|
||||
`forge.view.Main` then exits 1 with no output at all (the lab tool records every game as an instant draw: "Forge produced NO output"). Fix: the full `openjdk-17-jre`,
|
||||
`xvfb`, `DISPLAY=:99`, and the entrypoint starts `Xvfb :99`.
|
||||
- **Three folders, one deck.** The app writes saved decks to `USER_DECKS_DIR`, the lab's Java tool resolves deck files through `PRECON_DECKS_DIR`, and Forge finds a deck BY
|
||||
NAME in its own commander folder ("Could not load deck"). All three are the Forge folder (`FORGE_DECKS_DIR`), which the entrypoint seeds with the 163 bundled precons.
|
||||
- **Writable folders.** The tool writes `results/` beside Forge's data and Forge a debug log beside its jar; both folders are owned by uid 10001 in the image.
|
||||
- **Result path.** The app asked for `results/ml-decision-<id>.json` (relative) but ran the tool from Forge's folder, so the file landed there and `/api/lab/result` answered
|
||||
"Result file not found". The path is made absolute (patch `forge-results-abs`) and `/app/results` is linked to `/state/results`.
|
||||
- `docker cp` cannot write into a tmpfs and Docker mounts `/tmp` `noexec` (it stops SQLite's native library loading): test-harness traps, not cluster problems.
|
||||
|
||||
Speed: about 25 seconds per game, one at a time (the project's own figure is ~0.04 simulations/s single-threaded). For many games with real rules, the
|
||||
phase-rs engine (`farmhouse/phase/`) is far faster. Forge is GPL-3.0: private use, do not redistribute the built image.
|
||||
@@ -1,37 +0,0 @@
|
||||
#!/bin/sh
|
||||
# Keep the app's state on the persistent volume (/state) while the code stays in the image.
|
||||
# The upstream app writes next to its code: collection.db, scryfall_cache.db, data/ (Scryfall bulk DB, ChromaDB index),
|
||||
# user-decks/. Point each at /state with a symlink; seed data/ and user-decks/ from the image the first time.
|
||||
set -eu
|
||||
S="${STATE_DIR:-/state}"
|
||||
mkdir -p "$S"
|
||||
|
||||
for d in data user-decks results; do
|
||||
if [ ! -e "$S/$d" ]; then
|
||||
if [ -d "/app/$d" ]; then cp -a "/app/$d" "$S/$d"; else mkdir -p "$S/$d"; fi
|
||||
fi
|
||||
rm -rf "/app/$d"
|
||||
ln -s "$S/$d" "/app/$d"
|
||||
done
|
||||
|
||||
for f in collection.db scryfall_cache.db; do
|
||||
rm -f "/app/$f"
|
||||
ln -s "$S/$f" "/app/$f" # sqlite creates the target on first open
|
||||
done
|
||||
|
||||
# Forge looks decks up by name in its user folder; the app exports the decks it simulates there.
|
||||
mkdir -p "${FORGE_DECKS_DIR:-/tmp/.forge/decks/commander}"
|
||||
# Seed it with the bundled precon decks (163 small files); never overwrite a deck that is already there.
|
||||
if [ -d /app/precon-decks ]; then cp -n /app/precon-decks/*.dck "${FORGE_DECKS_DIR:-/tmp/.forge/decks/commander}"/ 2>/dev/null || true; fi
|
||||
|
||||
# A virtual display for Forge (it initialises AWT even in command-line mode; see the Dockerfile). Best effort: the app itself does not need it.
|
||||
if command -v Xvfb >/dev/null 2>&1; then
|
||||
mkdir -p /tmp/.X11-unix && chmod 1777 /tmp/.X11-unix 2>/dev/null || true
|
||||
Xvfb "${DISPLAY:-:99}" -screen 0 1280x800x24 -nolisten tcp >/tmp/xvfb.log 2>&1 &
|
||||
for _ in 1 2 3 4 5 6 7 8 9 10; do
|
||||
[ -S "/tmp/.X11-unix/X${DISPLAY#:}" ] && break
|
||||
sleep 0.3
|
||||
done
|
||||
fi
|
||||
|
||||
exec "$@"
|
||||
@@ -1,34 +0,0 @@
|
||||
# Laya (https://github.com/NandhaKishorM/laya, Apache-2.0): a "decision model". It answers typed multiple-choice / yes-no / score
|
||||
# questions about a piece of text in ONE forward pass (about 33 ms on a GPU) and never writes text. Here it is staged for the Commander
|
||||
# AI Lab simulator, which asks an LLM "which action?" about 25 times per game; a small classifier can answer that class of question
|
||||
# far faster, if it is accurate enough (it is measured in shadow mode first, see ../README.md).
|
||||
#
|
||||
# CPU only. The weights are baked in at a pinned Hugging Face commit and the pod has NO network access (NetworkPolicy), so the
|
||||
# running service fetches nothing. Two checkpoints are included so they can be compared: the English base and the fine-tuned
|
||||
# "typed-decisions" one (about 840 MB each).
|
||||
FROM python:3.12-slim-bookworm
|
||||
|
||||
ARG LAYA_VERSION=0.4.15
|
||||
ARG LAYA_HF_REVISION=7b928d828b7b0e022f929d9bd2e44165aa270148
|
||||
|
||||
ENV PIP_NO_CACHE_DIR=1 PIP_DISABLE_PIP_VERSION_CHECK=1 PYTHONUNBUFFERED=1 HF_HOME=/opt/hf
|
||||
|
||||
RUN pip install torch --index-url https://download.pytorch.org/whl/cpu \
|
||||
&& pip install "laya[serve]==${LAYA_VERSION}"
|
||||
|
||||
# Weights at the pinned commit. HF's offline lookup resolves the "main" ref, which a commit-pinned download does not create, so it is
|
||||
# written by hand to the same commit.
|
||||
RUN python -c "from huggingface_hub import snapshot_download; snapshot_download('convaiinnovations/laya', revision='${LAYA_HF_REVISION}', ignore_patterns=['multilingual/*','assets/*','eval/*'])" \
|
||||
&& mkdir -p /opt/hf/hub/models--convaiinnovations--laya/refs \
|
||||
&& printf '%s' "${LAYA_HF_REVISION}" > /opt/hf/hub/models--convaiinnovations--laya/refs/main \
|
||||
&& chmod -R a+rX /opt/hf
|
||||
|
||||
RUN useradd --uid 10001 --no-create-home --shell /usr/sbin/nologin laya
|
||||
USER 10001
|
||||
|
||||
ENV HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
|
||||
LAYA_HOST=0.0.0.0 LAYA_PORT=8080 \
|
||||
LAYA_MODELS=english,typed-decisions LAYA_DEFAULT_MODEL=english LAYA_MAX_LOADED=2 LAYA_PRELOAD=1 LAYA_THREADS=4
|
||||
|
||||
EXPOSE 8080
|
||||
CMD ["laya-serve"]
|
||||
@@ -1,52 +0,0 @@
|
||||
# Laya as the simulator's "which action?" model: evaluation, 2026-10-26
|
||||
|
||||
**Result: not usable as shipped.** Both checkpoints agreed with the LLM less often than a model that always answers the commonest action.
|
||||
|
||||
## What was measured
|
||||
The simulator's LLM brain ("twin-turbo") picks one of nine actions about 25 times a game. A decision model (Laya, or Kev; both serve
|
||||
`POST /v1/systemone`) could answer that kind of question in one forward pass instead of a 7-second generation. To find out whether it
|
||||
could stand in, every LLM decision the app had recorded (`/app/logs/decisions/*.jsonl`: the game state and the action the LLM chose)
|
||||
was replayed through Laya with the same question, and the answers compared. The LLM is the *teacher*, not the truth: this measures
|
||||
whether a fast model can imitate the slow one, not which plays better.
|
||||
|
||||
Script: `image/farmhouse_decision_eval.py` (in the app image as `/app/scripts/farmhouse_decision_eval.py`):
|
||||
`DECISION_URL=http://laya:8080 python scripts/farmhouse_decision_eval.py /app/logs/decisions english typed-decisions`
|
||||
|
||||
## Numbers (149 recorded decisions)
|
||||
LLM's choices: play_land 41, hold 31, attack_all 28, cast_creature 20, cast_spell 13, attack_safe 7, cast_removal 5, cast_ramp 4.
|
||||
|
||||
| | agrees with the LLM | answers | time per answer | what it picked |
|
||||
|---|---|---|---|---|
|
||||
| always "play_land" (baseline) | **27.5%** | 149 | 0 | |
|
||||
| `english` (ModernBERT-large, base) | 16.1% | 149 | 1.9 s | cast_creature 105, attack_all 26; never hold, almost never play_land |
|
||||
| `typed-decisions` (fine-tuned) | 18.6% | 140 (9 timed out at 5 s) | ~4 s (shared CPU) | cast_creature 99, attack_all 26 |
|
||||
|
||||
It could say "cast_creature" and "attack_all" and little else; it never recognised "hold" (31 of the LLM's choices) or a removal spell.
|
||||
|
||||
## Why, and caveats
|
||||
- The shipped checkpoints are trained on generic ticket-triage style decisions. Laya's own README says fine-tuning "on decisions from your
|
||||
own domain is where accuracy jumps" (0.766 against 0.362 for the base on its own benchmark). This game's decisions were never in its training.
|
||||
- Speed on this cluster's CPU (4 threads) was 2-4 s per answer, not the ~33 ms quoted for a GPU. Against the LLM's ~7.5 s that is not the
|
||||
speed-up that was hoped for either, without a GPU.
|
||||
- The question wording (the nine action descriptions) and the compact text it reads (`farmhouse_decide.compact_state`) are mine; a
|
||||
better prompt might help a little, not from 18% to usable.
|
||||
- Part of the recorded data predates the card-data fix (v10), so some states had thinner hands than they would now.
|
||||
- The LLM being the "truth" also caps the result: where the LLM is wrong, agreeing with it is not a win.
|
||||
|
||||
## Where this leaves it
|
||||
- Laya is **staged but scaled to 0 replicas** (`deployment-laya.yaml`: `replicas: 0`; set to 1 to run it). The image
|
||||
(`ghcr.io/fez5587/farmhouse-laya:v1`, offline, weights baked in), the service and the network policy remain.
|
||||
- The app is wired to use it (`DECISION_URL`, `DECISION_MODE`) and `DECISION_MODE` is `off`. `shadow` records agreement without
|
||||
affecting games; `assist` uses it when it is `DECISION_MIN_CONF` sure. Kev speaks the same API, so swapping is a URL.
|
||||
- To make it worth using: fine-tune on this game's decisions (Laya ships a Kaggle 2xT4 notebook, about 4 hours, and an Apple/CPU
|
||||
script). The labels would be the LLM's recorded decisions; a few thousand are needed, so generate them first (LLM games at ~7 s a
|
||||
decision, or label states with the engine's own heuristic for the cheap, abundant version). Then re-run this evaluation.
|
||||
- Node memory: it needed about 2 GB (3.6 GB with both checkpoints, which pushed the control-plane node to 92%; it is pinned to the other node).
|
||||
|
||||
## Later the same day: the new connections (see docs/LAYA-CONNECTIONS.md in the fork)
|
||||
- **Forge-shaped priority choice** (CSIE's ranking in the text, three scenarios): agreed with CSIE each time, 42-48% sure, 7.5-8.4 s per call on the CPU against Forge's
|
||||
1.5 s budget. Not usable on a CPU; the code falls back silently.
|
||||
- **Card roles from Oracle text** (150 unambiguous cards, 6 roles present, 9 offered): 48% against 17% by chance (counterspell 76%, board wipe 64%, removal 40%, tutor 40%,
|
||||
ramp 36%, card draw 32%), 3.2 s per card in batches. Much better than the action choice; useful as on-demand evidence for the cards in a hand, too slow to tag the whole pool.
|
||||
- **Extra questions** in the same call: "threat" said *high* for everything; "must answer" gave 0.48 for a lethal stack against 0.25-0.29 otherwise (right direction, weak).
|
||||
- Capture, dataset export and the rest are built and off by default; fine-tuning on the captured decisions is the path to making it useful.
|
||||
@@ -1,21 +0,0 @@
|
||||
# Laya in this repository
|
||||
|
||||
Laya (https://github.com/NandhaKishorM/laya, Apache-2.0) is a **decision model**: it answers typed multiple-choice, yes/no and score
|
||||
questions about a piece of text in one forward pass and never writes text. Here it is a fast second opinion beside the LLM and CSIE.
|
||||
Kev (https://github.com/jaredpalmer/kev) serves the same `POST /v1/systemone` API, so swapping is a URL change (Kev needs a GPU).
|
||||
|
||||
| File | What |
|
||||
|---|---|
|
||||
| `Dockerfile` | CPU image: `laya[serve]` 0.4.15 + CPU torch, English and `typed-decisions` checkpoints baked in at Hugging Face commit `7b928d8`; runs offline |
|
||||
| `deployment-laya.yaml` | Kubernetes Deployment + Service + NetworkPolicy (no egress; only the app may call it). Currently `replicas: 0` |
|
||||
| `build-farmhouse-laya.workflow.yml` | the CI workflow that builds and smoke-tests the image offline (it lives in `.github/workflows/` of the farmhouse-os GitOps repo) |
|
||||
| `LAYA-EVALUATION.md` | why it is off: zero-shot both checkpoints agreed with the LLM less often than always guessing the commonest action |
|
||||
|
||||
The deployment source of truth is the farmhouse-os repo (`infrastructure/apps/commander-lab/`); these are reference copies so the
|
||||
fork is self-contained. Things learned running it:
|
||||
|
||||
- A Kubernetes Service named `laya` injects `LAYA_PORT=tcp://...`, which `laya-serve` reads as its own port setting and refuses. Set
|
||||
`enableServiceLinks: false` and `LAYA_PORT=8080` explicitly.
|
||||
- Two checkpoints resident use 3.6 GB; one uses about 1.9 GB. Do not place it on a control-plane node.
|
||||
- The CPU answers in 2-4 s, not the ~33 ms quoted for a GPU.
|
||||
- How the code in this repository connects to it, and how to fine-tune it: `docs/LAYA-CONNECTIONS.md`.
|
||||
@@ -1,96 +0,0 @@
|
||||
name: Build FarmHouse Laya
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
paths:
|
||||
- "infrastructure/apps/commander-lab/laya/**"
|
||||
- ".github/workflows/build-farmhouse-laya.yml"
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
packages: write
|
||||
|
||||
jobs:
|
||||
build:
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
env:
|
||||
IMAGE_NAME: ghcr.io/${{ github.repository_owner }}/farmhouse-laya
|
||||
# Bump together with the image tag in infrastructure/apps/commander-lab/deployment-laya.yaml.
|
||||
IMAGE_TAG: v1
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Set image name lowercase
|
||||
shell: bash
|
||||
run: |
|
||||
echo "IMAGE=$(echo "${IMAGE_NAME}" | tr '[:upper:]' '[:lower:]')" >> "$GITHUB_ENV"
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
|
||||
- name: Build validation image
|
||||
uses: docker/build-push-action@v6
|
||||
with:
|
||||
context: infrastructure/apps/commander-lab/laya
|
||||
load: true
|
||||
push: false
|
||||
tags: laya:test
|
||||
|
||||
# The service must answer with NO network (that is how it runs in the cluster) as the unprivileged user, for both checkpoints.
|
||||
- name: Smoke test (boots offline, answers a game-state question with both checkpoints)
|
||||
shell: bash
|
||||
run: |
|
||||
set -euo pipefail
|
||||
docker run -d --name laya --network none laya:test
|
||||
cat > /tmp/ask.py <<'PY'
|
||||
import json, sys, time, urllib.request
|
||||
model = sys.argv[1]
|
||||
body = {"model": model,
|
||||
"state": {"body": "Turn 4. You have 4 lands untapped. Hand: Llanowar Elves, Swords to Plowshares, Forest. Opponent has a 6/6 creature attacking you."},
|
||||
"questions": {"action": {"type": "choice", "instructions": "Which action is best this turn?",
|
||||
"criteria": {"cast_creature": "put a creature onto the battlefield",
|
||||
"cast_removal": "destroy or exile an opposing permanent",
|
||||
"play_land": "play a land",
|
||||
"hold": "do nothing this turn"}}}}
|
||||
req = urllib.request.Request("http://127.0.0.1:8080/v1/systemone", data=json.dumps(body).encode(), headers={"content-type": "application/json"})
|
||||
t = time.time()
|
||||
out = json.load(urllib.request.urlopen(req, timeout=120))
|
||||
print(model, "%.0f ms" % ((time.time() - t) * 1000), json.dumps(out)[:600])
|
||||
assert "answers" in out and "action" in out["answers"], out
|
||||
PY
|
||||
docker cp /tmp/ask.py laya:/tmp/ask.py
|
||||
ok=0
|
||||
for i in $(seq 1 120); do
|
||||
if docker exec laya python -c "import socket; socket.create_connection(('127.0.0.1', 8080), 2)" 2>/dev/null; then ok=1; break; fi
|
||||
sleep 3
|
||||
done
|
||||
if [ "$ok" != 1 ]; then echo "laya did not open its port"; docker logs laya | tail -60; exit 1; fi
|
||||
for m in english typed-decisions; do docker exec laya python /tmp/ask.py "$m"; done
|
||||
docker exec laya id
|
||||
docker rm -f laya >/dev/null
|
||||
echo SMOKE OK
|
||||
|
||||
- name: Login to GitHub Container Registry
|
||||
uses: docker/login-action@v3
|
||||
with:
|
||||
registry: ghcr.io
|
||||
username: ${{ github.actor }}
|
||||
password: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
||||
- name: Build and publish
|
||||
uses: docker/build-push-action@v6
|
||||
with:
|
||||
context: infrastructure/apps/commander-lab/laya
|
||||
push: true
|
||||
tags: |
|
||||
${{ env.IMAGE }}:${{ env.IMAGE_TAG }}
|
||||
${{ env.IMAGE }}:${{ github.sha }}
|
||||
labels: |
|
||||
org.opencontainers.image.source=${{ github.server_url }}/${{ github.repository }}
|
||||
org.opencontainers.image.revision=${{ github.sha }}
|
||||
@@ -1,93 +0,0 @@
|
||||
# Laya, a CPU decision model (laya/Dockerfile), staged for the simulator. Reached only by the app, over the cluster network, on 8080.
|
||||
# No internet, no volume (weights are in the image), no service-account token.
|
||||
apiVersion: apps/v1
|
||||
kind: Deployment
|
||||
metadata:
|
||||
name: laya
|
||||
namespace: commander-lab
|
||||
labels: {app: laya}
|
||||
spec:
|
||||
# 0 since 2026-10-26: both checkpoints scored BELOW the always-guess baseline against the LLM's recorded decisions (English 16.1%, typed-decisions
|
||||
# 18.6%, baseline 27.5%, 149 states), and ~2 GB of the node's memory was needed for the card-search index build. Set to 1 to bring it back
|
||||
# (image, service and policy stay in place); it is only worth running once it is fine-tuned on this game's decisions (README).
|
||||
replicas: 0
|
||||
selector:
|
||||
matchLabels: {app: laya}
|
||||
template:
|
||||
metadata:
|
||||
labels: {app: laya}
|
||||
spec:
|
||||
automountServiceAccountToken: false
|
||||
# Kubernetes injects LAYA_PORT=tcp://<ip>:8080 for a Service named laya, and laya-serve reads LAYA_PORT as its own port setting
|
||||
# ("invalid LAYA_PORT 'tcp://...'", crash loop). Service-link variables are not wanted here at all.
|
||||
enableServiceLinks: false
|
||||
# NOT on talos-gmw: that node runs the Kubernetes API server, and Laya's first placement there (two checkpoints, 3.6 GB) took it from
|
||||
# 68% to 92% memory on 2026-10-26.
|
||||
nodeSelector: {kubernetes.io/hostname: talos-noy-0r4}
|
||||
imagePullSecrets:
|
||||
- name: ghcr-pull
|
||||
securityContext:
|
||||
runAsNonRoot: true
|
||||
runAsUser: 10001
|
||||
runAsGroup: 10001
|
||||
seccompProfile: {type: RuntimeDefault}
|
||||
containers:
|
||||
- name: laya
|
||||
image: ghcr.io/fez5587/farmhouse-laya:v1
|
||||
ports:
|
||||
- {name: http, containerPort: 8080}
|
||||
env:
|
||||
- {name: LAYA_THREADS, value: "4"}
|
||||
- {name: LAYA_PORT, value: "8080"}
|
||||
# One checkpoint only (about 1.9 GB): the English base scored below the always-guess baseline against the LLM; typed-decisions is the
|
||||
# fine-tuned one still being measured.
|
||||
- {name: LAYA_MODELS, value: "typed-decisions"}
|
||||
- {name: LAYA_DEFAULT_MODEL, value: "typed-decisions"}
|
||||
- {name: LAYA_MAX_LOADED, value: "1"}
|
||||
securityContext:
|
||||
allowPrivilegeEscalation: false
|
||||
capabilities: {drop: [ALL]}
|
||||
resources:
|
||||
requests: {cpu: "500m", memory: 2500Mi}
|
||||
limits: {cpu: "4", memory: 3Gi}
|
||||
# Loading two checkpoints takes a while; the port opens when the server is up.
|
||||
startupProbe:
|
||||
tcpSocket: {port: http}
|
||||
periodSeconds: 5
|
||||
failureThreshold: 90
|
||||
readinessProbe:
|
||||
tcpSocket: {port: http}
|
||||
periodSeconds: 10
|
||||
livenessProbe:
|
||||
tcpSocket: {port: http}
|
||||
periodSeconds: 30
|
||||
failureThreshold: 6
|
||||
---
|
||||
apiVersion: v1
|
||||
kind: Service
|
||||
metadata:
|
||||
name: laya
|
||||
namespace: commander-lab
|
||||
spec:
|
||||
type: ClusterIP
|
||||
selector: {app: laya}
|
||||
ports:
|
||||
- {name: http, port: 8080, targetPort: http}
|
||||
---
|
||||
# IN: only the app. OUT: nothing at all (the weights are in the image; it needs no DNS and calls no one).
|
||||
apiVersion: networking.k8s.io/v1
|
||||
kind: NetworkPolicy
|
||||
metadata:
|
||||
name: laya
|
||||
namespace: commander-lab
|
||||
spec:
|
||||
podSelector:
|
||||
matchLabels: {app: laya}
|
||||
policyTypes: [Ingress, Egress]
|
||||
ingress:
|
||||
- from:
|
||||
- podSelector:
|
||||
matchLabels: {app: commander-lab}
|
||||
ports:
|
||||
- {protocol: TCP, port: 8080}
|
||||
egress: []
|
||||
@@ -1,61 +0,0 @@
|
||||
# phase-sim: rules-accurate four-player Commander games with phase-rs's headless AI (github.com/phase-rs/phase), behind a small HTTP service.
|
||||
#
|
||||
# phase is a Rust rules engine (stack, priority, combat, layers, triggers, replacement effects), dual licensed Apache-2.0 / MIT, with card
|
||||
# data from MTGJSON (MIT). Pinned to release v0.118.0 (commit ad0de6a) and to the card database that release was published with
|
||||
# (`card-data-5e541a38c99b97f0.json`, named in data.phase-rs.dev/card-data-meta.json for that commit); the file's SHA-256 is checked, so a
|
||||
# changed file fails the build instead of silently changing results. Nothing is fetched at run time, and the pod has no network.
|
||||
#
|
||||
# Built: the `ai-commander` binary (4-player Commander, batch mode) and `ai-duel` with the repo's `server-release` profile (unwinding
|
||||
# panics, so one game's panic cannot kill a batch). The toolchain is whatever rust-toolchain.toml pins (a dated nightly).
|
||||
FROM rust:slim-bookworm AS build
|
||||
|
||||
ARG PHASE_REPO=https://github.com/phase-rs/phase.git
|
||||
ARG PHASE_TAG=v0.118.0
|
||||
ARG PHASE_SHA=ad0de6adf00153a6e6eb89604f6670be5b1d5168
|
||||
ARG CARD_DATA_URL=https://data.phase-rs.dev/card-data-5e541a38c99b97f0.json
|
||||
ARG CARD_DATA_SHA256=5e541a38c99b97f0ef12c81f7b249e7cd253f38b32bd5c156cf19608cb6f1218
|
||||
|
||||
RUN apt-get update \
|
||||
&& apt-get install -y --no-install-recommends git ca-certificates curl build-essential pkg-config python3 \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
RUN set -eux; \
|
||||
mkdir /phase; cd /phase; \
|
||||
git init -q; \
|
||||
git remote add origin "$PHASE_REPO"; \
|
||||
git fetch -q --depth 1 origin "$PHASE_SHA"; \
|
||||
git checkout -q FETCH_HEAD; \
|
||||
test "$(git rev-parse HEAD)" = "$PHASE_SHA"
|
||||
|
||||
WORKDIR /phase
|
||||
# phase's server-release profile uses thin LTO and 16 code-generation units; compiling its big engine crate that way ran a 7 GB CI machine
|
||||
# out of memory ("cannot allocate memory", 2026-10-27). LTO off and two parallel jobs trade some build time (and a few percent of run
|
||||
# speed) for a build that fits; the optimisation level and panic=unwind (which batch isolation needs) are unchanged.
|
||||
ENV CARGO_BUILD_JOBS=2 \
|
||||
CARGO_PROFILE_SERVER_RELEASE_LTO=off \
|
||||
CARGO_PROFILE_SERVER_RELEASE_CODEGEN_UNITS=8
|
||||
RUN cargo build --profile server-release -p phase-ai --bin ai-commander --bin ai-duel
|
||||
|
||||
RUN set -eux; \
|
||||
curl -fsSL --retry 3 -o /card-data.json "$CARD_DATA_URL"; \
|
||||
echo "$CARD_DATA_SHA256 /card-data.json" | sha256sum -c -; \
|
||||
cp /phase/client/public/feeds/mtggoldfish-commander.json /sample-feed.json
|
||||
|
||||
FROM python:3.12-slim-bookworm
|
||||
ARG PHASE_TAG=v0.118.0
|
||||
|
||||
RUN groupadd -g 10001 phase && useradd -u 10001 -g 10001 -d /tmp -s /usr/sbin/nologin phase
|
||||
COPY --from=build /phase/target/server-release/ai-commander /opt/phase/ai-commander
|
||||
COPY --from=build /phase/target/server-release/ai-duel /opt/phase/ai-duel
|
||||
COPY --from=build /card-data.json /opt/phase/data/card-data.json
|
||||
COPY --from=build /sample-feed.json /opt/phase/sample-feed.json
|
||||
COPY phase_sim.py phase_smoke.py /opt/phase/
|
||||
|
||||
ENV PHASE_ENGINE_VERSION="phase-rs ${PHASE_TAG}" \
|
||||
PHASE_AI_COMMANDER=/opt/phase/ai-commander \
|
||||
PHASE_CARD_DATA=/opt/phase/data/card-data.json \
|
||||
PYTHONUNBUFFERED=1 PORT=8090
|
||||
USER 10001
|
||||
WORKDIR /tmp
|
||||
EXPOSE 8090
|
||||
CMD ["python", "/opt/phase/phase_sim.py"]
|
||||
@@ -1,72 +0,0 @@
|
||||
# phase-sim: rules-accurate four-player Commander games with phase-rs's headless AI (github.com/phase-rs/phase), behind a small HTTP service.
|
||||
#
|
||||
# phase is a Rust rules engine (stack, priority, combat, layers, triggers, replacement effects), dual licensed Apache-2.0 / MIT, with card
|
||||
# data from MTGJSON (MIT). Pinned to release v0.118.0 (commit ad0de6a) and to the card database that release was published with
|
||||
# (`card-data-5e541a38c99b97f0.json`, named in data.phase-rs.dev/card-data-meta.json for that commit); the file's SHA-256 is checked, so a
|
||||
# changed file fails the build instead of silently changing results. Nothing is fetched at run time, and the pod has no network.
|
||||
#
|
||||
# Built: the `ai-commander` binary (4-player Commander, batch mode) and `ai-duel` with the repo's `server-release` profile (unwinding
|
||||
# panics, so one game's panic cannot kill a batch). The toolchain is whatever rust-toolchain.toml pins (a dated nightly).
|
||||
FROM rust:slim-bookworm AS build
|
||||
|
||||
ARG PHASE_REPO=https://github.com/phase-rs/phase.git
|
||||
ARG PHASE_TAG=v0.118.0
|
||||
ARG PHASE_SHA=ad0de6adf00153a6e6eb89604f6670be5b1d5168
|
||||
ARG CARD_DATA_URL=https://data.phase-rs.dev/card-data-5e541a38c99b97f0.json
|
||||
ARG CARD_DATA_SHA256=5e541a38c99b97f0ef12c81f7b249e7cd253f38b32bd5c156cf19608cb6f1218
|
||||
|
||||
RUN apt-get update \
|
||||
&& apt-get install -y --no-install-recommends git ca-certificates curl build-essential pkg-config python3 \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
RUN set -eux; \
|
||||
mkdir /phase; cd /phase; \
|
||||
git init -q; \
|
||||
git remote add origin "$PHASE_REPO"; \
|
||||
git fetch -q --depth 1 origin "$PHASE_SHA"; \
|
||||
git checkout -q FETCH_HEAD; \
|
||||
test "$(git rev-parse HEAD)" = "$PHASE_SHA"
|
||||
|
||||
WORKDIR /phase
|
||||
# phase's server-release profile uses thin LTO and 16 code-generation units; compiling its big engine crate that way ran a 7 GB CI machine
|
||||
# out of memory ("cannot allocate memory", 2026-10-27). LTO off and two parallel jobs trade some build time (and a few percent of run
|
||||
# speed) for a build that fits; the optimisation level and panic=unwind (which batch isolation needs) are unchanged.
|
||||
ENV CARGO_BUILD_JOBS=2 \
|
||||
CARGO_PROFILE_SERVER_RELEASE_LTO=off \
|
||||
CARGO_PROFILE_SERVER_RELEASE_CODEGEN_UNITS=8
|
||||
RUN cargo build --profile server-release -p phase-ai --bin ai-commander --bin ai-duel
|
||||
|
||||
# ---- model seats (this Dockerfile only) -------------------------------------------------------------------------------------------
|
||||
# Everything above is identical to Dockerfile, so the long compile above comes from the build cache. These steps add `ai-commander-llm`: the
|
||||
# same runner with a hook that lets a language model or a decision model (Laya / Kev) choose for named seats among the engine's OWN legal
|
||||
# options. Two anchored edits to phase's source (apply_phase_patches.py: the build stops if phase moved either line) and one new crate.
|
||||
# phase-llm depends on phase-ai, so the hook cannot live in phase-ai's binary; the new crate depends on both and includes the runner.
|
||||
COPY llm-seat /llm-seat
|
||||
RUN python3 /llm-seat/apply_phase_patches.py /phase /llm-seat
|
||||
RUN cargo build --profile server-release -p farmhouse-llm-seat --bin ai-commander-llm
|
||||
|
||||
RUN set -eux; \
|
||||
curl -fsSL --retry 3 -o /card-data.json "$CARD_DATA_URL"; \
|
||||
echo "$CARD_DATA_SHA256 /card-data.json" | sha256sum -c -; \
|
||||
cp /phase/client/public/feeds/mtggoldfish-commander.json /sample-feed.json
|
||||
|
||||
FROM python:3.12-slim-bookworm
|
||||
ARG PHASE_TAG=v0.118.0
|
||||
|
||||
RUN groupadd -g 10001 phase && useradd -u 10001 -g 10001 -d /tmp -s /usr/sbin/nologin phase
|
||||
COPY --from=build /phase/target/server-release/ai-commander /opt/phase/ai-commander
|
||||
COPY --from=build /phase/target/server-release/ai-duel /opt/phase/ai-duel
|
||||
COPY --from=build /phase/target/server-release/ai-commander-llm /opt/phase/ai-commander-llm
|
||||
COPY --from=build /card-data.json /opt/phase/data/card-data.json
|
||||
COPY --from=build /sample-feed.json /opt/phase/sample-feed.json
|
||||
COPY phase_sim.py phase_smoke.py /opt/phase/
|
||||
|
||||
ENV PHASE_ENGINE_VERSION="phase-rs ${PHASE_TAG}" \
|
||||
PHASE_AI_COMMANDER=/opt/phase/ai-commander \
|
||||
PHASE_AI_COMMANDER_LLM=/opt/phase/ai-commander-llm \
|
||||
PHASE_CARD_DATA=/opt/phase/data/card-data.json \
|
||||
PYTHONUNBUFFERED=1 PORT=8090
|
||||
USER 10001
|
||||
WORKDIR /tmp
|
||||
EXPOSE 8090
|
||||
CMD ["python", "/opt/phase/phase_sim.py"]
|
||||
@@ -1,36 +0,0 @@
|
||||
# phase-sim: rules-accurate Commander games with phase-rs
|
||||
|
||||
Reference copies of what runs in the cluster (source of truth: `infrastructure/apps/commander-lab/` in the farmhouse-os repo). Background and the assessment
|
||||
that led here: `docs/PHASE-AND-PLAYTESTER.md`.
|
||||
|
||||
| File | What |
|
||||
|---|---|
|
||||
| `phase_sim.py` | the service: stdlib HTTP, one job at a time. `POST /run` four decks -> `GET /job/<id>` per-game results, wins by seat, 95% interval, warnings. Optional model seats and two loopback-only relays (`/llm/v1/chat/completions`, `/jev/systemone`) |
|
||||
| `Dockerfile` | builds phase-rs `v0.118.0` (`ai-commander`, `ai-duel`) and takes the card database that release was published with (SHA-256 checked) |
|
||||
| `Dockerfile.llm` | the same plus `ai-commander-llm`: model-driven seats |
|
||||
| `llm-seat/` | the Rust crate and the two anchored edits to phase's source that make model seats possible |
|
||||
| `phase_smoke.py`, `test_phase_sim.py` | CI: real games through the service; 49 offline checks against a stub engine |
|
||||
| `deployment-phase.yaml` | Deployment, Service and a NetworkPolicy (in: the app only; out: DNS, Olla and Laya only) |
|
||||
|
||||
The app side is `routes/farmhouse_phase.py` (four saved decks -> the service) and `ui/phase-sim.html`; both are real source in this repository, registered by
|
||||
`lab_api.py` and linked from the home page.
|
||||
|
||||
## Model seats: how they work and why they are safe
|
||||
phase's headless runner has no model support; the model seats exist only in phase's browser client. `phase-llm` (the crate) deliberately does no networking:
|
||||
it renders the engine's option list into a prompt, builds the HTTP request, and parses the reply, and the host performs the call. `llm-seat/` is that host
|
||||
for the headless runner:
|
||||
|
||||
1. `apply_phase_patches.py` adds a process-wide `ACTION_OVERRIDE` hook in `phase-ai/src/auto_play.rs` (asked before the built-in AI chooses) and makes the runner's `main` `pub`.
|
||||
2. The crate `farmhouse-llm-seat` includes the runner's source as a module (`#[path]`) and installs the hook. `phase-llm` depends on `phase-ai`, so the hook cannot
|
||||
live inside phase-ai's own binary.
|
||||
3. For each decision on a model seat: the engine issues its option list (`AiDecisionContract`); phase-llm builds the prompt (a forced move with one option is never asked);
|
||||
this crate makes the HTTP call (blocking, no TLS, 20 s limit); phase-llm turns the reply into one of the engine's options; the choice is checked against the contract
|
||||
here and again by `run_ai_action`. **Any** problem returns "no opinion" and phase's own AI plays the move. A cap on model calls per run (default 300) bounds the cost.
|
||||
4. The service supplies the endpoints through its own loopback-only relays: the chat relay switches the model's thinking off (twin-turbo otherwise thinks for tens of
|
||||
seconds a move) and caps the answer; the decision relay unwraps phase's `Jev` envelope and forwards the System One request to Laya or Kev.
|
||||
|
||||
The model never authors an action and never sees hidden cards beyond what phase-llm renders for that seat.
|
||||
|
||||
## Status
|
||||
Written 2026-10-27. The service logic is tested offline; the Rust crate has not compiled outside CI when this was written (no Rust toolchain on the workstation), so
|
||||
its first CI run is the first real test. Check `.github/workflows/build-farmhouse-phase-sim.yml` results in the farmhouse-os repo.
|
||||
@@ -1,111 +0,0 @@
|
||||
# phase-sim (phase/Dockerfile): rules-accurate four-player Commander games over a small HTTP API. Only the app may call it; it has no
|
||||
# network and no volume (engine and card data are in the image). One game at a time, CPU heavy.
|
||||
apiVersion: apps/v1
|
||||
kind: Deployment
|
||||
metadata:
|
||||
name: phase-sim
|
||||
namespace: commander-lab
|
||||
labels: {app: phase-sim}
|
||||
spec:
|
||||
replicas: 1
|
||||
selector:
|
||||
matchLabels: {app: phase-sim}
|
||||
template:
|
||||
metadata:
|
||||
labels: {app: phase-sim}
|
||||
spec:
|
||||
automountServiceAccountToken: false
|
||||
# A Service named like the app injects PHASE_SIM_* variables into the pod; nothing here wants them (the same kind of collision
|
||||
# crash-looped Laya on LAYA_PORT).
|
||||
enableServiceLinks: false
|
||||
imagePullSecrets:
|
||||
- name: ghcr-pull
|
||||
securityContext:
|
||||
runAsNonRoot: true
|
||||
runAsUser: 10001
|
||||
runAsGroup: 10001
|
||||
seccompProfile: {type: RuntimeDefault}
|
||||
containers:
|
||||
- name: phase-sim
|
||||
image: ghcr.io/fez5587/farmhouse-phase-sim:v1-llm # -llm = the baseline plus ai-commander-llm (model seats)
|
||||
ports:
|
||||
- {name: http, containerPort: 8090}
|
||||
env:
|
||||
- {name: PORT, value: "8090"}
|
||||
- {name: PHASE_SIM_MAX_GAMES, value: "50"}
|
||||
- {name: PHASE_SIM_TIMEOUT, value: "3600"}
|
||||
# Model seats. The engine talks to THIS service's loopback-only relays, which forward to these (see phase_sim.py): chat = an
|
||||
# OpenAI-style endpoint (Olla -> twin-turbo; the relay switches thinking off and caps the answer), decision = a System One server
|
||||
# (Laya / Kev; the deployment is scaled to 0 until it is fine-tuned, so a decision-seat run answers 502 and falls back to phase's AI).
|
||||
- {name: PHASE_LLM_CHAT_URL, value: "http://olla.ai-system.svc.cluster.local:40114/olla/openai/v1"}
|
||||
- {name: PHASE_LLM_DECISION_URL, value: "http://laya.commander-lab.svc.cluster.local:8080/v1/systemone"}
|
||||
- {name: PHASE_LLM_REASONING, value: "none"}
|
||||
- {name: PHASE_LLM_MAX_TOKENS_CAP, value: "256"}
|
||||
securityContext:
|
||||
allowPrivilegeEscalation: false
|
||||
readOnlyRootFilesystem: true
|
||||
capabilities: {drop: [ALL]}
|
||||
volumeMounts:
|
||||
- {name: tmp, mountPath: /tmp}
|
||||
resources:
|
||||
requests: {cpu: "500m", memory: 1Gi}
|
||||
limits: {cpu: "3", memory: 3Gi}
|
||||
readinessProbe:
|
||||
httpGet: {path: /health, port: http}
|
||||
periodSeconds: 10
|
||||
livenessProbe:
|
||||
httpGet: {path: /health, port: http}
|
||||
periodSeconds: 30
|
||||
failureThreshold: 6
|
||||
volumes:
|
||||
- name: tmp
|
||||
emptyDir: {sizeLimit: 512Mi}
|
||||
---
|
||||
apiVersion: v1
|
||||
kind: Service
|
||||
metadata:
|
||||
name: phase-sim
|
||||
namespace: commander-lab
|
||||
spec:
|
||||
type: ClusterIP
|
||||
selector: {app: phase-sim}
|
||||
ports:
|
||||
- {name: http, port: 8090, targetPort: http}
|
||||
---
|
||||
# IN: only the app. OUT: DNS and the two model endpoints the relays use (Olla for the chat model, Laya for the decision model); nothing else.
|
||||
apiVersion: networking.k8s.io/v1
|
||||
kind: NetworkPolicy
|
||||
metadata:
|
||||
name: phase-sim
|
||||
namespace: commander-lab
|
||||
spec:
|
||||
podSelector:
|
||||
matchLabels: {app: phase-sim}
|
||||
policyTypes: [Ingress, Egress]
|
||||
ingress:
|
||||
- from:
|
||||
- podSelector:
|
||||
matchLabels: {app: commander-lab}
|
||||
ports:
|
||||
- {protocol: TCP, port: 8090}
|
||||
egress:
|
||||
- to:
|
||||
- namespaceSelector:
|
||||
matchLabels: {kubernetes.io/metadata.name: kube-system}
|
||||
podSelector:
|
||||
matchLabels: {k8s-app: kube-dns}
|
||||
ports:
|
||||
- {protocol: UDP, port: 53}
|
||||
- {protocol: TCP, port: 53}
|
||||
- to:
|
||||
- namespaceSelector:
|
||||
matchLabels: {kubernetes.io/metadata.name: ai-system}
|
||||
podSelector:
|
||||
matchLabels: {app.kubernetes.io/name: olla}
|
||||
ports:
|
||||
- {protocol: TCP, port: 40114}
|
||||
- to:
|
||||
- podSelector:
|
||||
matchLabels: {app: laya}
|
||||
ports:
|
||||
- {protocol: TCP, port: 8080}
|
||||
@@ -1,26 +0,0 @@
|
||||
[package]
|
||||
name = "farmhouse-llm-seat"
|
||||
version.workspace = true
|
||||
edition = "2021"
|
||||
license.workspace = true
|
||||
publish = false
|
||||
|
||||
# FarmHouse: phase's headless four-player Commander runner (`ai-commander`) with MODEL-DRIVEN SEATS. The runner's own source is included
|
||||
# as a module (src/main.rs, #[path]); this crate adds only the network transport phase-llm deliberately leaves to the host. phase-llm
|
||||
# depends on phase-ai, so the hook cannot live inside phase-ai's own binary: a new crate that depends on both is the way round that.
|
||||
|
||||
[[bin]]
|
||||
name = "ai-commander-llm"
|
||||
path = "src/main.rs"
|
||||
|
||||
[dependencies]
|
||||
engine = { path = "../engine", package = "phase-engine" }
|
||||
phase-ai = { path = "../phase-ai" }
|
||||
phase-llm = { path = "../phase-llm" }
|
||||
serde = { workspace = true }
|
||||
serde_json = "1"
|
||||
rand = "0.9"
|
||||
tracing = "0.1"
|
||||
mimalloc = "0.1"
|
||||
# Blocking HTTP, no TLS: the model endpoints are cluster-internal http:// relays (phase-sim's own /llm and /jev routes).
|
||||
ureq = { version = "2", default-features = false }
|
||||
@@ -1,74 +0,0 @@
|
||||
"""Apply the FarmHouse edits to a phase-rs checkout (run in the Docker build, from the repository root).
|
||||
|
||||
python apply_phase_patches.py <phase-checkout> <llm-seat-crate-dir>
|
||||
|
||||
1. crates/phase-ai/src/auto_play.rs: a process-wide ACTION_OVERRIDE hook, asked before the built-in AI chooses. It returns Option<GameAction>;
|
||||
None (the default, and what every failure returns) leaves phase's own choice untouched. The chosen action still goes through the same
|
||||
contract check (`contract.permits`) as before.
|
||||
2. crates/phase-ai/src/bin/ai_commander.rs: `fn main` becomes `pub fn main` so the new crate can include and call it.
|
||||
3. The new crate is copied into crates/farmhouse-llm-seat (the workspace takes every crates/* member).
|
||||
|
||||
Every anchor must match exactly once or the build stops, so a phase update that moves a line is reported here, not in a compile error."""
|
||||
import os
|
||||
import shutil
|
||||
import sys
|
||||
|
||||
root, crate = sys.argv[1], sys.argv[2]
|
||||
|
||||
|
||||
def edit(rel, edits):
|
||||
path = os.path.join(root, rel)
|
||||
text = open(path, encoding="utf-8", newline="").read()
|
||||
for old, new in edits:
|
||||
n = text.count(old)
|
||||
if n != 1:
|
||||
raise SystemExit("%s: anchor found %d times (need 1): %r" % (rel, n, old[:80]))
|
||||
text = text.replace(old, new)
|
||||
open(path, "w", encoding="utf-8", newline="").write(text)
|
||||
print("patched", rel)
|
||||
|
||||
|
||||
HOOK = '''use crate::search::choose_action_with_session;
|
||||
|
||||
/// FarmHouse: an optional process-wide override, asked BEFORE the built-in AI chooses for a seat. A host (see crates/farmhouse-llm-seat)
|
||||
/// installs one to let a model pick among the engine's own options. It returns None to leave the choice to the built-in AI, which is also
|
||||
/// what it must return on any failure; whatever it returns still has to pass the same decision-contract check below.
|
||||
pub type ActionOverride = Box<
|
||||
dyn Fn(
|
||||
&engine::types::game_state::GameState,
|
||||
&engine::ai_support::AiDecisionContract,
|
||||
engine::types::player::PlayerId,
|
||||
engine::types::player::PlayerId,
|
||||
) -> Option<engine::types::actions::GameAction>
|
||||
+ Send
|
||||
+ Sync,
|
||||
>;
|
||||
static ACTION_OVERRIDE: std::sync::OnceLock<ActionOverride> = std::sync::OnceLock::new();
|
||||
|
||||
/// Install the override. Returns false if one is already installed (it can be set once per process).
|
||||
pub fn install_action_override(hook: ActionOverride) -> bool {
|
||||
ACTION_OVERRIDE.set(hook).is_ok()
|
||||
}
|
||||
'''
|
||||
|
||||
edit("crates/phase-ai/src/auto_play.rs", [
|
||||
("use crate::search::choose_action_with_session;\n", HOOK),
|
||||
(" let action = match choose_action_with_session(state, semantic_owner, config, rng, session) {\n Some(action) => action,\n None => {\n tracing::warn!(player = ?actor, \"choose_action returned None",
|
||||
" // The override's pick must be one the engine will really APPLY, not only one it lists: a model chose both modes of a modal spell whose\n"
|
||||
" // entwine cost it could not pay, the contract listed the option, apply refused it, and the whole game stalled (2026-10-27). Try it on a\n"
|
||||
" // copy of the game first; anything the engine refuses is dropped and the built-in AI plays the move. (Stack-pass rechecks have their\n"
|
||||
" // own verified path and are left alone.)\n"
|
||||
" let farmhouse_choice = ACTION_OVERRIDE\n .get()\n .and_then(|hook| hook(&*state, &contract, semantic_owner, actor))\n"
|
||||
" .filter(|candidate| {\n"
|
||||
" verified_ai_stack_pass_player(&*state, candidate).is_some()\n"
|
||||
" || apply_interaction(&mut (*state).clone(), actor, semantic_owner, candidate.clone()).is_ok()\n"
|
||||
" });\n"
|
||||
" let action = match farmhouse_choice\n .or_else(|| choose_action_with_session(state, semantic_owner, config, rng, session))\n {\n Some(action) => action,\n None => {\n tracing::warn!(player = ?actor, \"choose_action returned None"),
|
||||
])
|
||||
edit("crates/phase-ai/src/bin/ai_commander.rs", [("\nfn main() {\n", "\npub fn main() {\n")])
|
||||
|
||||
dest = os.path.join(root, "crates", "farmhouse-llm-seat")
|
||||
os.makedirs(os.path.join(dest, "src"), exist_ok=True)
|
||||
shutil.copy(os.path.join(crate, "Cargo.toml"), os.path.join(dest, "Cargo.toml"))
|
||||
shutil.copy(os.path.join(crate, "src", "main.rs"), os.path.join(dest, "src", "main.rs"))
|
||||
print("added crates/farmhouse-llm-seat")
|
||||
@@ -1,237 +0,0 @@
|
||||
//! ai-commander with model-driven seats (FarmHouse).
|
||||
//!
|
||||
//! phase's headless runner plays four-player Commander games with the built-in AI on every seat. This binary is that same runner (its
|
||||
//! source is included below, unchanged but for `fn main` being `pub`) plus a hook, installed before the games start, that asks a
|
||||
//! language model or a decision model (Laya / Kev, over the System One protocol) to choose for the seats named in `PHASE_LLM_SEATS`.
|
||||
//!
|
||||
//! The model never authors an action. For each decision the engine issues its own option list (`AiDecisionContract`), phase-llm renders
|
||||
//! it and builds the HTTP request, this file performs the HTTP call (the one thing phase-llm leaves to the host), and phase-llm turns the
|
||||
//! reply into one of the engine's options. The pick is then checked against the contract a second time here, and again by
|
||||
//! `run_ai_action`. ANY problem (forced move, timeout, an unreadable or stale answer, the call cap) returns `None` and phase's own AI
|
||||
//! plays the move, so a model that is down, slow or wrong cannot stall or corrupt a game.
|
||||
//!
|
||||
//! Settings (environment): PHASE_LLM_SEATS "1,3" (seats 0-3; empty = none, the plain runner), PHASE_LLM_PROVIDER (OpenAiCompatible | Jev),
|
||||
//! PHASE_LLM_BASE_URL, PHASE_LLM_MODEL, PHASE_LLM_API_KEY (leave empty for http:// endpoints), PHASE_LLM_MAX_TOKENS (256),
|
||||
//! PHASE_LLM_TEMPERATURE (0.2), PHASE_LLM_TIMEOUT_S (20), PHASE_LLM_MAX_CALLS (total model calls for the whole process, default 300),
|
||||
//! PHASE_LLM_DIFFICULTY (Medium: the strength hint in the prompt), PHASE_LLM_MAX_FAILS (4: model failures IN A ROW before every model seat
|
||||
//! switches itself off for the rest of the process).
|
||||
|
||||
use std::collections::HashSet;
|
||||
use std::path::PathBuf;
|
||||
use std::sync::atomic::{AtomicBool, AtomicU64, Ordering};
|
||||
use std::sync::OnceLock;
|
||||
use std::time::{Duration, Instant};
|
||||
|
||||
use engine::ai_support::AiDecisionContract;
|
||||
use engine::database::CardDatabase;
|
||||
use engine::types::actions::GameAction;
|
||||
use engine::types::game_state::GameState;
|
||||
use engine::types::player::PlayerId;
|
||||
use phase_ai::auto_play::install_action_override;
|
||||
use phase_ai::config::AiDifficulty;
|
||||
use phase_llm::{LlmEndpointConfig, LlmProvider, LlmReply};
|
||||
|
||||
// The unchanged runner, with `fn main` made `pub` by apply_phase_patches.py.
|
||||
#[path = "../../phase-ai/src/bin/ai_commander.rs"]
|
||||
#[allow(dead_code, unused_imports, unused_variables)]
|
||||
mod commander;
|
||||
|
||||
struct Seats {
|
||||
seats: HashSet<u8>,
|
||||
endpoint: LlmEndpointConfig,
|
||||
difficulty: AiDifficulty,
|
||||
agent: ureq::Agent,
|
||||
cards_path: PathBuf,
|
||||
db: OnceLock<Option<CardDatabase>>,
|
||||
max_calls: u64,
|
||||
asked: [AtomicU64; 4],
|
||||
used: [AtomicU64; 4],
|
||||
fell_back: [AtomicU64; 4],
|
||||
total: AtomicU64,
|
||||
// Cost visibility and the circuit-breaker. A late-game prompt is thousands of tokens and a big-model server needs longer than the
|
||||
// per-call limit to read it; every such call used to burn the whole limit (a 9-minute game, 25 questions, 1 answer used). After
|
||||
// `max_fails` failures IN A ROW the model seats switch themselves off for the rest of the process and phase's AI plays on.
|
||||
timeouts: AtomicU64,
|
||||
consecutive_fails: AtomicU64,
|
||||
max_fails: u64,
|
||||
disabled: AtomicBool,
|
||||
millis: AtomicU64,
|
||||
answered: AtomicU64,
|
||||
prompt_chars: AtomicU64,
|
||||
}
|
||||
|
||||
impl Seats {
|
||||
fn database(&self) -> Option<&CardDatabase> {
|
||||
self.db
|
||||
.get_or_init(|| CardDatabase::from_export(&self.cards_path.join("card-data.json")).ok())
|
||||
.as_ref()
|
||||
}
|
||||
|
||||
/// The model's pick for `actor`, or None to let phase's AI play. Never panics on a bad answer.
|
||||
/// One failed model call (timeout, error status, unusable answer). Returns None so callers can `return self.failed(..)`.
|
||||
fn failed(&self, timeout: bool) -> Option<GameAction> {
|
||||
if timeout {
|
||||
self.timeouts.fetch_add(1, Ordering::Relaxed);
|
||||
}
|
||||
let in_a_row = self.consecutive_fails.fetch_add(1, Ordering::Relaxed) + 1;
|
||||
if in_a_row >= self.max_fails && !self.disabled.swap(true, Ordering::Relaxed) {
|
||||
println!(
|
||||
"LLM-SEAT-DISABLED after {in_a_row} consecutive failures ({} timeouts of {} questions): phase's own AI plays the rest",
|
||||
self.timeouts.load(Ordering::Relaxed),
|
||||
self.total.load(Ordering::Relaxed)
|
||||
);
|
||||
}
|
||||
None
|
||||
}
|
||||
|
||||
fn decide(&self, state: &GameState, contract: &AiDecisionContract, actor: PlayerId) -> Option<GameAction> {
|
||||
if self.disabled.load(Ordering::Relaxed) {
|
||||
return None;
|
||||
}
|
||||
let seat = (actor.0 as usize).min(3);
|
||||
// A forced move (one legal option) is never put to a model: build_game_decision_prompt refuses it.
|
||||
let request = phase_llm::build_game_decision_prompt(
|
||||
state,
|
||||
contract,
|
||||
self.difficulty,
|
||||
self.database(),
|
||||
&Vec::new(),
|
||||
)
|
||||
.ok()?;
|
||||
if self.total.fetch_add(1, Ordering::Relaxed) >= self.max_calls {
|
||||
return None; // the call cap: every later decision is phase's AI
|
||||
}
|
||||
self.asked[seat].fetch_add(1, Ordering::Relaxed);
|
||||
self.prompt_chars.fetch_add(request.prompt.char_count() as u64, Ordering::Relaxed);
|
||||
let http = phase_llm::build_chat_request(&self.endpoint, &request.prompt).ok()?;
|
||||
let mut call = self.agent.request(http.method, &http.url);
|
||||
for header in &http.headers {
|
||||
call = call.set(&header.name, &header.value);
|
||||
}
|
||||
let started = Instant::now();
|
||||
let (status, body) = match call.send_string(&http.body) {
|
||||
Ok(response) => {
|
||||
let status = response.status();
|
||||
match response.into_string() {
|
||||
Ok(text) => (status, text),
|
||||
Err(_) => return self.failed(true),
|
||||
}
|
||||
}
|
||||
// A 4xx/5xx still carries a body phase-llm knows how to classify.
|
||||
Err(ureq::Error::Status(code, response)) => (code, response.into_string().unwrap_or_default()),
|
||||
Err(_) => return self.failed(true), // transport error: a timeout, or the endpoint is down
|
||||
};
|
||||
self.millis.fetch_add(started.elapsed().as_millis() as u64, Ordering::Relaxed);
|
||||
self.answered.fetch_add(1, Ordering::Relaxed);
|
||||
let selection = match phase_llm::select_action_from_response(
|
||||
state,
|
||||
contract,
|
||||
&request.fingerprint,
|
||||
LlmReply { provider: self.endpoint.provider, status, body: &body },
|
||||
) {
|
||||
Ok(selection) => selection,
|
||||
Err(_) => return self.failed(false),
|
||||
};
|
||||
if !contract.contains_action(state, &selection.action) {
|
||||
return self.failed(false);
|
||||
}
|
||||
self.consecutive_fails.store(0, Ordering::Relaxed);
|
||||
self.used[seat].fetch_add(1, Ordering::Relaxed);
|
||||
Some(selection.action)
|
||||
}
|
||||
|
||||
fn report(&self, seat: usize) {
|
||||
let asked = self.asked[seat].load(Ordering::Relaxed);
|
||||
if asked > 0 && asked % 25 == 0 {
|
||||
let answered = self.answered.load(Ordering::Relaxed).max(1);
|
||||
let questions = self.total.load(Ordering::Relaxed).max(1);
|
||||
println!(
|
||||
"LLM-SEAT seat={seat} asked={asked} used={} total_calls={} timeouts={} avg_ms={} avg_prompt_chars={}",
|
||||
self.used[seat].load(Ordering::Relaxed),
|
||||
self.total.load(Ordering::Relaxed),
|
||||
self.timeouts.load(Ordering::Relaxed),
|
||||
self.millis.load(Ordering::Relaxed) / answered,
|
||||
self.prompt_chars.load(Ordering::Relaxed) / questions
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fn env_or(name: &str, default: &str) -> String {
|
||||
std::env::var(name).ok().filter(|v| !v.trim().is_empty()).unwrap_or_else(|| default.to_string())
|
||||
}
|
||||
|
||||
fn install_model_seats() {
|
||||
let seats: HashSet<u8> = env_or("PHASE_LLM_SEATS", "")
|
||||
.split(',')
|
||||
.filter_map(|s| s.trim().parse::<u8>().ok())
|
||||
.filter(|s| *s < 4)
|
||||
.collect();
|
||||
if seats.is_empty() {
|
||||
println!("LLM seats: none (plain ai-commander)");
|
||||
return;
|
||||
}
|
||||
let provider = LlmProvider::from_label(&env_or("PHASE_LLM_PROVIDER", "OpenAiCompatible"));
|
||||
let endpoint = LlmEndpointConfig {
|
||||
provider,
|
||||
base_url: std::env::var("PHASE_LLM_BASE_URL").ok().filter(|v| !v.trim().is_empty()),
|
||||
api_key: env_or("PHASE_LLM_API_KEY", ""),
|
||||
model: env_or("PHASE_LLM_MODEL", "model"),
|
||||
max_output_tokens: env_or("PHASE_LLM_MAX_TOKENS", "256").parse().ok(),
|
||||
temperature: env_or("PHASE_LLM_TEMPERATURE", "0.2").parse().ok(),
|
||||
};
|
||||
let timeout = env_or("PHASE_LLM_TIMEOUT_S", "20").parse::<u64>().unwrap_or(20);
|
||||
// The runner's first positional argument is the data folder (card-data.json lives there).
|
||||
let cards_path = PathBuf::from(std::env::args().nth(1).unwrap_or_else(|| ".".to_string()));
|
||||
let state = Seats {
|
||||
seats: seats.clone(),
|
||||
endpoint,
|
||||
difficulty: AiDifficulty::from_label(&env_or("PHASE_LLM_DIFFICULTY", "Medium")),
|
||||
agent: ureq::AgentBuilder::new().timeout(Duration::from_secs(timeout)).redirects(0).build(),
|
||||
cards_path,
|
||||
db: OnceLock::new(),
|
||||
max_calls: env_or("PHASE_LLM_MAX_CALLS", "300").parse().unwrap_or(300),
|
||||
asked: Default::default(),
|
||||
used: Default::default(),
|
||||
fell_back: Default::default(),
|
||||
total: AtomicU64::new(0),
|
||||
timeouts: AtomicU64::new(0),
|
||||
consecutive_fails: AtomicU64::new(0),
|
||||
max_fails: env_or("PHASE_LLM_MAX_FAILS", "4").parse().unwrap_or(4),
|
||||
disabled: AtomicBool::new(false),
|
||||
millis: AtomicU64::new(0),
|
||||
answered: AtomicU64::new(0),
|
||||
prompt_chars: AtomicU64::new(0),
|
||||
};
|
||||
println!(
|
||||
"LLM seats: {:?} provider={} model={} max_calls={}",
|
||||
{
|
||||
let mut v: Vec<u8> = seats.iter().copied().collect();
|
||||
v.sort();
|
||||
v
|
||||
},
|
||||
provider.label(),
|
||||
state.endpoint.model,
|
||||
state.max_calls
|
||||
);
|
||||
let installed = install_action_override(Box::new(move |game, contract, _owner, actor| {
|
||||
if !state.seats.contains(&actor.0) {
|
||||
return None;
|
||||
}
|
||||
let seat = (actor.0 as usize).min(3);
|
||||
let pick = state.decide(game, contract, actor);
|
||||
if pick.is_none() {
|
||||
state.fell_back[seat].fetch_add(1, Ordering::Relaxed);
|
||||
}
|
||||
state.report(seat);
|
||||
pick
|
||||
}));
|
||||
if !installed {
|
||||
eprintln!("LLM seats: an action override was already installed; model seats are off");
|
||||
}
|
||||
}
|
||||
|
||||
fn main() {
|
||||
install_model_seats();
|
||||
commander::main();
|
||||
}
|
||||
@@ -1,501 +0,0 @@
|
||||
"""phase-sim: run rules-accurate four-player Commander games with phase-rs's headless `ai-commander` and report what happened.
|
||||
|
||||
Standard library only. One job runs at a time (a game is CPU-heavy); more wait in a queue. The service never touches the network.
|
||||
|
||||
GET /health {"ok": true, "engine": ..., "queued": n, "running": id|null}
|
||||
POST /run {"decks": [4 x {"name", "commander": ["Card"], "main": [{"name","count"}, ...]}],
|
||||
"games": 1..MAX_GAMES, "seed": int, "difficulty": "Medium", "seat_difficulty": {"2": "Hard"}, "action_cap": int}
|
||||
-> {"job": id}
|
||||
GET /job/<id> status queued|running|done|error, games finished, and when done: per-game rows, wins by seat and by deck,
|
||||
win rate with a 95% interval, the cards the engine could not resolve, and the tail of the engine's output
|
||||
|
||||
Seat i is the i-th deck in the request (P0 = first). The engine treats every seat as AI; difficulty is per seat if given.
|
||||
A game that stalls, hits the action cap, or panics is reported as such and is NEVER counted as a win or a draw.
|
||||
|
||||
Model seats (optional, needs the image's ai-commander-llm binary): "llm_seats": [1, 3] and "llm": {"kind": "chat" | "decision", "model": "...",
|
||||
"max_calls": 300}. Those seats ask a model to pick among the ENGINE'S OWN legal options (phase-llm builds the prompt and checks the answer;
|
||||
anything wrong or slow falls back to phase's AI). kind "chat" goes to PHASE_LLM_CHAT_URL (an OpenAI-style endpoint, e.g. Olla) through this
|
||||
service's /llm relay, which switches the model's thinking off and caps its answer; kind "decision" goes to PHASE_LLM_DECISION_URL (a System One
|
||||
server: Laya or Kev) through /jev/systemone. Both relays accept connections from this machine only.
|
||||
"""
|
||||
import json
|
||||
import math
|
||||
import os
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
import tempfile
|
||||
import threading
|
||||
import time
|
||||
import urllib.error
|
||||
import urllib.request
|
||||
import uuid
|
||||
from collections import deque
|
||||
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
|
||||
|
||||
BIN = os.environ.get("PHASE_AI_COMMANDER", "/opt/phase/ai-commander")
|
||||
CARD_DATA = os.environ.get("PHASE_CARD_DATA", "/opt/phase/data/card-data.json")
|
||||
ENGINE = os.environ.get("PHASE_ENGINE_VERSION", "phase-rs (version unknown)")
|
||||
MAX_GAMES = int(os.environ.get("PHASE_SIM_MAX_GAMES", "50"))
|
||||
JOB_TIMEOUT = int(os.environ.get("PHASE_SIM_TIMEOUT", "3600"))
|
||||
PORT = int(os.environ.get("PORT", "8090"))
|
||||
DIFFICULTIES = ("VeryEasy", "Easy", "Medium", "Hard", "VeryHard")
|
||||
BIN_LLM = os.environ.get("PHASE_AI_COMMANDER_LLM", "/opt/phase/ai-commander-llm")
|
||||
CHAT_URL = os.environ.get("PHASE_LLM_CHAT_URL", "").rstrip("/") # e.g. http://olla...:40114/olla/openai/v1
|
||||
DECISION_URL = os.environ.get("PHASE_LLM_DECISION_URL", "") # e.g. http://laya...:8080/v1/systemone
|
||||
DECISION_KEY = os.environ.get("PHASE_LLM_DECISION_KEY", "")
|
||||
CHAT_REASONING = os.environ.get("PHASE_LLM_REASONING", "none")
|
||||
CHAT_MAX_TOKENS = int(os.environ.get("PHASE_LLM_MAX_TOKENS_CAP", "256"))
|
||||
MAX_LLM_CALLS = int(os.environ.get("PHASE_LLM_MAX_CALLS", "600"))
|
||||
LOCAL_ONLY = ("127.0.0.1", "::1")
|
||||
# A late-game prompt is thousands of tokens; reading it takes a 27B model on a V100 20-30 s. The engine waits MODEL_TIMEOUT per question and the
|
||||
# relay a little less, and the engine switches the model seats off after PHASE_LLM_MAX_FAILS failures in a row, so a dead model costs a few minutes at most.
|
||||
RELAY_TIMEOUT = float(os.environ.get("PHASE_LLM_RELAY_TIMEOUT", "55"))
|
||||
MODEL_TIMEOUT = os.environ.get("PHASE_LLM_TIMEOUT_S", "60")
|
||||
MAX_DECK_ENTRIES = 400
|
||||
|
||||
_jobs = {}
|
||||
_queue = deque()
|
||||
_cv = threading.Condition()
|
||||
_running = {"id": None}
|
||||
|
||||
|
||||
# ---------- request -> engine inputs ------------------------------------------------------------------------------------------
|
||||
|
||||
class BadRequest(ValueError):
|
||||
pass
|
||||
|
||||
|
||||
def validate(req):
|
||||
decks = req.get("decks")
|
||||
if not isinstance(decks, list) or len(decks) != 4:
|
||||
raise BadRequest("decks must be a list of exactly 4 decks (one per seat)")
|
||||
feed = []
|
||||
for i, d in enumerate(decks):
|
||||
if not isinstance(d, dict) or not str(d.get("name") or "").strip():
|
||||
raise BadRequest("deck %d needs a name" % i)
|
||||
cmd = d.get("commander")
|
||||
if isinstance(cmd, str):
|
||||
cmd = [cmd]
|
||||
if not isinstance(cmd, list) or not cmd or not all(isinstance(c, str) and c.strip() for c in cmd):
|
||||
raise BadRequest("deck %d needs commander: [\"Card Name\"]" % i)
|
||||
main = d.get("main")
|
||||
if main is None and isinstance(d.get("cards"), list):
|
||||
counts = {}
|
||||
for n in d["cards"]:
|
||||
counts[str(n)] = counts.get(str(n), 0) + 1
|
||||
main = [{"name": k, "count": v} for k, v in counts.items()]
|
||||
if not isinstance(main, list) or not main or len(main) > MAX_DECK_ENTRIES:
|
||||
raise BadRequest("deck %d needs a non-empty main list (at most %d entries)" % (i, MAX_DECK_ENTRIES))
|
||||
rows = []
|
||||
for e in main:
|
||||
try:
|
||||
name, count = str(e["name"]), int(e["count"])
|
||||
except (KeyError, TypeError, ValueError):
|
||||
raise BadRequest("deck %d has a bad main entry: %r" % (i, e))
|
||||
if not name.strip() or not 1 <= count <= 99:
|
||||
raise BadRequest("deck %d has a bad main entry: %r" % (i, e))
|
||||
rows.append({"name": name, "count": count})
|
||||
feed.append({"name": str(d["name"]), "commander": [c.strip() for c in cmd], "main": rows})
|
||||
try:
|
||||
games = int(req.get("games", 1))
|
||||
seed = int(req.get("seed", 1))
|
||||
except (TypeError, ValueError):
|
||||
raise BadRequest("games and seed must be integers")
|
||||
if not 1 <= games <= MAX_GAMES:
|
||||
raise BadRequest("games must be 1 to %d" % MAX_GAMES)
|
||||
difficulty = req.get("difficulty", "Medium")
|
||||
if difficulty not in DIFFICULTIES:
|
||||
raise BadRequest("difficulty must be one of %s" % ", ".join(DIFFICULTIES))
|
||||
seat_diff = {}
|
||||
for k, v in (req.get("seat_difficulty") or {}).items():
|
||||
if str(k) not in ("0", "1", "2", "3") or v not in DIFFICULTIES:
|
||||
raise BadRequest("seat_difficulty keys are seats 0-3, values one of %s" % ", ".join(DIFFICULTIES))
|
||||
seat_diff[int(k)] = v
|
||||
cap = req.get("action_cap")
|
||||
if cap is not None:
|
||||
try:
|
||||
cap = int(cap)
|
||||
except (TypeError, ValueError):
|
||||
raise BadRequest("action_cap must be an integer")
|
||||
if not 100 <= cap <= 200000:
|
||||
raise BadRequest("action_cap must be 100 to 200000")
|
||||
llm = None
|
||||
seats = req.get("llm_seats") or []
|
||||
if seats:
|
||||
if not isinstance(seats, list) or not all(isinstance(x, int) and 0 <= x <= 3 for x in seats):
|
||||
raise BadRequest("llm_seats must be a list of seats 0-3")
|
||||
cfg = req.get("llm") or {}
|
||||
kind = cfg.get("kind", "chat")
|
||||
if kind not in ("chat", "decision"):
|
||||
raise BadRequest("llm.kind must be chat or decision")
|
||||
if (kind == "chat" and not CHAT_URL) or (kind == "decision" and not DECISION_URL):
|
||||
raise BadRequest("this service has no %s model endpoint configured" % kind)
|
||||
if not os.path.isfile(BIN_LLM) and not os.environ.get("PHASE_SIM_ALLOW_MISSING_LLM_BIN"):
|
||||
raise BadRequest("this image has no model-seat engine (ai-commander-llm)")
|
||||
model = str(cfg.get("model") or "").strip()
|
||||
if kind == "chat" and not model:
|
||||
raise BadRequest("llm.model is required for a chat model")
|
||||
try:
|
||||
max_calls = int(cfg.get("max_calls", 300))
|
||||
except (TypeError, ValueError):
|
||||
raise BadRequest("llm.max_calls must be an integer")
|
||||
if not 1 <= max_calls <= MAX_LLM_CALLS:
|
||||
raise BadRequest("llm.max_calls must be 1 to %d" % MAX_LLM_CALLS)
|
||||
llm = {"seats": sorted(set(seats)), "kind": kind, "model": model or "decision-model", "max_calls": max_calls}
|
||||
return {"feed": feed, "games": games, "seed": seed, "difficulty": difficulty, "seat_difficulty": seat_diff, "action_cap": cap, "llm": llm}
|
||||
|
||||
|
||||
def engine_env(spec):
|
||||
"""The environment for the engine process: model-seat settings, or None (inherit) for the plain runner."""
|
||||
llm = spec.get("llm")
|
||||
if not llm:
|
||||
return None
|
||||
env = dict(os.environ)
|
||||
local = "http://127.0.0.1:%d" % PORT
|
||||
env.update(PHASE_LLM_SEATS=",".join(str(x) for x in llm["seats"]), PHASE_LLM_MODEL=llm["model"], PHASE_LLM_API_KEY="",
|
||||
PHASE_LLM_MAX_CALLS=str(llm["max_calls"]), PHASE_LLM_DIFFICULTY=spec["difficulty"], PHASE_LLM_TIMEOUT_S=MODEL_TIMEOUT)
|
||||
if llm["kind"] == "chat":
|
||||
env.update(PHASE_LLM_PROVIDER="OpenAiCompatible", PHASE_LLM_BASE_URL=local + "/llm/v1")
|
||||
else:
|
||||
env.update(PHASE_LLM_PROVIDER="Jev", PHASE_LLM_BASE_URL=local)
|
||||
return env
|
||||
|
||||
|
||||
def command(spec, root):
|
||||
cmd = [BIN_LLM if spec.get("llm") else BIN, root, "--feed", "feed.json", "--games-file", os.path.join(root, "games.txt")]
|
||||
for seat, level in sorted(spec["seat_difficulty"].items()):
|
||||
cmd += ["--difficulty-p%d" % seat, level]
|
||||
if spec["action_cap"]:
|
||||
cmd += ["--action-cap", str(spec["action_cap"])]
|
||||
return cmd
|
||||
|
||||
|
||||
# ---------- engine output -> results ------------------------------------------------------------------------------------------
|
||||
|
||||
GAME_HEAD = re.compile(r"^--- GAME\b(.*)$")
|
||||
WINNER = re.compile(r"Game ended cleanly\. Winner: (draw|P(\d))")
|
||||
TURNS = re.compile(r"^Turns played: (\d+)")
|
||||
ELIM = re.compile(r"^ELIMINATED: P(\d)\s+turn=(\d+)")
|
||||
RESOLVED = re.compile(r"^\s+P(\d)\s+main=\s*(\d+)\s+commander=(\d+)")
|
||||
LLM_STAT = re.compile(r"^LLM-SEAT seat=(\d) asked=(\d+) used=(\d+)(?: total_calls=(\d+))?(?: timeouts=(\d+))?(?: avg_ms=(\d+))?(?: avg_prompt_chars=(\d+))?")
|
||||
LLM_OFF = re.compile(r"^LLM-SEAT-DISABLED after (\d+) consecutive failures")
|
||||
SKIP = re.compile(r"^\s+SKIP (.+?): commander '(.+)' not in card db")
|
||||
PRE = re.compile(r"^\s+(.+?)\s+\|\s+commander: (.+?)\s+\|\s+main: (\d+) cards")
|
||||
|
||||
|
||||
def parse(output):
|
||||
"""Per-game rows and the deck-resolution facts from the engine's stdout. Games that did not finish are kept, labelled, never counted."""
|
||||
lines = output.splitlines()
|
||||
pre, resolved, skipped, games, cur = [], {}, [], [], None
|
||||
|
||||
def finish():
|
||||
if cur is not None:
|
||||
games.append(cur)
|
||||
|
||||
def new_game(label=""):
|
||||
return {"label": label, "status": "unfinished", "winner_seat": None, "turns": None, "eliminated": []}
|
||||
|
||||
result_line = re.compile(r"Game ended cleanly|^Turns played|^ELIMINATED:|^STALL:|ABORT: hit action cap|PANICKED")
|
||||
for ln in lines:
|
||||
if cur is None and not games: # the preamble: decks listed, then their resolved sizes
|
||||
m = PRE.match(ln)
|
||||
if m:
|
||||
pre.append({"deck": m.group(1), "commander": m.group(2), "listed": int(m.group(3))})
|
||||
continue
|
||||
m = SKIP.match(ln)
|
||||
if m:
|
||||
skipped.append({"deck": m.group(1), "commander": m.group(2)})
|
||||
continue
|
||||
m = RESOLVED.match(ln)
|
||||
if m and int(m.group(1)) not in resolved:
|
||||
resolved[int(m.group(1))] = {"main": int(m.group(2)), "commanders": int(m.group(3))}
|
||||
continue
|
||||
if GAME_HEAD.match(ln):
|
||||
finish()
|
||||
cur = new_game(GAME_HEAD.match(ln).group(1).strip(" -"))
|
||||
continue
|
||||
if cur is None:
|
||||
if not result_line.search(ln):
|
||||
continue # banner and preamble lines are not a game
|
||||
cur = new_game() # a single-game run prints no "--- GAME" header
|
||||
m = WINNER.search(ln)
|
||||
if m:
|
||||
cur["status"] = "draw" if m.group(1) == "draw" else "won"
|
||||
cur["winner_seat"] = None if m.group(2) is None else int(m.group(2))
|
||||
continue
|
||||
m = TURNS.match(ln)
|
||||
if m:
|
||||
cur["turns"] = int(m.group(1))
|
||||
continue
|
||||
m = ELIM.match(ln)
|
||||
if m:
|
||||
cur["eliminated"].append({"seat": int(m.group(1)), "turn": int(m.group(2))})
|
||||
continue
|
||||
if ln.startswith("STALL:"):
|
||||
cur["status"] = "stalled"
|
||||
cur.setdefault("detail", ln)
|
||||
elif "ABORT: hit action cap" in ln:
|
||||
cur["status"] = "action_cap"
|
||||
elif re.search(r"GAME .* PANICKED", ln):
|
||||
cur["status"] = "panicked"
|
||||
cur["detail"] = ln[:300]
|
||||
finish()
|
||||
games = [g for g in games if g["status"] != "unfinished" or g["turns"] is not None or g["eliminated"] or g["label"]]
|
||||
return {"pre": pre, "resolved": resolved, "skipped": skipped, "games": games}
|
||||
|
||||
|
||||
def llm_summary(llm, lines):
|
||||
"""The model seats' own counters (the engine prints one every 25th question), last value per seat, plus the breaker and the cost."""
|
||||
per_seat, extra, disabled = {}, {}, None
|
||||
for ln in lines:
|
||||
m = LLM_STAT.match(ln)
|
||||
if m:
|
||||
per_seat[int(m.group(1))] = {"asked": int(m.group(2)), "used": int(m.group(3))}
|
||||
for key, grp in (("timeouts", 5), ("avg_ms", 6), ("avg_prompt_chars", 7)):
|
||||
if m.group(grp) is not None:
|
||||
extra[key] = int(m.group(grp))
|
||||
continue
|
||||
m = LLM_OFF.match(ln)
|
||||
if m:
|
||||
disabled = int(m.group(1))
|
||||
out = {"seats": llm["seats"], "kind": llm["kind"], "model": llm["model"], "max_calls": llm["max_calls"], "per_seat": per_seat,
|
||||
"note": "counters are printed every 25th question, so the last few may be missing"}
|
||||
out.update(extra)
|
||||
if disabled is not None:
|
||||
out["switched_off_after_failures_in_a_row"] = disabled
|
||||
return out
|
||||
|
||||
|
||||
def wilson(wins, n, z=1.96):
|
||||
if n == 0:
|
||||
return [0.0, 0.0]
|
||||
p = wins / n
|
||||
d = 1 + z * z / n
|
||||
c = p + z * z / (2 * n)
|
||||
h = z * math.sqrt(p * (1 - p) / n + z * z / (4 * n * n))
|
||||
return [round(max(0.0, (c - h) / d), 4), round(min(1.0, (c + h) / d), 4)]
|
||||
|
||||
|
||||
def summarize(spec, parsed, seeds):
|
||||
decks = [d["name"] for d in spec["feed"]]
|
||||
finished = [g for g in parsed["games"] if g["status"] in ("won", "draw")]
|
||||
won = [g for g in finished if g["status"] == "won"]
|
||||
by_seat = [sum(1 for g in won if g["winner_seat"] == i) for i in range(4)]
|
||||
n = len(finished)
|
||||
warnings = []
|
||||
for s in parsed["skipped"]:
|
||||
warnings.append("deck %r was skipped: commander %r is not in the engine's card database" % (s["deck"], s["commander"]))
|
||||
for i, pre in enumerate(parsed["pre"][:4]):
|
||||
got = (parsed["resolved"].get(i) or {}).get("main")
|
||||
if got is not None and got < pre["listed"]:
|
||||
warnings.append("seat %d (%s): %d of %d cards were not recognised by the engine and were left out" % (
|
||||
i, pre["deck"], pre["listed"] - got, pre["listed"]))
|
||||
bad = [g for g in parsed["games"] if g["status"] not in ("won", "draw")]
|
||||
if bad:
|
||||
warnings.append("%d game(s) did not finish (%s): not counted as wins or draws" % (
|
||||
len(bad), ", ".join(sorted({g["status"] for g in bad}))))
|
||||
for i, g in enumerate(parsed["games"]):
|
||||
if i < len(seeds):
|
||||
g["seed"] = seeds[i]
|
||||
return {"games_requested": spec["games"], "games_finished": n, "games_not_finished": len(bad), "draws": n - len(won),
|
||||
"wins_by_seat": by_seat,
|
||||
"decks": [{"seat": i, "name": decks[i], "wins": by_seat[i], "win_rate": round(by_seat[i] / n, 4) if n else None,
|
||||
"win_rate_95": wilson(by_seat[i], n)} for i in range(4)],
|
||||
"avg_turns": round(sum(g["turns"] or 0 for g in finished) / n, 1) if n else None,
|
||||
"warnings": warnings, "games": parsed["games"]}
|
||||
|
||||
|
||||
# ---------- jobs ----------------------------------------------------------------------------------------------------------------
|
||||
|
||||
def run_job(job):
|
||||
spec = job["spec"]
|
||||
root = tempfile.mkdtemp(prefix="phase-sim-")
|
||||
out_lines = []
|
||||
try:
|
||||
os.symlink(CARD_DATA, os.path.join(root, "card-data.json"))
|
||||
with open(os.path.join(root, "feed.json"), "w", encoding="utf-8") as f:
|
||||
json.dump({"decks": spec["feed"]}, f)
|
||||
seeds = [spec["seed"] + i for i in range(spec["games"])]
|
||||
with open(os.path.join(root, "games.txt"), "w", encoding="utf-8") as f:
|
||||
f.write("".join("%d,%s\n" % (s, spec["difficulty"]) for s in seeds))
|
||||
job["status"], job["started"] = "running", time.time()
|
||||
proc = subprocess.Popen(command(spec, root), stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, errors="replace", cwd=root,
|
||||
env=engine_env(spec))
|
||||
timer = threading.Timer(JOB_TIMEOUT, proc.kill)
|
||||
timer.start()
|
||||
try:
|
||||
for ln in proc.stdout:
|
||||
out_lines.append(ln.rstrip("\n"))
|
||||
if GAME_HEAD.match(ln):
|
||||
job["games_started"] += 1
|
||||
if WINNER.search(ln) or "GAME" in ln and "PANICKED" in ln or ln.startswith("STALL:") or "ABORT: hit action cap" in ln:
|
||||
job["games_done"] += 1
|
||||
code = proc.wait()
|
||||
finally:
|
||||
timer.cancel()
|
||||
text = "\n".join(out_lines)
|
||||
parsed = parse(text)
|
||||
job["result"] = summarize(spec, parsed, seeds)
|
||||
if spec.get("llm"):
|
||||
job["result"]["llm"] = llm_summary(spec["llm"], out_lines)
|
||||
if "switched_off_after_failures_in_a_row" in job["result"]["llm"]:
|
||||
job["result"]["warnings"].append(
|
||||
"the model seats switched themselves off after %d failures in a row (usually timeouts: late-game prompts are thousands of tokens "
|
||||
"and the model server could not read them in time); phase's own AI played the rest of the run, so the later moves are NOT the model's"
|
||||
% job["result"]["llm"]["switched_off_after_failures_in_a_row"])
|
||||
job["result"]["warnings"].append(
|
||||
"model seats %s: the model is asked about each non-forced decision, up to %d calls for the whole run; an answer the engine "
|
||||
"cannot use, a timeout, or any decision after the cap is played by phase's own AI instead" % (spec["llm"]["seats"], spec["llm"]["max_calls"]))
|
||||
job["engine_exit_code"] = code
|
||||
job["output_tail"] = out_lines[-40:]
|
||||
job["status"] = "done" if parsed["games"] else "error"
|
||||
if not parsed["games"]:
|
||||
job["error"] = "the engine produced no game results (exit code %s); see output_tail" % code
|
||||
except Exception as exc: # the job fails, the service does not
|
||||
job["status"], job["error"] = "error", "%s: %s" % (type(exc).__name__, exc)
|
||||
job["output_tail"] = out_lines[-40:]
|
||||
finally:
|
||||
job["finished"] = time.time()
|
||||
try:
|
||||
for name in os.listdir(root):
|
||||
os.unlink(os.path.join(root, name))
|
||||
os.rmdir(root)
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
|
||||
def worker():
|
||||
while True:
|
||||
with _cv:
|
||||
while not _queue:
|
||||
_cv.wait()
|
||||
job = _queue.popleft()
|
||||
_running["id"] = job["id"]
|
||||
run_job(job)
|
||||
with _cv:
|
||||
_running["id"] = None
|
||||
|
||||
|
||||
def submit(req):
|
||||
spec = validate(req)
|
||||
job = {"id": uuid.uuid4().hex[:12], "status": "queued", "created": time.time(), "spec": spec, "games_started": 0, "games_done": 0}
|
||||
with _cv:
|
||||
_jobs[job["id"]] = job
|
||||
_queue.append(job)
|
||||
for old in sorted(_jobs.values(), key=lambda j: j["created"])[:-50]: # remember the last 50
|
||||
if old["status"] in ("done", "error"):
|
||||
_jobs.pop(old["id"], None)
|
||||
_cv.notify()
|
||||
return job["id"]
|
||||
|
||||
|
||||
def public(job):
|
||||
out = {k: job[k] for k in ("id", "status", "games_started", "games_done") if k in job}
|
||||
out["games_requested"] = job["spec"]["games"]
|
||||
for k in ("result", "error", "output_tail", "engine_exit_code"):
|
||||
if k in job:
|
||||
out[k] = job[k]
|
||||
if "started" in job:
|
||||
out["seconds"] = round((job.get("finished") or time.time()) - job["started"], 1)
|
||||
return out
|
||||
|
||||
|
||||
def forward(url, data, headers):
|
||||
req = urllib.request.Request(url, data=data, headers=dict({"content-type": "application/json"}, **headers))
|
||||
try:
|
||||
with urllib.request.urlopen(req, timeout=RELAY_TIMEOUT) as r:
|
||||
return r.status, json.loads(r.read() or b"{}")
|
||||
except urllib.error.HTTPError as exc:
|
||||
try:
|
||||
return exc.code, json.loads(exc.read() or b"{}")
|
||||
except ValueError:
|
||||
return exc.code, {"error": "upstream error"}
|
||||
except (urllib.error.URLError, OSError, ValueError):
|
||||
return 502, {"error": "the model endpoint is not reachable"}
|
||||
|
||||
|
||||
def relay_chat(body, remote):
|
||||
"""POST /llm/v1/chat/completions -> the OpenAI-style endpoint, with the model's thinking off and its answer capped."""
|
||||
if remote not in LOCAL_ONLY or not CHAT_URL:
|
||||
return 403, {"error": "not available"}
|
||||
try:
|
||||
req = json.loads(body)
|
||||
req["max_tokens"] = min(int(req.get("max_tokens") or CHAT_MAX_TOKENS), CHAT_MAX_TOKENS)
|
||||
req["stream"] = False
|
||||
if CHAT_REASONING:
|
||||
req["reasoning_effort"] = CHAT_REASONING
|
||||
data = json.dumps(req).encode()
|
||||
except (ValueError, TypeError):
|
||||
return 400, {"error": "bad chat request"}
|
||||
return forward(CHAT_URL + "/chat/completions", data, {})
|
||||
|
||||
|
||||
def relay_decision(body, remote):
|
||||
"""POST /jev/systemone: phase's Jev envelope {"apiKey", "request": {...}} -> a System One server (Laya / Kev); the key comes from the environment."""
|
||||
if remote not in LOCAL_ONLY or not DECISION_URL:
|
||||
return 403, {"error": "not available"}
|
||||
try:
|
||||
data = json.dumps(json.loads(body)["request"]).encode()
|
||||
except (ValueError, KeyError, TypeError):
|
||||
return 400, {"error": "bad decision request"}
|
||||
return forward(DECISION_URL, data, {"authorization": "Bearer " + DECISION_KEY} if DECISION_KEY else {})
|
||||
|
||||
|
||||
class Handler(BaseHTTPRequestHandler):
|
||||
protocol_version = "HTTP/1.1"
|
||||
server_version = "phase-sim/1"
|
||||
|
||||
def log_message(self, fmt, *args):
|
||||
if os.environ.get("PHASE_SIM_LOG") == "1":
|
||||
sys.stderr.write("%s\n" % (fmt % args))
|
||||
|
||||
def _send(self, code, obj):
|
||||
body = json.dumps(obj).encode()
|
||||
self.send_response(code)
|
||||
self.send_header("Content-Type", "application/json")
|
||||
self.send_header("Content-Length", str(len(body)))
|
||||
self.end_headers()
|
||||
self.wfile.write(body)
|
||||
|
||||
def do_GET(self):
|
||||
if self.path == "/health":
|
||||
with _cv:
|
||||
return self._send(200, {"ok": os.path.isfile(BIN) and os.path.isfile(CARD_DATA), "engine": ENGINE,
|
||||
"queued": len(_queue), "running": _running["id"], "max_games": MAX_GAMES})
|
||||
m = re.match(r"^/job/([0-9a-f]+)$", self.path)
|
||||
if m:
|
||||
with _cv:
|
||||
job = _jobs.get(m.group(1))
|
||||
return self._send(200, public(job)) if job else self._send(404, {"error": "no such job"})
|
||||
self._send(404, {"error": "not found"})
|
||||
|
||||
def do_POST(self):
|
||||
if self.path in ("/llm/v1/chat/completions", "/jev/systemone"):
|
||||
n = int(self.headers.get("Content-Length") or 0)
|
||||
if n > 4_000_000:
|
||||
return self._send(413, {"error": "request too large"})
|
||||
fn = relay_chat if self.path.startswith("/llm") else relay_decision
|
||||
code, obj = fn(self.rfile.read(n), self.client_address[0])
|
||||
return self._send(code, obj)
|
||||
if self.path != "/run":
|
||||
return self._send(404, {"error": "not found"})
|
||||
try:
|
||||
n = int(self.headers.get("Content-Length") or 0)
|
||||
if n > 2_000_000:
|
||||
return self._send(413, {"error": "request too large"})
|
||||
req = json.loads(self.rfile.read(n) or b"{}")
|
||||
if not isinstance(req, dict):
|
||||
raise BadRequest("the body must be a JSON object")
|
||||
self._send(202, {"job": submit(req)})
|
||||
except (BadRequest, json.JSONDecodeError) as exc:
|
||||
self._send(400, {"error": str(exc)})
|
||||
|
||||
|
||||
def main():
|
||||
threading.Thread(target=worker, daemon=True).start()
|
||||
ThreadingHTTPServer(("0.0.0.0", PORT), Handler).serve_forever()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,108 +0,0 @@
|
||||
"""Smoke test, run INSIDE the image (CI and by hand): four real decks from phase's own sample feed play real games through the service.
|
||||
|
||||
python /opt/phase/phase_smoke.py [games] [difficulty] plain engine
|
||||
python /opt/phase/phase_smoke.py --llm model seats against a stub model inside this process
|
||||
|
||||
Exits non-zero unless at least one game finished with a winner or a draw. Prints what the engine reported, including any cards it did
|
||||
not recognise, so a bad data/engine pairing shows up here and not in a result someone trusts.
|
||||
|
||||
--llm starts a stub OpenAI-style model on 127.0.0.1:9099 (the service must have been started with PHASE_LLM_CHAT_URL=http://127.0.0.1:9099/v1),
|
||||
puts seats 1 and 3 on it, and passes when the engine's own counters show the model was asked AND its answers were used by the engine: that
|
||||
proves the Rust hook, the relay and phase's answer check end to end without any outside service. The stub always answers option 0, so the
|
||||
game itself may not finish (it can hit the action cap); that is not what is being tested.
|
||||
"""
|
||||
import json
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
import urllib.request
|
||||
from http.server import BaseHTTPRequestHandler, HTTPServer
|
||||
|
||||
BASE = "http://127.0.0.1:8090"
|
||||
LLM = "--llm" in sys.argv
|
||||
args = [a for a in sys.argv[1:] if not a.startswith("--")]
|
||||
games = int(args[0]) if args else (1 if LLM else 2)
|
||||
difficulty = args[1] if len(args) > 1 else "VeryEasy"
|
||||
|
||||
ASKED = []
|
||||
|
||||
|
||||
class StubModel(BaseHTTPRequestHandler):
|
||||
def log_message(self, *a):
|
||||
pass
|
||||
|
||||
def do_POST(self):
|
||||
body = json.loads(self.rfile.read(int(self.headers.get("Content-Length") or 0)))
|
||||
ASKED.append(body)
|
||||
out = json.dumps({"choices": [{"index": 0, "message": {"role": "assistant", "content": "{\"choice\": 0, \"reason\": \"stub\"}"}, "finish_reason": "stop"}],
|
||||
"usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}).encode()
|
||||
self.send_response(200)
|
||||
self.send_header("content-type", "application/json")
|
||||
self.send_header("content-length", str(len(out)))
|
||||
self.end_headers()
|
||||
self.wfile.write(out)
|
||||
|
||||
|
||||
if LLM:
|
||||
threading.Thread(target=HTTPServer(("127.0.0.1", 9099), StubModel).serve_forever, daemon=True).start()
|
||||
|
||||
|
||||
def call(path, body=None):
|
||||
req = urllib.request.Request(BASE + path, data=None if body is None else json.dumps(body).encode(), headers={"content-type": "application/json"})
|
||||
with urllib.request.urlopen(req, timeout=60) as r:
|
||||
return json.load(r)
|
||||
|
||||
|
||||
for _ in range(60):
|
||||
try:
|
||||
h = call("/health")
|
||||
break
|
||||
except Exception:
|
||||
time.sleep(1)
|
||||
else:
|
||||
sys.exit("the service did not come up")
|
||||
print("health:", h)
|
||||
assert h["ok"], "binary or card data missing"
|
||||
|
||||
feed = json.load(open("/opt/phase/sample-feed.json", encoding="utf-8"))["decks"]
|
||||
decks = []
|
||||
for d in feed:
|
||||
cmd = d.get("commander") or [d["name"]]
|
||||
decks.append({"name": d["name"], "commander": cmd, "main": d["main"]})
|
||||
if len(decks) == 4:
|
||||
break
|
||||
request = {"decks": decks, "games": games, "seed": 7, "difficulty": difficulty, "action_cap": 60000}
|
||||
if LLM:
|
||||
request.update(action_cap=6000, llm_seats=[1, 3], llm={"kind": "chat", "model": "stub-model", "max_calls": 60})
|
||||
job = call("/run", request)["job"]
|
||||
t0 = time.time()
|
||||
while True:
|
||||
j = call("/job/" + job)
|
||||
if j["status"] in ("done", "error"):
|
||||
break
|
||||
if time.time() - t0 > 1500:
|
||||
sys.exit("timed out; last: %s" % json.dumps(j)[:300])
|
||||
time.sleep(3)
|
||||
print("job: %s in %.0f s" % (j["status"], time.time() - t0))
|
||||
res = j.get("result") or {}
|
||||
print(json.dumps({k: res.get(k) for k in ("games_requested", "games_finished", "games_not_finished", "draws", "wins_by_seat", "avg_turns", "warnings")}, indent=1))
|
||||
for g in res.get("games", []):
|
||||
print(" game seed=%s %s winner=%s turns=%s eliminated=%s" % (g.get("seed"), g["status"], g.get("winner_seat"), g.get("turns"), g.get("eliminated")))
|
||||
if LLM:
|
||||
llm = res.get("llm") or {}
|
||||
print("model seats:", json.dumps(llm), "| requests the stub model received:", len(ASKED))
|
||||
print("\n".join((j.get("output_tail") or [])[-15:]))
|
||||
seats = llm.get("per_seat") or {}
|
||||
asked = sum(v["asked"] for v in seats.values())
|
||||
used = sum(v["used"] for v in seats.values())
|
||||
reasoned = [a for a in ASKED if a.get("reasoning_effort") == "none"]
|
||||
if not ASKED or not asked or not used:
|
||||
sys.exit("the model seats were not exercised: stub got %d requests, engine counters asked=%d used=%d" % (len(ASKED), asked, used))
|
||||
if len(reasoned) != len(ASKED):
|
||||
sys.exit("the relay did not switch the model's thinking off on every request")
|
||||
print("PHASE LLM SMOKE OK (asked %d, engine used %d, stub saw %d requests, all with thinking off)" % (asked, used, len(ASKED)))
|
||||
sys.exit(0)
|
||||
if j["status"] != "done" or res.get("games_finished", 0) < 1:
|
||||
print("\n".join(j.get("output_tail") or []))
|
||||
sys.exit("no game finished")
|
||||
print("PHASE SMOKE OK")
|
||||
@@ -1,272 +0,0 @@
|
||||
"""Run: python -I -X utf8 test_phase_sim.py (offline: a stub engine prints output in the format phase's ai-commander prints)"""
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import tempfile
|
||||
import threading
|
||||
import time
|
||||
import urllib.request
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
import phase_sim as P # noqa: E402
|
||||
|
||||
FAILS = 0
|
||||
|
||||
|
||||
def ck(name, cond, detail=""):
|
||||
global FAILS
|
||||
print(" %s %s%s" % ("ok " if cond else "FAIL", name, (" -- " + str(detail)[:260]) if detail and not cond else ""))
|
||||
if not cond:
|
||||
FAILS += 1
|
||||
|
||||
|
||||
def deck(name, cmd, n=3):
|
||||
return {"name": name, "commander": [cmd], "main": [{"name": "Sol Ring", "count": 1}, {"name": "Forest", "count": n}]}
|
||||
|
||||
|
||||
GOOD = {"decks": [deck("A", "Cmd A"), deck("B", "Cmd B"), deck("C", "Cmd C"), deck("D", "Cmd D")], "games": 3, "seed": 10}
|
||||
|
||||
# --- validation
|
||||
def bad(mut, why):
|
||||
req = json.loads(json.dumps(GOOD))
|
||||
mut(req)
|
||||
try:
|
||||
P.validate(req)
|
||||
ck("rejects " + why, False)
|
||||
except P.BadRequest as exc:
|
||||
ck("rejects " + why, True, exc)
|
||||
|
||||
ck("a good request validates", P.validate(GOOD)["games"] == 3)
|
||||
bad(lambda r: r["decks"].pop(), "three decks")
|
||||
bad(lambda r: r["decks"][0].pop("commander"), "a deck with no commander")
|
||||
bad(lambda r: r["decks"][1].update(main=[]), "an empty main list")
|
||||
bad(lambda r: r["decks"][2]["main"].append({"name": "X", "count": 0}), "a zero count")
|
||||
bad(lambda r: r.update(games=P.MAX_GAMES + 1), "too many games")
|
||||
bad(lambda r: r.update(difficulty="Impossible"), "an unknown difficulty")
|
||||
bad(lambda r: r.update(seat_difficulty={"7": "Easy"}), "a seat outside 0-3")
|
||||
bad(lambda r: r.update(action_cap=5), "a tiny action cap")
|
||||
v = P.validate(dict(GOOD, decks=[dict(deck("A", "Cmd A"), commander="Cmd A", main=None, cards=["Forest", "Forest", "Sol Ring"])] + GOOD["decks"][1:]))
|
||||
ck("a plain card list becomes counted rows and a commander string becomes a list",
|
||||
v["feed"][0]["commander"] == ["Cmd A"] and {"name": "Forest", "count": 2} in v["feed"][0]["main"], v["feed"][0])
|
||||
|
||||
# --- parsing the engine's output
|
||||
OUT = """=== 4-player Commander AI test ===
|
||||
Feed: feed.json
|
||||
Batch mode: 5 games from games.txt
|
||||
|
||||
A | commander: Cmd A | main: 99 cards
|
||||
B | commander: Cmd B | main: 99 cards
|
||||
SKIP C: commander 'Cmd C' not in card db
|
||||
D | commander: Cmd D | main: 99 cards
|
||||
Resolved deck sizes (post name-resolution, 0-indexed by seat):
|
||||
P0 main= 99 commander=1
|
||||
P1 main= 97 commander=1
|
||||
P2 main= 99 commander=1
|
||||
P3 main= 99 commander=1
|
||||
|
||||
ExecutionMode: measurement
|
||||
--- GAME seed=10 difficulty=Medium ---
|
||||
Game started. 4 players.
|
||||
ELIMINATED: P1 turn=9 actions= 400 elapsed=3.1s
|
||||
ELIMINATED: P3 turn=12 actions= 600 elapsed=5.0s
|
||||
|
||||
=== RESULT ===
|
||||
Elapsed: 8.0s
|
||||
Turns played: 14
|
||||
|
||||
Game ended cleanly. Winner: P2
|
||||
--- GAME seed=11 difficulty=Medium ---
|
||||
Game started. 4 players.
|
||||
=== RESULT ===
|
||||
Turns played: 40
|
||||
Game ended cleanly. Winner: draw
|
||||
--- GAME seed=12 difficulty=Medium ---
|
||||
=== RESULT ===
|
||||
Turns played: 7
|
||||
STALL: waiting_for=ChooseTarget turn=7 active=P0 priority=P0 pending_cast=none
|
||||
Game did NOT reach GameOver. waiting_for = ChooseTarget
|
||||
--- GAME seed=13 difficulty=Medium ---
|
||||
ABORT: hit action cap=50000
|
||||
=== RESULT ===
|
||||
Turns played: 31
|
||||
Game did NOT reach GameOver. waiting_for = Priority
|
||||
--- GAME seed=14 difficulty=Medium ---
|
||||
GAME 14 PANICKED: index out of bounds
|
||||
"""
|
||||
parsed = P.parse(OUT)
|
||||
st = [g["status"] for g in parsed["games"]]
|
||||
ck("five games found with their outcomes in order", st == ["won", "draw", "stalled", "action_cap", "panicked"], st)
|
||||
g0 = parsed["games"][0]
|
||||
ck("winner seat, turns and elimination order are read", g0["winner_seat"] == 2 and g0["turns"] == 14
|
||||
and [e["seat"] for e in g0["eliminated"]] == [1, 3], g0)
|
||||
ck("a draw has no winner seat", parsed["games"][1]["winner_seat"] is None)
|
||||
ck("pre-resolution and resolved deck sizes and the skipped deck are read", len(parsed["pre"]) == 3 and parsed["resolved"][1]["main"] == 97
|
||||
and parsed["skipped"] == [{"deck": "C", "commander": "Cmd C"}], (parsed["pre"], parsed["resolved"], parsed["skipped"]))
|
||||
|
||||
spec = P.validate(dict(GOOD, games=5))
|
||||
res = P.summarize(spec, parsed, [10, 11, 12, 13, 14])
|
||||
ck("only finished games are counted", res["games_finished"] == 2 and res["games_not_finished"] == 3 and res["draws"] == 1, res)
|
||||
ck("wins by seat and per-deck win rate", res["wins_by_seat"] == [0, 0, 1, 0] and res["decks"][2]["win_rate"] == 0.5 and res["decks"][0]["win_rate"] == 0.0)
|
||||
lo, hi = res["decks"][2]["win_rate_95"]
|
||||
ck("a 95% interval wraps the rate and stays in 0..1", 0 <= lo < 0.5 < hi <= 1)
|
||||
w = " | ".join(res["warnings"])
|
||||
ck("warnings: a skipped deck, unrecognised cards, and unfinished games (never counted)",
|
||||
"was skipped" in w and "2 of 99" in w and "not counted as wins or draws" in w and "stalled" in w, w)
|
||||
ck("each game carries its seed", [g["seed"] for g in res["games"]] == [10, 11, 12, 13, 14])
|
||||
ck("interval of nothing is zeros, of a sweep is high", P.wilson(0, 0) == [0.0, 0.0] and P.wilson(10, 10)[0] > 0.7)
|
||||
|
||||
# --- a whole job through the real worker path with a stub engine
|
||||
STUB = os.path.join(tempfile.mkdtemp(), "stub_engine.py")
|
||||
with open(STUB, "w", encoding="utf-8") as f:
|
||||
f.write('''import sys, os
|
||||
root = sys.argv[1]
|
||||
seeds = [l.split(",")[0] for l in open(os.path.join(root, "games.txt")) if l.strip()]
|
||||
feed = __import__("json").load(open(os.path.join(root, "feed.json")))
|
||||
assert os.path.exists(os.path.join(root, "card-data.json")) and len(feed["decks"]) == 4
|
||||
for d in feed["decks"]:
|
||||
print(" %s | commander: %s | main: %d cards" % (d["name"], d["commander"][0], sum(e["count"] for e in d["main"])))
|
||||
print("Resolved deck sizes (post name-resolution, 0-indexed by seat):")
|
||||
for i, d in enumerate(feed["decks"]):
|
||||
print(" P%d main=%3d commander=1" % (i, sum(e["count"] for e in d["main"])))
|
||||
for k, s in enumerate(seeds):
|
||||
print("--- GAME seed=%s ---" % s)
|
||||
print("Turns played: %d" % (10 + k))
|
||||
print("Game ended cleanly. Winner: P%d" % (k % 4))
|
||||
''')
|
||||
CD = os.path.join(os.path.dirname(STUB), "card-data.json")
|
||||
open(CD, "w").write("{}")
|
||||
P.CARD_DATA = CD
|
||||
ORIG_COMMAND = P.command
|
||||
P.command = lambda spec, root: [sys.executable, STUB, root]
|
||||
job_id = P.submit(dict(GOOD, games=4))
|
||||
threading.Thread(target=P.worker, daemon=True).start()
|
||||
for _ in range(100):
|
||||
j = P._jobs[job_id]
|
||||
if j["status"] in ("done", "error"):
|
||||
break
|
||||
time.sleep(0.1)
|
||||
pub = P.public(P._jobs[job_id])
|
||||
ck("a queued job runs to done with four finished games, one win each", pub["status"] == "done" and pub["result"]["games_finished"] == 4
|
||||
and pub["result"]["wins_by_seat"] == [1, 1, 1, 1] and pub["games_done"] == 4, pub)
|
||||
ck("no warnings when every card resolved", pub["result"]["warnings"] == [], pub["result"]["warnings"])
|
||||
ck("the temp folder is cleaned up", not [d for d in os.listdir(tempfile.gettempdir()) if d.startswith("phase-sim-")])
|
||||
|
||||
# --- the HTTP surface
|
||||
srv = P.ThreadingHTTPServer(("127.0.0.1", 0), P.Handler)
|
||||
threading.Thread(target=srv.serve_forever, daemon=True).start()
|
||||
base = "http://127.0.0.1:%d" % srv.server_port
|
||||
def call(path, body=None):
|
||||
req = urllib.request.Request(base + path, data=None if body is None else json.dumps(body).encode(), headers={"content-type": "application/json"})
|
||||
try:
|
||||
r = urllib.request.urlopen(req, timeout=10)
|
||||
return r.status, json.load(r)
|
||||
except urllib.error.HTTPError as e:
|
||||
return e.code, json.load(e)
|
||||
code, h = call("/health")
|
||||
ck("/health answers (ok is false only because the real binary is not here)", code == 200 and "engine" in h and h["max_games"] == P.MAX_GAMES, h)
|
||||
code, r = call("/run", dict(GOOD, games=1))
|
||||
ck("POST /run is accepted with a job id", code == 202 and len(r["job"]) == 12, r)
|
||||
for _ in range(100):
|
||||
code, j = call("/job/" + r["job"])
|
||||
if j["status"] in ("done", "error"):
|
||||
break
|
||||
time.sleep(0.1)
|
||||
ck("GET /job/<id> returns the result", code == 200 and j["status"] == "done" and j["result"]["games_finished"] == 1, j)
|
||||
ck("a bad request is a 400 with a reason", call("/run", {"decks": []})[0] == 400)
|
||||
ck("an unknown job is a 404", call("/job/deadbeef")[0] == 404)
|
||||
ck("an unknown path is a 404", call("/nope")[0] == 404)
|
||||
# --- model seats: validation, environment, relays
|
||||
from http.server import BaseHTTPRequestHandler, HTTPServer
|
||||
|
||||
UP = []
|
||||
|
||||
|
||||
class Upstream(BaseHTTPRequestHandler):
|
||||
def log_message(self, *a):
|
||||
pass
|
||||
|
||||
def do_POST(self):
|
||||
body = json.loads(self.rfile.read(int(self.headers.get("Content-Length") or 0)))
|
||||
UP.append((self.path, body, self.headers.get("authorization")))
|
||||
out = json.dumps({"echo": True, "path": self.path}).encode()
|
||||
self.send_response(200)
|
||||
self.send_header("content-type", "application/json")
|
||||
self.send_header("content-length", str(len(out)))
|
||||
self.end_headers()
|
||||
self.wfile.write(out)
|
||||
|
||||
|
||||
up = HTTPServer(("127.0.0.1", 0), Upstream)
|
||||
threading.Thread(target=up.serve_forever, daemon=True).start()
|
||||
UPBASE = "http://127.0.0.1:%d" % up.server_port
|
||||
os.environ["PHASE_SIM_ALLOW_MISSING_LLM_BIN"] = "1"
|
||||
LLMREQ = dict(GOOD, llm_seats=[1, 3], llm={"kind": "chat", "model": "twin-turbo", "max_calls": 120})
|
||||
|
||||
P.CHAT_URL, P.DECISION_URL = "", ""
|
||||
try:
|
||||
P.validate(LLMREQ)
|
||||
ck("model seats are refused when no model endpoint is configured", False)
|
||||
except P.BadRequest as exc:
|
||||
ck("model seats are refused when no model endpoint is configured", "no chat model endpoint" in str(exc), exc)
|
||||
P.CHAT_URL, P.DECISION_URL, P.DECISION_KEY = UPBASE + "/v1", UPBASE + "/v1/systemone", "sekret"
|
||||
v = P.validate(LLMREQ)
|
||||
ck("a chat model seat request validates", v["llm"] == {"seats": [1, 3], "kind": "chat", "model": "twin-turbo", "max_calls": 120}, v["llm"])
|
||||
ck("a request with no model seats has no llm settings", P.validate(GOOD)["llm"] is None)
|
||||
for mut, why in ((lambda r: r.update(llm_seats=[4]), "a seat outside 0-3"),
|
||||
(lambda r: r["llm"].pop("model"), "a chat model with no name"),
|
||||
(lambda r: r["llm"].update(max_calls=P.MAX_LLM_CALLS + 1), "too many model calls"),
|
||||
(lambda r: r["llm"].update(kind="telepathy"), "an unknown kind")):
|
||||
req = json.loads(json.dumps(LLMREQ))
|
||||
mut(req)
|
||||
try:
|
||||
P.validate(req)
|
||||
ck("rejects " + why, False)
|
||||
except P.BadRequest:
|
||||
ck("rejects " + why, True)
|
||||
dv = P.validate(dict(GOOD, llm_seats=[0], llm={"kind": "decision"}))
|
||||
ck("a decision-model seat needs no model name", dv["llm"]["kind"] == "decision" and dv["llm"]["model"] == "decision-model")
|
||||
|
||||
env = P.engine_env(v)
|
||||
ck("chat seats: the engine is pointed at this service's /llm relay as an OpenAI-compatible endpoint with no key",
|
||||
env["PHASE_LLM_SEATS"] == "1,3" and env["PHASE_LLM_PROVIDER"] == "OpenAiCompatible" and env["PHASE_LLM_BASE_URL"].endswith("/llm/v1")
|
||||
and env["PHASE_LLM_MODEL"] == "twin-turbo" and env["PHASE_LLM_API_KEY"] == "" and env["PHASE_LLM_MAX_CALLS"] == "120", env)
|
||||
denv = P.engine_env(dv)
|
||||
ck("decision seats: provider Jev, base is this service (the engine adds /jev/systemone)", denv["PHASE_LLM_PROVIDER"] == "Jev"
|
||||
and denv["PHASE_LLM_BASE_URL"] == "http://127.0.0.1:%d" % P.PORT)
|
||||
ck("no model seats: no special environment, the plain engine", P.engine_env(P.validate(GOOD)) is None
|
||||
and ORIG_COMMAND(P.validate(GOOD), "/r")[0] == P.BIN and ORIG_COMMAND(v, "/r")[0] == P.BIN_LLM)
|
||||
|
||||
code, out = P.relay_chat(json.dumps({"model": "twin-turbo", "messages": [{"role": "user", "content": "x"}], "max_tokens": 4000, "stream": True}).encode(), "127.0.0.1")
|
||||
path, body, auth = UP[-1]
|
||||
ck("the chat relay forwards to /chat/completions with thinking off, the answer capped and streaming off",
|
||||
code == 200 and path == "/v1/chat/completions" and body["reasoning_effort"] == "none" and body["max_tokens"] == P.CHAT_MAX_TOKENS
|
||||
and body["stream"] is False and body["model"] == "twin-turbo", (code, path, body))
|
||||
ck("a request without max_tokens gets the cap", (P.relay_chat(b'{"model": "m", "messages": []}', "127.0.0.1"), UP[-1][1]["max_tokens"] == P.CHAT_MAX_TOKENS)[1])
|
||||
ck("the relays refuse anyone but this machine", P.relay_chat(b"{}", "10.1.2.3")[0] == 403 and P.relay_decision(b"{}", "10.1.2.3")[0] == 403)
|
||||
ck("a malformed chat body is a 400, not an exception", P.relay_chat(b"not json", "127.0.0.1")[0] == 400)
|
||||
envelope = {"apiKey": "ignored-by-us", "request": {"state": {"body": "t"}, "questions": {"pick": {"type": "choice"}}}}
|
||||
code, out = P.relay_decision(json.dumps(envelope).encode(), "127.0.0.1")
|
||||
path, body, auth = UP[-1]
|
||||
ck("the decision relay unwraps phase's envelope and sends only the request, with OUR key", code == 200 and path == "/v1/systemone"
|
||||
and body == envelope["request"] and auth == "Bearer sekret", (code, path, body, auth))
|
||||
ck("a decision body with no request is a 400", P.relay_decision(b'{"apiKey": "k"}', "127.0.0.1")[0] == 400)
|
||||
P.CHAT_URL = "http://127.0.0.1:1/v1"
|
||||
ck("an unreachable model endpoint is a 502", P.relay_chat(b'{"model": "m", "messages": []}', "127.0.0.1")[0] == 502)
|
||||
P.CHAT_URL = UPBASE + "/v1"
|
||||
|
||||
summ = P.llm_summary({"seats": [1], "kind": "chat", "model": "m", "max_calls": 9},
|
||||
["LLM-SEAT seat=1 asked=25 used=20 total_calls=25", "noise", "LLM-SEAT seat=1 asked=50 used=41 total_calls=50"])
|
||||
ck("the model seats' counters are read from the engine's output (last value wins)", summ["per_seat"] == {1: {"asked": 50, "used": 41}}, summ)
|
||||
|
||||
summ2 = P.llm_summary({"seats": [1], "kind": "chat", "model": "m", "max_calls": 9},
|
||||
["LLM-SEAT seat=1 asked=25 used=1 total_calls=25 timeouts=20 avg_ms=1800 avg_prompt_chars=31000",
|
||||
"LLM-SEAT-DISABLED after 4 consecutive failures (20 timeouts of 25 questions): phase's own AI plays the rest"])
|
||||
ck("cost counters and the circuit-breaker are read from the engine's output", summ2["timeouts"] == 20 and summ2["avg_ms"] == 1800
|
||||
and summ2["avg_prompt_chars"] == 31000 and summ2["switched_off_after_failures_in_a_row"] == 4 and summ2["per_seat"] == {1: {"asked": 25, "used": 1}}, summ2)
|
||||
ck("old three-field counter lines still parse", P.llm_summary({"seats": [1], "kind": "chat", "model": "m", "max_calls": 9},
|
||||
["LLM-SEAT seat=2 asked=25 used=24 total_calls=25"])["per_seat"] == {2: {"asked": 25, "used": 24}})
|
||||
|
||||
code, r = call("/llm/v1/chat/completions", {"model": "m", "messages": []})
|
||||
ck("over HTTP from this machine the chat relay answers", code == 200 and r["echo"] is True, (code, r))
|
||||
print("\nFAILURES: %d" % FAILS)
|
||||
sys.exit(1 if FAILS else 0)
|
||||
@@ -1,42 +0,0 @@
|
||||
with open('ml/eval/forge_evaluator.py', 'rb') as f:
|
||||
data = f.read()
|
||||
|
||||
# Fix 1: absolute JAR path in subprocess call
|
||||
data = data.replace(
|
||||
b'"--forge-jar", self.cfg.forge_jar,',
|
||||
b'"--forge-jar", str(Path(self.cfg.forge_jar).resolve()),'
|
||||
)
|
||||
|
||||
# Fix 2: resolve forge_work_dir to absolute path too
|
||||
data = data.replace(
|
||||
b'"--forge-dir", self.cfg.forge_work_dir,',
|
||||
b'"--forge-dir", str(Path(self.cfg.forge_work_dir).resolve()),'
|
||||
)
|
||||
|
||||
# Fix 3: change default forge_work_dir
|
||||
data = data.replace(
|
||||
b'forge_work_dir: str = "forge"',
|
||||
b'forge_work_dir: str = "D:/ForgeCommander/forge-repo/forge-gui"'
|
||||
)
|
||||
|
||||
with open('ml/eval/forge_evaluator.py', 'wb') as f:
|
||||
f.write(data)
|
||||
|
||||
import py_compile
|
||||
py_compile.compile('ml/eval/forge_evaluator.py', doraise=True)
|
||||
print('forge_evaluator.py - Syntax OK')
|
||||
|
||||
# Fix eval_policy.py too
|
||||
with open('scripts/eval_policy.py', 'rb') as f:
|
||||
data = f.read()
|
||||
|
||||
data = data.replace(
|
||||
b'"--forge-dir", default="forge"',
|
||||
b'"--forge-dir", default="D:/ForgeCommander/forge-repo/forge-gui"'
|
||||
)
|
||||
|
||||
with open('scripts/eval_policy.py', 'wb') as f:
|
||||
f.write(data)
|
||||
|
||||
py_compile.compile('scripts/eval_policy.py', doraise=True)
|
||||
print('eval_policy.py - Syntax OK')
|
||||
@@ -137,7 +137,7 @@ if (-not (Test-Path $envFile)) {
|
||||
# Uncomment and fill in the values you need:
|
||||
|
||||
# XIMILAR_API_KEY=your_ximilar_key_here
|
||||
# ANTHROPIC_API_KEY=your_anthropic_api_key_here
|
||||
# PPLX_API_KEY=your_perplexity_key_here
|
||||
# FORGE_JAR=C:\Path\To\forge-gui-desktop.jar
|
||||
# FORGE_DIR=C:\Path\To\Forge
|
||||
# LAB_PORT=8080
|
||||
|
||||
+80
-161
@@ -3,13 +3,9 @@
|
||||
Commander AI Lab — FastAPI Backend
|
||||
Thin entry point: registers routers and starts uvicorn.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
from contextlib import asynccontextmanager
|
||||
from pathlib import Path
|
||||
|
||||
try:
|
||||
@@ -26,97 +22,35 @@ except ImportError:
|
||||
print("ERROR: FastAPI not installed. Run: pip install fastapi uvicorn")
|
||||
import sys; sys.exit(1)
|
||||
|
||||
from models.state import CFG, COMMANDER_META, load_commander_meta, validate_forge_config
|
||||
from services.database import init_collection_db
|
||||
from services.logging import setup_logging
|
||||
from services.precon_service import PRECON_INDEX, download_precon_database
|
||||
from routes.shared import (
|
||||
CFG,
|
||||
setup_logging,
|
||||
init_collection_db,
|
||||
download_precon_database,
|
||||
load_commander_meta,
|
||||
PRECON_INDEX,
|
||||
COMMANDER_META,
|
||||
)
|
||||
from routes.collection import router as collection_router
|
||||
from routes.deckbuilder import router as deckbuilder_router
|
||||
from routes.precon import router as precon_router
|
||||
from routes.import_routes import router as import_router
|
||||
from routes.lab import router as lab_router
|
||||
from routes.scanner import router as scanner_router
|
||||
from routes.rag import router as rag_router
|
||||
from routes.deepseek import router as deepseek_router
|
||||
from routes.farmhouse_phase import router as farmhouse_phase_router # FARMHOUSE_PHASE_IMPORT
|
||||
from routes.deckgen import router as deckgen_router
|
||||
from routes.coach import router as coach_router, init_coach_service
|
||||
from routes.ml import router as ml_router
|
||||
from routes.ws_game import router as ws_game_router
|
||||
from routes.game import router as game_router
|
||||
|
||||
log = logging.getLogger("commander_ai_lab.api")
|
||||
|
||||
_DEFAULT_ORIGINS = "http://localhost:5173,http://localhost:3000,http://localhost:8080"
|
||||
# ── CORS origins (env-configurable) ────────────────────────────
|
||||
_DEFAULT_ORIGINS = "http://localhost:5173,http://localhost:8080,http://192.168.0.240:5173,http://192.168.0.240:8080"
|
||||
_allowed = os.environ.get("ALLOWED_ORIGINS", _DEFAULT_ORIGINS)
|
||||
allowed_origins = [o.strip() for o in _allowed.split(",") if o.strip()]
|
||||
|
||||
# Module-level handle so lifespan can call .load() after routes are registered.
|
||||
_POLICY_SVC = None
|
||||
|
||||
|
||||
# ── Lifespan (replaces deprecated @app.on_event) ────────────────────────────────────────────────────────
|
||||
@asynccontextmanager
|
||||
async def _lifespan(application: FastAPI):
|
||||
# ──── STARTUP ────
|
||||
if not CFG.precon_dir:
|
||||
CFG.precon_dir = _resolve_precon_dir()
|
||||
init_collection_db()
|
||||
validate_forge_config()
|
||||
init_coach_service() # FARMHOUSE_COACH_INIT_V1
|
||||
if not PRECON_INDEX:
|
||||
download_precon_database()
|
||||
if not COMMANDER_META:
|
||||
load_commander_meta()
|
||||
|
||||
loop = asyncio.get_running_loop()
|
||||
|
||||
# RAG Phase 1: kick off Scryfall bulk download in background thread.
|
||||
from services.scryfall_bulk import ensure_bulk_db
|
||||
loop.run_in_executor(None, ensure_bulk_db)
|
||||
|
||||
# RAG Phase 2: kick off ChromaDB vector store build in background thread.
|
||||
from services.rag_store import ensure_rag_store
|
||||
loop.run_in_executor(None, ensure_rag_store)
|
||||
|
||||
# RAG Phase 3: background staleness monitor (rebuilds if >14 days old)
|
||||
async def _rag_staleness_monitor():
|
||||
"""Periodically check if the RAG index needs rebuilding."""
|
||||
while True:
|
||||
await asyncio.sleep(6 * 3600) # check every 6 hours
|
||||
try:
|
||||
from services.rag_store import check_staleness, build_index
|
||||
info = check_staleness()
|
||||
if info.get("stale") and os.environ.get("RAG_AUTOBUILD", "0") == "1": # FARMHOUSE_RAG_MONITOR_V1
|
||||
log.info("RAG staleness monitor: %s — triggering rebuild.", info["reason"])
|
||||
loop.run_in_executor(None, lambda: build_index(force=True))
|
||||
else:
|
||||
log.debug("RAG staleness monitor: index is current (%.1f days).", info.get("age_days", 0))
|
||||
except Exception as exc:
|
||||
log.warning("RAG staleness monitor failed: %s", exc)
|
||||
asyncio.ensure_future(_rag_staleness_monitor())
|
||||
|
||||
# Policy model load — routes were already registered at module scope (before
|
||||
# any app.mount() calls) so /api/policy/* is reachable regardless of the
|
||||
# StaticFiles root mount. Only the heavyweight model load happens here.
|
||||
try:
|
||||
if _POLICY_SVC is not None:
|
||||
if _POLICY_SVC.load():
|
||||
log.info("Policy model loaded successfully")
|
||||
else:
|
||||
log.warning("Policy model not loaded — /api/policy/health will report degraded")
|
||||
except Exception as e:
|
||||
log.warning("Policy model load failed (non-fatal): %s", e)
|
||||
|
||||
yield # ← server is running
|
||||
|
||||
# ──── SHUTDOWN ────
|
||||
from services.async_db import async_close_db
|
||||
await async_close_db()
|
||||
|
||||
|
||||
# ── App ─────────────────────────────────────────────────────────────────────────────
|
||||
app = FastAPI(title="Commander AI Lab API", version="3.0.0", lifespan=_lifespan)
|
||||
# ── App ────────────────────────────────────────────────────────
|
||||
app = FastAPI(title="Commander AI Lab API", version="3.0.0")
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=allowed_origins,
|
||||
@@ -130,63 +64,35 @@ app.include_router(deckbuilder_router)
|
||||
app.include_router(precon_router)
|
||||
app.include_router(import_router)
|
||||
app.include_router(lab_router)
|
||||
app.include_router(farmhouse_phase_router)
|
||||
app.include_router(scanner_router)
|
||||
app.include_router(rag_router)
|
||||
app.include_router(deepseek_router)
|
||||
app.include_router(deckgen_router)
|
||||
app.include_router(coach_router)
|
||||
app.include_router(ws_game_router)
|
||||
app.include_router(ml_router)
|
||||
app.include_router(game_router) # live game session routes
|
||||
|
||||
# ── Policy routes — MUST be registered here, before any app.mount() calls ────────────────────
|
||||
# StaticFiles mounted at "/" acts as a sub-application and returns its own 404 for
|
||||
# paths it doesn't serve, rather than falling through to later-registered routes.
|
||||
# Any include_router() called after app.mount("/", ...) will be unreachable.
|
||||
# Registering here (module scope) guarantees the /api/policy/* endpoints exist in
|
||||
# the route list before the root static mount is added below.
|
||||
try:
|
||||
from ml.serving.policy_server import PolicyInferenceService
|
||||
from routes.policy import register_policy_routes
|
||||
_POLICY_SVC = PolicyInferenceService()
|
||||
register_policy_routes(app, _POLICY_SVC)
|
||||
log.info("Policy routes registered (/api/policy/*)")
|
||||
except ImportError:
|
||||
log.info("Policy service not available (ml.serving not installed)")
|
||||
except Exception as e:
|
||||
log.warning("Policy route registration failed (non-fatal): %s", e)
|
||||
|
||||
|
||||
# ── API endpoints ─────────────────────────────────────────────────────────────────────────────
|
||||
# NOTE: all @app.get routes MUST be registered before app.mount() calls.
|
||||
# Once StaticFiles is mounted at "/", it registers a wildcard catch-all that
|
||||
# shadows any routes added after it.
|
||||
|
||||
@app.get("/api/health")
|
||||
async def health_check():
|
||||
"""Health check endpoint for load balancers and Unity client polling."""
|
||||
return {"status": "ok"}
|
||||
|
||||
# ── Static UI (MUST come after all @app.get / include_router calls) ──────────────────────────
|
||||
from pathlib import Path as _Path
|
||||
_legacy_ui_dir = _Path(__file__).parent / "ui"
|
||||
_lan_client_dir = _Path(__file__).parent / "lan-client"
|
||||
_precon_decks_dir = _Path(__file__).parent / "precon-decks"
|
||||
_user_decks_dir = _Path(__file__).parent / "user-decks"
|
||||
_spa_dir = _Path(__file__).parent / "frontend" / "commander-ai-lab-ui" / "dist"
|
||||
|
||||
# Mount LAN client at /lan (served before the root catch-all)
|
||||
if _lan_client_dir.exists():
|
||||
app.mount("/lan", StaticFiles(directory=str(_lan_client_dir), html=True), name="lan-client")
|
||||
@app.on_event("startup")
|
||||
async def _on_startup():
|
||||
"""Ensure DB and precon index are ready (supports uvicorn --reload)."""
|
||||
import routes.shared as _shared
|
||||
# Ensure precon_dir is resolved even when started via uvicorn directly
|
||||
if not CFG.precon_dir:
|
||||
CFG.precon_dir = _resolve_precon_dir()
|
||||
_shared.init_collection_db()
|
||||
if not _shared.PRECON_INDEX:
|
||||
_shared.download_precon_database()
|
||||
if not _shared.COMMANDER_META:
|
||||
_shared.load_commander_meta()
|
||||
|
||||
# Mount precon-decks/ so .dck files are fetchable at /precon-decks/{file}.dck
|
||||
if _precon_decks_dir.exists():
|
||||
app.mount("/precon-decks", StaticFiles(directory=str(_precon_decks_dir)), name="precon-decks")
|
||||
|
||||
# Mount user-decks/ so exported .dck files are fetchable at /user-decks/{file}.dck
|
||||
if _user_decks_dir.exists():
|
||||
app.mount("/user-decks", StaticFiles(directory=str(_user_decks_dir)), name="user-decks")
|
||||
# ── Static UI ──────────────────────────────────────────────────
|
||||
_legacy_ui_dir = Path(__file__).parent / "ui"
|
||||
_spa_dir = Path(__file__).parent / "frontend" / "commander-ai-lab-ui" / "dist"
|
||||
|
||||
if _legacy_ui_dir.exists():
|
||||
app.mount("/", StaticFiles(directory=str(_legacy_ui_dir), html=True), name="ui")
|
||||
@@ -194,7 +100,6 @@ elif _spa_dir.exists():
|
||||
_spa_assets = _spa_dir / "assets"
|
||||
if _spa_assets.exists():
|
||||
app.mount("/assets", StaticFiles(directory=str(_spa_assets)), name="spa-assets")
|
||||
|
||||
@app.get("/{full_path:path}")
|
||||
async def _spa_catchall(full_path: str):
|
||||
from fastapi.responses import FileResponse
|
||||
@@ -203,24 +108,20 @@ elif _spa_dir.exists():
|
||||
return FileResponse(str(requested_file))
|
||||
return FileResponse(str(_spa_dir / "index.html"))
|
||||
|
||||
|
||||
# ── Argument parsing + auto-detection helpers ────────────────────────────────────────────────────────
|
||||
# ── Startup ────────────────────────────────────────────────────
|
||||
def _parse_args():
|
||||
p = argparse.ArgumentParser(description="Commander AI Lab API Server")
|
||||
p.add_argument("--forge-jar", default=os.environ.get("FORGE_JAR", ""))
|
||||
# Support both FORGE_DIR and FORGE_WORKING_DIR (alias for backwards compat)
|
||||
p.add_argument("--forge-dir", default=os.environ.get("FORGE_DIR") or os.environ.get("FORGE_WORKING_DIR", ""))
|
||||
p.add_argument("--forge-dir", default=os.environ.get("FORGE_DIR", ""))
|
||||
p.add_argument("--forge-decks-dir", default=os.environ.get("FORGE_DECKS_DIR", ""))
|
||||
p.add_argument("--lab-jar", default=os.environ.get("LAB_JAR", ""))
|
||||
p.add_argument("--precon-dir", default=os.environ.get("PRECON_DIR", ""))
|
||||
p.add_argument("--results-dir", default=os.environ.get("RESULTS_DIR", ""))
|
||||
p.add_argument("--port", type=int, default=int(os.environ.get("LAB_PORT", "8080")))
|
||||
p.add_argument("--ximilar-key", default=os.environ.get("XIMILAR_API_KEY", ""))
|
||||
p.add_argument("--anthropic-key", default=os.environ.get("ANTHROPIC_API_KEY", ""))
|
||||
p.add_argument("--pplx-key", default=os.environ.get("PPLX_API_KEY", ""))
|
||||
p.add_argument("--verbose", "-v", action="store_true")
|
||||
return p.parse_args()
|
||||
|
||||
|
||||
def _resolve_forge_decks_dir() -> str:
|
||||
import sys
|
||||
if sys.platform == "win32":
|
||||
@@ -238,37 +139,52 @@ def _resolve_forge_decks_dir() -> str:
|
||||
return str(candidate)
|
||||
return ""
|
||||
|
||||
|
||||
def _resolve_forge_dir() -> str:
|
||||
"""Auto-detect the Forge installation directory."""
|
||||
"""Auto-detect the Forge installation directory.
|
||||
|
||||
The Forge dir is the root of a Forge install — it typically contains
|
||||
a 'res' subdirectory with card data. We derive it from the JAR
|
||||
location (parent of 'target/') or look in common install paths.
|
||||
"""
|
||||
import sys
|
||||
|
||||
# 1. If forge_jar is already resolved, derive forge_dir from it.
|
||||
# JAR lives in <forge-root>/forge-gui-desktop/target/<jar>
|
||||
# so forge_dir = JAR's grandparent's parent.
|
||||
if CFG.forge_jar and os.path.isfile(CFG.forge_jar):
|
||||
jar_path = Path(CFG.forge_jar)
|
||||
# target/ -> forge-gui-desktop/ -> <forge-root>
|
||||
candidate = jar_path.parent.parent.parent
|
||||
if candidate.is_dir() and (candidate / "res").is_dir():
|
||||
return str(candidate)
|
||||
# Also try just one level up from target/
|
||||
candidate = jar_path.parent.parent
|
||||
# Check forge-gui sibling of forge-gui-desktop (res/ lives there)
|
||||
forge_gui_sibling = jar_path.parent.parent.parent / "forge-gui"
|
||||
if forge_gui_sibling.is_dir() and (forge_gui_sibling / "res").is_dir():
|
||||
return str(forge_gui_sibling)
|
||||
if candidate.is_dir() and (candidate / "res").is_dir():
|
||||
return str(candidate)
|
||||
# Fall back to the directory containing the JAR itself
|
||||
candidate = jar_path.parent
|
||||
if candidate.is_dir():
|
||||
return str(candidate)
|
||||
|
||||
# 2. Check common sibling/child directories relative to project root
|
||||
project_root = Path(__file__).parent
|
||||
for candidate in [
|
||||
project_root / "forge",
|
||||
project_root.parent / "Forge-Repo",
|
||||
project_root.parent / "Forge-Repo" / "forge-gui",
|
||||
project_root.parent / "forge-repo", # legacy fallback
|
||||
project_root.parent / "forge-repo" / "forge-gui",
|
||||
project_root.parent / "forge-repo",
|
||||
project_root.parent / "forge-repo" / "forge-gui",
|
||||
project_root.parent / "forge",
|
||||
project_root / "forge-gui-desktop",
|
||||
]:
|
||||
if candidate.is_dir():
|
||||
# Prefer directory that has 'res' subfolder
|
||||
if (candidate / "res").is_dir():
|
||||
return str(candidate)
|
||||
|
||||
# 3. Platform-specific user data directories
|
||||
if sys.platform == "win32":
|
||||
appdata = os.environ.get("APPDATA", "")
|
||||
if appdata:
|
||||
@@ -276,26 +192,32 @@ def _resolve_forge_dir() -> str:
|
||||
if candidate.is_dir():
|
||||
return str(candidate)
|
||||
home = Path.home()
|
||||
for candidate in [home / ".forge", home / "Forge"]:
|
||||
for candidate in [
|
||||
home / ".forge",
|
||||
home / "Forge",
|
||||
]:
|
||||
if candidate.is_dir():
|
||||
return str(candidate)
|
||||
return ""
|
||||
|
||||
|
||||
def _resolve_forge_jar() -> str:
|
||||
"""Auto-detect the Forge GUI desktop JAR in common locations."""
|
||||
project_root = Path(__file__).parent
|
||||
|
||||
# Candidate directories where the Forge JAR might live
|
||||
candidates = [
|
||||
project_root / "forge-gui-desktop" / "target",
|
||||
project_root.parent / "Forge-Repo" / "forge-gui-desktop" / "target",
|
||||
project_root.parent / "forge-repo" / "forge-gui-desktop" / "target", # legacy fallback
|
||||
project_root.parent / "forge-repo" / "forge-gui-desktop" / "target",
|
||||
project_root.parent / "forge" / "forge-gui-desktop" / "target",
|
||||
]
|
||||
forge_dir_env = os.environ.get("FORGE_DIR", "") or os.environ.get("FORGE_WORKING_DIR", "")
|
||||
|
||||
# Also check FORGE_DIR/../forge-gui-desktop/target if FORGE_DIR is set
|
||||
forge_dir_env = os.environ.get("FORGE_DIR", "")
|
||||
if forge_dir_env:
|
||||
fd = Path(forge_dir_env)
|
||||
candidates.append(fd.parent / "forge-gui-desktop" / "target")
|
||||
candidates.append(fd / "target")
|
||||
|
||||
for target_dir in candidates:
|
||||
if target_dir.is_dir():
|
||||
for pattern in [
|
||||
@@ -309,7 +231,6 @@ def _resolve_forge_jar() -> str:
|
||||
return str(jars[0])
|
||||
return ""
|
||||
|
||||
|
||||
def _resolve_lab_jar() -> str:
|
||||
target_dir = Path(__file__).parent / "target"
|
||||
if target_dir.exists():
|
||||
@@ -324,25 +245,36 @@ def _resolve_lab_jar() -> str:
|
||||
return str(jars[0])
|
||||
return ""
|
||||
|
||||
|
||||
def _resolve_precon_dir() -> str:
|
||||
"""Auto-detect the precon-decks directory."""
|
||||
"""Auto-detect the precon-decks directory.
|
||||
|
||||
The precon-decks dir lives in the project root and contains downloaded
|
||||
.dck files plus precon-index.json. We check relative to this file
|
||||
(lab_api.py) first, then common sibling paths.
|
||||
"""
|
||||
project_root = Path(__file__).parent
|
||||
|
||||
# 1. Standard location: <project-root>/precon-decks
|
||||
candidate = project_root / "precon-decks"
|
||||
if candidate.is_dir():
|
||||
return str(candidate)
|
||||
|
||||
# 2. Check parent directory (in case running from a subdirectory)
|
||||
candidate = project_root.parent / "precon-decks"
|
||||
if candidate.is_dir():
|
||||
return str(candidate)
|
||||
|
||||
# 3. Derive from forge_dir if available
|
||||
if CFG.forge_dir:
|
||||
candidate = Path(CFG.forge_dir) / "precon-decks"
|
||||
if candidate.is_dir():
|
||||
return str(candidate)
|
||||
|
||||
# 4. Fall back: create the directory in the project root so downloads work
|
||||
fallback = project_root / "precon-decks"
|
||||
fallback.mkdir(parents=True, exist_ok=True)
|
||||
return str(fallback)
|
||||
|
||||
|
||||
def main():
|
||||
args = _parse_args()
|
||||
setup_logging(logging.DEBUG if args.verbose else logging.INFO)
|
||||
@@ -354,18 +286,9 @@ def main():
|
||||
CFG.precon_dir = args.precon_dir or _resolve_precon_dir()
|
||||
CFG.port = args.port
|
||||
CFG.ximilar_api_key = args.ximilar_key
|
||||
CFG.anthropic_api_key = args.anthropic_key
|
||||
CFG.pplx_api_key = args.pplx_key
|
||||
|
||||
# Wire RESULTS_DIR: use explicit env/arg if set, otherwise keep default "results"
|
||||
# but resolve it to an absolute path relative to this file so it works
|
||||
# regardless of what directory the server is launched from.
|
||||
if args.results_dir:
|
||||
CFG.results_dir = args.results_dir
|
||||
else:
|
||||
# Default: results/ next to lab_api.py (always absolute)
|
||||
CFG.results_dir = str(Path(__file__).parent / "results")
|
||||
|
||||
from services.forge_runner import get_java17
|
||||
from routes.shared import get_java17
|
||||
j17 = get_java17()
|
||||
|
||||
log.info("Commander AI Lab — API Server v3.0.0")
|
||||
@@ -377,7 +300,7 @@ def main():
|
||||
log.info(f" Lab JAR: {CFG.lab_jar or 'NOT SET'}")
|
||||
log.info(f" Results: {CFG.results_dir}")
|
||||
log.info(f" Ximilar: {'configured' if CFG.ximilar_api_key else 'NOT SET'}")
|
||||
log.info(f" Anthropic: {'configured' if CFG.anthropic_api_key else 'NOT SET'}")
|
||||
log.info(f" Perplexity: {'configured' if CFG.pplx_api_key else 'NOT SET'}")
|
||||
log.info(f" Java 17: {j17}")
|
||||
|
||||
load_commander_meta()
|
||||
@@ -385,12 +308,8 @@ def main():
|
||||
init_collection_db()
|
||||
init_coach_service()
|
||||
|
||||
# RAG Phase 1 + 2 are handled by _lifespan when uvicorn starts.
|
||||
# No need to call them here — doing so causes a double-run.
|
||||
|
||||
import uvicorn
|
||||
uvicorn.run(app, host="0.0.0.0", port=CFG.port, log_level="info")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
@@ -1,34 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Commander AI Lab — Collection</title>
|
||||
<link rel="preconnect" href="https://fonts.googleapis.com" />
|
||||
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
|
||||
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet" />
|
||||
<link rel="stylesheet" href="global.css" />
|
||||
<link rel="stylesheet" href="collection.css" />
|
||||
<style>
|
||||
/* No nav bar when embedded in LAN client iframe */
|
||||
:root { --nav-height: 0px; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<!-- nav.js removed — collection is embedded via iframe in the LAN client -->
|
||||
<div id="collection-container"></div>
|
||||
<script>
|
||||
window.LAB_API_BASE = window.LAB_API_BASE || '';
|
||||
</script>
|
||||
<script src="collection.js"></script>
|
||||
<script>
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
Collection.init();
|
||||
});
|
||||
</script>
|
||||
<!-- Card Preview Popup (hover tooltip) -->
|
||||
<div id="coll-card-preview" style="display:none; position:fixed; pointer-events:none; z-index:9999;">
|
||||
<img id="coll-card-preview-img" src="" alt="Card preview" style="width:220px; border-radius:8px; box-shadow:0 8px 24px rgba(0,0,0,0.7);" />
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
@@ -1,495 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Commander AI Lab — Deck Builder</title>
|
||||
<link rel="preconnect" href="https://fonts.googleapis.com" />
|
||||
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
|
||||
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet" />
|
||||
<link rel="stylesheet" href="global.css" />
|
||||
<link rel="stylesheet" href="deckbuilder.css" />
|
||||
<style>
|
||||
/* No nav bar when embedded in LAN client iframe */
|
||||
:root { --nav-height: 0px; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<!-- nav.js removed — deck builder is embedded via iframe in the LAN client -->
|
||||
|
||||
<div id="deck-builder-page">
|
||||
|
||||
<!-- TOP BAR -->
|
||||
<header class="deck-topbar">
|
||||
<div class="deck-topbar-commander">
|
||||
<div class="deck-commander-portrait" id="deck-commander-portrait">
|
||||
<span class="deck-commander-portrait-placeholder">⚔</span>
|
||||
<img class="deck-commander-portrait-img" id="deck-commander-img" src="" alt="" style="display:none" />
|
||||
</div>
|
||||
<div class="deck-name-block">
|
||||
<input
|
||||
type="text"
|
||||
class="deck-name-input"
|
||||
id="deck-name-input"
|
||||
placeholder="Untitled Deck"
|
||||
value=""
|
||||
/>
|
||||
<div class="deck-commander-label" id="deck-commander-label">No commander selected</div>
|
||||
</div>
|
||||
<div class="deck-color-chips" id="deck-color-chips"></div>
|
||||
</div>
|
||||
|
||||
<div class="deck-topbar-center">
|
||||
<div class="deck-count-display">
|
||||
<span class="deck-count-num" id="deck-total-count">0</span>
|
||||
<span class="deck-count-sep">/</span>
|
||||
<span class="deck-count-target">100</span>
|
||||
<span class="deck-count-label">cards</span>
|
||||
</div>
|
||||
<button class="deck-btn deck-btn-ghost" id="deck-new-btn">+ New Deck</button>
|
||||
<div class="deck-selector-wrap">
|
||||
<select class="deck-selector" id="deck-selector">
|
||||
<option value="">— Select Deck —</option>
|
||||
</select>
|
||||
</div>
|
||||
<button class="deck-btn deck-btn-danger" id="deck-delete-btn" title="Delete this deck" style="display:none;">✕ Delete</button>
|
||||
<button class="deck-btn deck-btn-danger-outline" id="deck-clear-all-btn" title="Delete all decks">Clear All</button>
|
||||
</div>
|
||||
|
||||
<div class="deck-topbar-actions">
|
||||
<button class="deck-btn deck-btn-import" id="deck-import-btn" title="Import a decklist">
|
||||
<span>⬆</span> Import
|
||||
</button>
|
||||
<button class="deck-btn deck-btn-ghost" id="deck-export-btn" title="Export as .dck file">
|
||||
<span>⬇</span> Export .dck
|
||||
</button>
|
||||
<button class="deck-btn deck-btn-ghost" id="deck-export-sim-btn" title="Export deck as .dck to Forge sim folder">
|
||||
<span>📤</span> Export to Sim
|
||||
</button>
|
||||
<button class="deck-btn deck-btn-ghost" id="deck-export-game-btn" title="Send deck to LAN game setup" style="border-color:rgba(91,141,217,0.4);color:rgba(91,141,217,0.9);">
|
||||
<span>🎮</span> Use in Game
|
||||
</button>
|
||||
<button class="deck-btn deck-btn-sim" id="deck-sim-btn" title="Run quick simulation">
|
||||
<span class="deck-btn-icon">▶</span> Run Simulation
|
||||
</button>
|
||||
</div>
|
||||
</header>
|
||||
|
||||
<!-- MAIN 3-COLUMN LAYOUT -->
|
||||
<main class="deck-main">
|
||||
|
||||
<!-- LEFT COLUMN: Collection Search -->
|
||||
<aside class="deck-col-left">
|
||||
<div class="deck-panel deck-panel-search">
|
||||
<div class="deck-panel-header">
|
||||
<span class="deck-panel-title">Collection Search</span>
|
||||
<span class="deck-search-count" id="deck-search-count">0 results</span>
|
||||
</div>
|
||||
|
||||
<div class="deck-search-bar-wrap">
|
||||
<input
|
||||
type="search"
|
||||
class="deck-search-bar"
|
||||
id="deck-search-bar"
|
||||
placeholder="Search cards..."
|
||||
autocomplete="off"
|
||||
/>
|
||||
<span class="deck-search-icon">🔍</span>
|
||||
</div>
|
||||
|
||||
<div class="deck-filter-row">
|
||||
<select class="deck-filter-select" id="deck-type-filter">
|
||||
<option value="">All Types</option>
|
||||
<option value="Land">Land</option>
|
||||
<option value="Creature">Creature</option>
|
||||
<option value="Instant">Instant</option>
|
||||
<option value="Sorcery">Sorcery</option>
|
||||
<option value="Artifact">Artifact</option>
|
||||
<option value="Enchantment">Enchantment</option>
|
||||
<option value="Planeswalker">Planeswalker</option>
|
||||
</select>
|
||||
<div class="deck-cmc-filter">
|
||||
<input type="number" class="deck-cmc-input" id="deck-cmc-min" placeholder="0" min="0" max="20" />
|
||||
<span class="deck-cmc-sep">–</span>
|
||||
<input type="number" class="deck-cmc-input" id="deck-cmc-max" placeholder="∞" min="0" max="20" />
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="deck-color-filter-row" id="deck-color-filter">
|
||||
<button class="deck-color-pip deck-color-W" data-color="W" title="White">W</button>
|
||||
<button class="deck-color-pip deck-color-U" data-color="U" title="Blue">U</button>
|
||||
<button class="deck-color-pip deck-color-B" data-color="B" title="Black">B</button>
|
||||
<button class="deck-color-pip deck-color-R" data-color="R" title="Red">R</button>
|
||||
<button class="deck-color-pip deck-color-G" data-color="G" title="Green">G</button>
|
||||
<button class="deck-color-pip deck-color-C" data-color="C" title="Colorless">C</button>
|
||||
</div>
|
||||
|
||||
<!-- Results list -->
|
||||
<div class="deck-search-results" id="deck-search-results">
|
||||
<div class="deck-empty-state">
|
||||
<div class="deck-empty-icon">🃏</div>
|
||||
<div class="deck-empty-text">Search your collection to add cards</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="deck-search-footer">
|
||||
<button class="deck-btn deck-btn-success deck-btn-full" id="deck-add-all-owned-btn" style="display:none">
|
||||
Add All Matching (Owned)
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</aside>
|
||||
|
||||
<!-- CENTER COLUMN: Deck Card List -->
|
||||
<section class="deck-col-center">
|
||||
<div class="deck-panel deck-panel-cards" id="deck-card-list-panel">
|
||||
<div class="deck-panel-header deck-card-list-header">
|
||||
<span class="deck-panel-title">Deck Contents</span>
|
||||
<div class="deck-card-list-actions">
|
||||
<span class="deck-card-list-meta" id="deck-card-list-meta">Select or create a deck</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Card groups by type -->
|
||||
<div class="deck-card-groups" id="deck-card-groups">
|
||||
<div class="deck-empty-state deck-empty-state-lg">
|
||||
<div class="deck-empty-icon">⚔</div>
|
||||
<div class="deck-empty-text">Create or select a deck to start building</div>
|
||||
<button class="deck-btn deck-btn-accent" id="deck-empty-new-btn">+ New Deck</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Quick add by name -->
|
||||
<div class="deck-quickadd-row" id="deck-quickadd-row" style="display:none">
|
||||
<div class="deck-quickadd-wrap">
|
||||
<input
|
||||
type="text"
|
||||
class="deck-quickadd-input"
|
||||
id="deck-quickadd-input"
|
||||
placeholder="Add card by name..."
|
||||
autocomplete="off"
|
||||
/>
|
||||
<div class="deck-quickadd-dropdown" id="deck-quickadd-dropdown" style="display:none"></div>
|
||||
</div>
|
||||
<button class="deck-btn deck-btn-accent-sm" id="deck-quickadd-btn">Add</button>
|
||||
</div>
|
||||
|
||||
<!-- Deck stats footer -->
|
||||
<div class="deck-stats-footer" id="deck-stats-footer" style="display:none">
|
||||
<div class="deck-stat-pill">
|
||||
<span class="deck-stat-pill-num" id="deck-footer-total">0</span>
|
||||
<span class="deck-stat-pill-label">total</span>
|
||||
</div>
|
||||
<div class="deck-stat-pill">
|
||||
<span class="deck-stat-pill-num deck-stat-owned" id="deck-footer-owned">0</span>
|
||||
<span class="deck-stat-pill-label">owned</span>
|
||||
</div>
|
||||
<div class="deck-stat-pill">
|
||||
<span class="deck-stat-pill-num" id="deck-footer-avg-cmc">0.0</span>
|
||||
<span class="deck-stat-pill-label">avg CMC</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
<!-- RIGHT COLUMN: EDH Advisor -->
|
||||
<aside class="deck-col-right">
|
||||
|
||||
<!-- Ratio Dashboard -->
|
||||
<div class="deck-panel deck-panel-ratios" id="deck-ratio-panel">
|
||||
<div class="deck-panel-header deck-panel-header-collapsible" data-target="deck-ratio-body">
|
||||
<span class="deck-panel-title">📊 Ratio Dashboard</span>
|
||||
<span class="deck-collapse-arrow">▼</span>
|
||||
</div>
|
||||
<div class="deck-panel-body" id="deck-ratio-body">
|
||||
<div class="deck-ratio-bars" id="deck-ratio-bars">
|
||||
<div class="deck-ratio-placeholder">Load a deck to see ratios</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Fix My Ratios -->
|
||||
<div class="deck-panel deck-panel-fix" id="deck-fix-panel">
|
||||
<div class="deck-panel-header deck-panel-header-collapsible" data-target="deck-fix-body">
|
||||
<span class="deck-panel-title">🔧 Fix My Ratios</span>
|
||||
<span class="deck-collapse-arrow">▼</span>
|
||||
</div>
|
||||
<div class="deck-panel-body" id="deck-fix-body">
|
||||
<div class="deck-shortfalls" id="deck-shortfalls">
|
||||
<div class="deck-ratio-placeholder">No analysis available</div>
|
||||
</div>
|
||||
<div class="deck-fix-actions" id="deck-fix-actions" style="display:none">
|
||||
<button class="deck-btn deck-btn-ghost deck-btn-full" id="deck-autosuggest-btn">
|
||||
⚡ Auto-suggest from Collection
|
||||
</button>
|
||||
</div>
|
||||
<div class="deck-fix-suggestions" id="deck-fix-suggestions" style="display:none"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- EDHREC Recommendations -->
|
||||
<div class="deck-panel deck-panel-edhrec" id="deck-edhrec-panel">
|
||||
<div class="deck-panel-header deck-panel-header-collapsible" data-target="deck-edhrec-body">
|
||||
<span class="deck-panel-title">✨ EDHREC Recs</span>
|
||||
<span class="deck-collapse-arrow">▼</span>
|
||||
</div>
|
||||
<div class="deck-panel-body" id="deck-edhrec-body">
|
||||
<div class="deck-edhrec-controls">
|
||||
<label class="deck-toggle-label">
|
||||
<input type="checkbox" class="deck-toggle-check" id="deck-edhrec-owned-only" />
|
||||
<span class="deck-toggle-track"><span class="deck-toggle-thumb"></span></span>
|
||||
Owned Only
|
||||
</label>
|
||||
<button class="deck-btn deck-btn-ghost deck-btn-sm" id="deck-edhrec-refresh-btn">
|
||||
↺ Refresh
|
||||
</button>
|
||||
</div>
|
||||
<div class="deck-edhrec-list" id="deck-edhrec-list">
|
||||
<div class="deck-ratio-placeholder">Load a deck to see recommendations</div>
|
||||
</div>
|
||||
<div class="deck-edhrec-bulk" id="deck-edhrec-bulk" style="display:none">
|
||||
<button class="deck-btn deck-btn-success deck-btn-full" id="deck-edhrec-bulk-owned-btn">
|
||||
Add All Owned (respect ratios)
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- AI Research (Perplexity) — hidden until pplx status check -->
|
||||
<div class="deck-panel deck-panel-ai" id="deck-ai-research-panel" style="display:none">
|
||||
<div class="deck-panel-header deck-panel-header-collapsible" data-target="deck-ai-research-body">
|
||||
<span class="deck-panel-title">🧠 AI Research</span>
|
||||
<span class="deck-collapse-arrow">▼</span>
|
||||
</div>
|
||||
<div class="deck-panel-body" id="deck-ai-research-body">
|
||||
<div class="deck-ai-controls">
|
||||
<div class="deck-form-field deck-form-field-sm">
|
||||
<label class="deck-form-label-sm">Analysis Goal</label>
|
||||
<input type="text" class="deck-form-input deck-form-input-sm" id="ai-research-goal"
|
||||
placeholder="Identify weaknesses and suggest upgrades" />
|
||||
</div>
|
||||
<div class="deck-ai-row">
|
||||
<div class="deck-form-field deck-form-field-sm deck-form-field-half">
|
||||
<label class="deck-form-label-sm">Budget ($)</label>
|
||||
<input type="number" class="deck-form-input deck-form-input-sm" id="ai-research-budget"
|
||||
placeholder="No limit" min="0" step="10" />
|
||||
</div>
|
||||
<label class="deck-toggle-label deck-toggle-label-sm">
|
||||
<input type="checkbox" class="deck-toggle-check" id="ai-research-use-collection" checked />
|
||||
<span class="deck-toggle-track"><span class="deck-toggle-thumb"></span></span>
|
||||
Use Collection
|
||||
</label>
|
||||
</div>
|
||||
<div class="deck-form-field deck-form-field-sm">
|
||||
<label class="deck-form-label-sm">Omit Cards (comma-separated)</label>
|
||||
<input type="text" class="deck-form-input deck-form-input-sm" id="ai-research-omit"
|
||||
placeholder="Dockside Extortionist, Rhystic Study" />
|
||||
</div>
|
||||
<button class="deck-btn deck-btn-ai deck-btn-full" id="ai-research-btn" disabled>
|
||||
🔍 Analyze Deck
|
||||
</button>
|
||||
</div>
|
||||
<div class="deck-ai-loading" id="ai-research-loading" style="display:none">
|
||||
<div class="deck-ai-spinner"></div>
|
||||
<span>Analyzing with Claude Opus...</span>
|
||||
</div>
|
||||
<div class="deck-ai-results" id="ai-research-results" style="display:none"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- AI Deck Generate (Perplexity) — hidden until pplx status check -->
|
||||
<div class="deck-panel deck-panel-ai" id="deck-ai-generate-panel" style="display:none">
|
||||
<div class="deck-panel-header deck-panel-header-collapsible" data-target="deck-ai-generate-body">
|
||||
<span class="deck-panel-title">✨ AI Generate Deck</span>
|
||||
<span class="deck-collapse-arrow">▼</span>
|
||||
</div>
|
||||
<div class="deck-panel-body" id="deck-ai-generate-body">
|
||||
<div class="deck-ai-controls">
|
||||
<div class="deck-form-field deck-form-field-sm">
|
||||
<label class="deck-form-label-sm">Commander</label>
|
||||
<div class="deck-ai-commander-wrap">
|
||||
<input type="text" class="deck-form-input deck-form-input-sm" id="ai-gen-commander"
|
||||
placeholder="Search for a commander..." autocomplete="off" />
|
||||
<div class="deck-ai-commander-dropdown" id="ai-gen-commander-dropdown" style="display:none"></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="deck-ai-row">
|
||||
<div class="deck-form-field deck-form-field-sm deck-form-field-half">
|
||||
<label class="deck-form-label-sm">Budget ($)</label>
|
||||
<input type="number" class="deck-form-input deck-form-input-sm" id="ai-gen-budget"
|
||||
placeholder="No limit" min="0" step="10" />
|
||||
</div>
|
||||
<label class="deck-toggle-label deck-toggle-label-sm">
|
||||
<input type="checkbox" class="deck-toggle-check" id="ai-gen-use-collection" checked />
|
||||
<span class="deck-toggle-track"><span class="deck-toggle-thumb"></span></span>
|
||||
Use Collection
|
||||
</label>
|
||||
</div>
|
||||
<div class="deck-form-field deck-form-field-sm">
|
||||
<label class="deck-form-label-sm">Omit Cards (comma-separated)</label>
|
||||
<input type="text" class="deck-form-input deck-form-input-sm" id="ai-gen-omit"
|
||||
placeholder="Rhystic Study, Cyclonic Rift" />
|
||||
</div>
|
||||
<button class="deck-btn deck-btn-ai deck-btn-full" id="ai-gen-btn">
|
||||
✨ Generate Full Deck
|
||||
</button>
|
||||
</div>
|
||||
<div class="deck-ai-loading" id="ai-gen-loading" style="display:none">
|
||||
<div class="deck-ai-spinner"></div>
|
||||
<span>Generating deck with Claude Opus...</span>
|
||||
</div>
|
||||
<div class="deck-ai-results" id="ai-gen-results" style="display:none"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</aside>
|
||||
</main>
|
||||
|
||||
<!-- CARD PREVIEW TOOLTIP -->
|
||||
<div class="deck-card-preview" id="deck-card-preview" style="display:none">
|
||||
<img class="deck-card-preview-img" id="deck-card-preview-img" src="" alt="" />
|
||||
</div>
|
||||
|
||||
<!-- TOAST CONTAINER -->
|
||||
<div class="deck-toast-container" id="deck-toast-container"></div>
|
||||
|
||||
<!-- CONFIRM MODAL -->
|
||||
<div class="deck-modal-overlay" id="deck-confirm-modal" style="display:none">
|
||||
<div class="deck-modal">
|
||||
<div class="deck-modal-header">
|
||||
<h3 class="deck-modal-title" id="deck-confirm-title">Confirm Action</h3>
|
||||
<button class="deck-modal-close-btn" id="deck-confirm-close">✕</button>
|
||||
</div>
|
||||
<div class="deck-modal-body">
|
||||
<p class="deck-confirm-message" id="deck-confirm-message"></p>
|
||||
<div class="deck-confirm-changes" id="deck-confirm-changes"></div>
|
||||
</div>
|
||||
<div class="deck-modal-footer">
|
||||
<button class="deck-btn deck-btn-ghost" id="deck-confirm-cancel">Cancel</button>
|
||||
<button class="deck-btn deck-btn-accent" id="deck-confirm-ok">Confirm</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- NEW DECK MODAL -->
|
||||
<div class="deck-modal-overlay" id="deck-new-modal" style="display:none">
|
||||
<div class="deck-modal">
|
||||
<div class="deck-modal-header">
|
||||
<h3 class="deck-modal-title">Create New Deck</h3>
|
||||
<button class="deck-modal-close-btn" id="deck-new-modal-close">✕</button>
|
||||
</div>
|
||||
<div class="deck-modal-body">
|
||||
<div class="deck-form-field">
|
||||
<label class="deck-form-label">Deck Name</label>
|
||||
<input type="text" class="deck-form-input" id="deck-new-name" placeholder="My Commander Deck" />
|
||||
</div>
|
||||
<div class="deck-form-field">
|
||||
<label class="deck-form-label">Commander (optional)</label>
|
||||
<div class="deck-commander-search-wrap">
|
||||
<input
|
||||
type="text"
|
||||
class="deck-form-input"
|
||||
id="deck-new-commander-search"
|
||||
placeholder="Search Scryfall for commander..."
|
||||
autocomplete="off"
|
||||
/>
|
||||
<div class="deck-commander-dropdown" id="deck-new-commander-dropdown" style="display:none"></div>
|
||||
</div>
|
||||
<div class="deck-new-commander-selected" id="deck-new-commander-selected" style="display:none">
|
||||
<span class="deck-selected-commander-name" id="deck-new-commander-name"></span>
|
||||
<button class="deck-clear-commander-btn" id="deck-clear-commander-btn">✕</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="deck-modal-footer">
|
||||
<button class="deck-btn deck-btn-ghost" id="deck-new-cancel">Cancel</button>
|
||||
<button class="deck-btn deck-btn-accent" id="deck-new-create">Create Deck</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- IMPORT MODAL -->
|
||||
<div class="deck-modal-overlay" id="deck-import-modal" style="display:none">
|
||||
<div class="deck-modal deck-modal-wide">
|
||||
<div class="deck-modal-header">
|
||||
<h3 class="deck-modal-title">Import Decklist</h3>
|
||||
<button class="deck-modal-close-btn" id="deck-import-modal-close">✕</button>
|
||||
</div>
|
||||
<div class="deck-modal-body">
|
||||
<div class="deck-import-tabs">
|
||||
<button class="deck-import-tab active" data-tab="paste">Paste Text</button>
|
||||
<button class="deck-import-tab" data-tab="file">Upload File</button>
|
||||
</div>
|
||||
|
||||
<div id="deck-import-paste-panel" class="deck-import-panel">
|
||||
<textarea id="deck-import-textarea" rows="14" placeholder="Paste your decklist here...
|
||||
|
||||
Supported formats:
|
||||
[Commander]
|
||||
1 Korvold, Fae-Cursed King
|
||||
|
||||
[Deck]
|
||||
1 Sol Ring
|
||||
1 Cultivate
|
||||
37 Forest
|
||||
|
||||
Or plain list:
|
||||
Sol Ring
|
||||
Cultivate
|
||||
Forest"></textarea>
|
||||
</div>
|
||||
|
||||
<div id="deck-import-file-panel" class="deck-import-panel" style="display:none">
|
||||
<div class="deck-import-dropzone" id="deck-import-dropzone">
|
||||
<div class="deck-import-dropzone-label">Drop a .dck or .txt file here, or click to browse</div>
|
||||
<input type="file" id="deck-import-file" accept=".dck,.txt,.dec,.mwDeck" style="display:none" />
|
||||
</div>
|
||||
<div class="deck-import-file-info" id="deck-import-file-name"></div>
|
||||
</div>
|
||||
|
||||
<div class="deck-import-options">
|
||||
<label class="deck-import-option-label">
|
||||
<input type="radio" name="import-mode" value="new" checked /> Create new deck
|
||||
</label>
|
||||
<label class="deck-import-option-label">
|
||||
<input type="radio" name="import-mode" value="add" /> Add to current deck
|
||||
</label>
|
||||
<label class="deck-import-option-label">
|
||||
<input type="radio" name="import-mode" value="replace" /> Replace current deck cards
|
||||
</label>
|
||||
</div>
|
||||
|
||||
<div id="deck-import-new-name-row" class="deck-import-name-row">
|
||||
<label>Deck name (optional):</label>
|
||||
<input type="text" id="deck-import-name" placeholder="Auto-detected from commander" />
|
||||
</div>
|
||||
|
||||
<div class="deck-import-progress-wrap" id="deck-import-progress" style="display:none">
|
||||
<div class="deck-import-progress-label">Importing cards (Scryfall lookup)...</div>
|
||||
<div class="deck-import-progress-track">
|
||||
<div class="deck-import-progress-fill" id="deck-import-progress-bar"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div id="deck-import-result" style="display:none"></div>
|
||||
</div>
|
||||
<div class="deck-modal-footer">
|
||||
<button class="deck-btn deck-btn-ghost" id="deck-import-cancel">Cancel</button>
|
||||
<button class="deck-btn deck-btn-accent" id="deck-import-go">Import</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div><!-- /#deck-builder-page -->
|
||||
|
||||
<script>
|
||||
// Set API base URL — when served from a different host, override this value
|
||||
window.LAB_API_BASE = window.LAB_API_BASE || '';
|
||||
</script>
|
||||
<script src="deckbuilder.js"></script>
|
||||
<!-- Card Preview Popup (hover tooltip) -->
|
||||
<div id="deck-card-preview" style="display:none; position:fixed; pointer-events:none; z-index:60;">
|
||||
<img id="deck-card-preview-img" src="" alt="Card preview" style="width:220px; border-radius:8px; box-shadow:0 8px 24px rgba(0,0,0,0.7);" />
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
@@ -1,245 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Commander AI Lab — Auto Deck Generator V3</title>
|
||||
<link rel="preconnect" href="https://fonts.googleapis.com" />
|
||||
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
|
||||
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet" />
|
||||
<link rel="stylesheet" href="global.css" />
|
||||
<link rel="stylesheet" href="deckgenerator.css" />
|
||||
<style>
|
||||
/* No nav bar when embedded in LAN client iframe */
|
||||
:root { --nav-height: 0px; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<!-- nav.js removed — deck generator is embedded via iframe in the LAN client -->
|
||||
|
||||
<div id="deckgen-page">
|
||||
|
||||
<!-- MAIN LAYOUT -->
|
||||
<main class="dg-main">
|
||||
|
||||
<!-- LEFT: Configuration -->
|
||||
<section class="dg-col-config">
|
||||
|
||||
<!-- Commander Search -->
|
||||
<div class="dg-panel dg-panel-commander">
|
||||
<div class="dg-panel-header">
|
||||
<span class="dg-panel-title">Commander</span>
|
||||
</div>
|
||||
<div class="dg-panel-body">
|
||||
<div class="dg-commander-search-wrap">
|
||||
<input
|
||||
type="text"
|
||||
class="dg-input"
|
||||
id="dg-commander-input"
|
||||
placeholder="Search for a legendary creature..."
|
||||
autocomplete="off"
|
||||
/>
|
||||
<div class="dg-commander-dropdown" id="dg-commander-dropdown"></div>
|
||||
</div>
|
||||
<div class="dg-commander-selected" id="dg-commander-selected" style="display:none">
|
||||
<div class="dg-commander-card">
|
||||
<button class="dg-commander-clear" id="dg-commander-clear" title="Clear commander">×</button>
|
||||
<img class="dg-commander-img" id="dg-commander-img" src="" alt="" />
|
||||
<div class="dg-commander-info">
|
||||
<div class="dg-commander-name" id="dg-commander-name"></div>
|
||||
<div class="dg-commander-type" id="dg-commander-type"></div>
|
||||
<div class="dg-commander-colors" id="dg-commander-colors"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Strategy & Bracket -->
|
||||
<div class="dg-panel">
|
||||
<div class="dg-panel-header">
|
||||
<span class="dg-panel-title">Strategy & Power</span>
|
||||
</div>
|
||||
<div class="dg-panel-body">
|
||||
<input
|
||||
type="text"
|
||||
class="dg-input"
|
||||
id="dg-strategy"
|
||||
placeholder="e.g. zombie tokens, voltron, spellslinger..."
|
||||
title="Describe your deck strategy — the AI uses this to pick thematically aligned cards"
|
||||
/>
|
||||
<div class="dg-bracket-row">
|
||||
<label class="dg-label">
|
||||
Target Bracket
|
||||
<span class="tip-icon" data-tip="B1 = precon-level casual. B2 = upgraded with synergies. B3 = high power with strong combos. B4 = cEDH with maximum optimization, fast mana, and instant-speed interaction.">?</span>
|
||||
</label>
|
||||
<div class="dg-bracket-btns" id="dg-bracket-btns">
|
||||
<button class="dg-bracket-btn" data-bracket="1" title="Casual — precon level">B1</button>
|
||||
<button class="dg-bracket-btn" data-bracket="2" title="Upgraded Casual">B2</button>
|
||||
<button class="dg-bracket-btn dg-bracket-active" data-bracket="3" title="High Power">B3</button>
|
||||
<button class="dg-bracket-btn" data-bracket="4" title="cEDH — maximum power">B4</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Deck Name -->
|
||||
<div class="dg-panel">
|
||||
<div class="dg-panel-header">
|
||||
<span class="dg-panel-title">Deck Name</span>
|
||||
</div>
|
||||
<div class="dg-panel-body">
|
||||
<input
|
||||
type="text"
|
||||
class="dg-input"
|
||||
id="dg-deck-name"
|
||||
placeholder="Auto-generated from commander name"
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Budget -->
|
||||
<div class="dg-panel">
|
||||
<div class="dg-panel-header">
|
||||
<span class="dg-panel-title">Budget</span>
|
||||
</div>
|
||||
<div class="dg-panel-body">
|
||||
<div class="dg-budget-row">
|
||||
<input
|
||||
type="number"
|
||||
class="dg-input dg-input-narrow"
|
||||
id="dg-budget"
|
||||
placeholder="No limit"
|
||||
min="0"
|
||||
step="25"
|
||||
/>
|
||||
<select class="dg-select" id="dg-budget-mode" title="Total Deck: caps the combined price of all 100 cards. Per Card Max: caps each individual card price.">
|
||||
<option value="total">Total Deck $</option>
|
||||
<option value="per_card">Per Card Max $</option>
|
||||
</select>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Options -->
|
||||
<div class="dg-panel">
|
||||
<div class="dg-panel-header">
|
||||
<span class="dg-panel-title">Options</span>
|
||||
</div>
|
||||
<div class="dg-panel-body">
|
||||
<label class="dg-toggle-row">
|
||||
<span class="dg-toggle-label">
|
||||
Use my collection
|
||||
<span class="tip-icon" data-tip="Prioritizes cards you already own before pulling from the full card pool. Saves money and ensures the deck is ready to play. Requires an imported collection.">?</span>
|
||||
</span>
|
||||
<input type="checkbox" class="dg-toggle-check" id="dg-use-collection" checked />
|
||||
<span class="dg-toggle-track"><span class="dg-toggle-thumb"></span></span>
|
||||
</label>
|
||||
<label class="dg-toggle-row">
|
||||
<span class="dg-toggle-label">
|
||||
Smart Substitution
|
||||
<span class="tip-icon" data-tip="When a suggested card isn't in your collection, automatically finds the closest functional replacement you do own. Keeps the deck playable without buying extra cards.">?</span>
|
||||
</span>
|
||||
<input type="checkbox" class="dg-toggle-check" id="dg-substitution" checked />
|
||||
<span class="dg-toggle-track"><span class="dg-toggle-thumb"></span></span>
|
||||
</label>
|
||||
<div class="dg-model-row">
|
||||
<label class="dg-label">
|
||||
AI Model
|
||||
<span class="tip-icon" data-tip="Deck generation is powered by Claude Opus 4, Anthropic's most capable model.">?</span>
|
||||
</label>
|
||||
<input type="hidden" id="dg-model" value="claude-opus-4-20250514" />
|
||||
<span style="font-size:0.8rem; color:var(--text-muted, #888);">claude-opus-4-20250514</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Omit Cards (chips input) -->
|
||||
<div class="dg-panel">
|
||||
<div class="dg-panel-header">
|
||||
<span class="dg-panel-title">Omit Cards</span>
|
||||
</div>
|
||||
<div class="dg-panel-body">
|
||||
<div class="dg-omit-wrap" id="dg-omit-wrap">
|
||||
<div class="dg-omit-chips" id="dg-omit-chips"></div>
|
||||
<input
|
||||
type="text"
|
||||
class="dg-input dg-omit-input"
|
||||
id="dg-omit-input"
|
||||
placeholder="Type a card name and press Enter…"
|
||||
/>
|
||||
</div>
|
||||
<small class="dg-hint">Cards listed here will be excluded from the generated deck.</small>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Action Buttons -->
|
||||
<div class="dg-actions">
|
||||
<button class="dg-btn dg-btn-preview" id="dg-preview-btn" disabled>
|
||||
<span class="dg-btn-icon">⚡</span> Generate Deck
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<!-- Conversation Log -->
|
||||
<div id="dg-conversation-log" class="dg-conversation-log">
|
||||
<div class="dg-log-entry dg-log-info"><span class="dg-log-ts">[Ready]</span> Waiting for deck generation...</div>
|
||||
</div>
|
||||
|
||||
</section>
|
||||
|
||||
<!-- RIGHT: Preview Results -->
|
||||
<section class="dg-col-results">
|
||||
<!-- Empty State -->
|
||||
<div class="dg-empty-state" id="dg-empty-state">
|
||||
<div class="dg-empty-icon">⚡</div>
|
||||
<div class="dg-empty-title">V3 AI Deck Generator</div>
|
||||
<div class="dg-empty-text">Select a commander, set your bracket and strategy, then click "Generate Deck" to build a complete 100-card deck using Claude Opus with structured output.</div>
|
||||
</div>
|
||||
|
||||
<!-- Loading State -->
|
||||
<div class="dg-loading" id="dg-loading" style="display:none">
|
||||
<div class="dg-spinner"></div>
|
||||
<div class="dg-loading-text" id="dg-loading-text">Generating deck via Claude Opus 4...</div>
|
||||
</div>
|
||||
<div class="dg-progress-wrap" id="dg-progress-wrap" style="display:none">
|
||||
<div class="dg-progress-bar">
|
||||
<div class="dg-progress-fill" id="dg-progress-fill"></div>
|
||||
</div>
|
||||
<div class="dg-progress-meta">
|
||||
<span class="dg-progress-phase" id="dg-progress-phase"></span>
|
||||
<span class="dg-progress-elapsed" id="dg-progress-elapsed">0s elapsed</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Results -->
|
||||
<div class="dg-results" id="dg-results" style="display:none">
|
||||
<div class="dg-results-header">
|
||||
<div id="dg-strategy-bar"></div>
|
||||
<div id="dg-stats-bar" class="dg-stats-bar"></div>
|
||||
<div id="dg-sub-summary" style="display:none"></div>
|
||||
<div class="dg-result-actions">
|
||||
<button class="dg-btn dg-btn-secondary" id="dg-regenerate-btn">↻ Regenerate</button>
|
||||
<button class="dg-btn dg-btn-primary" id="dg-save-btn">💾 Save to Deck Builder</button>
|
||||
<button class="dg-btn dg-btn-ghost" id="dg-export-csv">CSV</button>
|
||||
<button class="dg-btn dg-btn-ghost" id="dg-export-dck">Forge .dck</button>
|
||||
<button class="dg-btn dg-btn-ghost" id="dg-export-moxfield">Moxfield</button>
|
||||
<button class="dg-btn dg-btn-ghost" id="dg-export-shopping">🛒 Shopping</button>
|
||||
</div>
|
||||
</div>
|
||||
<div id="dg-card-groups" class="dg-card-groups"></div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
</main>
|
||||
|
||||
<!-- TOAST CONTAINER -->
|
||||
<div class="dg-toast-container" id="dg-toast-container"></div>
|
||||
|
||||
</div>
|
||||
|
||||
<script>
|
||||
window.LAB_API_BASE = window.LAB_API_BASE || '';
|
||||
</script>
|
||||
<script src="deckgenerator.js"></script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,795 +0,0 @@
|
||||
/**
|
||||
* Commander AI Lab — Auto Deck Generator V3 UI
|
||||
* ═══════════════════════════════════════════════
|
||||
*
|
||||
* V3 Endpoints:
|
||||
* GET /api/deck/v3/status — check V3 generator status
|
||||
* GET /api/deck-generator/commander-search — autocomplete commanders
|
||||
* POST /api/deck/v3/generate — generate deck (local AI structured output)
|
||||
* POST /api/deck/v3/commit — generate + save to Deck Builder
|
||||
* POST /api/deck/v3/export/csv — export as CSV
|
||||
* POST /api/deck/v3/export/dck — export as Forge .dck
|
||||
* POST /api/deck/v3/export/moxfield — export in Moxfield paste format
|
||||
* POST /api/deck/v3/export/shopping — shopping list of missing cards
|
||||
*/
|
||||
|
||||
const DeckGenerator = (() => {
|
||||
'use strict';
|
||||
|
||||
const API_BASE = window.LAB_API_BASE || window.location.origin;
|
||||
const DEBOUNCE_MS = 350;
|
||||
|
||||
const COLOR_MAP = {
|
||||
W: { label: 'W', cls: 'dg-pip-W' },
|
||||
U: { label: 'U', cls: 'dg-pip-U' },
|
||||
B: { label: 'B', cls: 'dg-pip-B' },
|
||||
R: { label: 'R', cls: 'dg-pip-R' },
|
||||
G: { label: 'G', cls: 'dg-pip-G' },
|
||||
C: { label: 'C', cls: 'dg-pip-C' },
|
||||
};
|
||||
|
||||
const TYPE_ORDER = ['Creature', 'Instant', 'Sorcery', 'Artifact', 'Enchantment', 'Planeswalker', 'Land', 'Battle', 'Other'];
|
||||
const TYPE_ICONS = {
|
||||
'Creature': '🐉', 'Instant': '⚡', 'Sorcery': '🌀', 'Artifact': '🔧',
|
||||
'Enchantment': '✨', 'Planeswalker': '🌟', 'Land': '🏔', 'Battle': '⚔',
|
||||
'Other': '🃏',
|
||||
};
|
||||
|
||||
const STATUS_BADGES = {
|
||||
owned: { label: 'Owned', cls: 'dg-badge-owned' },
|
||||
substituted: { label: 'Substituted', cls: 'dg-badge-substituted' },
|
||||
missing: { label: 'Missing', cls: 'dg-badge-missing' },
|
||||
};
|
||||
|
||||
// ── State ──────────────────────────────────────────────
|
||||
let state = {
|
||||
commander: null,
|
||||
targetBracket: 3,
|
||||
previewResult: null,
|
||||
lastRequestBody: null,
|
||||
isLoading: false,
|
||||
v3Ready: false,
|
||||
};
|
||||
|
||||
let searchTimeout = null;
|
||||
|
||||
// ── Progress bar state ──────────────────────────────
|
||||
const PROGRESS_PHASES = [
|
||||
{ at: 0, pct: 5, msg: 'Connecting to AI model...' },
|
||||
{ at: 3, pct: 10, msg: 'Resolving commander on Scryfall...' },
|
||||
{ at: 6, pct: 18, msg: 'Building collection summary...' },
|
||||
{ at: 10, pct: 25, msg: 'Sending prompt to LLM...' },
|
||||
{ at: 20, pct: 40, msg: 'AI is building the 99...' },
|
||||
{ at: 35, pct: 55, msg: 'Selecting cards and filling slots...' },
|
||||
{ at: 50, pct: 65, msg: 'Running smart substitution...' },
|
||||
{ at: 70, pct: 78, msg: 'Cross-referencing with collection...' },
|
||||
{ at: 90, pct: 88, msg: 'Almost there — finalizing deck...' },
|
||||
{ at: 110, pct: 93, msg: 'Still working — large model can take a moment...' },
|
||||
];
|
||||
let _progressInterval = null;
|
||||
let _progressStart = 0;
|
||||
|
||||
function startProgress() {
|
||||
_progressStart = Date.now();
|
||||
const wrap = $('dg-progress-wrap');
|
||||
const fill = $('dg-progress-fill');
|
||||
const phase = $('dg-progress-phase');
|
||||
const elapsed = $('dg-progress-elapsed');
|
||||
if (!wrap) return;
|
||||
wrap.style.display = 'block';
|
||||
fill.style.width = '0%';
|
||||
phase.textContent = '';
|
||||
elapsed.textContent = '0s elapsed';
|
||||
let lastPhaseIdx = -1;
|
||||
_progressInterval = setInterval(() => {
|
||||
const secs = Math.floor((Date.now() - _progressStart) / 1000);
|
||||
elapsed.textContent = secs + 's elapsed';
|
||||
for (let i = PROGRESS_PHASES.length - 1; i >= 0; i--) {
|
||||
if (secs >= PROGRESS_PHASES[i].at) {
|
||||
fill.style.width = PROGRESS_PHASES[i].pct + '%';
|
||||
if (i !== lastPhaseIdx) {
|
||||
phase.textContent = PROGRESS_PHASES[i].msg;
|
||||
logMessage(PROGRESS_PHASES[i].msg, 'info');
|
||||
lastPhaseIdx = i;
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
}, 1000);
|
||||
}
|
||||
|
||||
function stopProgress(success) {
|
||||
if (_progressInterval) {
|
||||
clearInterval(_progressInterval);
|
||||
_progressInterval = null;
|
||||
}
|
||||
const fill = $('dg-progress-fill');
|
||||
const phase = $('dg-progress-phase');
|
||||
const elapsed = $('dg-progress-elapsed');
|
||||
if (fill) fill.style.width = success ? '100%' : '0%';
|
||||
if (phase) phase.textContent = success ? 'Done!' : 'Failed';
|
||||
const secs = Math.floor((Date.now() - _progressStart) / 1000);
|
||||
if (elapsed) elapsed.textContent = secs + 's total';
|
||||
}
|
||||
|
||||
// ── Omit Cards (chips) ─────────────────────────────
|
||||
let omitCards = [];
|
||||
|
||||
function addOmitCard(name) {
|
||||
const trimmed = name.trim();
|
||||
if (!trimmed) return;
|
||||
if (omitCards.some(c => c.toLowerCase() === trimmed.toLowerCase())) return;
|
||||
omitCards.push(trimmed);
|
||||
renderOmitChips();
|
||||
}
|
||||
|
||||
function removeOmitCard(idx) {
|
||||
omitCards.splice(idx, 1);
|
||||
renderOmitChips();
|
||||
}
|
||||
|
||||
function renderOmitChips() {
|
||||
const container = $('dg-omit-chips');
|
||||
if (!container) return;
|
||||
container.innerHTML = omitCards.map((name, i) =>
|
||||
'<span class="dg-chip">' +
|
||||
escHtml(name) +
|
||||
'<span class="dg-chip-x" data-idx="' + i + '">×</span>' +
|
||||
'</span>'
|
||||
).join('');
|
||||
container.querySelectorAll('.dg-chip-x').forEach(el => {
|
||||
el.addEventListener('click', () => removeOmitCard(parseInt(el.dataset.idx, 10)));
|
||||
});
|
||||
}
|
||||
|
||||
const $ = (id) => document.getElementById(id);
|
||||
|
||||
// ── Init ────────────────────────────────────────────────
|
||||
async function init() {
|
||||
await checkV3Status();
|
||||
bindEvents();
|
||||
checkQueryParams();
|
||||
}
|
||||
|
||||
async function checkV3Status() {
|
||||
try {
|
||||
const resp = await fetch(API_BASE + '/api/deck/v3/status');
|
||||
const data = await resp.json();
|
||||
state.v3Ready = data.initialized;
|
||||
if (!state.v3Ready) {
|
||||
// Warn but do not block — local/Ollama mode may still work
|
||||
console.warn('V3 status: not initialized. Error:', data.error || 'unknown');
|
||||
toast('V3 generator not ready — check that the Anthropic API key is configured', 'warning');
|
||||
}
|
||||
} catch (e) {
|
||||
console.warn('V3 status check failed:', e);
|
||||
}
|
||||
}
|
||||
|
||||
function bindEvents() {
|
||||
// Commander search
|
||||
const input = $('dg-commander-input');
|
||||
input.addEventListener('input', () => {
|
||||
clearTimeout(searchTimeout);
|
||||
const q = input.value.trim();
|
||||
if (q.length < 2) { hideDropdown(); return; }
|
||||
searchTimeout = setTimeout(() => searchCommander(q), DEBOUNCE_MS);
|
||||
});
|
||||
|
||||
input.addEventListener('focus', () => {
|
||||
const q = input.value.trim();
|
||||
if (q.length >= 2) searchCommander(q);
|
||||
});
|
||||
|
||||
document.addEventListener('click', (e) => {
|
||||
const wrap = document.querySelector('.dg-commander-search-wrap');
|
||||
if (wrap && !wrap.contains(e.target)) hideDropdown();
|
||||
});
|
||||
|
||||
$('dg-commander-clear').addEventListener('click', clearCommander);
|
||||
|
||||
// Bracket buttons
|
||||
document.querySelectorAll('.dg-bracket-btn').forEach(btn => {
|
||||
btn.addEventListener('click', () => {
|
||||
document.querySelectorAll('.dg-bracket-btn').forEach(b => b.classList.remove('dg-bracket-active'));
|
||||
btn.classList.add('dg-bracket-active');
|
||||
state.targetBracket = parseInt(btn.dataset.bracket, 10);
|
||||
});
|
||||
});
|
||||
|
||||
// Generate & save
|
||||
$('dg-preview-btn').addEventListener('click', generateDeck);
|
||||
$('dg-save-btn').addEventListener('click', commitDeck);
|
||||
$('dg-regenerate-btn').addEventListener('click', generateDeck);
|
||||
|
||||
// Export buttons
|
||||
$('dg-export-csv').addEventListener('click', () => exportDeck('csv'));
|
||||
$('dg-export-dck').addEventListener('click', () => exportDeck('dck'));
|
||||
$('dg-export-moxfield').addEventListener('click', () => exportMoxfield());
|
||||
$('dg-export-shopping').addEventListener('click', () => exportShopping());
|
||||
|
||||
// Omit cards chips input
|
||||
const omitInput = $('dg-omit-input');
|
||||
if (omitInput) {
|
||||
omitInput.addEventListener('keydown', (e) => {
|
||||
if (e.key === 'Enter') {
|
||||
e.preventDefault();
|
||||
addOmitCard(omitInput.value);
|
||||
omitInput.value = '';
|
||||
}
|
||||
});
|
||||
}
|
||||
const omitWrap = $('dg-omit-wrap');
|
||||
if (omitWrap) {
|
||||
omitWrap.addEventListener('click', () => omitInput && omitInput.focus());
|
||||
}
|
||||
}
|
||||
|
||||
function checkQueryParams() {
|
||||
const params = new URLSearchParams(window.location.search);
|
||||
const name = params.get('commander');
|
||||
if (name) {
|
||||
$('dg-commander-input').value = name;
|
||||
searchCommander(name);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Build Request Body ──────────────────────────────────
|
||||
function buildRequestBody() {
|
||||
const budget = parseFloat($('dg-budget').value) || null;
|
||||
return {
|
||||
commander_name: state.commander.name,
|
||||
strategy: $('dg-strategy').value.trim(),
|
||||
target_bracket: state.targetBracket,
|
||||
budget_usd: budget,
|
||||
budget_mode: $('dg-budget-mode').value,
|
||||
omit_cards: omitCards.slice(),
|
||||
use_collection: $('dg-use-collection').checked,
|
||||
run_substitution: $('dg-substitution').checked,
|
||||
model: $('dg-model').value,
|
||||
deck_name: $('dg-deck-name').value.trim() || '',
|
||||
};
|
||||
}
|
||||
|
||||
// ── Commander Search ────────────────────────────────────
|
||||
function positionDropdown() {
|
||||
const input = $('dg-commander-input');
|
||||
const dropdown = $('dg-commander-dropdown');
|
||||
const rect = input.getBoundingClientRect();
|
||||
dropdown.style.top = (rect.bottom + 4) + 'px';
|
||||
dropdown.style.left = rect.left + 'px';
|
||||
dropdown.style.width = rect.width + 'px';
|
||||
}
|
||||
|
||||
async function searchCommander(q) {
|
||||
const dropdown = $('dg-commander-dropdown');
|
||||
dropdown.innerHTML = '<div class="dg-dropdown-loading">Searching...</div>';
|
||||
dropdown.classList.add('open');
|
||||
positionDropdown();
|
||||
|
||||
try {
|
||||
const resp = await fetch(API_BASE + '/api/deck-generator/commander-search?q=' + encodeURIComponent(q));
|
||||
const data = await resp.json();
|
||||
renderDropdown(data.results || []);
|
||||
} catch (e) {
|
||||
dropdown.innerHTML = '<div class="dg-dropdown-empty">Search failed</div>';
|
||||
}
|
||||
}
|
||||
|
||||
function renderDropdown(results) {
|
||||
const dropdown = $('dg-commander-dropdown');
|
||||
|
||||
if (results.length === 0) {
|
||||
dropdown.innerHTML = '<div class="dg-dropdown-empty">No legendary creatures found</div>';
|
||||
return;
|
||||
}
|
||||
|
||||
dropdown.innerHTML = results.map((r, idx) => {
|
||||
const colors = (r.color_identity || [])
|
||||
.map(c => COLOR_MAP[c] ? '<span class="dg-color-pip ' + COLOR_MAP[c].cls + '">' + COLOR_MAP[c].label + '</span>' : '')
|
||||
.join('');
|
||||
|
||||
const badge = r.in_collection
|
||||
? '<span class="dg-dropdown-item-badge dg-badge-owned">Owned</span>'
|
||||
: '<span class="dg-dropdown-item-badge dg-badge-scryfall">Scryfall</span>';
|
||||
|
||||
const imgSrc = r.image_url ? r.image_url.replace('version=normal', 'version=small') : '';
|
||||
const imgTag = imgSrc
|
||||
? '<img class="dg-dropdown-item-img" src="' + imgSrc + '" alt="" loading="lazy" />'
|
||||
: '<div class="dg-dropdown-item-img" style="background:var(--lab-surface)"></div>';
|
||||
|
||||
return '<div class="dg-dropdown-item" data-idx="' + idx + '">'
|
||||
+ imgTag
|
||||
+ '<div class="dg-dropdown-item-info">'
|
||||
+ ' <div class="dg-dropdown-item-name">' + escHtml(r.name) + '</div>'
|
||||
+ ' <div class="dg-dropdown-item-type">' + escHtml(r.type_line || '') + '</div>'
|
||||
+ '</div>'
|
||||
+ '<div style="display:flex;gap:3px;align-items:center">' + colors + '</div>'
|
||||
+ badge
|
||||
+ '</div>';
|
||||
}).join('');
|
||||
|
||||
dropdown.querySelectorAll('.dg-dropdown-item').forEach(el => {
|
||||
el.addEventListener('click', () => {
|
||||
selectCommander(results[parseInt(el.dataset.idx, 10)]);
|
||||
hideDropdown();
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
function selectCommander(cmdr) {
|
||||
state.commander = cmdr;
|
||||
$('dg-commander-input').style.display = 'none';
|
||||
$('dg-commander-selected').style.display = 'block';
|
||||
$('dg-commander-name').textContent = cmdr.name;
|
||||
$('dg-commander-type').textContent = cmdr.type_line || '';
|
||||
|
||||
$('dg-commander-colors').innerHTML = (cmdr.color_identity || [])
|
||||
.map(c => COLOR_MAP[c] ? '<span class="dg-color-pip ' + COLOR_MAP[c].cls + '">' + COLOR_MAP[c].label + '</span>' : '')
|
||||
.join('');
|
||||
|
||||
const img = $('dg-commander-img');
|
||||
if (cmdr.image_url) {
|
||||
var imgUrl = cmdr.image_url;
|
||||
if (imgUrl.indexOf('version=small') !== -1) {
|
||||
imgUrl = imgUrl.replace('version=small', 'version=normal');
|
||||
} else if (imgUrl.indexOf('version=') === -1 && imgUrl.indexOf('?') !== -1) {
|
||||
imgUrl += '&version=normal';
|
||||
} else if (imgUrl.indexOf('version=') === -1) {
|
||||
imgUrl += '?version=normal';
|
||||
}
|
||||
img.src = imgUrl;
|
||||
img.style.display = 'block';
|
||||
} else {
|
||||
img.style.display = 'none';
|
||||
}
|
||||
|
||||
$('dg-preview-btn').disabled = false;
|
||||
|
||||
// Auto-fill deck name if empty
|
||||
const deckNameInput = $('dg-deck-name');
|
||||
if (deckNameInput && !deckNameInput.value.trim()) {
|
||||
deckNameInput.value = 'Auto - ' + cmdr.name;
|
||||
}
|
||||
}
|
||||
|
||||
function clearCommander() {
|
||||
state.commander = null;
|
||||
$('dg-commander-input').style.display = '';
|
||||
$('dg-commander-input').value = '';
|
||||
$('dg-commander-selected').style.display = 'none';
|
||||
$('dg-preview-btn').disabled = true;
|
||||
}
|
||||
|
||||
function hideDropdown() {
|
||||
$('dg-commander-dropdown').classList.remove('open');
|
||||
}
|
||||
|
||||
// -- Conversation Log Helper --
|
||||
function logMessage(msg, type = 'info') {
|
||||
const log = $('dg-conversation-log');
|
||||
if (!log) return;
|
||||
const entry = document.createElement('div');
|
||||
entry.className = 'dg-log-entry dg-log-' + type;
|
||||
const ts = new Date().toLocaleTimeString();
|
||||
entry.innerHTML = '<span class="dg-log-ts">[' + ts + ']</span> ' + msg;
|
||||
log.appendChild(entry);
|
||||
log.scrollTop = log.scrollHeight;
|
||||
}
|
||||
|
||||
function clearLog() {
|
||||
const log = $('dg-conversation-log');
|
||||
if (!log) return;
|
||||
log.innerHTML = '';
|
||||
}
|
||||
|
||||
// ── Generate Deck ───────────────────────────────────────
|
||||
async function generateDeck() {
|
||||
clearLog();
|
||||
logMessage('Connecting to Claude AI...', 'info');
|
||||
if (!state.commander) { toast('Select a commander first', 'error'); return; }
|
||||
// Non-blocking warning only — local Ollama mode does not require v3Ready
|
||||
if (!state.v3Ready) {
|
||||
logMessage('Warning: V3 status check failed. Attempting generation anyway via local AI...', 'warning');
|
||||
}
|
||||
logMessage('Building request body with commander: <b>' + state.commander.name + '</b>', 'info');
|
||||
|
||||
setLoading(true, 'Generating deck via Local AI...');
|
||||
startProgress();
|
||||
|
||||
const body = buildRequestBody();
|
||||
logMessage('Sending request to AI model... this may take a moment.', 'info');
|
||||
state.lastRequestBody = body;
|
||||
|
||||
try {
|
||||
const resp = await fetch(API_BASE + '/api/deck/v3/generate', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
if (!resp.ok) {
|
||||
const err = await resp.json().catch(() => ({}));
|
||||
throw new Error(err.error || err.detail || 'Generation failed');
|
||||
}
|
||||
|
||||
state.previewResult = await resp.json();
|
||||
logMessage('AI response received! Processing deck data...', 'success');
|
||||
|
||||
const stats = state.previewResult.stats || {};
|
||||
const cardCount = (stats.total_cards || 0);
|
||||
logMessage('Deck generated: <b>' + cardCount + ' cards</b> | $' + (stats.total_price_usd || 0).toFixed(2) + ' estimated', 'success');
|
||||
stopProgress(true);
|
||||
renderResults(state.previewResult);
|
||||
|
||||
toast('Deck generated: ' + (stats.total_cards || 0) + ' cards | $' + (stats.total_price_usd || 0), 'success');
|
||||
} catch (e) {
|
||||
stopProgress(false);
|
||||
toast(e.message || 'Generation failed', 'error');
|
||||
logMessage('Error: ' + (e.message || 'Unknown error'), 'error');
|
||||
setLoading(false);
|
||||
}
|
||||
}
|
||||
|
||||
function setLoading(loading, text) {
|
||||
state.isLoading = loading;
|
||||
$('dg-empty-state').style.display = 'none';
|
||||
$('dg-loading').style.display = loading ? 'flex' : 'none';
|
||||
$('dg-results').style.display = loading ? 'none' : 'block';
|
||||
$('dg-preview-btn').disabled = loading || !state.commander;
|
||||
if (text) $('dg-loading-text').textContent = text;
|
||||
}
|
||||
|
||||
// ── Render Results ──────────────────────────────────────
|
||||
function renderResults(result) {
|
||||
$('dg-loading').style.display = 'none';
|
||||
$('dg-results').style.display = 'block';
|
||||
$('dg-preview-btn').disabled = false;
|
||||
|
||||
renderStrategyBar(result);
|
||||
renderStatsBar(result);
|
||||
renderSubstitutionSummary(result);
|
||||
renderCardGroups(result);
|
||||
renderUseInGameButton(result);
|
||||
}
|
||||
|
||||
function renderUseInGameButton(result) {
|
||||
// Only show when running inside the LAN client iframe
|
||||
if (window.parent === window) return;
|
||||
|
||||
var existing = document.getElementById('dg-export-game-btn');
|
||||
if (existing) existing.remove();
|
||||
|
||||
var btn = document.createElement('button');
|
||||
btn.id = 'dg-export-game-btn';
|
||||
btn.className = 'dg-btn dg-btn-preview';
|
||||
btn.style.cssText = 'margin:12px auto;display:block;border-color:rgba(91,141,217,0.4);color:rgba(91,141,217,0.9);';
|
||||
btn.innerHTML = '<span class="dg-btn-icon">🎮</span> Use in Game';
|
||||
btn.addEventListener('click', function() {
|
||||
var cards = (result.cards || []).map(function(c) {
|
||||
return { name: c.name, quantity: c.count || 1, is_commander: false };
|
||||
});
|
||||
if (result.commander) {
|
||||
cards.unshift({ name: result.commander.name, quantity: 1, is_commander: true });
|
||||
}
|
||||
window.parent.postMessage({
|
||||
type: 'DECK_BUILDER_EXPORT',
|
||||
deck: cards,
|
||||
deckName: result.commander ? result.commander.name + ' Deck' : 'Generated Deck',
|
||||
commander: result.commander ? result.commander.name : '',
|
||||
}, '*');
|
||||
toast('Deck sent to game setup!', 'success');
|
||||
});
|
||||
|
||||
$('dg-results').appendChild(btn);
|
||||
}
|
||||
|
||||
function renderStrategyBar(result) {
|
||||
const bar = $('dg-strategy-bar');
|
||||
if (!bar) return;
|
||||
const bracket = result.bracket || {};
|
||||
const archetype = result.archetype || '';
|
||||
const strategy = result.strategy_summary || '';
|
||||
|
||||
bar.innerHTML = '<div class="dg-strategy-info">'
|
||||
+ '<span class="dg-bracket-badge dg-bracket-' + (bracket.level || 0) + '">B' + (bracket.level || '?') + '</span>'
|
||||
+ '<span class="dg-archetype-badge">' + escHtml(archetype) + '</span>'
|
||||
+ '<span class="dg-strategy-text">' + escHtml(strategy) + '</span>'
|
||||
+ '</div>'
|
||||
+ (bracket.reasoning ? '<div class="dg-bracket-reasoning">' + escHtml(bracket.reasoning) + '</div>' : '')
|
||||
+ (bracket.game_changers && bracket.game_changers.length
|
||||
? '<div class="dg-game-changers">Game Changers: ' + bracket.game_changers.map(c => '<span class="dg-gc-chip">' + escHtml(c) + '</span>').join('') + '</div>'
|
||||
: '');
|
||||
}
|
||||
|
||||
function renderStatsBar(result) {
|
||||
const stats = result.stats || {};
|
||||
const container = $('dg-stats-bar');
|
||||
if (!container) return;
|
||||
|
||||
const chips = [
|
||||
chipHtml('Total', stats.total_cards || 0, ''),
|
||||
chipHtml('Owned', (stats.by_status || {}).owned || 0, 'dg-stat-owned'),
|
||||
chipHtml('Subbed', (stats.by_status || {}).substituted || 0, 'dg-stat-substituted'),
|
||||
chipHtml('Missing', (stats.by_status || {}).missing || 0, 'dg-stat-missing'),
|
||||
chipHtml('Lands', stats.land_count || 0, ''),
|
||||
chipHtml('$' + (stats.total_price_usd || 0).toFixed(0), 'Total', 'dg-stat-price'),
|
||||
];
|
||||
|
||||
container.innerHTML = chips.join('');
|
||||
}
|
||||
|
||||
function chipHtml(label, num, cls) {
|
||||
return '<div class="dg-stat-chip ' + cls + '">'
|
||||
+ '<span class="dg-stat-num">' + num + '</span>'
|
||||
+ '<span class="dg-stat-label">' + label + '</span>'
|
||||
+ '</div>';
|
||||
}
|
||||
|
||||
function renderSubstitutionSummary(result) {
|
||||
const el = $('dg-sub-summary');
|
||||
const sub = result.substitution_stats;
|
||||
if (!sub) { el.style.display = 'none'; return; }
|
||||
|
||||
el.style.display = 'block';
|
||||
el.innerHTML = '<div class="dg-sub-bar">'
|
||||
+ '<span class="dg-sub-stat dg-sub-owned">' + sub.owned + ' owned</span>'
|
||||
+ '<span class="dg-sub-stat dg-sub-substituted">' + sub.substituted + ' substituted</span>'
|
||||
+ '<span class="dg-sub-stat dg-sub-missing">' + sub.missing + ' still missing</span>'
|
||||
+ '</div>';
|
||||
}
|
||||
|
||||
function renderCardGroups(result) {
|
||||
const container = $('dg-card-groups');
|
||||
if (!container) return;
|
||||
const cards = result.cards || [];
|
||||
const commander = result.commander;
|
||||
|
||||
const groups = {};
|
||||
for (const card of cards) {
|
||||
const cat = card.category || 'Other';
|
||||
if (!groups[cat]) groups[cat] = [];
|
||||
groups[cat].push(card);
|
||||
}
|
||||
|
||||
const sortedTypes = TYPE_ORDER.filter(t => groups[t] && groups[t].length > 0);
|
||||
for (const t of Object.keys(groups)) {
|
||||
if (!sortedTypes.includes(t)) sortedTypes.push(t);
|
||||
}
|
||||
|
||||
let html = '';
|
||||
|
||||
if (commander) {
|
||||
html += '<div class="dg-card-group">'
|
||||
+ '<div class="dg-group-header">'
|
||||
+ '<span class="dg-group-title">⚔ Commander</span>'
|
||||
+ '<span class="dg-group-count">1</span>'
|
||||
+ '</div>'
|
||||
+ '<div class="dg-group-body">'
|
||||
+ '<div class="dg-card-row dg-card-row-commander">'
|
||||
+ '<span class="dg-card-row-name" style="font-weight:600;color:#fff">' + escHtml(commander.name) + '</span>'
|
||||
+ '<span class="dg-card-row-source dg-src-collection">Commander</span>'
|
||||
+ '</div>'
|
||||
+ '</div></div>';
|
||||
}
|
||||
|
||||
for (const type of sortedTypes) {
|
||||
const groupCards = groups[type];
|
||||
if (!groupCards || groupCards.length === 0) continue;
|
||||
|
||||
groupCards.sort((a, b) => (a.name || '').localeCompare(b.name || ''));
|
||||
|
||||
const icon = TYPE_ICONS[type] || '🃏';
|
||||
html += '<div class="dg-card-group">'
|
||||
+ '<div class="dg-group-header" data-type="' + type + '">'
|
||||
+ '<span class="dg-group-title">' + icon + ' ' + escHtml(type) + '</span>'
|
||||
+ '<span class="dg-group-count">' + groupCards.reduce(function(s,c){ return s + (c.count || 1); }, 0) + '</span>'
|
||||
+ '</div>'
|
||||
+ '<div class="dg-group-body" data-group="' + type + '">';
|
||||
|
||||
for (const card of groupCards) {
|
||||
html += renderCardRow(card);
|
||||
}
|
||||
|
||||
html += '</div></div>';
|
||||
}
|
||||
|
||||
container.innerHTML = html;
|
||||
|
||||
container.querySelectorAll('.dg-group-header').forEach(header => {
|
||||
header.addEventListener('click', () => {
|
||||
const type = header.dataset.type;
|
||||
if (!type) return;
|
||||
const body = container.querySelector('.dg-group-body[data-group="' + type + '"]');
|
||||
if (body) body.classList.toggle('collapsed');
|
||||
});
|
||||
});
|
||||
}
|
||||
|
||||
function renderCardRow(card) {
|
||||
const statusInfo = STATUS_BADGES[card.status] || STATUS_BADGES.missing;
|
||||
const statusBadge = '<span class="dg-card-status ' + statusInfo.cls + '">' + statusInfo.label + '</span>';
|
||||
|
||||
const roleTags = (card.role_tags || []).slice(0, 3)
|
||||
.map(r => '<span class="dg-role-chip">' + escHtml(r) + '</span>')
|
||||
.join('');
|
||||
|
||||
let subHtml = '';
|
||||
if (card.status === 'substituted' && card.selected_substitute) {
|
||||
subHtml = '<div class="dg-card-sub-line">'
|
||||
+ '<span class="dg-sub-arrow">→</span> '
|
||||
+ '<span class="dg-sub-name">' + escHtml(card.selected_substitute) + '</span>'
|
||||
+ '</div>';
|
||||
} else if (card.status === 'missing' && card.alternatives && card.alternatives.length > 0) {
|
||||
subHtml = '<div class="dg-card-sub-line dg-sub-suggestions">'
|
||||
+ '<span class="dg-sub-label">Alternatives:</span> '
|
||||
+ card.alternatives.slice(0, 3).map(a =>
|
||||
'<span class="dg-alt-chip" title="' + escAttr(a.reason || '') + '">'
|
||||
+ escHtml(a.name) + ' (' + (a.similarity_score * 100).toFixed(0) + '%)'
|
||||
+ '</span>'
|
||||
).join('')
|
||||
+ '</div>';
|
||||
}
|
||||
|
||||
const price = card.estimated_price_usd
|
||||
? '<span class="dg-card-price">$' + card.estimated_price_usd.toFixed(2) + '</span>'
|
||||
: '';
|
||||
|
||||
return '<div class="dg-card-row dg-card-status-' + card.status + '">'
|
||||
+ '<div class="dg-card-row-main">'
|
||||
+ '<span class="dg-card-row-name">' + ((card.count || 1) > 1 ? '<span class="dg-card-count">' + card.count + 'x</span> ' : '') + escHtml(card.name) + '</span>'
|
||||
+ '<span class="dg-card-row-roles">' + roleTags + '</span>'
|
||||
+ statusBadge
|
||||
+ price
|
||||
+ '</div>'
|
||||
+ (card.reason ? '<div class="dg-card-reason">' + escHtml(card.reason) + '</div>' : '')
|
||||
+ subHtml
|
||||
+ '</div>';
|
||||
}
|
||||
|
||||
// ── Commit Deck ─────────────────────────────────────────
|
||||
async function commitDeck() {
|
||||
if (!state.commander) { toast('No commander selected', 'error'); return; }
|
||||
|
||||
$('dg-save-btn').disabled = true;
|
||||
$('dg-save-btn').textContent = 'Saving...';
|
||||
|
||||
try {
|
||||
const body = state.lastRequestBody || buildRequestBody();
|
||||
const resp = await fetch(API_BASE + '/api/deck/v3/commit', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
if (!resp.ok) {
|
||||
const err = await resp.json().catch(() => ({}));
|
||||
throw new Error(err.error || err.detail || 'Save failed');
|
||||
}
|
||||
|
||||
const result = await resp.json();
|
||||
toast('Deck saved: ' + (result.deck_name || 'Unknown'), 'success');
|
||||
|
||||
setTimeout(() => {
|
||||
window.location.href = 'deckbuilder.html?deck_id=' + result.deck_id;
|
||||
}, 1200);
|
||||
} catch (e) {
|
||||
toast(e.message || 'Save failed', 'error');
|
||||
} finally {
|
||||
$('dg-save-btn').disabled = false;
|
||||
$('dg-save-btn').innerHTML = '<span class="dg-btn-icon">💾</span> Save to Deck Builder';
|
||||
}
|
||||
}
|
||||
|
||||
// ── Exports ─────────────────────────────────────────────
|
||||
async function exportDeck(format) {
|
||||
if (!state.commander) { toast('Generate a deck first', 'error'); return; }
|
||||
|
||||
const body = state.lastRequestBody || buildRequestBody();
|
||||
|
||||
try {
|
||||
const resp = await fetch(API_BASE + '/api/deck/v3/export/' + format, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
if (!resp.ok) throw new Error('Export failed');
|
||||
|
||||
const blob = await resp.blob();
|
||||
const url = URL.createObjectURL(blob);
|
||||
const a = document.createElement('a');
|
||||
a.href = url;
|
||||
a.download = (state.commander.name || 'deck').replace(/\s+/g, '_') + '.' + (format === 'csv' ? 'csv' : 'dck');
|
||||
a.click();
|
||||
URL.revokeObjectURL(url);
|
||||
|
||||
toast('Exported ' + format.toUpperCase(), 'success');
|
||||
} catch (e) {
|
||||
toast(e.message || 'Export failed', 'error');
|
||||
}
|
||||
}
|
||||
|
||||
async function exportMoxfield() {
|
||||
if (!state.commander) { toast('Generate a deck first', 'error'); return; }
|
||||
|
||||
const body = state.lastRequestBody || buildRequestBody();
|
||||
|
||||
try {
|
||||
const resp = await fetch(API_BASE + '/api/deck/v3/export/moxfield', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
const data = await resp.json();
|
||||
if (data.content) {
|
||||
await navigator.clipboard.writeText(data.content);
|
||||
toast('Moxfield deck copied to clipboard', 'success');
|
||||
}
|
||||
} catch (e) {
|
||||
toast('Moxfield export failed', 'error');
|
||||
}
|
||||
}
|
||||
|
||||
async function exportShopping() {
|
||||
if (!state.commander) { toast('Generate a deck first', 'error'); return; }
|
||||
|
||||
const body = state.lastRequestBody || buildRequestBody();
|
||||
|
||||
try {
|
||||
const resp = await fetch(API_BASE + '/api/deck/v3/export/shopping', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
|
||||
const data = await resp.json();
|
||||
const list = data.shopping_list || [];
|
||||
|
||||
if (list.length === 0) {
|
||||
toast('All cards are owned — nothing to buy', 'success');
|
||||
return;
|
||||
}
|
||||
|
||||
const lines = ['Shopping List — ' + (data.commander || 'Deck'), ''];
|
||||
for (const item of list) {
|
||||
lines.push(item.count + 'x ' + item.name + ' ($' + (item.estimated_price_usd || 0).toFixed(2) + ') — ' + item.category);
|
||||
}
|
||||
lines.push('', 'Total: ' + list.length + ' cards, ~$' + (data.estimated_cost_usd || 0).toFixed(2));
|
||||
|
||||
await navigator.clipboard.writeText(lines.join('\n'));
|
||||
toast('Shopping list copied (' + list.length + ' cards, ~$' + (data.estimated_cost_usd || 0).toFixed(0) + ')', 'success');
|
||||
} catch (e) {
|
||||
toast('Shopping list failed', 'error');
|
||||
}
|
||||
}
|
||||
|
||||
// ── Toast ───────────────────────────────────────────────
|
||||
function toast(msg, type) {
|
||||
type = type || 'info';
|
||||
const container = $('dg-toast-container');
|
||||
const el = document.createElement('div');
|
||||
el.className = 'dg-toast dg-toast-' + type;
|
||||
el.textContent = msg;
|
||||
container.appendChild(el);
|
||||
setTimeout(() => el.remove(), 4000);
|
||||
}
|
||||
|
||||
// ── Helpers ─────────────────────────────────────────────
|
||||
function escHtml(str) {
|
||||
const div = document.createElement('div');
|
||||
div.textContent = str;
|
||||
return div.innerHTML;
|
||||
}
|
||||
|
||||
function escAttr(str) {
|
||||
return str.replace(/"/g, '"').replace(/'/g, ''').replace(/</g, '<').replace(/>/g, '>');
|
||||
}
|
||||
|
||||
return { init };
|
||||
})();
|
||||
|
||||
document.addEventListener('DOMContentLoaded', () => {
|
||||
DeckGenerator.init();
|
||||
});
|
||||
-25735
File diff suppressed because it is too large.
Load diff
@@ -1,955 +0,0 @@
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
Commander AI Lab — Global Design System
|
||||
Dark & Polished · Premium Dashboard Aesthetic
|
||||
═══════════════════════════════════════════════════════════ */
|
||||
|
||||
/* ── Reset ──────────────────────────────────────────────── */
|
||||
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
|
||||
|
||||
/* ── Design Tokens ──────────────────────────────────────── */
|
||||
:root {
|
||||
/* Surfaces — layered dark with subtle warmth */
|
||||
--bg: #0c0e14;
|
||||
--surface-0: #12141c;
|
||||
--surface-1: #171a24;
|
||||
--surface-2: #1c1f2b;
|
||||
--surface-3: #222533;
|
||||
--surface-hover: #252839;
|
||||
|
||||
/* Borders */
|
||||
--border: #2a2d3a;
|
||||
--border-subtle: #1e2130;
|
||||
--border-focus: #4a6fa5;
|
||||
|
||||
/* Text */
|
||||
--text: #e1e4eb;
|
||||
--text-secondary:#8b90a0;
|
||||
--text-tertiary: #5c6070;
|
||||
--text-inverse: #0c0e14;
|
||||
|
||||
/* Accent — cool blue with subtle depth */
|
||||
--accent: #5b9ef0;
|
||||
--accent-hover: #7ab4ff;
|
||||
--accent-dim: rgba(91, 158, 240, 0.12);
|
||||
--accent-glow: rgba(91, 158, 240, 0.25);
|
||||
|
||||
/* Semantic */
|
||||
--success: #4ade80;
|
||||
--success-dim: rgba(74, 222, 128, 0.12);
|
||||
--warning: #fbbf24;
|
||||
--warning-dim: rgba(251, 191, 36, 0.12);
|
||||
--error: #f87171;
|
||||
--error-dim: rgba(248, 113, 113, 0.12);
|
||||
--purple: #a78bfa;
|
||||
--purple-dim: rgba(167, 139, 250, 0.12);
|
||||
--gold: #fcd34d;
|
||||
|
||||
/* Radius */
|
||||
--radius-sm: 6px;
|
||||
--radius-md: 10px;
|
||||
--radius-lg: 14px;
|
||||
--radius-xl: 20px;
|
||||
|
||||
/* Shadows */
|
||||
--shadow-sm: 0 1px 3px rgba(0,0,0,0.3);
|
||||
--shadow-md: 0 4px 16px rgba(0,0,0,0.35);
|
||||
--shadow-lg: 0 12px 40px rgba(0,0,0,0.45);
|
||||
--shadow-glow: 0 0 20px var(--accent-dim);
|
||||
|
||||
/* Transitions */
|
||||
--ease: cubic-bezier(0.16, 1, 0.3, 1);
|
||||
--duration: 180ms;
|
||||
|
||||
/* Typography */
|
||||
--font: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif;
|
||||
--font-mono: 'JetBrains Mono', 'Fira Code', 'Cascadia Code', monospace;
|
||||
|
||||
/* Spacing scale */
|
||||
--sp-1: 4px; --sp-2: 8px; --sp-3: 12px;
|
||||
--sp-4: 16px; --sp-5: 20px; --sp-6: 24px;
|
||||
--sp-8: 32px; --sp-10: 40px; --sp-12: 48px;
|
||||
--sp-16: 64px;
|
||||
|
||||
/* Layout */
|
||||
--content-max: 1280px;
|
||||
--sidebar-width: 280px;
|
||||
--nav-height: 56px;
|
||||
}
|
||||
|
||||
/* ── Base ───────────────────────────────────────────────── */
|
||||
html {
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-moz-osx-font-smoothing: grayscale;
|
||||
text-rendering: optimizeLegibility;
|
||||
scroll-behavior: smooth;
|
||||
}
|
||||
|
||||
body {
|
||||
min-height: 100vh;
|
||||
font-family: var(--font);
|
||||
font-size: 14px;
|
||||
line-height: 1.6;
|
||||
color: var(--text);
|
||||
background: var(--bg);
|
||||
}
|
||||
|
||||
h1, h2, h3, h4, h5, h6 {
|
||||
color: #fff;
|
||||
font-weight: 600;
|
||||
line-height: 1.25;
|
||||
}
|
||||
|
||||
h1 { font-size: 24px; }
|
||||
h2 { font-size: 18px; }
|
||||
h3 { font-size: 16px; }
|
||||
|
||||
a { color: var(--accent); text-decoration: none; transition: color var(--duration) var(--ease); }
|
||||
a:hover { color: var(--accent-hover); }
|
||||
|
||||
img, svg { display: block; max-width: 100%; }
|
||||
button { cursor: pointer; font: inherit; }
|
||||
input, select, textarea { font: inherit; color: inherit; }
|
||||
table { border-collapse: collapse; width: 100%; }
|
||||
|
||||
::selection {
|
||||
background: var(--accent-dim);
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
:focus-visible {
|
||||
outline: 2px solid var(--accent);
|
||||
outline-offset: 2px;
|
||||
border-radius: var(--radius-sm);
|
||||
}
|
||||
|
||||
/* ── Scrollbar ──────────────────────────────────────────── */
|
||||
::-webkit-scrollbar { width: 8px; height: 8px; }
|
||||
::-webkit-scrollbar-track { background: transparent; }
|
||||
::-webkit-scrollbar-thumb { background: var(--border); border-radius: 4px; }
|
||||
::-webkit-scrollbar-thumb:hover { background: var(--text-tertiary); }
|
||||
|
||||
/* ── Navigation ─────────────────────────────────────────── */
|
||||
.nav {
|
||||
position: sticky;
|
||||
top: 0;
|
||||
z-index: 100;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
height: var(--nav-height);
|
||||
padding: 0 var(--sp-6);
|
||||
background: rgba(12, 14, 20, 0.85);
|
||||
backdrop-filter: blur(16px) saturate(1.5);
|
||||
-webkit-backdrop-filter: blur(16px) saturate(1.5);
|
||||
border-bottom: 1px solid var(--border-subtle);
|
||||
}
|
||||
|
||||
.nav-brand {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: var(--sp-3);
|
||||
font-size: 15px;
|
||||
font-weight: 700;
|
||||
color: #fff;
|
||||
letter-spacing: -0.01em;
|
||||
text-decoration: none;
|
||||
white-space: nowrap;
|
||||
margin-right: var(--sp-8);
|
||||
}
|
||||
|
||||
.nav-brand svg {
|
||||
width: 28px;
|
||||
height: 28px;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.nav-links {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: var(--sp-1);
|
||||
overflow-x: auto;
|
||||
scrollbar-width: none;
|
||||
}
|
||||
|
||||
.nav-links::-webkit-scrollbar { display: none; }
|
||||
|
||||
.nav-links a {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: var(--sp-2);
|
||||
padding: 6px 12px;
|
||||
font-size: 13px;
|
||||
font-weight: 500;
|
||||
color: var(--text-secondary);
|
||||
border-radius: var(--radius-sm);
|
||||
white-space: nowrap;
|
||||
transition: all var(--duration) var(--ease);
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
.nav-links a:hover {
|
||||
color: var(--text);
|
||||
background: var(--surface-2);
|
||||
}
|
||||
|
||||
.nav-links a.active {
|
||||
color: #fff;
|
||||
background: var(--accent-dim);
|
||||
}
|
||||
|
||||
.nav-links a .nav-icon {
|
||||
width: 16px;
|
||||
height: 16px;
|
||||
opacity: 0.7;
|
||||
}
|
||||
|
||||
.nav-links a.active .nav-icon,
|
||||
.nav-links a:hover .nav-icon {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
/* ── Page Layout ────────────────────────────────────────── */
|
||||
.page {
|
||||
max-width: var(--content-max);
|
||||
margin: 0 auto;
|
||||
padding: var(--sp-8) var(--sp-6);
|
||||
}
|
||||
|
||||
.page-header {
|
||||
margin-bottom: var(--sp-8);
|
||||
}
|
||||
|
||||
.page-header h1 {
|
||||
font-size: 28px;
|
||||
font-weight: 700;
|
||||
letter-spacing: -0.02em;
|
||||
margin-bottom: var(--sp-2);
|
||||
}
|
||||
|
||||
.page-header p {
|
||||
color: var(--text-secondary);
|
||||
font-size: 15px;
|
||||
}
|
||||
|
||||
/* ── Cards / Panels ─────────────────────────────────────── */
|
||||
.panel {
|
||||
background: var(--surface-1);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: var(--radius-lg);
|
||||
padding: var(--sp-6);
|
||||
margin-bottom: var(--sp-6);
|
||||
transition: border-color var(--duration) var(--ease);
|
||||
}
|
||||
|
||||
.panel:hover {
|
||||
border-color: var(--border-focus);
|
||||
}
|
||||
|
||||
.panel-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
margin-bottom: var(--sp-5);
|
||||
}
|
||||
|
||||
.panel-header h2 {
|
||||
font-size: 16px;
|
||||
font-weight: 600;
|
||||
}
|
||||
|
||||
.panel-header .badge {
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
padding: 3px 10px;
|
||||
border-radius: var(--radius-sm);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.05em;
|
||||
}
|
||||
|
||||
/* ── Stat Cards ─────────────────────────────────────────── */
|
||||
.stat-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
|
||||
gap: var(--sp-4);
|
||||
margin-bottom: var(--sp-6);
|
||||
}
|
||||
|
||||
.stat-card {
|
||||
background: var(--surface-1);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: var(--radius-md);
|
||||
padding: var(--sp-5);
|
||||
transition: all var(--duration) var(--ease);
|
||||
}
|
||||
|
||||
.stat-card:hover {
|
||||
border-color: var(--border-focus);
|
||||
transform: translateY(-1px);
|
||||
box-shadow: var(--shadow-md);
|
||||
}
|
||||
|
||||
.stat-label {
|
||||
font-size: 12px;
|
||||
font-weight: 500;
|
||||
color: var(--text-secondary);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.06em;
|
||||
margin-bottom: var(--sp-2);
|
||||
}
|
||||
|
||||
.stat-value {
|
||||
font-size: 28px;
|
||||
font-weight: 700;
|
||||
color: #fff;
|
||||
line-height: 1.1;
|
||||
font-variant-numeric: tabular-nums;
|
||||
}
|
||||
|
||||
.stat-sub {
|
||||
font-size: 12px;
|
||||
color: var(--text-tertiary);
|
||||
margin-top: var(--sp-1);
|
||||
}
|
||||
|
||||
/* ── Buttons ────────────────────────────────────────────── */
|
||||
.btn {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
gap: var(--sp-2);
|
||||
padding: 8px 18px;
|
||||
font-size: 13px;
|
||||
font-weight: 600;
|
||||
border-radius: var(--radius-sm);
|
||||
border: none;
|
||||
cursor: pointer;
|
||||
transition: all var(--duration) var(--ease);
|
||||
white-space: nowrap;
|
||||
line-height: 1.4;
|
||||
}
|
||||
|
||||
.btn-primary {
|
||||
background: var(--accent);
|
||||
color: var(--text-inverse);
|
||||
}
|
||||
|
||||
.btn-primary:hover {
|
||||
background: var(--accent-hover);
|
||||
box-shadow: 0 4px 14px var(--accent-glow);
|
||||
transform: translateY(-1px);
|
||||
}
|
||||
|
||||
.btn-primary:active {
|
||||
transform: translateY(0);
|
||||
}
|
||||
|
||||
.btn-primary:disabled {
|
||||
opacity: 0.4;
|
||||
cursor: not-allowed;
|
||||
transform: none;
|
||||
box-shadow: none;
|
||||
}
|
||||
|
||||
.btn-secondary {
|
||||
background: var(--surface-3);
|
||||
color: var(--text);
|
||||
border: 1px solid var(--border);
|
||||
}
|
||||
|
||||
.btn-secondary:hover {
|
||||
background: var(--surface-hover);
|
||||
border-color: var(--text-tertiary);
|
||||
}
|
||||
|
||||
.btn-ghost {
|
||||
background: transparent;
|
||||
color: var(--text-secondary);
|
||||
padding: 6px 12px;
|
||||
}
|
||||
|
||||
.btn-ghost:hover {
|
||||
color: var(--text);
|
||||
background: var(--surface-2);
|
||||
}
|
||||
|
||||
.btn-success { background: var(--success); color: var(--text-inverse); }
|
||||
.btn-success:hover { box-shadow: 0 4px 14px var(--success-dim); }
|
||||
|
||||
.btn-purple { background: var(--purple); color: var(--text-inverse); }
|
||||
.btn-purple:hover { box-shadow: 0 4px 14px var(--purple-dim); }
|
||||
|
||||
.btn-danger { background: var(--error); color: #fff; }
|
||||
.btn-danger:hover { box-shadow: 0 4px 14px var(--error-dim); }
|
||||
|
||||
.btn-sm { padding: 5px 12px; font-size: 12px; }
|
||||
.btn-lg { padding: 12px 24px; font-size: 15px; }
|
||||
.btn-icon-only { padding: 8px; width: 36px; height: 36px; }
|
||||
|
||||
/* ── Forms ──────────────────────────────────────────────── */
|
||||
.form-group {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
gap: var(--sp-2);
|
||||
}
|
||||
|
||||
.form-label {
|
||||
font-size: 12px;
|
||||
font-weight: 600;
|
||||
color: var(--text-secondary);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.05em;
|
||||
}
|
||||
|
||||
.form-input,
|
||||
.form-select,
|
||||
.form-textarea {
|
||||
width: 100%;
|
||||
padding: 9px 14px;
|
||||
font-size: 14px;
|
||||
color: var(--text);
|
||||
background: var(--surface-0);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: var(--radius-sm);
|
||||
transition: all var(--duration) var(--ease);
|
||||
}
|
||||
|
||||
.form-input:focus,
|
||||
.form-select:focus,
|
||||
.form-textarea:focus {
|
||||
border-color: var(--accent);
|
||||
box-shadow: 0 0 0 3px var(--accent-dim);
|
||||
outline: none;
|
||||
}
|
||||
|
||||
.form-input::placeholder,
|
||||
.form-textarea::placeholder {
|
||||
color: var(--text-tertiary);
|
||||
}
|
||||
|
||||
.form-select {
|
||||
cursor: pointer;
|
||||
appearance: none;
|
||||
background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='12' height='12' viewBox='0 0 24 24' fill='none' stroke='%235c6070' stroke-width='2'%3E%3Cpath d='M6 9l6 6 6-6'/%3E%3C/svg%3E");
|
||||
background-repeat: no-repeat;
|
||||
background-position: right 12px center;
|
||||
padding-right: 36px;
|
||||
}
|
||||
|
||||
.form-textarea {
|
||||
resize: vertical;
|
||||
min-height: 100px;
|
||||
}
|
||||
|
||||
.form-checkbox {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: var(--sp-2);
|
||||
font-size: 13px;
|
||||
color: var(--text-secondary);
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.form-checkbox input[type="checkbox"] {
|
||||
width: 16px;
|
||||
height: 16px;
|
||||
accent-color: var(--accent);
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.form-row {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(auto-fit, minmax(160px, 1fr));
|
||||
gap: var(--sp-4);
|
||||
}
|
||||
|
||||
/* ── Progress Bar ───────────────────────────────────────── */
|
||||
.progress-container {
|
||||
margin-top: var(--sp-4);
|
||||
padding: var(--sp-4);
|
||||
background: var(--surface-0);
|
||||
border-radius: var(--radius-md);
|
||||
border: 1px solid var(--border-subtle);
|
||||
}
|
||||
|
||||
.progress-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: var(--sp-3);
|
||||
margin-bottom: var(--sp-3);
|
||||
}
|
||||
|
||||
.progress-label {
|
||||
font-size: 13px;
|
||||
font-weight: 500;
|
||||
color: var(--text);
|
||||
}
|
||||
|
||||
.progress-track {
|
||||
height: 6px;
|
||||
background: var(--surface-3);
|
||||
border-radius: 3px;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.progress-fill {
|
||||
height: 100%;
|
||||
background: linear-gradient(90deg, var(--accent), var(--accent-hover));
|
||||
border-radius: 3px;
|
||||
transition: width 0.3s var(--ease);
|
||||
}
|
||||
|
||||
.progress-detail {
|
||||
margin-top: var(--sp-2);
|
||||
font-size: 12px;
|
||||
font-family: var(--font-mono);
|
||||
color: var(--text-tertiary);
|
||||
line-height: 1.6;
|
||||
}
|
||||
|
||||
/* ── Phase Badge ────────────────────────────────────────── */
|
||||
.badge-status {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
padding: 3px 10px;
|
||||
border-radius: var(--radius-sm);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.05em;
|
||||
}
|
||||
|
||||
.badge-status::before {
|
||||
content: '';
|
||||
width: 6px;
|
||||
height: 6px;
|
||||
border-radius: 50%;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.badge-idle { background: var(--surface-3); color: var(--text-secondary); }
|
||||
.badge-idle::before { background: var(--text-tertiary); }
|
||||
|
||||
.badge-running { background: var(--accent-dim); color: var(--accent); }
|
||||
.badge-running::before { background: var(--accent); animation: pulse-dot 1.5s ease-in-out infinite; }
|
||||
|
||||
.badge-success { background: var(--success-dim); color: var(--success); }
|
||||
.badge-success::before { background: var(--success); }
|
||||
|
||||
.badge-error { background: var(--error-dim); color: var(--error); }
|
||||
.badge-error::before { background: var(--error); }
|
||||
|
||||
.badge-warning { background: var(--warning-dim); color: var(--warning); }
|
||||
.badge-warning::before { background: var(--warning); }
|
||||
|
||||
@keyframes pulse-dot {
|
||||
0%, 100% { opacity: 1; }
|
||||
50% { opacity: 0.4; }
|
||||
}
|
||||
|
||||
/* ── Empty State ────────────────────────────────────────── */
|
||||
.empty-state {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
padding: var(--sp-12) var(--sp-6);
|
||||
text-align: center;
|
||||
color: var(--text-tertiary);
|
||||
}
|
||||
|
||||
.empty-state svg {
|
||||
width: 48px;
|
||||
height: 48px;
|
||||
margin-bottom: var(--sp-4);
|
||||
opacity: 0.3;
|
||||
}
|
||||
|
||||
.empty-state p {
|
||||
font-size: 14px;
|
||||
margin-bottom: var(--sp-3);
|
||||
}
|
||||
|
||||
/* ── Tables ─────────────────────────────────────────────── */
|
||||
.data-table {
|
||||
width: 100%;
|
||||
border-collapse: collapse;
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
.data-table thead th {
|
||||
text-align: left;
|
||||
padding: var(--sp-3) var(--sp-4);
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
color: var(--text-secondary);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.06em;
|
||||
border-bottom: 1px solid var(--border);
|
||||
background: var(--surface-0);
|
||||
}
|
||||
|
||||
.data-table tbody td {
|
||||
padding: var(--sp-3) var(--sp-4);
|
||||
border-bottom: 1px solid var(--border-subtle);
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
.data-table tbody tr:hover {
|
||||
background: var(--surface-2);
|
||||
}
|
||||
|
||||
.data-table tbody tr:last-child td {
|
||||
border-bottom: none;
|
||||
}
|
||||
|
||||
/* ── Tags ───────────────────────────────────────────────── */
|
||||
.tag {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
padding: 2px 8px;
|
||||
font-size: 11px;
|
||||
font-weight: 600;
|
||||
border-radius: var(--radius-sm);
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.tag-blue { background: var(--accent-dim); color: var(--accent); }
|
||||
.tag-green { background: var(--success-dim); color: var(--success); }
|
||||
.tag-yellow { background: var(--warning-dim); color: var(--warning); }
|
||||
.tag-red { background: var(--error-dim); color: var(--error); }
|
||||
.tag-purple { background: var(--purple-dim); color: var(--purple); }
|
||||
|
||||
/* ── Toast / Notification ───────────────────────────────── */
|
||||
.toast {
|
||||
position: fixed;
|
||||
bottom: var(--sp-6);
|
||||
right: var(--sp-6);
|
||||
padding: var(--sp-3) var(--sp-5);
|
||||
background: var(--surface-3);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: var(--radius-md);
|
||||
box-shadow: var(--shadow-lg);
|
||||
font-size: 13px;
|
||||
color: var(--text);
|
||||
z-index: 1000;
|
||||
animation: toast-in 0.3s var(--ease);
|
||||
}
|
||||
|
||||
.toast-success { border-left: 3px solid var(--success); }
|
||||
.toast-error { border-left: 3px solid var(--error); }
|
||||
.toast-warning { border-left: 3px solid var(--warning); }
|
||||
|
||||
@keyframes toast-in {
|
||||
from { opacity: 0; transform: translateY(16px); }
|
||||
to { opacity: 1; transform: translateY(0); }
|
||||
}
|
||||
|
||||
/* ── Loading Spinner ────────────────────────────────────── */
|
||||
.spinner {
|
||||
width: 20px;
|
||||
height: 20px;
|
||||
border: 2px solid var(--border);
|
||||
border-top-color: var(--accent);
|
||||
border-radius: 50%;
|
||||
animation: spin 0.6s linear infinite;
|
||||
}
|
||||
|
||||
@keyframes spin {
|
||||
to { transform: rotate(360deg); }
|
||||
}
|
||||
|
||||
/* ── Divider ────────────────────────────────────────────── */
|
||||
.divider {
|
||||
height: 1px;
|
||||
background: var(--border);
|
||||
margin: var(--sp-5) 0;
|
||||
}
|
||||
|
||||
/* ── Utility Classes ────────────────────────────────────── */
|
||||
.hidden { display: none !important; }
|
||||
.text-accent { color: var(--accent); }
|
||||
.text-success { color: var(--success); }
|
||||
.text-warning { color: var(--warning); }
|
||||
.text-error { color: var(--error); }
|
||||
.text-muted { color: var(--text-secondary); }
|
||||
.text-faint { color: var(--text-tertiary); }
|
||||
.text-mono { font-family: var(--font-mono); }
|
||||
.text-sm { font-size: 12px; }
|
||||
.text-xs { font-size: 11px; }
|
||||
.font-bold { font-weight: 700; }
|
||||
.text-center { text-align: center; }
|
||||
.text-right { text-align: right; }
|
||||
.mt-2 { margin-top: var(--sp-2); }
|
||||
.mt-4 { margin-top: var(--sp-4); }
|
||||
.mt-6 { margin-top: var(--sp-6); }
|
||||
.mb-2 { margin-bottom: var(--sp-2); }
|
||||
.mb-4 { margin-bottom: var(--sp-4); }
|
||||
.mb-6 { margin-bottom: var(--sp-6); }
|
||||
.gap-2 { gap: var(--sp-2); }
|
||||
.gap-4 { gap: var(--sp-4); }
|
||||
.flex { display: flex; }
|
||||
.flex-col { flex-direction: column; }
|
||||
.items-center { align-items: center; }
|
||||
.justify-between { justify-content: space-between; }
|
||||
.flex-1 { flex: 1; }
|
||||
.grid { display: grid; }
|
||||
.sr-only {
|
||||
position: absolute; width: 1px; height: 1px; padding: 0; margin: -1px;
|
||||
overflow: hidden; clip: rect(0,0,0,0); white-space: nowrap; border-width: 0;
|
||||
}
|
||||
|
||||
/* ── Responsive ─────────────────────────────────────────── */
|
||||
@media (max-width: 768px) {
|
||||
.page {
|
||||
padding: var(--sp-5) var(--sp-4);
|
||||
}
|
||||
|
||||
.nav {
|
||||
padding: 0 var(--sp-4);
|
||||
}
|
||||
|
||||
.nav-brand {
|
||||
margin-right: var(--sp-4);
|
||||
font-size: 13px;
|
||||
}
|
||||
|
||||
.stat-grid {
|
||||
grid-template-columns: repeat(2, 1fr);
|
||||
}
|
||||
|
||||
.form-row {
|
||||
grid-template-columns: 1fr;
|
||||
}
|
||||
}
|
||||
|
||||
@media (max-width: 480px) {
|
||||
.stat-grid {
|
||||
grid-template-columns: 1fr;
|
||||
}
|
||||
}
|
||||
|
||||
/* ═══════════════════════════════════════════════════════════
|
||||
PREMIUM UPGRADE LAYER
|
||||
═══════════════════════════════════════════════════════════ */
|
||||
|
||||
/* ── Ambient background glow (body) ─────────────────────── */
|
||||
body {
|
||||
background-image:
|
||||
radial-gradient(ellipse 80% 50% at 10% -10%, rgba(91,158,240,0.07) 0%, transparent 60%),
|
||||
radial-gradient(ellipse 60% 40% at 90% 110%, rgba(167,139,250,0.05) 0%, transparent 60%);
|
||||
background-attachment: fixed;
|
||||
}
|
||||
|
||||
/* ── Page title: subtle gradient text ───────────────────── */
|
||||
.page-header h1 {
|
||||
background: linear-gradient(135deg, #fff 60%, rgba(91,158,240,0.85) 100%);
|
||||
-webkit-background-clip: text;
|
||||
-webkit-text-fill-color: transparent;
|
||||
background-clip: text;
|
||||
}
|
||||
|
||||
/* ── Nav: stronger glass + accent underline ─────────────── */
|
||||
.nav {
|
||||
background: rgba(9,11,16,0.80);
|
||||
backdrop-filter: blur(20px) saturate(1.8);
|
||||
-webkit-backdrop-filter: blur(20px) saturate(1.8);
|
||||
box-shadow: 0 1px 0 rgba(91,158,240,0.08), 0 4px 24px rgba(0,0,0,0.3);
|
||||
border-bottom-color: rgba(255,255,255,0.06);
|
||||
}
|
||||
.nav-brand svg { filter: drop-shadow(0 0 8px rgba(91,158,240,0.4)); }
|
||||
.nav-links a.active { box-shadow: inset 0 0 0 1px rgba(91,158,240,0.2); }
|
||||
|
||||
/* ── Panels: gradient border on hover ───────────────────── */
|
||||
.panel {
|
||||
position: relative;
|
||||
transition: border-color var(--duration) var(--ease),
|
||||
box-shadow var(--duration) var(--ease);
|
||||
}
|
||||
.panel::before {
|
||||
content: '';
|
||||
position: absolute;
|
||||
inset: 0;
|
||||
border-radius: var(--radius-lg);
|
||||
padding: 1px;
|
||||
background: linear-gradient(135deg, rgba(91,158,240,0.18), transparent 50%, rgba(167,139,250,0.1));
|
||||
-webkit-mask: linear-gradient(#fff 0 0) content-box, linear-gradient(#fff 0 0);
|
||||
-webkit-mask-composite: xor;
|
||||
mask-composite: exclude;
|
||||
pointer-events: none;
|
||||
opacity: 0;
|
||||
transition: opacity var(--duration) var(--ease);
|
||||
}
|
||||
.panel:hover::before { opacity: 1; }
|
||||
.panel:hover {
|
||||
box-shadow: 0 8px 32px rgba(0,0,0,0.35), 0 0 0 1px rgba(91,158,240,0.08);
|
||||
}
|
||||
|
||||
/* ── Stat cards: gradient surface + shimmer top edge ─────── */
|
||||
.stat-card {
|
||||
position: relative;
|
||||
background: linear-gradient(145deg, var(--surface-2), var(--surface-1));
|
||||
overflow: hidden;
|
||||
}
|
||||
.stat-card::after {
|
||||
content: '';
|
||||
position: absolute;
|
||||
top: 0; left: 0; right: 0;
|
||||
height: 1px;
|
||||
background: linear-gradient(90deg, transparent, rgba(255,255,255,0.12), transparent);
|
||||
pointer-events: none;
|
||||
}
|
||||
.stat-card:hover {
|
||||
box-shadow: var(--shadow-md), 0 0 20px rgba(91,158,240,0.08);
|
||||
transform: translateY(-2px);
|
||||
}
|
||||
|
||||
/* ── Buttons: gradient fills + inner highlight ───────────── */
|
||||
.btn { position: relative; overflow: hidden; }
|
||||
.btn::after {
|
||||
content: '';
|
||||
position: absolute;
|
||||
top: 0; left: 0; right: 0;
|
||||
height: 50%;
|
||||
background: linear-gradient(to bottom, rgba(255,255,255,0.07), transparent);
|
||||
border-radius: inherit;
|
||||
pointer-events: none;
|
||||
}
|
||||
.btn-primary {
|
||||
background: linear-gradient(135deg, var(--accent), #4a8de0);
|
||||
box-shadow: 0 2px 8px rgba(91,158,240,0.25);
|
||||
}
|
||||
.btn-primary:hover {
|
||||
background: linear-gradient(135deg, var(--accent-hover), var(--accent));
|
||||
box-shadow: 0 4px 20px rgba(91,158,240,0.4);
|
||||
}
|
||||
.btn-success {
|
||||
background: linear-gradient(135deg, var(--success), #38c46c);
|
||||
box-shadow: 0 2px 8px rgba(74,222,128,0.2);
|
||||
}
|
||||
.btn-success:hover { box-shadow: 0 4px 20px rgba(74,222,128,0.4); transform: translateY(-1px); }
|
||||
.btn-purple {
|
||||
background: linear-gradient(135deg, var(--purple), #9171f0);
|
||||
box-shadow: 0 2px 8px rgba(167,139,250,0.2);
|
||||
}
|
||||
.btn-purple:hover { box-shadow: 0 4px 20px rgba(167,139,250,0.4); transform: translateY(-1px); }
|
||||
|
||||
/* ── Tags: subtle colored border ────────────────────────── */
|
||||
.tag-blue { border: 1px solid rgba(91,158,240,0.25); }
|
||||
.tag-green { border: 1px solid rgba(74,222,128,0.25); }
|
||||
.tag-yellow { border: 1px solid rgba(251,191,36,0.25); }
|
||||
.tag-red { border: 1px solid rgba(248,113,113,0.25); }
|
||||
.tag-purple { border: 1px solid rgba(167,139,250,0.25); }
|
||||
|
||||
/* ── Progress fill: glow ─────────────────────────────────── */
|
||||
.progress-fill { box-shadow: 0 0 8px rgba(91,158,240,0.5); }
|
||||
|
||||
/* ── Badge running: glow dot ─────────────────────────────── */
|
||||
.badge-running { box-shadow: 0 0 12px rgba(91,158,240,0.15); }
|
||||
.badge-running::before { box-shadow: 0 0 6px var(--accent); }
|
||||
|
||||
/* ── Toast: glassmorphism + colored glow ─────────────────── */
|
||||
.toast { backdrop-filter: blur(12px); }
|
||||
.toast-success { box-shadow: var(--shadow-lg), 0 0 20px rgba(74,222,128,0.1); }
|
||||
.toast-error { box-shadow: var(--shadow-lg), 0 0 20px rgba(248,113,113,0.1); }
|
||||
|
||||
/* ── Divider: fade-out gradient ──────────────────────────── */
|
||||
.divider {
|
||||
background: linear-gradient(90deg, transparent, var(--border), transparent);
|
||||
}
|
||||
|
||||
/* ── Inputs: inset shadow for depth ─────────────────────── */
|
||||
.form-input, .form-select, .form-textarea {
|
||||
box-shadow: inset 0 1px 3px rgba(0,0,0,0.25);
|
||||
}
|
||||
.form-input:focus, .form-select:focus, .form-textarea:focus {
|
||||
box-shadow: 0 0 0 3px var(--accent-dim), inset 0 1px 3px rgba(0,0,0,0.1);
|
||||
}
|
||||
|
||||
|
||||
/* ══════════════════════════════════════════════════════════════
|
||||
Tooltip System (.tip-icon + data-tip)
|
||||
Shared across all pages — do not duplicate in page-specific CSS
|
||||
══════════════════════════════════════════════════════════════ */
|
||||
|
||||
.tip-icon {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
width: 15px;
|
||||
height: 15px;
|
||||
border-radius: 50%;
|
||||
background: var(--surface-3);
|
||||
color: var(--text-secondary);
|
||||
font-size: 10px;
|
||||
font-weight: 700;
|
||||
font-style: normal;
|
||||
cursor: default;
|
||||
position: relative;
|
||||
vertical-align: middle;
|
||||
margin-left: 4px;
|
||||
flex-shrink: 0;
|
||||
transition: background 0.15s, color 0.15s;
|
||||
user-select: none;
|
||||
}
|
||||
|
||||
.tip-icon:hover {
|
||||
background: var(--accent-dim);
|
||||
color: var(--accent);
|
||||
}
|
||||
|
||||
.tip-icon::after {
|
||||
content: attr(data-tip);
|
||||
position: absolute;
|
||||
bottom: calc(100% + 8px);
|
||||
left: 50%;
|
||||
transform: translateX(-50%);
|
||||
background: var(--surface-0, #1a1a2e);
|
||||
color: var(--text, #e0e0e0);
|
||||
border: 1px solid var(--border, #333);
|
||||
border-radius: var(--radius-md, 6px);
|
||||
padding: 8px 12px;
|
||||
font-size: 12px;
|
||||
font-weight: 400;
|
||||
line-height: 1.5;
|
||||
white-space: normal;
|
||||
width: 240px;
|
||||
box-shadow: 0 4px 16px rgba(0, 0, 0, 0.4);
|
||||
pointer-events: none;
|
||||
opacity: 0;
|
||||
transition: opacity 0.15s ease;
|
||||
z-index: 1000;
|
||||
text-align: left;
|
||||
text-transform: none;
|
||||
letter-spacing: 0;
|
||||
}
|
||||
|
||||
.tip-icon::before {
|
||||
content: '';
|
||||
position: absolute;
|
||||
bottom: calc(100% + 3px);
|
||||
left: 50%;
|
||||
transform: translateX(-50%);
|
||||
border: 5px solid transparent;
|
||||
border-top-color: var(--border, #333);
|
||||
pointer-events: none;
|
||||
opacity: 0;
|
||||
transition: opacity 0.15s ease;
|
||||
z-index: 1001;
|
||||
}
|
||||
|
||||
.tip-icon:hover::after,
|
||||
.tip-icon:hover::before {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
.tip-icon.tip-below::after {
|
||||
bottom: auto;
|
||||
top: calc(100% + 8px);
|
||||
}
|
||||
|
||||
.tip-icon.tip-below::before {
|
||||
bottom: auto;
|
||||
top: calc(100% + 3px);
|
||||
border-top-color: transparent;
|
||||
border-bottom-color: var(--border, #333);
|
||||
}
|
||||
@@ -1,944 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>MTG Commander Battlefield</title>
|
||||
<link rel="stylesheet" href="style.css?v=20260403_0300">
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<!-- ====================================================
|
||||
SETUP SCREEN
|
||||
==================================================== -->
|
||||
<div id="setup-screen">
|
||||
<!-- Populated by ui.js initSetupScreen() -->
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
TABLE SURFACE — 4-Quadrant Battlefield
|
||||
==================================================== -->
|
||||
<div class="table-surface" id="game-battlefield" style="display:none;">
|
||||
|
||||
<!-- TOP BAR: Phase tracker + controls -->
|
||||
<div id="top-bar">
|
||||
<span class="top-bar-logo">Commander</span>
|
||||
|
||||
<div class="turn-counter-badge" id="turn-counter">Turn <span>1</span></div>
|
||||
|
||||
<div class="active-player-indicator" id="active-player-indicator">
|
||||
<span class="active-player-dot" id="active-dot" style="background:#c0392b;"></span>
|
||||
Player 1's Turn
|
||||
</div>
|
||||
|
||||
<!-- Phase track -->
|
||||
<div class="phase-track-wrapper">
|
||||
<div class="phase-track" id="phase-track"></div>
|
||||
</div>
|
||||
|
||||
<!-- Phase/turn action buttons -->
|
||||
<div class="top-bar-actions">
|
||||
<button class="btn-phase" onclick="nextPhase()" title="Advance to next phase (N)">Next Phase</button>
|
||||
<button class="btn-phase btn-next-turn" onclick="nextTurn()" title="End turn, advance to next player (T)">Next Turn</button>
|
||||
</div>
|
||||
|
||||
<!-- Utility buttons -->
|
||||
<div class="top-bar-utils">
|
||||
<button class="btn-util" id="btn-undo" onclick="undo()" title="Undo (Ctrl+Z)" disabled>↩</button>
|
||||
<button class="btn-util" id="btn-redo" onclick="redo()" title="Redo (Ctrl+Y)" disabled>↪</button>
|
||||
<button class="btn-util" id="btn-triggers" onclick="toggleTriggerStack()" title="Trigger Stack">❇</button>
|
||||
<button class="btn-util" onclick="openDiceModal()" title="Dice & Coins (G)">🎲</button>
|
||||
<button class="btn-util" onclick="checkStateBasedActions()" title="Check State-Based Actions">SBA</button>
|
||||
<button class="btn-util" onclick="toggleSettingsPanel()" title="Settings">⚙</button>
|
||||
<button class="btn-util" id="btn-search-toggle" onclick="document.getElementById('card-search-sidebar').classList.toggle('collapsed')" title="Card Search (S)">🔍</button>
|
||||
<button class="btn-util" id="btn-deckbuilder-toggle" onclick="toggleDeckBuilderPanel()" title="Deck Builder">🃏</button>
|
||||
<button class="btn-util btn-util-exit" onclick="location.reload()" title="Return to Start Screen">✕</button>
|
||||
</div>
|
||||
|
||||
<!-- Storm counter -->
|
||||
<span id="storm-badge" style="display:none;font-size:10px;color:var(--mtg-gold);background:rgba(200,168,67,0.15);border:1px solid rgba(200,168,67,0.3);border-radius:4px;padding:2px 7px;white-space:nowrap;">Storm: 0</span>
|
||||
</div>
|
||||
|
||||
<!-- DIVIDER LINES -->
|
||||
<div class="divider-h"></div>
|
||||
<div class="divider-v"></div>
|
||||
|
||||
<!-- CORNER ACCENTS -->
|
||||
<div class="corner-accent tl"></div>
|
||||
<div class="corner-accent tr"></div>
|
||||
<div class="corner-accent bl"></div>
|
||||
<div class="corner-accent br"></div>
|
||||
|
||||
|
||||
<!-- ================================================
|
||||
4-QUADRANT BATTLEFIELD
|
||||
================================================ -->
|
||||
<div class="battlefield">
|
||||
|
||||
<!-- PLAYER 1: Top-Left (Crimson) -->
|
||||
<div class="player-area p1" id="player-area-1">
|
||||
<span class="player-label" id="player-label-1">Player 1</span>
|
||||
|
||||
<!-- Zone Row: Library, Exile, Graveyard, Hand -->
|
||||
<div class="zone-row" id="zone-row-1">
|
||||
<div class="zone library" id="lib-zone-1" data-player="1" data-zone="library"
|
||||
onclick="openZoneModal(0,'library')" data-tooltip="Library">
|
||||
<span class="card-count" id="lib-count-1">0</span>
|
||||
<span class="zone-icon">📚</span>
|
||||
<span class="zone-name">Library</span>
|
||||
</div>
|
||||
<div class="zone exile" id="exile-zone-1" data-player="1" data-zone="exile"
|
||||
onclick="openZoneModal(0,'exile')" data-tooltip="Exile">
|
||||
<span class="card-count" id="exile-count-1" style="display:none;">0</span>
|
||||
<span class="zone-icon">⬡</span>
|
||||
<span class="zone-name">Exile</span>
|
||||
</div>
|
||||
<div class="zone graveyard" id="gv-zone-1" data-player="1" data-zone="graveyard"
|
||||
onclick="openZoneModal(0,'graveyard')" data-tooltip="Graveyard">
|
||||
<span class="card-count" id="gv-count-1" style="display:none;">0</span>
|
||||
<span class="zone-icon">☠</span>
|
||||
<span class="zone-name">Grave</span>
|
||||
</div>
|
||||
<div class="zone hand" id="hand-zone-1" data-player="1" data-zone="hand"
|
||||
onclick="openZoneModal(0,'hand')" data-tooltip="Hand">
|
||||
<span class="zone-icon">🃏</span>
|
||||
<span class="zone-name">Hand (<span id="hand-count-1">0</span>)</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Commander Row -->
|
||||
<div class="commander-row" id="cmd-row-1">
|
||||
<div class="zone commander" id="cmd-zone-1" data-player="1" data-zone="command"
|
||||
onclick="openZoneModal(0,'command')" data-tooltip="Command Zone">
|
||||
<span class="zone-icon">👑</span>
|
||||
<span class="zone-name">Commander</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Battle Zone -->
|
||||
<div class="battle-zone" id="battle-zone-1">
|
||||
<div class="battle-zone-inner"></div>
|
||||
<span class="battle-zone-label">Battlefield</span>
|
||||
<!-- Battlefield cards injected here -->
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- PLAYER 2: Top-Right (Sapphire) -->
|
||||
<div class="player-area p2" id="player-area-2">
|
||||
<span class="player-label" id="player-label-2">Player 2</span>
|
||||
|
||||
<div class="zone-row" id="zone-row-2">
|
||||
<div class="zone library" id="lib-zone-2" data-player="2" data-zone="library"
|
||||
onclick="openZoneModal(1,'library')" data-tooltip="Library">
|
||||
<span class="card-count" id="lib-count-2">0</span>
|
||||
<span class="zone-icon">📚</span>
|
||||
<span class="zone-name">Library</span>
|
||||
</div>
|
||||
<div class="zone exile" id="exile-zone-2" data-player="2" data-zone="exile"
|
||||
onclick="openZoneModal(1,'exile')" data-tooltip="Exile">
|
||||
<span class="card-count" id="exile-count-2" style="display:none;">0</span>
|
||||
<span class="zone-icon">⬡</span>
|
||||
<span class="zone-name">Exile</span>
|
||||
</div>
|
||||
<div class="zone graveyard" id="gv-zone-2" data-player="2" data-zone="graveyard"
|
||||
onclick="openZoneModal(1,'graveyard')" data-tooltip="Graveyard">
|
||||
<span class="card-count" id="gv-count-2" style="display:none;">0</span>
|
||||
<span class="zone-icon">☠</span>
|
||||
<span class="zone-name">Grave</span>
|
||||
</div>
|
||||
<div class="zone hand" id="hand-zone-2" data-player="2" data-zone="hand"
|
||||
onclick="openZoneModal(1,'hand')" data-tooltip="Hand">
|
||||
<span class="zone-icon">🃏</span>
|
||||
<span class="zone-name">Hand (<span id="hand-count-2">0</span>)</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="commander-row" id="cmd-row-2">
|
||||
<div class="zone commander" id="cmd-zone-2" data-player="2" data-zone="command"
|
||||
onclick="openZoneModal(1,'command')" data-tooltip="Command Zone">
|
||||
<span class="zone-icon">👑</span>
|
||||
<span class="zone-name">Commander</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="battle-zone" id="battle-zone-2">
|
||||
<div class="battle-zone-inner"></div>
|
||||
<span class="battle-zone-label">Battlefield</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- PLAYER 3: Bottom-Left (Emerald) -->
|
||||
<div class="player-area p3" id="player-area-3">
|
||||
<!-- Battle zone on top for bottom players -->
|
||||
<div class="battle-zone" id="battle-zone-3">
|
||||
<div class="battle-zone-inner"></div>
|
||||
<span class="battle-zone-label">Battlefield</span>
|
||||
</div>
|
||||
|
||||
<div class="commander-row" id="cmd-row-3">
|
||||
<div class="zone commander" id="cmd-zone-3" data-player="3" data-zone="command"
|
||||
onclick="openZoneModal(2,'command')" data-tooltip="Command Zone">
|
||||
<span class="zone-icon">👑</span>
|
||||
<span class="zone-name">Commander</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="zone-row" id="zone-row-3">
|
||||
<div class="zone library" id="lib-zone-3" data-player="3" data-zone="library"
|
||||
onclick="openZoneModal(2,'library')" data-tooltip="Library">
|
||||
<span class="card-count" id="lib-count-3">0</span>
|
||||
<span class="zone-icon">📚</span>
|
||||
<span class="zone-name">Library</span>
|
||||
</div>
|
||||
<div class="zone exile" id="exile-zone-3" data-player="3" data-zone="exile"
|
||||
onclick="openZoneModal(2,'exile')" data-tooltip="Exile">
|
||||
<span class="card-count" id="exile-count-3" style="display:none;">0</span>
|
||||
<span class="zone-icon">⬡</span>
|
||||
<span class="zone-name">Exile</span>
|
||||
</div>
|
||||
<div class="zone graveyard" id="gv-zone-3" data-player="3" data-zone="graveyard"
|
||||
onclick="openZoneModal(2,'graveyard')" data-tooltip="Graveyard">
|
||||
<span class="card-count" id="gv-count-3" style="display:none;">0</span>
|
||||
<span class="zone-icon">☠</span>
|
||||
<span class="zone-name">Grave</span>
|
||||
</div>
|
||||
<div class="zone hand" id="hand-zone-3" data-player="3" data-zone="hand"
|
||||
onclick="openZoneModal(2,'hand')" data-tooltip="Hand">
|
||||
<span class="zone-icon">🃏</span>
|
||||
<span class="zone-name">Hand (<span id="hand-count-3">0</span>)</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<span class="player-label" id="player-label-3">Player 3</span>
|
||||
</div>
|
||||
|
||||
<!-- PLAYER 4: Bottom-Right (Gold) -->
|
||||
<div class="player-area p4" id="player-area-4">
|
||||
<div class="battle-zone" id="battle-zone-4">
|
||||
<div class="battle-zone-inner"></div>
|
||||
<span class="battle-zone-label">Battlefield</span>
|
||||
</div>
|
||||
|
||||
<div class="commander-row" id="cmd-row-4">
|
||||
<div class="zone commander" id="cmd-zone-4" data-player="4" data-zone="command"
|
||||
onclick="openZoneModal(3,'command')" data-tooltip="Command Zone">
|
||||
<span class="zone-icon">👑</span>
|
||||
<span class="zone-name">Commander</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="zone-row" id="zone-row-4">
|
||||
<div class="zone library" id="lib-zone-4" data-player="4" data-zone="library"
|
||||
onclick="openZoneModal(3,'library')" data-tooltip="Library">
|
||||
<span class="card-count" id="lib-count-4">0</span>
|
||||
<span class="zone-icon">📚</span>
|
||||
<span class="zone-name">Library</span>
|
||||
</div>
|
||||
<div class="zone exile" id="exile-zone-4" data-player="4" data-zone="exile"
|
||||
onclick="openZoneModal(3,'exile')" data-tooltip="Exile">
|
||||
<span class="card-count" id="exile-count-4" style="display:none;">0</span>
|
||||
<span class="zone-icon">⬡</span>
|
||||
<span class="zone-name">Exile</span>
|
||||
</div>
|
||||
<div class="zone graveyard" id="gv-zone-4" data-player="4" data-zone="graveyard"
|
||||
onclick="openZoneModal(3,'graveyard')" data-tooltip="Graveyard">
|
||||
<span class="card-count" id="gv-count-4" style="display:none;">0</span>
|
||||
<span class="zone-icon">☠</span>
|
||||
<span class="zone-name">Grave</span>
|
||||
</div>
|
||||
<div class="zone hand" id="hand-zone-4" data-player="4" data-zone="hand"
|
||||
onclick="openZoneModal(3,'hand')" data-tooltip="Hand">
|
||||
<span class="zone-icon">🃏</span>
|
||||
<span class="zone-name">Hand (<span id="hand-count-4">0</span>)</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<span class="player-label" id="player-label-4">Player 4</span>
|
||||
</div>
|
||||
|
||||
</div><!-- /.battlefield -->
|
||||
|
||||
<!-- LIFE PANELS OVERLAY (top-most layer) -->
|
||||
<div id="life-panels-overlay">
|
||||
<div class="life-panel lp-p1" id="life-panel-1">
|
||||
<div class="life-panel-box life-box">
|
||||
<span class="life-panel-label">Life</span>
|
||||
<div class="life-counter" id="life-counter-1">
|
||||
<button onclick="adjustLife(0,-1)" aria-label="Decrease P1 life">−</button>
|
||||
<span class="life-counter-value" id="life-0" onclick="showSetLifeDialog(0)">40</span>
|
||||
<button onclick="adjustLife(0,1)" aria-label="Increase P1 life">+</button>
|
||||
</div>
|
||||
</div>
|
||||
<div class="life-panel-box cmd-dmg-box" id="cmd-dmg-inline-0" onclick="openCmdDmgMatrix()" title="Open Commander Damage matrix">
|
||||
<span class="life-panel-label">Cmd Dmg</span>
|
||||
<div class="cmd-dmg-inline-row">—</div>
|
||||
</div>
|
||||
<div class="life-panel-box counters-box">
|
||||
<span class="life-panel-label">Counters</span>
|
||||
<div class="poison-badge" id="poison-0" onclick="adjustPoison(0,1)" title="Poison counters (click +1, right-click -1)" oncontextmenu="event.preventDefault();adjustPoison(0,-1)">0 poison</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="life-panel lp-p2" id="life-panel-2">
|
||||
<div class="life-panel-box life-box">
|
||||
<span class="life-panel-label">Life</span>
|
||||
<div class="life-counter" id="life-counter-2">
|
||||
<button onclick="adjustLife(1,-1)" aria-label="Decrease P2 life">−</button>
|
||||
<span class="life-counter-value" id="life-1" onclick="showSetLifeDialog(1)">40</span>
|
||||
<button onclick="adjustLife(1,1)" aria-label="Increase P2 life">+</button>
|
||||
</div>
|
||||
</div>
|
||||
<div class="life-panel-box cmd-dmg-box" id="cmd-dmg-inline-1" onclick="openCmdDmgMatrix()" title="Open Commander Damage matrix">
|
||||
<span class="life-panel-label">Cmd Dmg</span>
|
||||
<div class="cmd-dmg-inline-row">—</div>
|
||||
</div>
|
||||
<div class="life-panel-box counters-box">
|
||||
<span class="life-panel-label">Counters</span>
|
||||
<div class="poison-badge" id="poison-1" onclick="adjustPoison(1,1)" oncontextmenu="event.preventDefault();adjustPoison(1,-1)">0 poison</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="life-panel lp-p3" id="life-panel-3">
|
||||
<div class="life-panel-box life-box">
|
||||
<span class="life-panel-label">Life</span>
|
||||
<div class="life-counter" id="life-counter-3">
|
||||
<button onclick="adjustLife(2,-1)" aria-label="Decrease P3 life">−</button>
|
||||
<span class="life-counter-value" id="life-2" onclick="showSetLifeDialog(2)">40</span>
|
||||
<button onclick="adjustLife(2,1)" aria-label="Increase P3 life">+</button>
|
||||
</div>
|
||||
</div>
|
||||
<div class="life-panel-box cmd-dmg-box" id="cmd-dmg-inline-2" onclick="openCmdDmgMatrix()" title="Open Commander Damage matrix">
|
||||
<span class="life-panel-label">Cmd Dmg</span>
|
||||
<div class="cmd-dmg-inline-row">—</div>
|
||||
</div>
|
||||
<div class="life-panel-box counters-box">
|
||||
<span class="life-panel-label">Counters</span>
|
||||
<div class="poison-badge" id="poison-2" onclick="adjustPoison(2,1)" oncontextmenu="event.preventDefault();adjustPoison(2,-1)">0 poison</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="life-panel lp-p4" id="life-panel-4">
|
||||
<div class="life-panel-box life-box">
|
||||
<span class="life-panel-label">Life</span>
|
||||
<div class="life-counter" id="life-counter-4">
|
||||
<button onclick="adjustLife(3,-1)" aria-label="Decrease P4 life">−</button>
|
||||
<span class="life-counter-value" id="life-3" onclick="showSetLifeDialog(3)">40</span>
|
||||
<button onclick="adjustLife(3,1)" aria-label="Increase P4 life">+</button>
|
||||
</div>
|
||||
</div>
|
||||
<div class="life-panel-box cmd-dmg-box" id="cmd-dmg-inline-3" onclick="openCmdDmgMatrix()" title="Open Commander Damage matrix">
|
||||
<span class="life-panel-label">Cmd Dmg</span>
|
||||
<div class="cmd-dmg-inline-row">—</div>
|
||||
</div>
|
||||
<div class="life-panel-box counters-box">
|
||||
<span class="life-panel-label">Counters</span>
|
||||
<div class="poison-badge" id="poison-3" onclick="adjustPoison(3,1)" oncontextmenu="event.preventDefault();adjustPoison(3,-1)">0 poison</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- COMBAT SVG OVERLAY (arrows) -->
|
||||
<svg id="combat-overlay" xmlns="http://www.w3.org/2000/svg" style="position:absolute;inset:44px 0 0 0;pointer-events:none;z-index:30;">
|
||||
<defs>
|
||||
<marker id="arrowhead" markerWidth="10" markerHeight="7" refX="9" refY="3.5" orient="auto">
|
||||
<polygon points="0 0, 10 3.5, 0 7" fill="rgba(211,32,42,0.7)"/>
|
||||
</marker>
|
||||
</defs>
|
||||
<g id="combat-arrows-svg"></g>
|
||||
</svg>
|
||||
|
||||
<!-- FLOATING LOG -->
|
||||
<div id="floating-log" class="floating-log"></div>
|
||||
|
||||
<!-- SHORTCUT HINT -->
|
||||
<div class="shortcut-hint"></div>
|
||||
|
||||
<!-- CARD SEARCH SIDEBAR (right side, collapsible) -->
|
||||
<div id="card-search-sidebar" class="collapsed">
|
||||
<div class="sidebar-header">
|
||||
<span class="sidebar-title">Card Search</span>
|
||||
<button class="sidebar-toggle" onclick="document.getElementById('card-search-sidebar').classList.toggle('collapsed')" title="Close search">✕</button>
|
||||
</div>
|
||||
<div class="search-input-wrap">
|
||||
<input class="search-input" id="card-search-input" type="text" placeholder="Search Scryfall..." autocomplete="off"/>
|
||||
</div>
|
||||
<div class="search-status" id="search-status">Type to search cards...</div>
|
||||
<div class="card-results" id="card-results"></div>
|
||||
</div>
|
||||
|
||||
<!-- Sidebar edge tab -->
|
||||
<div id="sidebar-edge-tab" class="sidebar-edge-tab" title="Toggle card search">‹</div>
|
||||
|
||||
<!-- LEFT ACTION PANEL (triggers + quick actions) -->
|
||||
<div id="left-action-panel" class="collapsed">
|
||||
<div class="left-panel-header">
|
||||
<span class="left-panel-title">Triggers <span id="left-panel-trigger-count" class="left-panel-count">0</span></span>
|
||||
<button class="sidebar-toggle" onclick="toggleLeftPanel()" title="Close panel">✕</button>
|
||||
</div>
|
||||
<div class="left-panel-actions">
|
||||
<button class="left-panel-btn" onclick="openTokenModal((typeof gameState !== 'undefined' && gameState) ? (gameState.currentPlayerIndex || 0) : 0)" title="Create token">✨ Create Token</button>
|
||||
<button class="left-panel-btn" onclick="autoArrangeBattlefield()" title="Auto-arrange battlefield">⇄ Auto-Arrange</button>
|
||||
</div>
|
||||
<div class="left-panel-body" id="left-panel-triggers">
|
||||
<div class="left-panel-empty">No pending triggers.</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Left panel edge tab -->
|
||||
<div id="left-panel-edge-tab" class="left-panel-edge-tab" onclick="toggleLeftPanel()" title="Toggle quick actions">›</div>
|
||||
|
||||
</div><!-- /.table-surface / #game-battlefield -->
|
||||
|
||||
<!-- ====================================================
|
||||
CONTEXT MENU
|
||||
==================================================== -->
|
||||
<div id="context-menu"></div>
|
||||
|
||||
<!-- ====================================================
|
||||
FLOATING CARD PREVIEW
|
||||
==================================================== -->
|
||||
<div id="card-preview">
|
||||
<img src="" alt="Card Preview"/>
|
||||
<div class="card-preview-pt-overlay" id="card-preview-pt-overlay"></div>
|
||||
<div class="card-preview-info">
|
||||
<div class="card-preview-name"></div>
|
||||
<div class="card-preview-type"></div>
|
||||
<div class="card-preview-oracle"></div>
|
||||
<div class="card-preview-pt"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
ZONE MODAL
|
||||
==================================================== -->
|
||||
<div id="zone-modal">
|
||||
<div class="zone-modal-card">
|
||||
<div class="zone-modal-header">
|
||||
<span class="zone-modal-title" id="zone-modal-title">Player Zones</span>
|
||||
<button class="btn-close" onclick="closeZoneModal()">✕</button>
|
||||
</div>
|
||||
<div class="zone-modal-tabs" id="zone-modal-tabs">
|
||||
<!-- Populated by renderZoneModal() -->
|
||||
</div>
|
||||
<div class="zone-modal-body" id="zone-modal-body">
|
||||
<!-- Populated by renderZoneModal() -->
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
DICE MODAL
|
||||
==================================================== -->
|
||||
<div id="dice-modal">
|
||||
<div class="dice-modal-card">
|
||||
<div style="display:flex;justify-content:space-between;align-items:center;margin-bottom:1rem;">
|
||||
<span style="font-family:var(--font-display);font-size:14px;font-weight:700;color:var(--color-text-muted);text-transform:uppercase;letter-spacing:0.08em;">Dice & Coins</span>
|
||||
<button class="btn-close" onclick="closeDiceModal()">✕</button>
|
||||
</div>
|
||||
<div class="dice-result-display" id="dice-result-display">—</div>
|
||||
<div class="dice-label" id="dice-label">Choose a die</div>
|
||||
<div class="dice-buttons">
|
||||
<button class="btn-dice" onclick="rollDice(4)">d4</button>
|
||||
<button class="btn-dice" onclick="rollDice(6)">d6</button>
|
||||
<button class="btn-dice" onclick="rollDice(8)">d8</button>
|
||||
<button class="btn-dice" onclick="rollDice(10)">d10</button>
|
||||
<button class="btn-dice" onclick="rollDice(12)">d12</button>
|
||||
<button class="btn-dice" onclick="rollDice(20)">d20</button>
|
||||
<button class="btn-dice" onclick="rollDice(100)">d100</button>
|
||||
<button class="btn-dice coin" onclick="flipCoin()">Coin Flip</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
TOKEN MODAL
|
||||
==================================================== -->
|
||||
<div id="token-modal">
|
||||
<div class="token-modal-card">
|
||||
<div class="modal-header">
|
||||
<span class="modal-title">Create Token</span>
|
||||
<button class="btn-close" onclick="closeTokenModal()">✕</button>
|
||||
</div>
|
||||
<div class="modal-body" id="token-modal-body">
|
||||
<div style="display:grid;grid-template-columns:1fr 1fr;gap:10px;margin-bottom:12px;">
|
||||
<div>
|
||||
<label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Token Name</label>
|
||||
<input class="game-input" id="token-name" type="text" placeholder="e.g. 1/1 White Soldier"/>
|
||||
</div>
|
||||
<div>
|
||||
<label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Type Line</label>
|
||||
<input class="game-input" id="token-type" type="text" placeholder="Creature — Soldier"/>
|
||||
</div>
|
||||
<div>
|
||||
<label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Power/Toughness</label>
|
||||
<input class="game-input" id="token-pt" type="text" placeholder="1/1"/>
|
||||
</div>
|
||||
<div>
|
||||
<label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Count</label>
|
||||
<input class="game-input" id="token-count" type="number" value="1" min="1" max="20"/>
|
||||
</div>
|
||||
</div>
|
||||
<div style="margin-bottom:10px;">
|
||||
<label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Keywords (comma-separated)</label>
|
||||
<input class="game-input" id="token-keywords" type="text" placeholder="Flying, Vigilance"/>
|
||||
</div>
|
||||
<div style="margin-bottom:12px;">
|
||||
<label style="font-size:11px;font-weight:600;color:var(--color-text);display:block;margin-bottom:4px;">Player</label>
|
||||
<select class="game-input" id="token-player-select" style="height:28px;">
|
||||
<option value="0">Player 1</option>
|
||||
<option value="1">Player 2</option>
|
||||
<option value="2">Player 3</option>
|
||||
<option value="3">Player 4</option>
|
||||
</select>
|
||||
</div>
|
||||
<div style="display:flex;gap:8px;">
|
||||
<button class="btn btn-primary" onclick="createTokenFromModal()">Create Token(s)</button>
|
||||
<button class="btn btn-cancel" onclick="closeTokenModal()">Cancel</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
DECK MODAL
|
||||
==================================================== -->
|
||||
<div id="deck-modal">
|
||||
<div class="deck-modal-card">
|
||||
<div class="modal-header">
|
||||
<span class="modal-title">Deck Manager</span>
|
||||
<button class="btn-close" onclick="closeDeckModal()">✕</button>
|
||||
</div>
|
||||
<div class="modal-body" id="deck-modal-body">
|
||||
<!-- Populated by renderDeckModalContent() -->
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
COMMANDER DAMAGE MATRIX MODAL
|
||||
==================================================== -->
|
||||
<div id="cmd-dmg-matrix-modal">
|
||||
<!-- Populated by renderCmdDmgMatrix() -->
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
TRIGGER STACK PANEL
|
||||
==================================================== -->
|
||||
<div id="trigger-stack-panel">
|
||||
<div class="trigger-stack-card">
|
||||
<div class="trigger-stack-header">
|
||||
<span class="trigger-stack-title">Trigger Stack</span>
|
||||
<button class="btn-close" style="font-size:14px;width:20px;height:20px;" onclick="closeTriggerStack()">✕</button>
|
||||
</div>
|
||||
<div class="trigger-stack-body" id="trigger-stack-body">
|
||||
<div class="trigger-stack-empty">No pending triggers.</div>
|
||||
</div>
|
||||
<div class="trigger-stack-actions">
|
||||
<button class="btn-resolve" onclick="resolveNextTrigger()">Resolve Next</button>
|
||||
<button class="btn-resolve-all" onclick="resolveAllTriggers()">Resolve All</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
SETTINGS PANEL
|
||||
==================================================== -->
|
||||
<div id="settings-panel">
|
||||
<div class="modal-header" style="flex-shrink:0;">
|
||||
<span class="modal-title">Settings</span>
|
||||
<button class="btn-close" onclick="toggleSettingsPanel()">✕</button>
|
||||
</div>
|
||||
<div class="settings-body" id="settings-body" style="flex:1;overflow-y:auto;padding:12px;">
|
||||
<!-- Populated by renderSettingsPanel() -->
|
||||
</div>
|
||||
<div style="padding:10px 14px;border-top:1px solid var(--color-border);display:flex;gap:8px;flex-wrap:wrap;flex-shrink:0;">
|
||||
<button class="btn btn-primary" onclick="openDiceModal()">🎲 Dice</button>
|
||||
<button class="btn btn-gold" onclick="openCmdDmgMatrix()">CMD Damage Matrix</button>
|
||||
<button class="btn btn-cancel" onclick="checkStateBasedActions()">Check SBA</button>
|
||||
<button class="btn btn-cancel" onclick="autoArrangeBattlefield()">Auto-Arrange</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
PRIORITY BANNER
|
||||
==================================================== -->
|
||||
<div id="priority-banner">
|
||||
<span class="priority-text" id="priority-text">Waiting for priority...</span>
|
||||
<button class="btn-priority-pass" onclick="passPriority()">Pass Priority</button>
|
||||
<button class="btn-close" style="font-size:13px;" onclick="closePriorityWindow()">✕</button>
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
LEGAL ACTIONS BAR
|
||||
==================================================== -->
|
||||
<div id="legal-actions-bar">
|
||||
<!-- Populated by renderLegalActionsBar() -->
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
UNDO HISTORY PANEL
|
||||
==================================================== -->
|
||||
<div id="undo-history-panel">
|
||||
<!-- Populated by renderUndoHistory() -->
|
||||
</div>
|
||||
|
||||
<!-- ====================================================
|
||||
MANA COLOR PICKER (dynamically appended) -->
|
||||
<!-- Zone card context menu (dynamically appended) -->
|
||||
<!-- SBA alert panel (dynamically appended) -->
|
||||
<!-- Set-life dialog (dynamically appended) -->
|
||||
|
||||
<!-- ====================================================
|
||||
SCRIPTS
|
||||
Load order matters:
|
||||
1. precons.js — static PRECON_DATA fallback
|
||||
2. engine.js — game state & core logic
|
||||
3. ai-bridge.js — backend connector & DeckSourceClient
|
||||
4. precon-loader.js — startGameWithDecks() + loadPreconDeckFromDck()
|
||||
5. player-panels.js — player panel rendering
|
||||
6. ui.js — setup screen, deck picker, UI wiring
|
||||
==================================================== -->
|
||||
<script src="precons.js?v=20260403_0300"></script>
|
||||
<script src="engine.js?v=20260403_0300"></script>
|
||||
<script src="ai-bridge.js?v=20260403_0300"></script>
|
||||
<script src="precon-loader.js?v=20260403_0300"></script>
|
||||
<script src="player-panels.js?v=20260403_0300"></script>
|
||||
<script src="ui.js?v=20260403_0300"></script>
|
||||
|
||||
<script>
|
||||
// ============================================================
|
||||
// STARTUP & BRIDGE FUNCTIONS
|
||||
// ============================================================
|
||||
|
||||
// Token creation from modal
|
||||
function createTokenFromModal() {
|
||||
if (!gameState) return;
|
||||
const name = document.getElementById('token-name').value.trim() || '1/1 Token';
|
||||
const typeLine = document.getElementById('token-type').value.trim() || 'Creature';
|
||||
const pt = document.getElementById('token-pt').value.trim() || '1/1';
|
||||
const count = parseInt(document.getElementById('token-count').value) || 1;
|
||||
const keywords = document.getElementById('token-keywords').value.trim();
|
||||
const playerIdx = parseInt(document.getElementById('token-player-select').value) || 0;
|
||||
|
||||
const parts = pt.split('/');
|
||||
const power = parts[0] ? parts[0].trim() : '1';
|
||||
const toughness = parts[1] ? parts[1].trim() : '1';
|
||||
|
||||
for (let i = 0; i < count; i++) {
|
||||
addCardToBattlefield(name, '', typeLine, playerIdx, undefined, undefined,
|
||||
keywords ? keywords.replace(/,/g, ' ') : '',
|
||||
{ power, toughness, pt, isToken: true });
|
||||
}
|
||||
addLogEntry('Created ' + count + 'x <strong>' + name + '</strong> token(s) for Player ' + (playerIdx + 1));
|
||||
closeTokenModal();
|
||||
}
|
||||
|
||||
// Pass priority
|
||||
function passPriority() {
|
||||
addLogEntry('Priority passed.');
|
||||
closePriorityWindow();
|
||||
}
|
||||
|
||||
// Open precon browser (stub - opens deck modal for current player)
|
||||
function openPreconBrowser() {
|
||||
if (gameState) openDeckModal(gameState.currentPlayerIndex);
|
||||
}
|
||||
|
||||
// Open collection modal (stub)
|
||||
function openCollectionModal() { showToast('Collection tracker available in full version.'); }
|
||||
|
||||
// Scry dialog stub
|
||||
function showScryDialog(playerId) {
|
||||
if (!gameState) return;
|
||||
const player = gameState.players[playerId];
|
||||
if (!player || player.zones.library.length === 0) {
|
||||
addLogEntry('!! ' + (player ? player.name : 'Player') + ' has no cards to scry.');
|
||||
return;
|
||||
}
|
||||
const count = Math.min(parseInt(prompt('Scry how many? (1-7)', '1') || '1'), 7, player.zones.library.length);
|
||||
if (isNaN(count) || count < 1) return;
|
||||
const topCards = player.zones.library.slice(0, count);
|
||||
const names = topCards.map(c => c.name || '?').join(', ');
|
||||
addLogEntry('<strong>' + player.name + '</strong> scrys ' + count + ': [' + names + ']');
|
||||
}
|
||||
|
||||
// Mill dialog stub
|
||||
function showMillDialog(playerId) {
|
||||
if (!gameState) return;
|
||||
const count = parseInt(prompt('Mill how many cards?', '1') || '1');
|
||||
if (isNaN(count) || count < 1) return;
|
||||
millCards(playerId, count);
|
||||
}
|
||||
|
||||
// Become monarch
|
||||
function becomeMonarch(playerId) {
|
||||
if (!gameState) return;
|
||||
gameState.monarchPlayerIndex = playerId;
|
||||
const player = gameState.players[playerId];
|
||||
addLogEntry('👑 <strong>' + (player ? player.name : 'Player') + '</strong> becomes the Monarch!');
|
||||
updateMonarchBadges();
|
||||
}
|
||||
|
||||
// Empower the life counter right-click on the quadrant labels
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
// Wire right-click on life values to set-life dialog
|
||||
for (var i = 0; i < 4; i++) {
|
||||
(function(idx) {
|
||||
var lifeEl = document.getElementById('life-' + idx);
|
||||
if (lifeEl) {
|
||||
lifeEl.addEventListener('contextmenu', function(e) {
|
||||
e.preventDefault();
|
||||
showSetLifeDialog(idx);
|
||||
});
|
||||
}
|
||||
})(i);
|
||||
}
|
||||
|
||||
// Life counter right-click on quadrant buttons too
|
||||
// (already handled via inline onclick in HTML)
|
||||
|
||||
// Auto-detect Commander-AI-Lab backend on page load
|
||||
if (typeof AIBridge !== 'undefined') {
|
||||
AIBridge.init().then(function(status) {
|
||||
var indicator = document.getElementById('backend-status-indicator');
|
||||
if (indicator) {
|
||||
indicator.textContent = status.connected
|
||||
? (status.modelLoaded ? '\u{1F9E0} ML Backend Connected' : '\u26a1 Backend Connected (no ML model)')
|
||||
: '\u{1F4E1} Local Mode (no backend)';
|
||||
indicator.className = 'backend-status ' +
|
||||
(status.connected ? (status.modelLoaded ? 'connected-ml' : 'connected') : 'disconnected');
|
||||
}
|
||||
|
||||
// Wire up WebSocket push events from backend
|
||||
AIBridge.connector.on('ai:decision', function(msg) {
|
||||
if (!gameState || !msg.action) return;
|
||||
var playerIdx = gameState.currentPlayerIndex;
|
||||
var player = gameState.players[playerIdx];
|
||||
if (player && player.isAI) {
|
||||
AIBridge.MacroActionMapper.execute(msg.action, playerIdx, gameState, msg);
|
||||
}
|
||||
});
|
||||
|
||||
AIBridge.connector.on('state:snapshot', function(msg) {
|
||||
if (typeof AI_BRIDGE_CONFIG !== 'undefined' && AI_BRIDGE_CONFIG.verbose) {
|
||||
console.log('[WS] State snapshot received from backend');
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
});
|
||||
</script>
|
||||
|
||||
<!-- ====================================================
|
||||
DECK BUILDER PANEL (iframe)
|
||||
==================================================== -->
|
||||
<div id="panel-deckbuilder" class="fullscreen-panel hidden">
|
||||
<div class="fullscreen-panel-header">
|
||||
<span>Deck Builder</span>
|
||||
<button class="btn-util" onclick="toggleDeckBuilderPanel()" title="Close">×</button>
|
||||
</div>
|
||||
<iframe src="deckbuilder.html"
|
||||
style="width:100%;height:calc(100vh - 36px);border:none;"
|
||||
id="deckbuilder-iframe">
|
||||
</iframe>
|
||||
</div>
|
||||
|
||||
<div id="panel-collection" class="fullscreen-panel hidden">
|
||||
<div class="fullscreen-panel-header">
|
||||
<span>Collection</span>
|
||||
<button class="btn-util" onclick="toggleCollectionPanel()" title="Close">×</button>
|
||||
</div>
|
||||
<iframe src="collection.html"
|
||||
style="width:100%;height:calc(100vh - 36px);border:none;"
|
||||
id="collection-iframe">
|
||||
</iframe>
|
||||
</div>
|
||||
|
||||
<div id="panel-deckgenerator" class="fullscreen-panel hidden">
|
||||
<div class="fullscreen-panel-header">
|
||||
<span>Auto Deck Generator</span>
|
||||
<button class="btn-util" onclick="toggleDeckGenPanel()" title="Close">×</button>
|
||||
</div>
|
||||
<iframe src="deckgenerator.html"
|
||||
style="width:100%;height:calc(100vh - 36px);border:none;"
|
||||
id="deckgenerator-iframe">
|
||||
</iframe>
|
||||
</div>
|
||||
|
||||
<script>
|
||||
function toggleDeckBuilderPanel() {
|
||||
var panel = document.getElementById('panel-deckbuilder');
|
||||
if (panel) panel.classList.toggle('hidden');
|
||||
}
|
||||
|
||||
function toggleDeckGenPanel() {
|
||||
var panel = document.getElementById('panel-deckgenerator');
|
||||
if (panel) panel.classList.toggle('hidden');
|
||||
}
|
||||
|
||||
function toggleCollectionPanel() {
|
||||
var panel = document.getElementById('panel-collection');
|
||||
if (panel) panel.classList.toggle('hidden');
|
||||
}
|
||||
|
||||
function toggleLeftPanel() {
|
||||
var panel = document.getElementById('left-action-panel');
|
||||
if (panel) {
|
||||
panel.classList.toggle('collapsed');
|
||||
if (!panel.classList.contains('collapsed') && typeof refreshLeftPanelTriggers === 'function') {
|
||||
refreshLeftPanelTriggers();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Render the triggers list inside the left action panel.
|
||||
// Mirrors #trigger-stack-body rendering but stays embedded in the panel.
|
||||
function refreshLeftPanelTriggers() {
|
||||
var body = document.getElementById('left-panel-triggers');
|
||||
var countEl = document.getElementById('left-panel-trigger-count');
|
||||
if (!body) return;
|
||||
if (typeof gameState === 'undefined' || !gameState) {
|
||||
if (countEl) countEl.textContent = '0';
|
||||
body.innerHTML = '<div class="left-panel-empty">No pending triggers.</div>';
|
||||
return;
|
||||
}
|
||||
var pending = (gameState.triggerStack || []).filter(function(t){ return !t.resolved; });
|
||||
if (countEl) countEl.textContent = String(pending.length);
|
||||
if (pending.length === 0) {
|
||||
body.innerHTML = '<div class="left-panel-empty">No pending triggers.</div>';
|
||||
return;
|
||||
}
|
||||
var icons = (typeof TRIGGER_TYPE_ICONS !== 'undefined') ? TRIGGER_TYPE_ICONS : {};
|
||||
var labels = (typeof TRIGGER_TYPE_LABELS !== 'undefined') ? TRIGGER_TYPE_LABELS : {};
|
||||
var html = '';
|
||||
pending.forEach(function(t) {
|
||||
var icon = icons[t.type] || '\u2747';
|
||||
var label = labels[t.type] || t.type || '';
|
||||
var owner = (gameState.players && gameState.players[t.ownerIndex]) || {};
|
||||
var color = owner.color || '#888';
|
||||
var name = (t.cardName || 'Unknown').replace(/</g,'<');
|
||||
var snip = (t.oracleSnippet || '').replace(/</g,'<');
|
||||
html +=
|
||||
'<div class="lpt-item">' +
|
||||
'<div class="lpt-icon">' + icon + '</div>' +
|
||||
'<div class="lpt-body">' +
|
||||
'<div class="lpt-title">' + name + '</div>' +
|
||||
'<div class="lpt-type">' + label + '</div>' +
|
||||
'<div class="lpt-text">' + snip + '</div>' +
|
||||
'</div>' +
|
||||
'<div class="lpt-dot" style="background:' + color + '"></div>' +
|
||||
'<button class="lpt-dismiss" title="Dismiss" onclick="if(typeof dismissTrigger===\'function\')dismissTrigger(' + t.id + ');refreshLeftPanelTriggers();">✕</button>' +
|
||||
'</div>';
|
||||
});
|
||||
body.innerHTML = html;
|
||||
}
|
||||
|
||||
// Populate each seat's Cmd Dmg score-area box with per-opponent badges.
|
||||
// Skips the seat's own player number from the display.
|
||||
function renderCmdDmgInline() {
|
||||
if (typeof gameState === 'undefined' || !gameState || !gameState.players) return;
|
||||
var players = gameState.players;
|
||||
for (var i = 0; i < players.length; i++) {
|
||||
var box = document.getElementById('cmd-dmg-inline-' + i);
|
||||
if (!box) continue;
|
||||
var row = box.querySelector('.cmd-dmg-inline-row');
|
||||
if (!row) continue;
|
||||
var me = players[i];
|
||||
if (!me) continue;
|
||||
var html = '';
|
||||
for (var j = 0; j < players.length; j++) {
|
||||
if (j === i) continue; // leave own player number out
|
||||
var op = players[j];
|
||||
if (!op) continue;
|
||||
var dmg = (me.commanderDamage && me.commanderDamage[op.id]) || 0;
|
||||
var danger = dmg >= 21 ? ' danger' : dmg >= 15 ? ' crit' : dmg >= 8 ? ' warn' : '';
|
||||
html +=
|
||||
'<span class="cdi-badge' + danger + '" title="Commander damage from ' + (op.name||'P'+(j+1)) + '">' +
|
||||
'<span class="cdi-dot" style="background:' + (op.color||'#888') + '"></span>' +
|
||||
'P' + (j + 1) + ' <b>' + dmg + '</b>' +
|
||||
'</span>';
|
||||
}
|
||||
row.innerHTML = html || '—';
|
||||
}
|
||||
}
|
||||
|
||||
// Hook into the trigger-stack badge updater so the left panel stays in sync.
|
||||
(function() {
|
||||
if (typeof window === 'undefined') return;
|
||||
var tryHook = function() {
|
||||
if (typeof window.updateTriggerBadge === 'function' && !window.updateTriggerBadge._leftPanelHooked) {
|
||||
var orig = window.updateTriggerBadge;
|
||||
window.updateTriggerBadge = function() {
|
||||
var r = orig.apply(this, arguments);
|
||||
try { refreshLeftPanelTriggers(); } catch(e) {}
|
||||
return r;
|
||||
};
|
||||
window.updateTriggerBadge._leftPanelHooked = true;
|
||||
}
|
||||
// Also wrap adjustCommanderDamage so badges refresh after matrix clicks.
|
||||
if (typeof window.adjustCommanderDamage === 'function' && !window.adjustCommanderDamage._cdmInlineHooked) {
|
||||
var origAdj = window.adjustCommanderDamage;
|
||||
window.adjustCommanderDamage = function() {
|
||||
var r = origAdj.apply(this, arguments);
|
||||
try { renderCmdDmgInline(); } catch(e) {}
|
||||
return r;
|
||||
};
|
||||
window.adjustCommanderDamage._cdmInlineHooked = true;
|
||||
}
|
||||
};
|
||||
if (document.readyState === 'loading') {
|
||||
document.addEventListener('DOMContentLoaded', tryHook);
|
||||
} else {
|
||||
tryHook();
|
||||
}
|
||||
// Also attempt the hook again a tick later (engine.js loads after this script in some orderings)
|
||||
setTimeout(tryHook, 0);
|
||||
setTimeout(tryHook, 500);
|
||||
})();
|
||||
|
||||
// Listen for deck exports from the deck builder iframe
|
||||
window.addEventListener('message', function(e) {
|
||||
if (!e.data || e.data.type !== 'DECK_BUILDER_EXPORT') return;
|
||||
|
||||
var deckData = e.data;
|
||||
// Build a decklist text string compatible with importDeckForPlayer()
|
||||
var lines = [];
|
||||
var commanders = deckData.deck.filter(function(c) { return c.is_commander; });
|
||||
var nonCmd = deckData.deck.filter(function(c) { return !c.is_commander; });
|
||||
|
||||
if (commanders.length > 0) {
|
||||
lines.push('[Commander]');
|
||||
commanders.forEach(function(c) { lines.push('1 ' + c.name); });
|
||||
lines.push('');
|
||||
}
|
||||
lines.push('[Deck]');
|
||||
nonCmd.forEach(function(c) { lines.push((c.quantity || 1) + ' ' + c.name); });
|
||||
|
||||
// Store on window for the setup screen to pick up
|
||||
window._pendingDeckImport = {
|
||||
text: lines.join('\n'),
|
||||
deckName: deckData.deckName || 'Imported Deck',
|
||||
commander: deckData.commander || '',
|
||||
};
|
||||
|
||||
// If a game is in progress, import into current player's slot
|
||||
if (typeof gameState !== 'undefined' && gameState && typeof importDeckForPlayer === 'function') {
|
||||
var playerIdx = gameState.currentPlayerIndex || 0;
|
||||
importDeckForPlayer(playerIdx, window._pendingDeckImport.text).then(function() {
|
||||
if (typeof renderHandArea === 'function') renderHandArea();
|
||||
if (typeof renderPlayerPanels === 'function') renderPlayerPanels();
|
||||
if (typeof addLogEntry === 'function') {
|
||||
addLogEntry('Imported deck "' + window._pendingDeckImport.deckName + '" from Deck Builder');
|
||||
}
|
||||
});
|
||||
} else if (typeof addLogEntry === 'function') {
|
||||
addLogEntry('Deck "' + window._pendingDeckImport.deckName + '" ready — start a game to use it.');
|
||||
}
|
||||
|
||||
// Close the deck builder panel
|
||||
toggleDeckBuilderPanel();
|
||||
});
|
||||
</script>
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,275 +0,0 @@
|
||||
// player-panels.js — Enhanced Player Panel Component
|
||||
// Provides collapsible panels with life, commander damage, counters, and emblems.
|
||||
|
||||
'use strict';
|
||||
|
||||
/**
|
||||
* Create an enhanced player panel widget for a quadrant.
|
||||
* Replaces the basic life counter with a full tracking panel.
|
||||
*
|
||||
* @param {object} player - Player object from gameState
|
||||
* @param {number} quadrant - 1-4 (matches CSS class p1-p4)
|
||||
* @param {boolean} isActive - Whether this is the active player
|
||||
*/
|
||||
function createEnhancedPlayerPanel(player, quadrant, isActive) {
|
||||
var panel = document.createElement('div');
|
||||
panel.className = 'enhanced-player-panel' +
|
||||
(isActive ? ' active-turn' : '') +
|
||||
(player.eliminated ? ' eliminated' : '');
|
||||
panel.id = 'enhanced-panel-' + player.id;
|
||||
panel.setAttribute('data-player-id', player.id);
|
||||
|
||||
// Track collapse state
|
||||
var collapseKey = 'panel-collapsed-' + player.id;
|
||||
var isCollapsed = localStorage.getItem(collapseKey) === 'true';
|
||||
|
||||
// ── Header Row ──────────────────────────────────────────
|
||||
var headerHtml =
|
||||
'<div class="epp-header" onclick="togglePlayerPanel(' + player.id + ')">' +
|
||||
'<span class="epp-collapse-arrow">' + (isCollapsed ? '\u25b8' : '\u25be') + '</span>' +
|
||||
'<div class="epp-name-dot" style="background:' + player.color + '"></div>' +
|
||||
'<span class="epp-name">' + player.name + '</span>' +
|
||||
(player.isAI ? '<span class="epp-ai-badge">AI</span>' : '') +
|
||||
'<span class="epp-life-badge">' +
|
||||
'\u2665 ' + player.life +
|
||||
(player.life < player.startingLife
|
||||
? ' / ' + player.startingLife
|
||||
: '') +
|
||||
'</span>' +
|
||||
'</div>';
|
||||
|
||||
// ── Life Counter Row ────────────────────────────────────
|
||||
var lifeHtml =
|
||||
'<div class="epp-life-row">' +
|
||||
'<button class="epp-life-btn minus" onclick="adjustLife(' + player.id + ',-1)" ' +
|
||||
'oncontextmenu="event.preventDefault();adjustLife(' + player.id + ',-5)" ' +
|
||||
'title="Left: -1 | Right: -5">\u2212</button>' +
|
||||
'<div class="epp-life-value" id="epp-life-' + player.id + '" ' +
|
||||
'onclick="showSetLifeDialog(' + player.id + ')">' +
|
||||
player.life +
|
||||
'</div>' +
|
||||
'<button class="epp-life-btn plus" onclick="adjustLife(' + player.id + ',1)" ' +
|
||||
'oncontextmenu="event.preventDefault();adjustLife(' + player.id + ',5)" ' +
|
||||
'title="Left: +1 | Right: +5">+</button>' +
|
||||
'</div>';
|
||||
|
||||
// ── Commander Damage Section ────────────────────────────
|
||||
var otherPlayers = gameState.players.filter(function(p) {
|
||||
return p.id !== player.id && !p.eliminated;
|
||||
});
|
||||
|
||||
var cmdDmgHtml =
|
||||
'<div class="epp-section">' +
|
||||
'<div class="epp-section-label">Commander Damage</div>' +
|
||||
'<div class="epp-cmd-dmg-grid">';
|
||||
|
||||
otherPlayers.forEach(function(op) {
|
||||
var dmg = player.commanderDamage[op.id] || 0;
|
||||
var pct = Math.round((dmg / 21) * 100);
|
||||
var dangerClass = dmg >= 18 ? 'critical' : dmg >= 10 ? 'warning' : '';
|
||||
|
||||
cmdDmgHtml +=
|
||||
'<div class="epp-cmd-dmg-item ' + dangerClass + '">' +
|
||||
'<div class="epp-cmd-dmg-bar" style="width:' + pct + '%"></div>' +
|
||||
'<div class="epp-cmd-dmg-dot" style="background:' + op.color + '"></div>' +
|
||||
'<span class="epp-cmd-dmg-name">' + op.name + '</span>' +
|
||||
'<span class="epp-cmd-dmg-value">' + dmg + '/21</span>' +
|
||||
'<button class="epp-counter-btn" ' +
|
||||
'onclick="event.stopPropagation();adjustCommanderDamage(' + player.id + ',' + op.id + ',1)" ' +
|
||||
'title="Add commander damage">+</button>' +
|
||||
'<button class="epp-counter-btn" ' +
|
||||
'onclick="event.stopPropagation();adjustCommanderDamage(' + player.id + ',' + op.id + ',-1)" ' +
|
||||
'title="Remove commander damage">\u2212</button>' +
|
||||
'</div>';
|
||||
});
|
||||
|
||||
cmdDmgHtml += '</div></div>';
|
||||
|
||||
// ── Counters Section ────────────────────────────────────
|
||||
var poison = player.poison || 0;
|
||||
var energy = player.energy || 0;
|
||||
var experience = player.experience || 0;
|
||||
var rad = player.rad || 0;
|
||||
|
||||
var countersHtml =
|
||||
'<div class="epp-section">' +
|
||||
'<div class="epp-section-label">Counters</div>' +
|
||||
'<div class="epp-counters-grid">' +
|
||||
_buildCounterWidget('Poison', '\u2620', poison, 10,
|
||||
'adjustPoison(' + player.id + ',1)',
|
||||
'adjustPoison(' + player.id + ',-1)',
|
||||
poison >= 8 ? 'critical' : poison >= 5 ? 'warning' : '') +
|
||||
_buildCounterWidget('Energy', '\u269b', energy, 0,
|
||||
'adjustEnergy(' + player.id + ',1)',
|
||||
'adjustEnergy(' + player.id + ',-1)', '') +
|
||||
_buildCounterWidget('Experience', '\u2605', experience, 0,
|
||||
'adjustExperience(' + player.id + ',1)',
|
||||
'adjustExperience(' + player.id + ',-1)', '') +
|
||||
_buildCounterWidget('Rad', '\u2622', rad, 0,
|
||||
'adjustRad(' + player.id + ',1)',
|
||||
'adjustRad(' + player.id + ',-1)', '') +
|
||||
'</div>' +
|
||||
'</div>';
|
||||
|
||||
// ── Emblems Section ─────────────────────────────────────
|
||||
var emblems = player.emblems || [];
|
||||
var emblemsHtml =
|
||||
'<div class="epp-section">' +
|
||||
'<div class="epp-section-label">Emblems</div>' +
|
||||
'<div class="epp-emblems">';
|
||||
|
||||
if (emblems.length === 0) {
|
||||
emblemsHtml += '<span class="epp-no-emblems">None</span>';
|
||||
} else {
|
||||
emblems.forEach(function(emb, idx) {
|
||||
emblemsHtml +=
|
||||
'<div class="epp-emblem-chip" title="' + emb.name + '">' +
|
||||
'<span class="epp-emblem-name">' + emb.name + '</span>' +
|
||||
'<button class="epp-emblem-remove" ' +
|
||||
'onclick="event.stopPropagation();removeEmblem(' + player.id + ',' + idx + ')">\u00d7</button>' +
|
||||
'</div>';
|
||||
});
|
||||
}
|
||||
|
||||
emblemsHtml +=
|
||||
'<button class="epp-add-emblem-btn" ' +
|
||||
'onclick="event.stopPropagation();showAddEmblemDialog(' + player.id + ')" ' +
|
||||
'title="Add an emblem">+ Emblem</button>' +
|
||||
'</div>' +
|
||||
'</div>';
|
||||
|
||||
// ── Assemble Panel ──────────────────────────────────────
|
||||
var bodyHtml =
|
||||
'<div class="epp-body" id="epp-body-' + player.id + '" ' +
|
||||
'style="' + (isCollapsed ? 'display:none;' : '') + '">' +
|
||||
lifeHtml + cmdDmgHtml + countersHtml + emblemsHtml +
|
||||
'</div>';
|
||||
|
||||
panel.innerHTML = headerHtml + bodyHtml;
|
||||
return panel;
|
||||
}
|
||||
|
||||
function _buildCounterWidget(label, icon, value, threshold, onInc, onDec, dangerClass) {
|
||||
return '<div class="epp-counter ' + dangerClass + '">' +
|
||||
'<div class="epp-counter-icon">' + icon + '</div>' +
|
||||
'<div class="epp-counter-info">' +
|
||||
'<span class="epp-counter-value">' + value + '</span>' +
|
||||
'<span class="epp-counter-label">' + label + '</span>' +
|
||||
'</div>' +
|
||||
'<div class="epp-counter-btns">' +
|
||||
'<button class="epp-counter-btn" onclick="event.stopPropagation();' + onInc + '">+</button>' +
|
||||
'<button class="epp-counter-btn" onclick="event.stopPropagation();' + onDec + '">\u2212</button>' +
|
||||
'</div>' +
|
||||
'</div>';
|
||||
}
|
||||
|
||||
function togglePlayerPanel(playerId) {
|
||||
var body = document.getElementById('epp-body-' + playerId);
|
||||
var panel = document.getElementById('enhanced-panel-' + playerId);
|
||||
if (!body || !panel) return;
|
||||
|
||||
var isCollapsed = body.style.display === 'none';
|
||||
body.style.display = isCollapsed ? '' : 'none';
|
||||
|
||||
var arrow = panel.querySelector('.epp-collapse-arrow');
|
||||
if (arrow) arrow.textContent = isCollapsed ? '\u25be' : '\u25b8';
|
||||
|
||||
localStorage.setItem('panel-collapsed-' + playerId, !isCollapsed);
|
||||
}
|
||||
|
||||
// Counter adjustment functions
|
||||
function adjustEnergy(playerId, amount) {
|
||||
if (!gameState) return;
|
||||
var player = gameState.players[playerId];
|
||||
if (!player) return;
|
||||
player.energy = Math.max(0, (player.energy || 0) + amount);
|
||||
addLogEntry('<strong>' + player.name + '</strong> energy: ' + player.energy);
|
||||
renderEnhancedPanels();
|
||||
}
|
||||
|
||||
function adjustExperience(playerId, amount) {
|
||||
if (!gameState) return;
|
||||
var player = gameState.players[playerId];
|
||||
if (!player) return;
|
||||
player.experience = Math.max(0, (player.experience || 0) + amount);
|
||||
addLogEntry('<strong>' + player.name + '</strong> experience: ' + player.experience);
|
||||
renderEnhancedPanels();
|
||||
}
|
||||
|
||||
function adjustRad(playerId, amount) {
|
||||
if (!gameState) return;
|
||||
var player = gameState.players[playerId];
|
||||
if (!player) return;
|
||||
player.rad = Math.max(0, (player.rad || 0) + amount);
|
||||
addLogEntry('<strong>' + player.name + '</strong> rad counters: ' + player.rad);
|
||||
renderEnhancedPanels();
|
||||
}
|
||||
|
||||
function showAddEmblemDialog(playerId) {
|
||||
var name = prompt('Enter emblem name (e.g., "Sorin, Vengeful Bloodlord"):');
|
||||
if (!name || !name.trim()) return;
|
||||
if (!gameState) return;
|
||||
var player = gameState.players[playerId];
|
||||
if (!player) return;
|
||||
if (!player.emblems) player.emblems = [];
|
||||
player.emblems.push({ name: name.trim() });
|
||||
addLogEntry('<strong>' + player.name + '</strong> gained emblem: ' + name.trim());
|
||||
renderEnhancedPanels();
|
||||
}
|
||||
|
||||
function removeEmblem(playerId, idx) {
|
||||
if (!gameState) return;
|
||||
var player = gameState.players[playerId];
|
||||
if (!player || !player.emblems) return;
|
||||
var removed = player.emblems.splice(idx, 1);
|
||||
if (removed.length > 0) {
|
||||
addLogEntry('<strong>' + player.name + '</strong> lost emblem: ' + removed[0].name);
|
||||
}
|
||||
renderEnhancedPanels();
|
||||
}
|
||||
|
||||
// index: 0 1 2 3 → area: 1 2 4 3
|
||||
// players[2] → bottom-RIGHT (area-4), players[3] → bottom-LEFT (area-3)
|
||||
// Keeps turn order P1→P2→P3→P4 visually clockwise.
|
||||
var PLAYER_INDEX_TO_QUADRANT = [1, 2, 4, 3];
|
||||
|
||||
/**
|
||||
* Re-render all enhanced player panels in their quadrant positions.
|
||||
*/
|
||||
function renderEnhancedPanels() {
|
||||
if (!gameState) return;
|
||||
|
||||
for (var i = 0; i < gameState.players.length; i++) {
|
||||
var player = gameState.players[i];
|
||||
var quadrant = PLAYER_INDEX_TO_QUADRANT[i] || (i + 1);
|
||||
var areaEl = document.getElementById('player-area-' + quadrant);
|
||||
if (!areaEl) continue;
|
||||
|
||||
// Find or create the enhanced panel container
|
||||
var container = areaEl.querySelector('.epp-container');
|
||||
if (!container) {
|
||||
container = document.createElement('div');
|
||||
container.className = 'epp-container';
|
||||
// Insert before the zone row
|
||||
var zoneRow = areaEl.querySelector('.zone-row');
|
||||
if (zoneRow) {
|
||||
areaEl.insertBefore(container, zoneRow);
|
||||
} else {
|
||||
areaEl.appendChild(container);
|
||||
}
|
||||
}
|
||||
|
||||
var isActive = gameState.currentPlayerIndex === i;
|
||||
var panel = createEnhancedPlayerPanel(player, quadrant, isActive);
|
||||
container.innerHTML = '';
|
||||
container.appendChild(panel);
|
||||
|
||||
// Hide the old life counter (we have our own now)
|
||||
var oldLifeCounter = areaEl.querySelector('.life-counter');
|
||||
if (oldLifeCounter) oldLifeCounter.style.display = 'none';
|
||||
|
||||
// Hide old poison badge (we have counters section)
|
||||
var oldPoison = areaEl.querySelector('.poison-badge');
|
||||
if (oldPoison) oldPoison.style.display = 'none';
|
||||
}
|
||||
}
|
||||
@@ -1,426 +0,0 @@
|
||||
/**
|
||||
* precon-loader.js
|
||||
* Handles loading precon .dck files from the backend into the lan-client simulator.
|
||||
* Depends on: engine.js (gameState, initGame, addLogEntry, renderPlayerPanels)
|
||||
* ai-bridge.js (AIBridge.deckSource.fetchPreconDeck)
|
||||
* ui.js (setupDeckChoices, setupPlayerCount, setupStartingLife)
|
||||
*/
|
||||
|
||||
'use strict';
|
||||
|
||||
// Scryfall collection endpoint — accepts up to 75 card identifiers per call
|
||||
const SCRYFALL_COLLECTION_URL = 'https://api.scryfall.com/cards/collection';
|
||||
|
||||
// ────────────────────────────────────────────────────────────
|
||||
// Scryfall image cache: cardName -> { imageUrl, typeLine, ... }
|
||||
// Persisted to localStorage so repeat games don't re-hit Scryfall.
|
||||
// ────────────────────────────────────────────────────────────
|
||||
const _SCRYFALL_CACHE_KEY = 'commanderLab.scryfallCache.v1';
|
||||
const _SCRYFALL_CACHE_TTL_MS = 30 * 24 * 60 * 60 * 1000; // 30 days
|
||||
const _SCRYFALL_CACHE_MAX_ENTRIES = 20000;
|
||||
|
||||
let _scryfallCache = {};
|
||||
(function _loadScryfallCacheFromLocalStorage() {
|
||||
try {
|
||||
const raw = localStorage.getItem(_SCRYFALL_CACHE_KEY);
|
||||
if (!raw) return;
|
||||
const parsed = JSON.parse(raw);
|
||||
if (!parsed || typeof parsed !== 'object') return;
|
||||
const now = Date.now();
|
||||
const stamp = parsed.__stamp || 0;
|
||||
if (now - stamp > _SCRYFALL_CACHE_TTL_MS) {
|
||||
console.log('[scryfall-cache] expired (> 30 days), clearing');
|
||||
localStorage.removeItem(_SCRYFALL_CACHE_KEY);
|
||||
return;
|
||||
}
|
||||
delete parsed.__stamp;
|
||||
_scryfallCache = parsed;
|
||||
const n = Object.keys(_scryfallCache).length;
|
||||
console.log('[scryfall-cache] loaded ' + n + ' entries from localStorage');
|
||||
} catch (e) {
|
||||
console.warn('[scryfall-cache] load failed:', e);
|
||||
try { localStorage.removeItem(_SCRYFALL_CACHE_KEY); } catch (_) {}
|
||||
_scryfallCache = {};
|
||||
}
|
||||
})();
|
||||
|
||||
let _scryfallCacheSaveTimer = null;
|
||||
function _persistScryfallCache() {
|
||||
// Debounced — coalesce rapid writes during batch enrichment
|
||||
if (_scryfallCacheSaveTimer) return;
|
||||
_scryfallCacheSaveTimer = setTimeout(function () {
|
||||
_scryfallCacheSaveTimer = null;
|
||||
try {
|
||||
// Trim to MAX_ENTRIES if over (drop entries with no imageUrl first)
|
||||
const keys = Object.keys(_scryfallCache);
|
||||
if (keys.length > _SCRYFALL_CACHE_MAX_ENTRIES) {
|
||||
const trimmed = {};
|
||||
const sorted = keys.sort((a, b) =>
|
||||
(_scryfallCache[b].imageUrl ? 1 : 0) - (_scryfallCache[a].imageUrl ? 1 : 0)
|
||||
).slice(0, _SCRYFALL_CACHE_MAX_ENTRIES);
|
||||
for (const k of sorted) trimmed[k] = _scryfallCache[k];
|
||||
_scryfallCache = trimmed;
|
||||
}
|
||||
const payload = Object.assign({ __stamp: Date.now() }, _scryfallCache);
|
||||
localStorage.setItem(_SCRYFALL_CACHE_KEY, JSON.stringify(payload));
|
||||
} catch (e) {
|
||||
// Quota exceeded — clear and skip. Cache will rebuild next session.
|
||||
console.warn('[scryfall-cache] persist failed (quota?):', e);
|
||||
try { localStorage.removeItem(_SCRYFALL_CACHE_KEY); } catch (_) {}
|
||||
}
|
||||
}, 750);
|
||||
}
|
||||
|
||||
function clearScryfallCache() {
|
||||
_scryfallCache = {};
|
||||
try { localStorage.removeItem(_SCRYFALL_CACHE_KEY); } catch (_) {}
|
||||
console.log('[scryfall-cache] cleared');
|
||||
}
|
||||
|
||||
// ============================================================
|
||||
// startGameWithDecks
|
||||
// Called by ui.js startGame() when players have selected precon decks.
|
||||
// ============================================================
|
||||
async function startGameWithDecks() {
|
||||
const playerNames = [];
|
||||
for (let i = 0; i < (setupPlayerCount || 4); i++) {
|
||||
const inp = document.getElementById('player-name-' + i);
|
||||
playerNames.push(inp && inp.value.trim() ? inp.value.trim() : 'Player ' + (i + 1));
|
||||
}
|
||||
|
||||
initGame(setupPlayerCount || 4, playerNames, setupStartingLife || 40);
|
||||
|
||||
const choices = typeof setupDeckChoices !== 'undefined' ? setupDeckChoices : {};
|
||||
const loadPromises = Object.entries(choices).map(async ([idxStr, choice]) => {
|
||||
const playerIdx = parseInt(idxStr, 10);
|
||||
if (choice.type !== 'precon' || !choice.preconId) return;
|
||||
try {
|
||||
const fileName = choice.preconId.endsWith('.dck') ? choice.preconId : choice.preconId + '.dck';
|
||||
const data = await AIBridge.deckSource.fetchPreconDeck(fileName);
|
||||
if (!data || !data.decklist) {
|
||||
addLogEntry('\u26A0 Could not load deck for Player ' + (playerIdx + 1) + ' (no decklist returned)');
|
||||
return;
|
||||
}
|
||||
loadPreconDeckFromDck(playerIdx, data.decklist, choice.preconId);
|
||||
} catch (e) {
|
||||
console.warn('[precon-loader] Failed to load deck for player', playerIdx, e);
|
||||
addLogEntry('\u26A0 Deck load failed for Player ' + (playerIdx + 1) + ': ' + e.message);
|
||||
}
|
||||
});
|
||||
|
||||
await Promise.all(loadPromises);
|
||||
|
||||
if (typeof renderPlayerPanels === 'function') renderPlayerPanels();
|
||||
if (typeof updateActivePlayerHighlight === 'function') updateActivePlayerHighlight();
|
||||
|
||||
// Block on Scryfall enrichment BEFORE the game becomes interactive. This is
|
||||
// critical: the opening 7 cards are dealt via player.zones.hand.push(lib.shift())
|
||||
// directly (bypassing drawCard()), so they don't get _enrichCardFromCache'd at
|
||||
// draw time. Without awaiting here, the AI's first turn fires with empty
|
||||
// typeLine/cmc/oracleText/isRamp/isRemoval flags and can only play lands.
|
||||
addLogEntry('\u23F3 Loading card data from Scryfall...');
|
||||
try {
|
||||
await enrichAllZonesWithScryfall();
|
||||
} catch (e) {
|
||||
console.warn('[precon-loader] Enrichment failed, continuing anyway:', e);
|
||||
}
|
||||
addLogEntry('\u2705 Card data loaded. Game ready.');
|
||||
}
|
||||
|
||||
|
||||
// ============================================================
|
||||
// loadPreconDeckFromDck
|
||||
// Parses a Forge-format .dck file string and populates a player's zones.
|
||||
// ============================================================
|
||||
function loadPreconDeckFromDck(playerIdx, dckContent, deckLabel) {
|
||||
if (!gameState) return;
|
||||
const player = gameState.players[playerIdx];
|
||||
if (!player) return;
|
||||
|
||||
let commander = '';
|
||||
let section = '';
|
||||
const mainCards = [];
|
||||
|
||||
for (const rawLine of dckContent.split('\n')) {
|
||||
const line = rawLine.trim();
|
||||
if (!line || line.startsWith('//')) continue;
|
||||
|
||||
if (line.startsWith('[') && line.endsWith(']')) {
|
||||
section = line.slice(1, -1);
|
||||
continue;
|
||||
}
|
||||
if (line.includes('=') && !line.match(/^\d/)) continue;
|
||||
|
||||
const m = line.match(/^(\d+)\s+(.+?)(?:\|.*)?$/);
|
||||
if (!m) continue;
|
||||
|
||||
const qty = parseInt(m[1], 10);
|
||||
const name = m[2].trim();
|
||||
|
||||
if (section === 'Commander') {
|
||||
commander = name;
|
||||
} else if (section === 'Main') {
|
||||
for (let q = 0; q < qty; q++) mainCards.push(name);
|
||||
}
|
||||
}
|
||||
|
||||
player.zones.command = [];
|
||||
player.zones.library = [];
|
||||
player.zones.hand = [];
|
||||
player.zones.graveyard = [];
|
||||
player.zones.exile = [];
|
||||
|
||||
if (commander) {
|
||||
player.zones.command.push({
|
||||
id: 'cmd-' + playerIdx + '-' + Date.now(),
|
||||
name: commander,
|
||||
imageUrl: '',
|
||||
typeLine: 'Legendary Creature',
|
||||
oracleText: '',
|
||||
manaCost: '',
|
||||
});
|
||||
player.commanderCardName = commander;
|
||||
}
|
||||
|
||||
for (const cardName of mainCards) {
|
||||
player.zones.library.push({
|
||||
id: 'lib-' + playerIdx + '-' + cardName + '-' + Math.random().toString(36).slice(2, 6),
|
||||
name: cardName,
|
||||
imageUrl: '',
|
||||
typeLine: '',
|
||||
oracleText: '',
|
||||
manaCost: '',
|
||||
});
|
||||
}
|
||||
|
||||
// Fisher-Yates shuffle
|
||||
const lib = player.zones.library;
|
||||
for (let i = lib.length - 1; i > 0; i--) {
|
||||
const j = Math.floor(Math.random() * (i + 1));
|
||||
[lib[i], lib[j]] = [lib[j], lib[i]];
|
||||
}
|
||||
|
||||
// Draw opening hand
|
||||
for (let d = 0; d < 7 && lib.length > 0; d++) {
|
||||
player.zones.hand.push(lib.shift());
|
||||
}
|
||||
|
||||
if (typeof updateZoneCountsForPlayer === 'function') updateZoneCountsForPlayer(playerIdx);
|
||||
if (typeof renderCommanderZone === 'function') renderCommanderZone(playerIdx);
|
||||
if (typeof renderHandZone === 'function') renderHandZone(playerIdx);
|
||||
|
||||
const label = deckLabel ? deckLabel.replace('.dck', '') : 'Unknown Deck';
|
||||
addLogEntry(
|
||||
'\uD83C\uDCCF <strong>' + player.name + '</strong> loaded: <em>' + label + '</em>' +
|
||||
(commander ? ' \u2014 Commander: <strong>' + commander + '</strong>' : '') +
|
||||
' (' + lib.length + ' in library, 7 in hand)'
|
||||
);
|
||||
}
|
||||
|
||||
|
||||
// ============================================================
|
||||
// SCRYFALL IMAGE ENRICHMENT
|
||||
// After zones are populated with card names, batch-fetch card
|
||||
// data from Scryfall and backfill imageUrl on every card object.
|
||||
// Uses the /cards/collection endpoint (75 cards per request).
|
||||
// Results are cached in _scryfallCache so re-renders are instant.
|
||||
// ============================================================
|
||||
|
||||
async function enrichAllZonesWithScryfall() {
|
||||
if (!gameState) return;
|
||||
|
||||
// Gather all unique card names across all players and all zones
|
||||
const nameSet = new Set();
|
||||
let totalCards = 0;
|
||||
let cacheHits = 0;
|
||||
for (const player of gameState.players) {
|
||||
for (const zone of Object.values(player.zones)) {
|
||||
for (const card of zone) {
|
||||
if (!card.name) continue;
|
||||
totalCards++;
|
||||
if (_scryfallCache[card.name]) {
|
||||
cacheHits++;
|
||||
} else {
|
||||
nameSet.add(card.name);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (nameSet.size === 0) {
|
||||
// Everything was in cache — just apply and bail, no network
|
||||
addLogEntry('\u2705 Card images loaded from cache (' + cacheHits + ' cards).');
|
||||
_applyScryfallDataToAllCards();
|
||||
return;
|
||||
}
|
||||
|
||||
const names = Array.from(nameSet);
|
||||
const hitRatio = totalCards > 0 ? Math.round((cacheHits / totalCards) * 100) : 0;
|
||||
addLogEntry('\uD83C\uDF0D Fetching ' + names.length + ' new cards from Scryfall (' + hitRatio + '% cache hit rate)...');
|
||||
|
||||
// Split into batches of 75 (Scryfall max)
|
||||
const BATCH = 75;
|
||||
for (let i = 0; i < names.length; i += BATCH) {
|
||||
const batch = names.slice(i, i + BATCH);
|
||||
try {
|
||||
await _fetchScryfallBatch(batch);
|
||||
} catch (e) {
|
||||
console.warn('[precon-loader] Scryfall batch failed', e);
|
||||
}
|
||||
// Polite delay between batches to respect Scryfall rate limit (50-100ms)
|
||||
if (i + BATCH < names.length) await _sleep(100);
|
||||
}
|
||||
|
||||
// Apply cached data to every card object in every zone
|
||||
_applyScryfallDataToAllCards();
|
||||
|
||||
addLogEntry('\u2705 Card images loaded.');
|
||||
}
|
||||
|
||||
async function _fetchScryfallBatch(names) {
|
||||
const identifiers = names.map(name => ({ name }));
|
||||
const resp = await fetch(SCRYFALL_COLLECTION_URL, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({ identifiers }),
|
||||
});
|
||||
if (!resp.ok) throw new Error('Scryfall HTTP ' + resp.status);
|
||||
const json = await resp.json();
|
||||
|
||||
for (const card of (json.data || [])) {
|
||||
const name = card.name;
|
||||
// Pick the best image: normal > large > small, handle double-faced
|
||||
let imageUrl = '';
|
||||
if (card.image_uris) {
|
||||
imageUrl = card.image_uris.normal || card.image_uris.large || card.image_uris.small || '';
|
||||
} else if (card.card_faces && card.card_faces[0] && card.card_faces[0].image_uris) {
|
||||
imageUrl = card.card_faces[0].image_uris.normal || card.card_faces[0].image_uris.large || '';
|
||||
}
|
||||
_scryfallCache[name] = {
|
||||
imageUrl,
|
||||
typeLine: card.type_line || '',
|
||||
oracleText: card.oracle_text || (card.card_faces && card.card_faces[0] ? card.card_faces[0].oracle_text : '') || '',
|
||||
manaCost: card.mana_cost || (card.card_faces && card.card_faces[0] ? card.card_faces[0].mana_cost : '') || '',
|
||||
cmc: card.cmc || 0,
|
||||
power: card.power || '',
|
||||
toughness: card.toughness || '',
|
||||
};
|
||||
}
|
||||
|
||||
// Mark not-found cards so we don't re-fetch them on next enrichment
|
||||
for (const name of names) {
|
||||
if (!_scryfallCache[name]) _scryfallCache[name] = { imageUrl: '', typeLine: '', oracleText: '', manaCost: '', cmc: 0, power: '', toughness: '' };
|
||||
}
|
||||
|
||||
_persistScryfallCache();
|
||||
}
|
||||
|
||||
function _applyScryfallDataToAllCards() {
|
||||
if (!gameState) return;
|
||||
for (const player of gameState.players) {
|
||||
for (const zone of Object.values(player.zones)) {
|
||||
for (const card of zone) {
|
||||
const data = _scryfallCache[card.name];
|
||||
if (data) {
|
||||
card.imageUrl = data.imageUrl || card.imageUrl;
|
||||
card.typeLine = data.typeLine || card.typeLine;
|
||||
card.oracleText = data.oracleText || card.oracleText;
|
||||
card.manaCost = data.manaCost || card.manaCost;
|
||||
card.cmc = data.cmc || card.cmc || 0;
|
||||
if (data.power) card.power = data.power;
|
||||
if (data.toughness) card.toughness = data.toughness;
|
||||
|
||||
// Tag roles from oracle text so aiCategorizeHand works
|
||||
const ot = (card.oracleText || '').toLowerCase();
|
||||
if (!card.isRamp) card.isRamp = /search your library for a.*land|add \{|adds? one mana/i.test(ot);
|
||||
if (!card.isRemoval) card.isRemoval = /destroy target|exile target|counter target spell/i.test(ot);
|
||||
if (!card.isBoardWipe) card.isBoardWipe = /destroy all|exile all|all creatures get -/i.test(ot);
|
||||
}
|
||||
}
|
||||
}
|
||||
// Also enrich battlefield cards owned by this player
|
||||
if (gameState.battlefieldCards) {
|
||||
for (const card of gameState.battlefieldCards) {
|
||||
if (card.ownerIndex !== player.id) continue;
|
||||
const data = _scryfallCache[card.name];
|
||||
if (data) {
|
||||
if (!card.imageUrl) card.imageUrl = data.imageUrl || '';
|
||||
card.typeLine = data.typeLine || card.typeLine;
|
||||
card.oracleText = data.oracleText || card.oracleText;
|
||||
card.manaCost = data.manaCost || card.manaCost;
|
||||
card.cmc = data.cmc || card.cmc || 0;
|
||||
|
||||
const ot = (card.oracleText || '').toLowerCase();
|
||||
if (!card.isRamp) card.isRamp = /search your library for a.*land|add \{|adds? one mana/i.test(ot);
|
||||
if (!card.isRemoval) card.isRemoval = /destroy target|exile target|counter target spell/i.test(ot);
|
||||
if (!card.isBoardWipe) card.isBoardWipe = /destroy all|exile all|all creatures get -/i.test(ot);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Trigger UI refresh so images appear without a full reload
|
||||
if (typeof renderPlayerPanels === 'function') renderPlayerPanels();
|
||||
if (typeof renderCommanderZone === 'function') {
|
||||
for (let i = 0; i < gameState.players.length; i++) renderCommanderZone(i);
|
||||
}
|
||||
if (typeof renderHandZone === 'function') {
|
||||
for (let i = 0; i < gameState.players.length; i++) renderHandZone(i);
|
||||
}
|
||||
if (typeof updateZoneCounts === 'function') updateZoneCounts();
|
||||
}
|
||||
|
||||
/**
|
||||
* Synchronously enrich a single card from the Scryfall cache.
|
||||
* Called at draw time so cards have typeLine/cmc/oracle data
|
||||
* before the AI's first turn, even if async enrichment hasn't completed.
|
||||
*/
|
||||
function _enrichCardFromCache(card) {
|
||||
if (!card || !card.name) return;
|
||||
const data = _scryfallCache[card.name];
|
||||
if (!data) return;
|
||||
card.imageUrl = data.imageUrl || card.imageUrl || '';
|
||||
card.typeLine = data.typeLine || card.typeLine || '';
|
||||
card.oracleText = data.oracleText || card.oracleText || '';
|
||||
card.manaCost = data.manaCost || card.manaCost || '';
|
||||
card.cmc = data.cmc || card.cmc || 0;
|
||||
if (data.power) card.power = data.power;
|
||||
if (data.toughness) card.toughness = data.toughness;
|
||||
|
||||
// Tag oracle roles
|
||||
const ot = (card.oracleText || '').toLowerCase();
|
||||
if (!card.isRamp) card.isRamp = /search your library for a.*land|add \{|adds? one mana/i.test(ot);
|
||||
if (!card.isRemoval) card.isRemoval = /destroy target|exile target|counter target spell/i.test(ot);
|
||||
if (!card.isBoardWipe) card.isBoardWipe = /destroy all|exile all|all creatures get -/i.test(ot);
|
||||
}
|
||||
|
||||
/**
|
||||
* Enrich a single card object on-demand (useful for cards drawn or played after initial load).
|
||||
* Returns a Promise that resolves when the card's imageUrl is set.
|
||||
*/
|
||||
async function enrichCardWithScryfall(card) {
|
||||
if (!card || !card.name) return;
|
||||
if (card.imageUrl) return; // already has image
|
||||
|
||||
const cached = _scryfallCache[card.name];
|
||||
if (cached) {
|
||||
card.imageUrl = cached.imageUrl || '';
|
||||
card.typeLine = cached.typeLine || card.typeLine;
|
||||
card.oracleText = cached.oracleText || card.oracleText;
|
||||
card.manaCost = cached.manaCost || card.manaCost;
|
||||
return;
|
||||
}
|
||||
|
||||
await _fetchScryfallBatch([card.name]);
|
||||
const data = _scryfallCache[card.name];
|
||||
if (data) {
|
||||
card.imageUrl = data.imageUrl || '';
|
||||
card.typeLine = data.typeLine || card.typeLine;
|
||||
card.oracleText = data.oracleText || card.oracleText;
|
||||
card.manaCost = data.manaCost || card.manaCost;
|
||||
}
|
||||
}
|
||||
|
||||
function _sleep(ms) {
|
||||
return new Promise(resolve => setTimeout(resolve, ms));
|
||||
}
|
||||
@@ -1,167 +0,0 @@
|
||||
// Auto-generated from precon-decks/precon-index.json
|
||||
// Run: node scripts/generate-precons-js.js to regenerate
|
||||
var PRECON_DATA = [
|
||||
{"id":"Counterpunch","name":"Counterpunch","commander":"Ghave, Guru of Spores","set":"Commander 2011","fileName":"Counterpunch.dck","colors":[],"year":2011},
|
||||
{"id":"Devour_for_Power_CMD","name":"Devour for Power","commander":"The Mimeoplasm","set":"Commander 2011","fileName":"Devour_for_Power_CMD.dck","colors":[],"year":2011},
|
||||
{"id":"Heavenly_Inferno_CMD","name":"Heavenly Inferno","commander":"Kaalia of the Vast","set":"Commander 2011","fileName":"Heavenly_Inferno_CMD.dck","colors":[],"year":2011},
|
||||
{"id":"Mirror_Mastery","name":"Mirror Mastery","commander":"Riku of Two Reflections","set":"Commander 2011","fileName":"Mirror_Mastery.dck","colors":[],"year":2011},
|
||||
{"id":"Political_Puppets","name":"Political Puppets","commander":"Zedruu the Greathearted","set":"Commander 2011","fileName":"Political_Puppets.dck","colors":[],"year":2011},
|
||||
{"id":"Eternal_Bargain","name":"Eternal Bargain","commander":"Oloro, Ageless Ascetic","set":"Commander 2013","fileName":"Eternal_Bargain.dck","colors":[],"year":2013},
|
||||
{"id":"Evasive_Maneuvers_C13","name":"Evasive Maneuvers","commander":"Derevi, Empyrial Tactician","set":"Commander 2013","fileName":"Evasive_Maneuvers_C13.dck","colors":[],"year":2013},
|
||||
{"id":"Mind_Seize","name":"Mind Seize","commander":"Jeleva, Nephalia's Scourge","set":"Commander 2013","fileName":"Mind_Seize.dck","colors":[],"year":2013},
|
||||
{"id":"Nature_of_the_Beast","name":"Nature of the Beast","commander":"Marath, Will of the Wild","set":"Commander 2013","fileName":"Nature_of_the_Beast.dck","colors":[],"year":2013},
|
||||
{"id":"Power_Hungry","name":"Power Hungry","commander":"Prossh, Skyraider of Kher","set":"Commander 2013","fileName":"Power_Hungry.dck","colors":[],"year":2013},
|
||||
{"id":"Built_From_Scratch_C14","name":"Built From Scratch","commander":"Daretti, Scrap Savant","set":"Commander 2014","fileName":"Built_From_Scratch_C14.dck","colors":[],"year":2014},
|
||||
{"id":"Forged_In_Stone","name":"Forged In Stone","commander":"Nahiri, the Lithomancer","set":"Commander 2014","fileName":"Forged_In_Stone.dck","colors":[],"year":2014},
|
||||
{"id":"Guided_By_Nature","name":"Guided By Nature","commander":"Freyalise, Llanowar's Fury","set":"Commander 2014","fileName":"Guided_By_Nature.dck","colors":[],"year":2014},
|
||||
{"id":"Peer_Through_Time","name":"Peer Through Time","commander":"Teferi, Temporal Archmage","set":"Commander 2014","fileName":"Peer_Through_Time.dck","colors":[],"year":2014},
|
||||
{"id":"Sworn_To_Darkness","name":"Sworn To Darkness","commander":"Ob Nixilis of the Black Oath","set":"Commander 2014","fileName":"Sworn_To_Darkness.dck","colors":[],"year":2014},
|
||||
{"id":"Call_the_Spirits","name":"Call the Spirits","commander":"Daxos the Returned","set":"Commander 2015","fileName":"Call_the_Spirits.dck","colors":[],"year":2015},
|
||||
{"id":"Plunder_the_Graves_C15","name":"Plunder the Graves","commander":"Meren of Clan Nel Toth","set":"Commander 2015","fileName":"Plunder_the_Graves_C15.dck","colors":[],"year":2015},
|
||||
{"id":"Seize_Control","name":"Seize Control","commander":"Mizzix of the Izmagnus","set":"Commander 2015","fileName":"Seize_Control.dck","colors":[],"year":2015},
|
||||
{"id":"Swell_the_Host","name":"Swell the Host","commander":"Ezuri, Claw of Progress","set":"Commander 2015","fileName":"Swell_the_Host.dck","colors":[],"year":2015},
|
||||
{"id":"Wade_into_Battle_C15","name":"Wade into Battle","commander":"Kalemne, Disciple of Iroas","set":"Commander 2015","fileName":"Wade_into_Battle_C15.dck","colors":[],"year":2015},
|
||||
{"id":"Breed_Lethality_C16","name":"Breed Lethality","commander":"Atraxa, Praetors' Voice","set":"Commander 2016","fileName":"Breed_Lethality_C16.dck","colors":[],"year":2016},
|
||||
{"id":"Entropic_Uprising","name":"Entropic Uprising","commander":"Yidris, Maelstrom Wielder","set":"Commander 2016","fileName":"Entropic_Uprising.dck","colors":[],"year":2016},
|
||||
{"id":"Invent_Superiority","name":"Invent Superiority","commander":"Breya, Etherium Shaper","set":"Commander 2016","fileName":"Invent_Superiority.dck","colors":[],"year":2016},
|
||||
{"id":"Open_Hostility","name":"Open Hostility","commander":"Saskia the Unyielding","set":"Commander 2016","fileName":"Open_Hostility.dck","colors":[],"year":2016},
|
||||
{"id":"Stalwart_Unity","name":"Stalwart Unity","commander":"Kynaios and Tiro of Meletis","set":"Commander 2016","fileName":"Stalwart_Unity.dck","colors":[],"year":2016},
|
||||
{"id":"Evasive_Maneuvers_CMA","name":"Evasive Maneuvers","commander":"Derevi, Empyrial Tactician","set":"Commander Anthology","fileName":"Evasive_Maneuvers_CMA.dck","colors":[],"year":2017},
|
||||
{"id":"Guided_by_Nature","name":"Guided by Nature","commander":"Freyalise, Llanowar's Fury","set":"Commander Anthology","fileName":"Guided_by_Nature.dck","colors":[],"year":2017},
|
||||
{"id":"Heavenly_Inferno_CMA","name":"Heavenly Inferno","commander":"Kaalia of the Vast","set":"Commander Anthology","fileName":"Heavenly_Inferno_CMA.dck","colors":[],"year":2017},
|
||||
{"id":"Plunder_the_Graves_CMA","name":"Plunder the Graves","commander":"Meren of Clan Nel Toth","set":"Commander Anthology","fileName":"Plunder_the_Graves_CMA.dck","colors":[],"year":2017},
|
||||
{"id":"Arcane_Wizardry","name":"Arcane Wizardry","commander":"Inalla, Archmage Ritualist","set":"Commander 2017","fileName":"Arcane_Wizardry.dck","colors":[],"year":2017},
|
||||
{"id":"Draconic_Domination","name":"Draconic Domination","commander":"The Ur-Dragon","set":"Commander 2017","fileName":"Draconic_Domination.dck","colors":[],"year":2017},
|
||||
{"id":"Feline_Ferocity","name":"Feline Ferocity","commander":"Arahbo, Roar of the World","set":"Commander 2017","fileName":"Feline_Ferocity.dck","colors":[],"year":2017},
|
||||
{"id":"Vampiric_Bloodlust","name":"Vampiric Bloodlust","commander":"Edgar Markov","set":"Commander 2017","fileName":"Vampiric_Bloodlust.dck","colors":[],"year":2017},
|
||||
{"id":"Breed_Lethality_CM2","name":"Breed Lethality","commander":"Atraxa, Praetors' Voice","set":"Commander Anthology Volume II","fileName":"Breed_Lethality_CM2.dck","colors":[],"year":2018},
|
||||
{"id":"Built_From_Scratch_CM2","name":"Built From Scratch","commander":"Daretti, Scrap Savant","set":"Commander Anthology Volume II","fileName":"Built_From_Scratch_CM2.dck","colors":[],"year":2018},
|
||||
{"id":"Devour_for_Power_CM2","name":"Devour for Power","commander":"The Mimeoplasm","set":"Commander Anthology Volume II","fileName":"Devour_for_Power_CM2.dck","colors":[],"year":2018},
|
||||
{"id":"Wade_into_Battle_CM2","name":"Wade into Battle","commander":"Kalemne, Disciple of Iroas","set":"Commander Anthology Volume II","fileName":"Wade_into_Battle_CM2.dck","colors":[],"year":2018},
|
||||
{"id":"Adaptive_Enchantment","name":"Adaptive Enchantment","commander":"Estrid, the Masked","set":"Commander 2018","fileName":"Adaptive_Enchantment.dck","colors":[],"year":2018},
|
||||
{"id":"Exquisite_Invention","name":"Exquisite Invention","commander":"Saheeli, the Gifted","set":"Commander 2018","fileName":"Exquisite_Invention.dck","colors":[],"year":2018},
|
||||
{"id":"Natures_Vengeance","name":"Nature's Vengeance","commander":"Lord Windgrace","set":"Commander 2018","fileName":"Natures_Vengeance.dck","colors":[],"year":2018},
|
||||
{"id":"Subjective_Reality","name":"Subjective Reality","commander":"Aminatou, the Fateshifter","set":"Commander 2018","fileName":"Subjective_Reality.dck","colors":[],"year":2018},
|
||||
{"id":"Faceless_Menace","name":"Faceless Menace","commander":"Kadena, Slinking Sorcerer","set":"Commander 2019","fileName":"Faceless_Menace.dck","colors":[],"year":2019},
|
||||
{"id":"Merciless_Rage","name":"Merciless Rage","commander":"Anje Falkenrath","set":"Commander 2019","fileName":"Merciless_Rage.dck","colors":[],"year":2019},
|
||||
{"id":"Mystic_Intellect","name":"Mystic Intellect","commander":"Sevinne, the Chronoclasm","set":"Commander 2019","fileName":"Mystic_Intellect.dck","colors":[],"year":2019},
|
||||
{"id":"Primal_Genesis","name":"Primal Genesis","commander":"Ghired, Conclave Exile","set":"Commander 2019","fileName":"Primal_Genesis.dck","colors":[],"year":2019},
|
||||
{"id":"Arcane_Maelstrom","name":"Arcane Maelstrom","commander":"Kalamax, the Stormsire","set":"Commander 2020","fileName":"Arcane_Maelstrom.dck","colors":[],"year":2020},
|
||||
{"id":"Enhanced_Evolution","name":"Enhanced Evolution","commander":"Otrimi, the Ever-Playful","set":"Commander 2020","fileName":"Enhanced_Evolution.dck","colors":[],"year":2020},
|
||||
{"id":"Ruthless_Regiment","name":"Ruthless Regiment","commander":"Jirina Kudro","set":"Commander 2020","fileName":"Ruthless_Regiment.dck","colors":[],"year":2020},
|
||||
{"id":"Symbiotic_Swarm","name":"Symbiotic Swarm","commander":"Kathril, Aspect Warper","set":"Commander 2020","fileName":"Symbiotic_Swarm.dck","colors":[],"year":2020},
|
||||
{"id":"Timeless_Wisdom","name":"Timeless Wisdom","commander":"Gavi, Nest Warden","set":"Commander 2020","fileName":"Timeless_Wisdom.dck","colors":[],"year":2020},
|
||||
{"id":"Lands_Wrath","name":"Land's Wrath","commander":"Obuun, Mul Daya Ancestor","set":"Zendikar Rising Commander","fileName":"Lands_Wrath.dck","colors":[],"year":2020},
|
||||
{"id":"Sneak_Attack","name":"Sneak Attack","commander":"Anowon, the Ruin Thief","set":"Zendikar Rising Commander","fileName":"Sneak_Attack.dck","colors":[],"year":2020},
|
||||
{"id":"Arm_for_Battle","name":"Arm for Battle","commander":"Wyleth, Soul of Steel","set":"Commander Legends","fileName":"Arm_for_Battle.dck","colors":[],"year":2020},
|
||||
{"id":"Reap_the_Tides","name":"Reap the Tides","commander":"Aesi, Tyrant of Gyre Strait","set":"Commander Legends","fileName":"Reap_the_Tides.dck","colors":[],"year":2020},
|
||||
{"id":"Elven_Empire","name":"Elven Empire","commander":"Lathril, Blade of the Elves","set":"Kaldheim Commander","fileName":"Elven_Empire.dck","colors":[],"year":2021},
|
||||
{"id":"Phantom_Premonition","name":"Phantom Premonition","commander":"Ranar the Ever-Watchful","set":"Kaldheim Commander","fileName":"Phantom_Premonition.dck","colors":[],"year":2021},
|
||||
{"id":"Lorehold_Legacies","name":"Lorehold Legacies","commander":"Osgir, the Reconstructor","set":"Commander 2021","fileName":"Lorehold_Legacies.dck","colors":[],"year":2021},
|
||||
{"id":"Prismari_Performance","name":"Prismari Performance","commander":"Zaffai, Thunder Conductor","set":"Commander 2021","fileName":"Prismari_Performance.dck","colors":[],"year":2021},
|
||||
{"id":"Quantum_Quandrix","name":"Quantum Quandrix","commander":"Adrix and Nev, Twincasters","set":"Commander 2021","fileName":"Quantum_Quandrix.dck","colors":[],"year":2021},
|
||||
{"id":"Silverquill_Statement","name":"Silverquill Statement","commander":"Breena, the Demagogue","set":"Commander 2021","fileName":"Silverquill_Statement.dck","colors":[],"year":2021},
|
||||
{"id":"Witherbloom_Witchcraft","name":"Witherbloom Witchcraft","commander":"Willowdusk, Essence Seer","set":"Commander 2021","fileName":"Witherbloom_Witchcraft.dck","colors":[],"year":2021},
|
||||
{"id":"Aura_of_Courage","name":"Aura of Courage","commander":"Galea, Kindler of Hope","set":"Forgotten Realms Commander","fileName":"Aura_of_Courage.dck","colors":[],"year":2021},
|
||||
{"id":"Draconic_Rage","name":"Draconic Rage","commander":"Vrondiss, Rage of Ancients","set":"Forgotten Realms Commander","fileName":"Draconic_Rage.dck","colors":[],"year":2021},
|
||||
{"id":"Dungeons_of_Death","name":"Dungeons of Death","commander":"Sefris of the Hidden Ways","set":"Forgotten Realms Commander","fileName":"Dungeons_of_Death.dck","colors":[],"year":2021},
|
||||
{"id":"Planar_Portal","name":"Planar Portal","commander":"Prosper, Tome-Bound","set":"Forgotten Realms Commander","fileName":"Planar_Portal.dck","colors":[],"year":2021},
|
||||
{"id":"Coven_Counters","name":"Coven Counters","commander":"Leinore, Autumn Sovereign","set":"Midnight Hunt Commander","fileName":"Coven_Counters.dck","colors":[],"year":2021},
|
||||
{"id":"Undead_Unleashed","name":"Undead Unleashed","commander":"Wilhelt, the Rotcleaver","set":"Midnight Hunt Commander","fileName":"Undead_Unleashed.dck","colors":[],"year":2021},
|
||||
{"id":"Spirit_Squadron","name":"Spirit Squadron","commander":"Millicent, Restless Revenant","set":"Crimson Vow Commander","fileName":"Spirit_Squadron.dck","colors":[],"year":2021},
|
||||
{"id":"Vampiric_Bloodline","name":"Vampiric Bloodline","commander":"Strefan, Maurer Progenitor","set":"Crimson Vow Commander","fileName":"Vampiric_Bloodline.dck","colors":[],"year":2021},
|
||||
{"id":"Buckle_Up","name":"Buckle Up","commander":"Kotori, Pilot Prodigy","set":"Neon Dynasty Commander","fileName":"Buckle_Up.dck","colors":[],"year":2022},
|
||||
{"id":"Upgrades_Unleashed","name":"Upgrades Unleashed","commander":"Chishiro, the Shattered Blade","set":"Neon Dynasty Commander","fileName":"Upgrades_Unleashed.dck","colors":[],"year":2022},
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||||
{"id":"Heads_I_Win,_Tails_You_Lose","name":"Heads I Win, Tails You Lose","commander":"Zndrsplt, Eye of Wisdom","set":"Secret Lair Drop","fileName":"Heads_I_Win,_Tails_You_Lose.dck","colors":[],"year":2022},
|
||||
{"id":"Bedecked_Brokers","name":"Bedecked Brokers","commander":"Perrie, the Pulverizer","set":"New Capenna Commander","fileName":"Bedecked_Brokers.dck","colors":[],"year":2022},
|
||||
{"id":"Cabaretti_Cacophony","name":"Cabaretti Cacophony","commander":"Kitt Kanto, Mayhem Diva","set":"New Capenna Commander","fileName":"Cabaretti_Cacophony.dck","colors":[],"year":2022},
|
||||
{"id":"Maestros_Massacre","name":"Maestros Massacre","commander":"Anhelo, the Painter","set":"New Capenna Commander","fileName":"Maestros_Massacre.dck","colors":[],"year":2022},
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||||
{"id":"Obscura_Operation","name":"Obscura Operation","commander":"Kamiz, Obscura Oculus","set":"New Capenna Commander","fileName":"Obscura_Operation.dck","colors":[],"year":2022},
|
||||
{"id":"Riveteers_Rampage","name":"Riveteers Rampage","commander":"Henzie \"Toolbox\" Torre","set":"New Capenna Commander","fileName":"Riveteers_Rampage.dck","colors":[],"year":2022},
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||||
{"id":"Draconic_Dissent","name":"Draconic Dissent","commander":"Firkraag, Cunning Instigator","set":"Commander Legends: Battle for Baldur's Gate","fileName":"Draconic_Dissent.dck","colors":[],"year":2022},
|
||||
{"id":"Exit_from_Exile","name":"Exit from Exile","commander":"Faldorn, Dread Wolf Herald","set":"Commander Legends: Battle for Baldur's Gate","fileName":"Exit_from_Exile.dck","colors":[],"year":2022},
|
||||
{"id":"Mind_Flayarrrs","name":"Mind Flayarrrs","commander":"Captain N'ghathrod","set":"Commander Legends: Battle for Baldur's Gate","fileName":"Mind_Flayarrrs.dck","colors":[],"year":2022},
|
||||
{"id":"Party_Time","name":"Party Time","commander":"Nalia de'Arnise","set":"Commander Legends: Battle for Baldur's Gate","fileName":"Party_Time.dck","colors":[],"year":2022},
|
||||
{"id":"Legends_Legacy","name":"Legends' Legacy","commander":"Dihada, Binder of Wills","set":"Dominaria United Commander","fileName":"Legends_Legacy.dck","colors":[],"year":2022},
|
||||
{"id":"Painbow","name":"Painbow","commander":"Jared Carthalion","set":"Dominaria United Commander","fileName":"Painbow.dck","colors":[],"year":2022},
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||||
{"id":"Forces_of_the_Imperium","name":"Forces of the Imperium","commander":"Inquisitor Greyfax","set":"Warhammer 40,000 Commander","fileName":"Forces_of_the_Imperium.dck","colors":[],"year":2022},
|
||||
{"id":"Forces_of_the_Imperium_Collectors_Edition","name":"Forces of the Imperium Collector's Edition","commander":"Inquisitor Greyfax","set":"Warhammer 40,000 Commander","fileName":"Forces_of_the_Imperium_Collectors_Edition.dck","colors":[],"year":2022},
|
||||
{"id":"Necron_Dynasties","name":"Necron Dynasties","commander":"Szarekh, the Silent King","set":"Warhammer 40,000 Commander","fileName":"Necron_Dynasties.dck","colors":[],"year":2022},
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||||
{"id":"Necron_Dynasties_Collectors_Edition","name":"Necron Dynasties Collector's Edition","commander":"Szarekh, the Silent King","set":"Warhammer 40,000 Commander","fileName":"Necron_Dynasties_Collectors_Edition.dck","colors":[],"year":2022},
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||||
{"id":"The_Ruinous_Powers","name":"The Ruinous Powers","commander":"Abaddon the Despoiler","set":"Warhammer 40,000 Commander","fileName":"The_Ruinous_Powers.dck","colors":[],"year":2022},
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||||
{"id":"The_Ruinous_Powers_Collectors_Edition","name":"The Ruinous Powers Collector's Edition","commander":"Abaddon the Despoiler","set":"Warhammer 40,000 Commander","fileName":"The_Ruinous_Powers_Collectors_Edition.dck","colors":[],"year":2022},
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||||
{"id":"Tyranid_Swarm","name":"Tyranid Swarm","commander":"The Swarmlord","set":"Warhammer 40,000 Commander","fileName":"Tyranid_Swarm.dck","colors":[],"year":2022},
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||||
{"id":"Tyranid_Swarm_Collectors_Edition","name":"Tyranid Swarm Collector's Edition","commander":"The Swarmlord","set":"Warhammer 40,000 Commander","fileName":"Tyranid_Swarm_Collectors_Edition.dck","colors":[],"year":2022},
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||||
{"id":"Mishras_Burnished_Banner","name":"Mishra's Burnished Banner","commander":"Mishra, Eminent One","set":"The Brothers' War Commander","fileName":"Mishras_Burnished_Banner.dck","colors":[],"year":2022},
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||||
{"id":"Urzas_Iron_Alliance","name":"Urza's Iron Alliance","commander":"Urza, Chief Artificer","set":"The Brothers' War Commander","fileName":"Urzas_Iron_Alliance.dck","colors":[],"year":2022},
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||||
{"id":"Chaos_Incarnate","name":"Chaos Incarnate","commander":"Kardur, Doomscourge","set":"Starter Commander Decks","fileName":"Chaos_Incarnate.dck","colors":[],"year":2022},
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||||
{"id":"Draconic_Destruction","name":"Draconic Destruction","commander":"Atarka, World Render","set":"Starter Commander Decks","fileName":"Draconic_Destruction.dck","colors":[],"year":2022},
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||||
{"id":"First_Flight","name":"First Flight","commander":"Isperia, Supreme Judge","set":"Starter Commander Decks","fileName":"First_Flight.dck","colors":[],"year":2022},
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||||
{"id":"Grave_Danger","name":"Grave Danger","commander":"Gisa and Geralf","set":"Starter Commander Decks","fileName":"Grave_Danger.dck","colors":[],"year":2022},
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||||
{"id":"Token_Triumph","name":"Token Triumph","commander":"Emmara, Soul of the Accord","set":"Starter Commander Decks","fileName":"Token_Triumph.dck","colors":[],"year":2022},
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||||
{"id":"Corrupting_Influence","name":"Corrupting Influence","commander":"Ixhel, Scion of Atraxa","set":"Phyrexia: All Will Be One Commander","fileName":"Corrupting_Influence.dck","colors":[],"year":2023},
|
||||
{"id":"Rebellion_Rising","name":"Rebellion Rising","commander":"Neyali, Suns' Vanguard","set":"Phyrexia: All Will Be One Commander","fileName":"Rebellion_Rising.dck","colors":[],"year":2023},
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||||
{"id":"From_Cute_to_Brute","name":"From Cute to Brute","commander":"Esika, God of the Tree","set":"Secret Lair Drop","fileName":"From_Cute_to_Brute.dck","colors":[],"year":2023},
|
||||
{"id":"Elven_Council","name":"Elven Council","commander":"Galadriel, Elven-Queen","set":"Tales of Middle-earth Commander","fileName":"Elven_Council.dck","colors":[],"year":2023},
|
||||
{"id":"Food_and_Fellowship","name":"Food and Fellowship","commander":"Frodo, Adventurous Hobbit","set":"Tales of Middle-earth Commander","fileName":"Food_and_Fellowship.dck","colors":[],"year":2023},
|
||||
{"id":"Riders_of_Rohan","name":"Riders of Rohan","commander":"Éowyn, Shieldmaiden","set":"Tales of Middle-earth Commander","fileName":"Riders_of_Rohan.dck","colors":[],"year":2023},
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||||
{"id":"The_Hosts_of_Mordor","name":"The Hosts of Mordor","commander":"Sauron, Lord of the Rings","set":"Tales of Middle-earth Commander","fileName":"The_Hosts_of_Mordor.dck","colors":[],"year":2023},
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||||
{"id":"Eldrazi_Unbound","name":"Eldrazi Unbound","commander":"Zhulodok, Void Gorger","set":"Commander Masters","fileName":"Eldrazi_Unbound.dck","colors":[],"year":2023},
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||||
{"id":"Enduring_Enchantments","name":"Enduring Enchantments","commander":"Anikthea, Hand of Erebos","set":"Commander Masters","fileName":"Enduring_Enchantments.dck","colors":[],"year":2023},
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||||
{"id":"Planeswalker_Party","name":"Planeswalker Party","commander":"Commodore Guff","set":"Commander Masters","fileName":"Planeswalker_Party.dck","colors":[],"year":2023},
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||||
{"id":"Sliver_Swarm","name":"Sliver Swarm","commander":"Sliver Gravemother","set":"Commander Masters","fileName":"Sliver_Swarm.dck","colors":[],"year":2023},
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||||
{"id":"Angels_Theyre_Just_Like_Us_but_Cooler_and_with_Wings","name":"Angels: They're Just Like Us but Cooler and with Wings","commander":"Gisela, the Broken Blade","set":"Secret Lair Drop","fileName":"Angels_Theyre_Just_Like_Us_but_Cooler_and_with_Wings.dck","colors":[],"year":2023},
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||||
{"id":"Fae_Dominion","name":"Fae Dominion","commander":"Tegwyll, Duke of Splendor","set":"Wilds of Eldraine Commander","fileName":"Fae_Dominion.dck","colors":[],"year":2023},
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||||
{"id":"Virtue_and_Valor","name":"Virtue and Valor","commander":"Ellivere of the Wild Court","set":"Wilds of Eldraine Commander","fileName":"Virtue_and_Valor.dck","colors":[],"year":2023},
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||||
{"id":"Ahoy_Mateys","name":"Ahoy Mateys","commander":"Admiral Brass, Unsinkable","set":"The Lost Caverns of Ixalan Commander","fileName":"Ahoy_Mateys.dck","colors":[],"year":2023},
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||||
{"id":"Blood_Rites","name":"Blood Rites","commander":"Clavileño, First of the Blessed","set":"The Lost Caverns of Ixalan Commander","fileName":"Blood_Rites.dck","colors":[],"year":2023},
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||||
{"id":"Explorers_of_the_Deep","name":"Explorers of the Deep","commander":"Hakbal of the Surging Soul","set":"The Lost Caverns of Ixalan Commander","fileName":"Explorers_of_the_Deep.dck","colors":[],"year":2023},
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||||
{"id":"Veloci-Ramp-Tor","name":"Veloci-Ramp-Tor","commander":"Pantlaza, Sun-Favored","set":"The Lost Caverns of Ixalan Commander","fileName":"Veloci-Ramp-Tor.dck","colors":[],"year":2023},
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||||
{"id":"Raining_Cats_and_Dogs","name":"Raining Cats and Dogs","commander":"Rin and Seri, Inseparable","set":"Secret Lair Drop","fileName":"Raining_Cats_and_Dogs.dck","colors":[],"year":2024},
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||||
{"id":"Blame_Game","name":"Blame Game","commander":"Nelly Borca, Impulsive Accuser","set":"Murders at Karlov Manor Commander","fileName":"Blame_Game.dck","colors":[],"year":2024},
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||||
{"id":"Deadly_Disguise","name":"Deadly Disguise","commander":"Kaust, Eyes of the Glade","set":"Murders at Karlov Manor Commander","fileName":"Deadly_Disguise.dck","colors":[],"year":2024},
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||||
{"id":"Deep_Clue_Sea","name":"Deep Clue Sea","commander":"Morska, Undersea Sleuth","set":"Murders at Karlov Manor Commander","fileName":"Deep_Clue_Sea.dck","colors":[],"year":2024},
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||||
{"id":"Revenant_Recon","name":"Revenant Recon","commander":"Mirko, Obsessive Theorist","set":"Murders at Karlov Manor Commander","fileName":"Revenant_Recon.dck","colors":[],"year":2024},
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||||
{"id":"Hail,_Caesar","name":"Hail, Caesar","commander":"Caesar, Legion's Emperor","set":"Fallout","fileName":"Hail,_Caesar.dck","colors":[],"year":2024},
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||||
{"id":"Mutant_Menace","name":"Mutant Menace","commander":"The Wise Mothman","set":"Fallout","fileName":"Mutant_Menace.dck","colors":[],"year":2024},
|
||||
{"id":"Science","name":"Science!","commander":"Dr. Madison Li","set":"Fallout","fileName":"Science.dck","colors":[],"year":2024},
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||||
{"id":"Scrappy_Survivors","name":"Scrappy Survivors","commander":"Dogmeat, Ever Loyal","set":"Fallout","fileName":"Scrappy_Survivors.dck","colors":[],"year":2024},
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||||
{"id":"Desert_Bloom","name":"Desert Bloom","commander":"Yuma, Proud Protector","set":"Outlaws of Thunder Junction Commander","fileName":"Desert_Bloom.dck","colors":[],"year":2024},
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||||
{"id":"Grand_Larceny","name":"Grand Larceny","commander":"Gonti, Canny Acquisitor","set":"Outlaws of Thunder Junction Commander","fileName":"Grand_Larceny.dck","colors":[],"year":2024},
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||||
{"id":"Most_Wanted","name":"Most Wanted","commander":"Olivia, Opulent Outlaw","set":"Outlaws of Thunder Junction Commander","fileName":"Most_Wanted.dck","colors":[],"year":2024},
|
||||
{"id":"Quick_Draw","name":"Quick Draw","commander":"Stella Lee, Wild Card","set":"Outlaws of Thunder Junction Commander","fileName":"Quick_Draw.dck","colors":[],"year":2024},
|
||||
{"id":"Creative_Energy","name":"Creative Energy","commander":"Satya, Aetherflux Genius","set":"Modern Horizons 3 Commander","fileName":"Creative_Energy.dck","colors":[],"year":2024},
|
||||
{"id":"Creative_Energy_Collectors_Edition","name":"Creative Energy Collector's Edition","commander":"Satya, Aetherflux Genius","set":"Modern Horizons 3 Commander","fileName":"Creative_Energy_Collectors_Edition.dck","colors":[],"year":2024},
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||||
{"id":"Eldrazi_Incursion","name":"Eldrazi Incursion","commander":"Ulalek, Fused Atrocity","set":"Modern Horizons 3 Commander","fileName":"Eldrazi_Incursion.dck","colors":[],"year":2024},
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||||
{"id":"Eldrazi_Incursion_Collectors_Edition","name":"Eldrazi Incursion Collector's Edition","commander":"Ulalek, Fused Atrocity","set":"Modern Horizons 3 Commander","fileName":"Eldrazi_Incursion_Collectors_Edition.dck","colors":[],"year":2024},
|
||||
{"id":"Graveyard_Overdrive","name":"Graveyard Overdrive","commander":"Disa the Restless","set":"Modern Horizons 3 Commander","fileName":"Graveyard_Overdrive.dck","colors":[],"year":2024},
|
||||
{"id":"Graveyard_Overdrive_Collectors_Edition","name":"Graveyard Overdrive Collector's Edition","commander":"Disa the Restless","set":"Modern Horizons 3 Commander","fileName":"Graveyard_Overdrive_Collectors_Edition.dck","colors":[],"year":2024},
|
||||
{"id":"Tricky_Terrain","name":"Tricky Terrain","commander":"Omo, Queen of Vesuva","set":"Modern Horizons 3 Commander","fileName":"Tricky_Terrain.dck","colors":[],"year":2024},
|
||||
{"id":"Tricky_Terrain_Collectors_Edition","name":"Tricky Terrain Collector's Edition","commander":"Omo, Queen of Vesuva","set":"Modern Horizons 3 Commander","fileName":"Tricky_Terrain_Collectors_Edition.dck","colors":[],"year":2024},
|
||||
{"id":"Animated_Army","name":"Animated Army","commander":"Bello, Bard of the Brambles","set":"Bloomburrow Commander","fileName":"Animated_Army.dck","colors":[],"year":2024},
|
||||
{"id":"Family_Matters","name":"Family Matters","commander":"Zinnia, Valley's Voice","set":"Bloomburrow Commander","fileName":"Family_Matters.dck","colors":[],"year":2024},
|
||||
{"id":"Peace_Offering","name":"Peace Offering","commander":"Ms. Bumbleflower","set":"Bloomburrow Commander","fileName":"Peace_Offering.dck","colors":[],"year":2024},
|
||||
{"id":"Squirreled_Away","name":"Squirreled Away","commander":"Hazel of the Rootbloom","set":"Bloomburrow Commander","fileName":"Squirreled_Away.dck","colors":[],"year":2024},
|
||||
{"id":"20_Ways_to_Win","name":"20 Ways to Win","commander":"Go-Shintai of Life's Origin","set":"Secret Lair Drop","fileName":"20_Ways_to_Win.dck","colors":[],"year":2024},
|
||||
{"id":"Eternal_Might","name":"Eternal Might","commander":"Temmet, Naktamun's Will","set":"Aetherdrift Commander","fileName":"Eternal_Might.dck","colors":[],"year":2025},
|
||||
{"id":"Living_Energy","name":"Living Energy","commander":"Saheeli, Radiant Creator","set":"Aetherdrift Commander","fileName":"Living_Energy.dck","colors":[],"year":2025},
|
||||
{"id":"Abzan_Armor","name":"Abzan Armor","commander":"Felothar the Steadfast","set":"Tarkir: Dragonstorm Commander","fileName":"Abzan_Armor.dck","colors":[],"year":2025},
|
||||
{"id":"Jeskai_Striker","name":"Jeskai Striker","commander":"Shiko and Narset, Unified","set":"Tarkir: Dragonstorm Commander","fileName":"Jeskai_Striker.dck","colors":[],"year":2025},
|
||||
{"id":"Mardu_Surge","name":"Mardu Surge","commander":"Zurgo Stormrender","set":"Tarkir: Dragonstorm Commander","fileName":"Mardu_Surge.dck","colors":[],"year":2025},
|
||||
{"id":"Sultai_Arisen","name":"Sultai Arisen","commander":"Teval, the Balanced Scale","set":"Tarkir: Dragonstorm Commander","fileName":"Sultai_Arisen.dck","colors":[],"year":2025},
|
||||
{"id":"Temur_Roar","name":"Temur Roar","commander":"Eshki, Temur's Roar","set":"Tarkir: Dragonstorm Commander","fileName":"Temur_Roar.dck","colors":[],"year":2025},
|
||||
{"id":"Everyones_Invited","name":"Everyone's Invited!","commander":"Morophon, the Boundless","set":"Secret Lair Drop","fileName":"Everyones_Invited.dck","colors":[],"year":2025},
|
||||
{"id":"Counter_Blitz_(FINAL_FANTASY_X)","name":"Counter Blitz (FINAL FANTASY X)","commander":"Tidus, Yuna's Guardian","set":"Final Fantasy Commander","fileName":"Counter_Blitz_(FINAL_FANTASY_X).dck","colors":[],"year":2025},
|
||||
{"id":"Counter_Blitz_Collectors_Edition_(FINAL_FANTASY_X)","name":"Counter Blitz Collector's Edition (FINAL FANTASY X)","commander":"Tidus, Yuna's Guardian","set":"Final Fantasy Commander","fileName":"Counter_Blitz_Collectors_Edition_(FINAL_FANTASY_X).dck","colors":[],"year":2025},
|
||||
{"id":"Limit_Break_(FINAL_FANTASY_VII)","name":"Limit Break (FINAL FANTASY VII)","commander":"Cloud, Ex-SOLDIER","set":"Final Fantasy Commander","fileName":"Limit_Break_(FINAL_FANTASY_VII).dck","colors":[],"year":2025},
|
||||
{"id":"Limit_Break_Collectors_Edition_(FINAL_FANTASY_VII)","name":"Limit Break Collector's Edition (FINAL FANTASY VII)","commander":"Cloud, Ex-SOLDIER","set":"Final Fantasy Commander","fileName":"Limit_Break_Collectors_Edition_(FINAL_FANTASY_VII).dck","colors":[],"year":2025},
|
||||
{"id":"Revival_Trance_(FINAL_FANTASY_VI)","name":"Revival Trance (FINAL FANTASY VI)","commander":"Terra, Herald of Hope","set":"Final Fantasy Commander","fileName":"Revival_Trance_(FINAL_FANTASY_VI).dck","colors":[],"year":2025},
|
||||
{"id":"Revival_Trance_Collectors_Edition_(FINAL_FANTASY_VI)","name":"Revival Trance Collector's Edition (FINAL FANTASY VI)","commander":"Terra, Herald of Hope","set":"Final Fantasy Commander","fileName":"Revival_Trance_Collectors_Edition_(FINAL_FANTASY_VI).dck","colors":[],"year":2025},
|
||||
{"id":"Scions_&_Spellcraft_(FINAL_FANTASY_XIV)","name":"Scions & Spellcraft (FINAL FANTASY XIV)","commander":"Y'shtola, Night's Blessed","set":"Final Fantasy Commander","fileName":"Scions_&_Spellcraft_(FINAL_FANTASY_XIV).dck","colors":[],"year":2025},
|
||||
{"id":"Scions_&_Spellcraft_Collectors_Edition_(FINAL_FANTASY_XIV)","name":"Scions & Spellcraft Collector's Edition (FINAL FANTASY XIV)","commander":"Y'shtola, Night's Blessed","set":"Final Fantasy Commander","fileName":"Scions_&_Spellcraft_Collectors_Edition_(FINAL_FANTASY_XIV).dck","colors":[],"year":2025},
|
||||
{"id":"Counter_Intelligence","name":"Counter Intelligence","commander":"Inspirit, Flagship Vessel","set":"Edge of Eternities Commander","fileName":"Counter_Intelligence.dck","colors":[],"year":2025},
|
||||
{"id":"World_Shaper","name":"World Shaper","commander":"Hearthhull, the Worldseed","set":"Edge of Eternities Commander","fileName":"World_Shaper.dck","colors":[],"year":2025},
|
||||
{"id":"Blight_Curse","name":"Blight Curse","commander":"Auntie Ool, Cursewretch","set":"Lorwyn Eclipsed Commander","fileName":"Blight_Curse.dck","colors":[],"year":2026},
|
||||
{"id":"Dance_of_the_Elements","name":"Dance of the Elements","commander":"Ashling, the Limitless","set":"Lorwyn Eclipsed Commander","fileName":"Dance_of_the_Elements.dck","colors":[],"year":2026},
|
||||
{"id":"Turtle_Power","name":"Turtle Power!","commander":"Heroes in a Half Shell","set":"Teenage Mutant Ninja Turtles Eternal","fileName":"Turtle_Power.dck","colors":[],"year":2026}
|
||||
];
|
||||
File diff suppressed because it is too large.
Load diff
-2515
File diff suppressed because it is too large.
Load diff
+83
-204
@@ -4,15 +4,10 @@ Commander AI Lab — Action Labeler
|
||||
Maps raw Forge action records (from DecisionSnapshot JSONL)
|
||||
to one of 8 macro-action labels for supervised policy learning.
|
||||
|
||||
Classification logic (updated):
|
||||
1. Check action.type for unambiguous types (attack, land, pass, hold,
|
||||
cast_commander)
|
||||
2. For "cast" actions:
|
||||
a. If action.card is present, match directly against name databases
|
||||
b. If action.card is absent (Forge omits it), scan the active
|
||||
player's hand against name databases to infer the cast type.
|
||||
Priority: removal > ramp > draw > commander > creature (default)
|
||||
3. Fallback: PASS
|
||||
Classification logic:
|
||||
1. Check action.type (cast, attack, land, cast_commander, pass)
|
||||
2. For "cast" actions, classify by card oracle text / card type keywords
|
||||
3. For ambiguous cases, use the raw log line and card name heuristics
|
||||
|
||||
Macro-actions (from ml.config.scope):
|
||||
0: CAST_CREATURE — Play a creature
|
||||
@@ -46,6 +41,11 @@ logger = logging.getLogger("ml.labeler")
|
||||
# Card Type Keyword Databases
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
# Known removal spells (partial list — expanded from common Commander staples)
|
||||
REMOVAL_KEYWORDS = {
|
||||
"destroy", "exile", "sacrifice", "counter", "bounce", "return.*to.*hand",
|
||||
"-\\d+/-\\d+", "damage to", "fight", "deathtouch",
|
||||
}
|
||||
REMOVAL_CARD_NAMES = {
|
||||
"swords to plowshares", "path to exile", "beast within", "chaos warp",
|
||||
"counterspell", "swan song", "negate", "arcane denial", "doomblade",
|
||||
@@ -62,52 +62,45 @@ REMOVAL_CARD_NAMES = {
|
||||
"decree of pain", "in garruk's wake", "plague wind",
|
||||
"fire covenant", "imprisoned in the moon", "darksteel mutation",
|
||||
"kenrith's transformation", "song of the dryads",
|
||||
"abrade", "disenchant", "naturalize", "wear", "tear",
|
||||
"shattering spree", "shatter", "smash to smithereens",
|
||||
"lightning bolt", "lightning helix", "condemn", "oust",
|
||||
"pongify", "reality shift", "turn to frog", "polymorph",
|
||||
"boros charm", "utter end", "anguished unmaking", "mortify",
|
||||
"putrefy", "maelstrom pulse", "void rend", "render silent",
|
||||
"overwhelming denial", "test of talents", "dispel", "spell pierce",
|
||||
"flusterstorm", "mental misstep", "pact of negation",
|
||||
"opposition agent", "hullbreacher", "narset parter of veils",
|
||||
"grafdigger's cage", "rest in peace", "leyline of the void",
|
||||
"tormod's crypt", "relic of progenitus",
|
||||
}
|
||||
|
||||
# Known draw spells
|
||||
DRAW_KEYWORDS = {
|
||||
"draw", "scry", "look at the top", "reveal.*library",
|
||||
"search your library.*hand", "impulse", "brainstorm",
|
||||
}
|
||||
DRAW_CARD_NAMES = {
|
||||
"rhystic study", "mystic remora", "phyrexian arena", "sylvan library",
|
||||
"harmonize", "blue sun's zenith", "pull from tomorrow",
|
||||
"windfall", "wheel of fortune", "timetwister", "brainstorm",
|
||||
"ponder", "preordain", "opt", "consider", "treasure cruise",
|
||||
"dig through time", "fact or fiction", "night's whisper",
|
||||
"sign in blood", "read the bones", "painful truths",
|
||||
"ambition's cost", "promise of power", "necropotence",
|
||||
"dark confidant", "toski bearer of secrets", "beast whisperer",
|
||||
"guardian project", "the great henge", "skullclamp",
|
||||
"esper sentinel", "archivist of oghma", "mind's eye",
|
||||
"consecrated sphinx", "ancient craving", "faithless looting",
|
||||
"cathartic reunion", "thrill of possibility", "jeska's will",
|
||||
"valakut awakening", "stinging study", "notion thief",
|
||||
"tymna the weaver", "shamanic revelation", "rishkar's expertise",
|
||||
"return of the wildspeaker", "garruk's uprising", "elemental bond",
|
||||
"kindred discovery", "coastal piracy", "reconnaissance mission",
|
||||
"bident of thassa", "distant melody", "slate of ancestry",
|
||||
"wheel of fate", "reforge the soul", "magus of the wheel",
|
||||
"river's rebuke", "prosperity", "intellectual offering",
|
||||
"peer into the abyss", "mind spring", "stroke of genius",
|
||||
"jace's ingenuity", "tidings", "concentrate", "divination",
|
||||
"inspiration", "counsel of the soratami", "accumulated knowledge",
|
||||
"predict", "pressure point", "quicken", "serum visions",
|
||||
"gitaxian probe", "mental note", "thought scour",
|
||||
"dig through time", "fact or fiction", "nights whisper",
|
||||
"nights whisper", "sign in blood", "read the bones",
|
||||
"painful truths", "ambition's cost", "promise of power",
|
||||
"necropotence", "dark confidant", "toski bearer of secrets",
|
||||
"beast whisperer", "guardian project", "the great henge",
|
||||
"skullclamp", "esper sentinel", "archivist of oghma",
|
||||
"mind's eye", "consecrated sphinx", "ancient craving",
|
||||
"faithless looting", "cathartic reunion", "thrill of possibility",
|
||||
"jeska's will", "valakut awakening", "stinging study",
|
||||
"night's whisper", "notion thief", "tymna the weaver",
|
||||
"shamanic revelation", "rishkar's expertise", "return of the wildspeaker",
|
||||
"garruk's uprising", "elemental bond", "kindred discovery",
|
||||
"coastal piracy", "reconnaissance mission", "bident of thassa",
|
||||
}
|
||||
|
||||
# Known ramp cards
|
||||
RAMP_KEYWORDS = {
|
||||
"add {", "add one mana", "mana of any color",
|
||||
"land.*onto the battlefield", "search.*basic land.*battlefield",
|
||||
}
|
||||
RAMP_CARD_NAMES = {
|
||||
"sol ring", "mana crypt", "mana vault", "arcane signet",
|
||||
"commander's sphere", "chromatic lantern", "fellwar stone",
|
||||
"mind stone", "thought vessel", "thran dynamo", "worn powerstone",
|
||||
"gilded lotus", "hedron archive", "everflowing chalice",
|
||||
"coalition relic", "prismatic lens",
|
||||
"coalition relic", "prismatic lens", "signets", # catch-all partial
|
||||
"talisman", # catch-all for talisman cycle
|
||||
"cultivate", "kodama's reach", "rampant growth", "farseek",
|
||||
"nature's lore", "three visits", "skyshroud claim",
|
||||
"explosive vegetation", "migration path", "circuitous route",
|
||||
@@ -119,144 +112,13 @@ RAMP_CARD_NAMES = {
|
||||
"birds of paradise", "llanowar elves", "elvish mystic",
|
||||
"fyndhorn elves", "avacyn's pilgrim", "elves of deep shadow",
|
||||
"bloom tender", "priest of titania", "selvala heart of the wilds",
|
||||
"boreal druid", "green sun's zenith", "eternal witness",
|
||||
"reclamation sage", "mystic snake", "fierce empath",
|
||||
"signal the clans", "crop rotation", "harrow", "kodama's reach",
|
||||
"traverse the ulvenwald", "search for tomorrow", "primal growth",
|
||||
"land grant", "untamed wilds", "rangers path", "rangers' path",
|
||||
"nissa's pilgrimage", "forest grove", "grove of the burnwillows",
|
||||
"cabal stronghold", "urborg tomb of yawgmoth", "urborg, tomb of yawgmoth",
|
||||
"bojuka bog", "ghost quarter", "field of ruin", "strip mine",
|
||||
"wasteland", "tectonic edge", "dust bowl",
|
||||
"worn powerstone", "basalt monolith", "grim monolith",
|
||||
"mana geode", "darksteel ingot", "manalith", "opaline unicorn",
|
||||
"vessel of endless rest", "corrupted grafstone", "dreamstone hedron",
|
||||
"sisay's ring", "ur-golem's eye", "blinkmoth urn",
|
||||
"astral cornucopia", "druids' repository", "black market",
|
||||
"cryptolith rite", "earthcraft", "citanul hierophants",
|
||||
"zendikar resurgent", "mirari's wake", "mana reflection",
|
||||
"vorinclex voice of hunger", "vorinclex, voice of hunger",
|
||||
}
|
||||
|
||||
# Partial-match prefixes (for cycles like Signets, Talismans)
|
||||
RAMP_PARTIAL_PREFIXES = {
|
||||
"talisman", "signet", "locket", "banner", "obelisk",
|
||||
"keyrune", "cluestone", "monolith", "medallion",
|
||||
# Known creature-type indicators
|
||||
CREATURE_KEYWORDS = {
|
||||
"creature", "token", "enters the battlefield",
|
||||
}
|
||||
|
||||
DRAW_PARTIAL_PREFIXES = {
|
||||
"scroll", "tome", "journal",
|
||||
}
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# Core classification helpers
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
def _classify_card_name(name: str) -> Optional[MacroAction]:
|
||||
"""
|
||||
Classify a single card name into a MacroAction, or None if unknown.
|
||||
|
||||
Checks exact matches first, then partial prefix matches.
|
||||
Returns None for unrecognised cards (caller handles the default).
|
||||
"""
|
||||
n = name.lower().strip()
|
||||
if not n:
|
||||
return None
|
||||
|
||||
if n in REMOVAL_CARD_NAMES:
|
||||
return MacroAction.CAST_REMOVAL
|
||||
if n in DRAW_CARD_NAMES:
|
||||
return MacroAction.CAST_DRAW
|
||||
if n in RAMP_CARD_NAMES:
|
||||
return MacroAction.CAST_RAMP
|
||||
|
||||
# Partial prefix match for cycles (Talismans, Signets, etc.)
|
||||
for prefix in RAMP_PARTIAL_PREFIXES:
|
||||
if n.startswith(prefix):
|
||||
return MacroAction.CAST_RAMP
|
||||
for prefix in DRAW_PARTIAL_PREFIXES:
|
||||
if n.startswith(prefix):
|
||||
return MacroAction.CAST_DRAW
|
||||
|
||||
# Substring match (catches "Doom Blade" inside longer names etc.)
|
||||
for known in REMOVAL_CARD_NAMES:
|
||||
if known in n or n in known:
|
||||
return MacroAction.CAST_REMOVAL
|
||||
for known in DRAW_CARD_NAMES:
|
||||
if known in n or n in known:
|
||||
return MacroAction.CAST_DRAW
|
||||
for known in RAMP_CARD_NAMES:
|
||||
if known in n or n in known:
|
||||
return MacroAction.CAST_RAMP
|
||||
|
||||
return None # Unknown → caller defaults to CAST_CREATURE
|
||||
|
||||
|
||||
def _classify_from_hand(
|
||||
players: list,
|
||||
active_seat: int,
|
||||
commander_names: Optional[set] = None,
|
||||
) -> MacroAction:
|
||||
"""
|
||||
Infer the cast action type by scanning the active player's hand.
|
||||
|
||||
When action.card is absent (Forge omits it), the hand snapshot
|
||||
is the best available signal. We scan all hand cards and return
|
||||
the highest-priority match:
|
||||
|
||||
removal > ramp > draw > commander > creature (default)
|
||||
|
||||
Priority rationale: Forge's AI plays the highest-impact available
|
||||
action, so if any removal/ramp/draw spell is in hand during a cast
|
||||
decision, it's the most likely card played.
|
||||
"""
|
||||
if active_seat >= len(players):
|
||||
return MacroAction.CAST_CREATURE
|
||||
|
||||
hand: List[str] = players[active_seat].get("hand", [])
|
||||
command_zone: List[str] = players[active_seat].get("command_zone", [])
|
||||
|
||||
# Build commander name set from command_zone if not provided
|
||||
if commander_names is None:
|
||||
commander_names = {c.lower().strip() for c in command_zone if c}
|
||||
|
||||
found_removal = False
|
||||
found_ramp = False
|
||||
found_draw = False
|
||||
found_commander = False
|
||||
|
||||
for card in hand:
|
||||
card_lower = card.lower().strip()
|
||||
|
||||
# Commander check: hand card matches a command zone card
|
||||
if card_lower in commander_names:
|
||||
found_commander = True
|
||||
continue
|
||||
|
||||
result = _classify_card_name(card_lower)
|
||||
if result == MacroAction.CAST_REMOVAL:
|
||||
found_removal = True
|
||||
elif result == MacroAction.CAST_RAMP:
|
||||
found_ramp = True
|
||||
elif result == MacroAction.CAST_DRAW:
|
||||
found_draw = True
|
||||
|
||||
# Return highest-priority found
|
||||
if found_removal:
|
||||
return MacroAction.CAST_REMOVAL
|
||||
if found_ramp:
|
||||
return MacroAction.CAST_RAMP
|
||||
if found_draw:
|
||||
return MacroAction.CAST_DRAW
|
||||
if found_commander:
|
||||
return MacroAction.CAST_COMMANDER
|
||||
return MacroAction.CAST_CREATURE
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# Main label_action entry point
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
def label_action(decision: dict) -> int:
|
||||
"""
|
||||
@@ -271,52 +133,74 @@ def label_action(decision: dict) -> int:
|
||||
action = decision.get("action", {})
|
||||
action_type = action.get("type", "").lower()
|
||||
card_name = (action.get("card", "") or "").lower().strip()
|
||||
raw_line = (action.get("raw", "") or "").lower()
|
||||
|
||||
players = decision.get("players", [])
|
||||
active_seat = decision.get("active_seat", 0)
|
||||
|
||||
# ── Unambiguous direct-type matches ──────────────────────
|
||||
# ── Direct type matching ────────────────────────────
|
||||
|
||||
# Commander cast is unambiguous
|
||||
if action_type == "cast_commander":
|
||||
return ACTION_TO_IDX[MacroAction.CAST_COMMANDER]
|
||||
|
||||
# Attack is unambiguous
|
||||
if action_type == "attack":
|
||||
return ACTION_TO_IDX[MacroAction.ATTACK_OPPONENT]
|
||||
|
||||
# Land play → developing mana base = ramp
|
||||
# Land play → classify as CAST_RAMP (developing mana is ramp)
|
||||
if action_type == "land":
|
||||
return ACTION_TO_IDX[MacroAction.CAST_RAMP]
|
||||
|
||||
# Pass / hold mana — check available mana to distinguish
|
||||
# Pass / hold mana
|
||||
if action_type in ("pass", "hold"):
|
||||
# Check if player had mana open (from state)
|
||||
active_seat = decision.get("active_seat", 0)
|
||||
players = decision.get("players", [])
|
||||
if active_seat < len(players):
|
||||
mana = players[active_seat].get("mana", 0)
|
||||
if mana >= 2:
|
||||
return ACTION_TO_IDX[MacroAction.HOLD_MANA]
|
||||
return ACTION_TO_IDX[MacroAction.PASS]
|
||||
|
||||
# ── Cast actions ──────────────────────────────────────
|
||||
# ── Cast actions: classify by card identity ─────────
|
||||
|
||||
if action_type == "cast":
|
||||
# Path A: action.card is present — classify it directly
|
||||
if card_name:
|
||||
result = _classify_card_name(card_name)
|
||||
if result is not None:
|
||||
return ACTION_TO_IDX[result]
|
||||
# Unrecognised named card → default creature
|
||||
return ACTION_TO_IDX[MacroAction.CAST_CREATURE]
|
||||
# 1. Check known card name databases
|
||||
if card_name in REMOVAL_CARD_NAMES or _partial_match(card_name, REMOVAL_CARD_NAMES):
|
||||
return ACTION_TO_IDX[MacroAction.CAST_REMOVAL]
|
||||
|
||||
# Path B: action.card absent (Forge omits it) — infer from hand
|
||||
inferred = _classify_from_hand(players, active_seat)
|
||||
return ACTION_TO_IDX[inferred]
|
||||
if card_name in DRAW_CARD_NAMES or _partial_match(card_name, DRAW_CARD_NAMES):
|
||||
return ACTION_TO_IDX[MacroAction.CAST_DRAW]
|
||||
|
||||
# ── Fallback ──────────────────────────────────────────
|
||||
if card_name in RAMP_CARD_NAMES or _partial_match(card_name, RAMP_CARD_NAMES):
|
||||
return ACTION_TO_IDX[MacroAction.CAST_RAMP]
|
||||
|
||||
# 2. Check raw log line for keyword patterns
|
||||
for keyword in REMOVAL_KEYWORDS:
|
||||
if re.search(keyword, raw_line):
|
||||
return ACTION_TO_IDX[MacroAction.CAST_REMOVAL]
|
||||
|
||||
for keyword in DRAW_KEYWORDS:
|
||||
if re.search(keyword, raw_line):
|
||||
return ACTION_TO_IDX[MacroAction.CAST_DRAW]
|
||||
|
||||
for keyword in RAMP_KEYWORDS:
|
||||
if re.search(keyword, raw_line):
|
||||
return ACTION_TO_IDX[MacroAction.CAST_RAMP]
|
||||
|
||||
# 3. Default: if it's a spell cast and not recognized, assume creature
|
||||
# (most casts in Commander are creatures or creature-adjacent)
|
||||
return ACTION_TO_IDX[MacroAction.CAST_CREATURE]
|
||||
|
||||
# ── Fallback ────────────────────────────────────────
|
||||
return ACTION_TO_IDX[MacroAction.PASS]
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# File-level helpers
|
||||
# ══════════════════════════════════════════════════════════
|
||||
def _partial_match(name: str, name_set: set) -> bool:
|
||||
"""Check if card name partially matches any name in the set."""
|
||||
for known in name_set:
|
||||
if known in name or name in known:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def label_decisions_file(jsonl_path: str) -> Tuple[np.ndarray, List[dict]]:
|
||||
"""
|
||||
@@ -330,7 +214,7 @@ def label_decisions_file(jsonl_path: str) -> Tuple[np.ndarray, List[dict]]:
|
||||
labels = []
|
||||
raw = []
|
||||
|
||||
with open(jsonl_path, "r", encoding="utf-8") as f:
|
||||
with open(jsonl_path, "r") as f:
|
||||
for i, line in enumerate(f):
|
||||
line = line.strip()
|
||||
if not line:
|
||||
@@ -348,11 +232,7 @@ def label_decisions_file(jsonl_path: str) -> Tuple[np.ndarray, List[dict]]:
|
||||
|
||||
|
||||
def print_label_distribution(labels: np.ndarray) -> Dict[str, int]:
|
||||
"""Print and return the distribution of action labels.
|
||||
|
||||
Iterates MacroAction enum in index order so the display always
|
||||
matches ACTION_TO_IDX regardless of dict insertion order.
|
||||
"""
|
||||
"""Print and return the distribution of action labels."""
|
||||
dist = {}
|
||||
for action in MacroAction:
|
||||
idx = ACTION_TO_IDX[action]
|
||||
@@ -362,11 +242,10 @@ def print_label_distribution(labels: np.ndarray) -> Dict[str, int]:
|
||||
total = len(labels)
|
||||
logger.info(f"\n{'Action':<25} {'Count':>8} {'Pct':>8}")
|
||||
logger.info("─" * 43)
|
||||
# Sort descending by count for readability
|
||||
for action_name, count in sorted(dist.items(), key=lambda x: -x[1]):
|
||||
pct = (count / total * 100) if total > 0 else 0
|
||||
logger.info(f" {action_name:<23} {count:>8} {pct:>7.1f}%")
|
||||
logger.info("─" * 43)
|
||||
logger.info(f"{'─' * 43}")
|
||||
logger.info(f" {'TOTAL':<23} {total:>8}")
|
||||
|
||||
return dist
|
||||
+1
-9
@@ -292,15 +292,7 @@ class DistillationDefaults:
|
||||
ppo_episodes_per_iter: int = 64
|
||||
ppo_lr: float = 3e-4
|
||||
ppo_clip_epsilon: float = 0.2
|
||||
# Increased from 0.01 to 0.04 to prevent premature convergence on the
|
||||
# "pass"/"land" macro-actions. The policy-collapse bug (see
|
||||
# commander-ai-lab-land-only-bug-analysis.md) is exacerbated by a low
|
||||
# entropy bonus: PPO settled on argmax very early in training, so the
|
||||
# model never learned to cast creatures / removal / draw spells even
|
||||
# when they were affordable. A higher entropy bonus keeps the action
|
||||
# distribution flat enough during self-play for those alternatives to
|
||||
# accumulate reward signal.
|
||||
ppo_entropy_coeff: float = 0.04
|
||||
ppo_entropy_coeff: float = 0.01
|
||||
ppo_eval_episodes: int = 100
|
||||
|
||||
# Dataset mixing — Forge-only by default (Phase 5 follow-up decision)
|
||||
|
||||
+18
-33
@@ -6,9 +6,8 @@ Reads ML decision JSONL files, encodes states, labels actions,
|
||||
and produces a training-ready dataset saved as NPZ.
|
||||
|
||||
Phase 2: Source Awareness (Issue #66)
|
||||
- Scans Forge sim files (ml-decisions-sim-*.jsonl) → source "forge"
|
||||
- Scans PPO self-play (ml-decisions-ppo-*.jsonl) → source "ppo"
|
||||
- Scans DeepSeek files (ml-decisions-ds-*.jsonl) → source "ds"
|
||||
- Scans both Forge sim files (ml-decisions-sim-*.jsonl) and
|
||||
PPO self-play files (ml-decisions-ppo-*.jsonl)
|
||||
- Configurable source_weights for sampling ratios
|
||||
- min_reward_threshold filter for PPO data quality
|
||||
- Each sample tagged with source for downstream analysis
|
||||
@@ -22,7 +21,7 @@ Output NPZ structure:
|
||||
game_ids: object (N,) — game ID for each sample
|
||||
outcomes: object (N,) — game outcome per sample
|
||||
playstyles: object (N,) — deck archetype per sample
|
||||
sources: object (N,) — data source ("forge", "ppo", or "ds")
|
||||
sources: object (N,) — data source ("forge" or "ppo")
|
||||
"""
|
||||
|
||||
import argparse
|
||||
@@ -63,11 +62,11 @@ class DatasetConfig:
|
||||
embeddings_dir: Optional[str] = None
|
||||
max_samples: Optional[int] = None
|
||||
|
||||
# Source weights control sampling ratios between Forge, PPO, and DS data.
|
||||
# ds=4.0 oversamples the 8 DeepSeek files from ~5.8% → ~20% of effective
|
||||
# training tokens, matching their higher signal quality relative to sim data.
|
||||
# Source weights control sampling ratios between Forge and PPO data.
|
||||
# Default: Forge-only (ppo=0.0). Use MIXED_MODE_PRESETS from
|
||||
# ml.config.scope for blended configurations (e.g. 90/10, 80/20).
|
||||
source_weights: Dict[str, float] = field(
|
||||
default_factory=lambda: {"forge": 1.0, "ppo": 0.0, "ds": 4.0}
|
||||
default_factory=lambda: {"forge": 1.0, "ppo": 0.0}
|
||||
)
|
||||
|
||||
# Minimum reward threshold for PPO data — only include decisions
|
||||
@@ -82,13 +81,12 @@ class DatasetConfig:
|
||||
|
||||
def _discover_jsonl_files(results_dir: str) -> Dict[str, List[Path]]:
|
||||
"""
|
||||
Scan results directory for Forge sim, PPO, and DeepSeek decision files.
|
||||
Scan results directory for both Forge sim and PPO decision files.
|
||||
|
||||
Returns:
|
||||
Dict mapping source name to list of JSONL file paths.
|
||||
Keys: "forge" for ml-decisions-sim-*.jsonl
|
||||
"ppo" for ml-decisions-ppo-*.jsonl
|
||||
"ds" for ml-decisions-ds-*.jsonl
|
||||
"""
|
||||
results_path = Path(results_dir)
|
||||
sources = {}
|
||||
@@ -103,16 +101,10 @@ def _discover_jsonl_files(results_dir: str) -> Dict[str, List[Path]]:
|
||||
if ppo_files:
|
||||
sources["ppo"] = ppo_files
|
||||
|
||||
# DeepSeek high-quality decisions (upweighted by default)
|
||||
ds_files = sorted(results_path.glob("ml-decisions-ds-*.jsonl"))
|
||||
if ds_files:
|
||||
sources["ds"] = ds_files
|
||||
|
||||
# Legacy files that don't match any known prefix — treat as forge
|
||||
known = set(forge_files + ppo_files + ds_files)
|
||||
# Also pick up legacy files that don't have sim/ppo prefix
|
||||
legacy_files = sorted(
|
||||
f for f in results_path.glob("ml-decisions-*.jsonl")
|
||||
if f not in known
|
||||
if f not in set(forge_files + ppo_files)
|
||||
)
|
||||
if legacy_files:
|
||||
sources.setdefault("forge", []).extend(legacy_files)
|
||||
@@ -131,7 +123,7 @@ def _passes_reward_filter(
|
||||
) -> bool:
|
||||
"""
|
||||
Filter PPO decisions by reward threshold.
|
||||
Forge and DS data always passes. PPO data must meet the threshold.
|
||||
Forge data always passes. PPO data must meet the threshold.
|
||||
"""
|
||||
if source != "ppo":
|
||||
return True
|
||||
@@ -163,7 +155,7 @@ def _apply_source_weights(
|
||||
if weight <= 0:
|
||||
logger.info(" Skipping %d %s samples (weight=0)", len(samples), source)
|
||||
continue
|
||||
if weight == 1.0:
|
||||
if weight >= 1.0 and weight == 1.0:
|
||||
combined.extend(samples)
|
||||
elif weight < 1.0:
|
||||
# Subsample
|
||||
@@ -201,9 +193,8 @@ def build_dataset(
|
||||
Build complete training dataset from all ML decision files.
|
||||
|
||||
Phase 2 additions:
|
||||
- Scans Forge, PPO, and DeepSeek (ds) JSONL files
|
||||
- Scans both Forge and PPO JSONL files
|
||||
- Applies source_weights for configurable mixing
|
||||
(default: ds=4.0 to oversample high-quality DeepSeek decisions)
|
||||
- Filters PPO data by min_reward_threshold
|
||||
- Tags each sample with its source
|
||||
|
||||
@@ -211,15 +202,14 @@ def build_dataset(
|
||||
results_dir: Path to results/ directory containing JSONL files
|
||||
embeddings_dir: Path to embeddings/ directory (default: auto-detect)
|
||||
max_samples: Cap total samples (None = unlimited)
|
||||
source_weights: Dict of source → sampling weight
|
||||
(default: forge=1.0, ppo=0.0, ds=4.0)
|
||||
source_weights: Dict of source → sampling weight (default: forge=1.0, ppo=0.5)
|
||||
min_reward_threshold: Min episode_return for PPO data (default: 0.0)
|
||||
|
||||
Returns:
|
||||
Dict with keys: states, labels, game_ids, outcomes, playstyles, sources
|
||||
"""
|
||||
if source_weights is None:
|
||||
source_weights = {"forge": 1.0, "ppo": 0.0, "ds": 4.0}
|
||||
source_weights = {"forge": 1.0, "ppo": 0.0}
|
||||
|
||||
# Load card embeddings
|
||||
card_index = CardEmbeddingIndex(embeddings_dir)
|
||||
@@ -235,7 +225,7 @@ def build_dataset(
|
||||
|
||||
encoder = StateEncoder(card_index)
|
||||
|
||||
# --- Phase 2 Task 1: Discover files from all sources ---
|
||||
# --- Phase 2 Task 1: Discover files from both sources ---
|
||||
source_files = _discover_jsonl_files(results_dir)
|
||||
if not source_files:
|
||||
logger.error("No ML decision files found in %s", results_dir)
|
||||
@@ -266,7 +256,7 @@ def build_dataset(
|
||||
logger.info("Processing %s [%s] ...", jsonl_file.name, source)
|
||||
file_count = 0
|
||||
|
||||
with open(jsonl_file, "r", encoding="utf-8") as f:
|
||||
with open(jsonl_file, "r") as f:
|
||||
for line_num, line in enumerate(f):
|
||||
line = line.strip()
|
||||
if not line:
|
||||
@@ -513,11 +503,7 @@ def main():
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ppo-weight", type=float, default=0.0,
|
||||
help="Sampling weight for PPO self-play data (default: 0.0, disabled)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--ds-weight", type=float, default=4.0,
|
||||
help="Sampling weight for DeepSeek decision data (default: 4.0, ~20%% effective share)",
|
||||
help="Sampling weight for PPO self-play data (default: 0.0, Forge-only)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--min-reward", type=float, default=0.0,
|
||||
@@ -529,7 +515,6 @@ def main():
|
||||
source_weights = {
|
||||
"forge": args.forge_weight,
|
||||
"ppo": args.ppo_weight,
|
||||
"ds": args.ds_weight,
|
||||
}
|
||||
|
||||
# Build dataset
|
||||
|
||||
@@ -523,7 +523,7 @@ class QualityGate:
|
||||
report_path = os.path.join(
|
||||
self.config.reports_dir, f"quality_{timestamp}.json"
|
||||
)
|
||||
with open(report_path, "w", encoding="utf-8") as f:
|
||||
with open(report_path, "w") as f:
|
||||
json.dump(metrics, f, indent=2)
|
||||
logger.info("Quality report saved: %s", report_path)
|
||||
|
||||
@@ -582,6 +582,6 @@ class QualityGate:
|
||||
path = os.path.join(self.config.reports_dir, "verdict_history.json")
|
||||
|
||||
history = [v.to_dict() for v in self.verdicts]
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
with open(path, "w") as f:
|
||||
json.dump(history, f, indent=2)
|
||||
logger.info("Verdict history saved: %s (%d entries)", path, len(history))
|
||||
+38
-106
@@ -141,17 +141,8 @@ class CardEmbeddingIndex:
|
||||
logger.error("Auto-download failed: %s", e)
|
||||
return None
|
||||
|
||||
def get_embedding(self, card_name) -> np.ndarray:
|
||||
"""Get embedding vector for a card name. Returns zero vector if not found.
|
||||
|
||||
Accepts either a plain string or a dict with a 'name' key (defensive
|
||||
against the LAN client sending structured battlefield objects).
|
||||
"""
|
||||
# Handle dict objects from the LAN client's battlefield_detail
|
||||
if isinstance(card_name, dict):
|
||||
card_name = card_name.get('name', '')
|
||||
if not card_name or not isinstance(card_name, str):
|
||||
return self._zero_vec
|
||||
def get_embedding(self, card_name: str) -> np.ndarray:
|
||||
"""Get embedding vector for a card name. Returns zero vector if not found."""
|
||||
if not self._loaded:
|
||||
return self._zero_vec
|
||||
idx = self._name_to_idx.get(card_name.lower())
|
||||
@@ -164,11 +155,8 @@ class CardEmbeddingIndex:
|
||||
return self._embeddings[idx]
|
||||
return self._zero_vec
|
||||
|
||||
def mean_pool_zone(self, card_names: list) -> np.ndarray:
|
||||
"""Mean-pool embeddings for a list of card names (a game zone).
|
||||
|
||||
Each entry may be a plain string or a dict with a 'name' key.
|
||||
"""
|
||||
def mean_pool_zone(self, card_names: List[str]) -> np.ndarray:
|
||||
"""Mean-pool embeddings for a list of card names (a game zone)."""
|
||||
if not card_names:
|
||||
return self._zero_vec
|
||||
vectors = [self.get_embedding(name) for name in card_names]
|
||||
@@ -179,24 +167,6 @@ class CardEmbeddingIndex:
|
||||
return self._loaded
|
||||
|
||||
|
||||
def _resolve_cmdr_dmg(raw) -> int:
|
||||
"""Collapse cmdr_dmg to a scalar integer regardless of source format.
|
||||
|
||||
Forge sends cmdr_dmg as a dict keyed by opponent seat string, e.g.
|
||||
{"0": 14, "2": 7}. _build_encoder_snapshot already sums this before
|
||||
passing to predict(), but the live Forge IPC path (and legacy JSONL)
|
||||
may send the raw dict. Summing all values is correct for Commander:
|
||||
the total commander damage received from all opponents is what matters
|
||||
for the life-loss threshold (21).
|
||||
"""
|
||||
if isinstance(raw, dict):
|
||||
return sum(int(v) for v in raw.values())
|
||||
try:
|
||||
return int(raw)
|
||||
except (TypeError, ValueError):
|
||||
return 0
|
||||
|
||||
|
||||
class StateEncoder:
|
||||
"""
|
||||
Encodes a decision snapshot JSON dict into a fixed-size numpy vector.
|
||||
@@ -208,23 +178,6 @@ class StateEncoder:
|
||||
[0:29] - Global features (life, mana, turn, phase, etc.)
|
||||
[29:6173] - Zone embeddings (4 zones × 2 players × 768)
|
||||
[6173:6177] - Playstyle one-hot (4 dims)
|
||||
|
||||
Global feature layout per player block (14 features × 2 players + 1 turn = 29):
|
||||
idx 0 life (normalised /40)
|
||||
idx 1 cmdr_dmg (normalised /21)
|
||||
idx 2 mana (normalised /20, clamped)
|
||||
idx 3 cmdr_tax (normalised /10, clamped)
|
||||
idx 4 hand_size (normalised /15, clamped)
|
||||
idx 5 graveyard_size (normalised /100, clamped)
|
||||
idx 6 creatures (normalised /30, clamped)
|
||||
idx 7 total_power (normalised /100, clamped)
|
||||
└─ real Forge value when present;
|
||||
falls back to creatures*3 heuristic
|
||||
idx 8 is_active (binary 0/1)
|
||||
idx 9-12 phase one-hot (4 dims: main_1/combat/main_2/end)
|
||||
idx 13 lands (normalised /15, clamped)
|
||||
--- repeat for player 1 at idx 14-27 ---
|
||||
idx 28 turn (normalised /max_turns)
|
||||
"""
|
||||
|
||||
def __init__(self, card_index: CardEmbeddingIndex):
|
||||
@@ -244,6 +197,7 @@ class StateEncoder:
|
||||
"""
|
||||
players = decision.get("players", [])
|
||||
if len(players) < 2:
|
||||
# Pad to 2 players for 1v1
|
||||
while len(players) < 2:
|
||||
players.append(self._empty_player(len(players)))
|
||||
|
||||
@@ -264,76 +218,55 @@ class StateEncoder:
|
||||
return state.astype(np.float32)
|
||||
|
||||
def _encode_global(self, decision: dict, players: list) -> np.ndarray:
|
||||
"""Encode global scalar features. Shape: (29,)
|
||||
|
||||
Per-player block (14 features each, 2 players = 28) + 1 turn feature.
|
||||
|
||||
Index 7 priority for total_power:
|
||||
1. p['total_power'] — real Forge value from Step 4 / _build_encoder_snapshot()
|
||||
2. p['creatures'] * 3 — legacy heuristic for old JSONL snapshots
|
||||
|
||||
cmdr_dmg is resolved via _resolve_cmdr_dmg() which handles both the
|
||||
live Forge format (dict keyed by opponent seat) and the pre-collapsed
|
||||
int from _build_encoder_snapshot / JSONL training snapshots.
|
||||
"""
|
||||
"""Encode global scalar features. Shape: (29,)"""
|
||||
features = []
|
||||
|
||||
for p in players[:2]:
|
||||
# idx 0: life
|
||||
for p in players[:2]: # Exactly 2 players for 1v1
|
||||
# Per-player scalars (10 each)
|
||||
life = p.get("life", 40)
|
||||
features.append(life / 40.0)
|
||||
features.append(life / 40.0) # Normalized life
|
||||
|
||||
# idx 1: commander damage taken
|
||||
# _resolve_cmdr_dmg handles dict (live Forge IPC) and int (JSONL/pre-collapsed)
|
||||
cmdr_dmg = _resolve_cmdr_dmg(p.get("cmdr_dmg", 0))
|
||||
features.append(cmdr_dmg / 21.0)
|
||||
cmdr_dmg = p.get("cmdr_dmg", 0)
|
||||
features.append(cmdr_dmg / 21.0) # Normalized commander damage
|
||||
|
||||
# idx 2: mana available
|
||||
mana = p.get("mana", 0)
|
||||
features.append(min(mana / 20.0, 1.0))
|
||||
features.append(min(mana / 20.0, 1.0)) # Normalized mana
|
||||
|
||||
# idx 3: commander tax
|
||||
cmdr_tax = p.get("cmdr_tax", 0)
|
||||
features.append(min(cmdr_tax / 10.0, 1.0))
|
||||
features.append(min(cmdr_tax / 10.0, 1.0)) # Normalized tax
|
||||
|
||||
# idx 4: cards in hand
|
||||
hand_size = len(p.get("hand", []))
|
||||
features.append(min(hand_size / 15.0, 1.0))
|
||||
|
||||
# idx 5: graveyard size
|
||||
grave_size = len(p.get("graveyard", []))
|
||||
features.append(min(grave_size / 100.0, 1.0))
|
||||
|
||||
# idx 6: creatures on battlefield
|
||||
creatures = p.get("creatures", 0)
|
||||
features.append(min(creatures / 30.0, 1.0))
|
||||
|
||||
# idx 7: total power on board
|
||||
# Use real Forge value when available (Step 4); fall back to heuristic
|
||||
# for legacy JSONL snapshots that predate Step 4.
|
||||
raw_power = p.get("total_power", 0)
|
||||
total_power = raw_power if raw_power > 0 else creatures * 3
|
||||
# Total power on board (not yet tracked accurately; use creatures as proxy)
|
||||
total_power = creatures * 3 # rough heuristic: avg 3 power
|
||||
features.append(min(total_power / 100.0, 1.0))
|
||||
|
||||
# idx 8: is active player
|
||||
# Is active player
|
||||
is_active = 1.0 if p.get("seat") == decision.get("active_seat") else 0.0
|
||||
features.append(is_active)
|
||||
|
||||
# idx 9-12: phase one-hot (4 dims)
|
||||
# Land count (extra feature — helps distinguish ramp strategies)
|
||||
lands = p.get("lands", 0)
|
||||
features.append(min(lands / 15.0, 1.0))
|
||||
|
||||
# Phase one-hot (4 dims per player)
|
||||
phase_str = decision.get("phase", "main_1")
|
||||
phase_oh = [0.0] * 4
|
||||
try:
|
||||
phase_enum = GamePhase(phase_str)
|
||||
phase_oh[PHASE_TO_IDX[phase_enum]] = 1.0
|
||||
except (ValueError, KeyError):
|
||||
phase_oh[0] = 1.0 # default to main_1
|
||||
phase_oh[0] = 1.0 # Default to main_1
|
||||
features.extend(phase_oh)
|
||||
|
||||
# idx 13: land count
|
||||
lands = p.get("lands", 0)
|
||||
features.append(min(lands / 15.0, 1.0))
|
||||
|
||||
# idx 28: turn number (shared, 1 feature)
|
||||
# Turn number (1 feature, shared)
|
||||
turn = decision.get("turn", 1)
|
||||
features.append(min(turn / GAME_SCOPE.max_turns, 1.0))
|
||||
|
||||
@@ -357,6 +290,8 @@ class StateEncoder:
|
||||
for p in players[:2]:
|
||||
for zone_key in zone_keys:
|
||||
cards = p.get(zone_key, [])
|
||||
# mean_pool_zone returns a zero vector when cards is empty,
|
||||
# preserving the fixed 768-dim slot size.
|
||||
pooled = self.card_index.mean_pool_zone(cards)
|
||||
vectors.append(pooled)
|
||||
|
||||
@@ -369,27 +304,24 @@ class StateEncoder:
|
||||
style = Playstyle(playstyle_str.lower())
|
||||
vec[PLAYSTYLE_TO_IDX[style]] = 1.0
|
||||
except (ValueError, KeyError):
|
||||
# Default: midrange
|
||||
vec[PLAYSTYLE_TO_IDX[Playstyle.MIDRANGE]] = 1.0
|
||||
return vec
|
||||
|
||||
def _empty_player(self, seat: int) -> dict:
|
||||
"""Create an empty player dict for padding."""
|
||||
return {
|
||||
"seat": seat,
|
||||
"life": 40,
|
||||
"cmdr_dmg": 0,
|
||||
"mana": 0,
|
||||
"cmdr_tax": 0,
|
||||
"creatures": 0,
|
||||
"total_power": 0,
|
||||
"total_toughness": 0,
|
||||
"artifacts": 0,
|
||||
"enchantments": 0,
|
||||
"lands": 0,
|
||||
"hand": [],
|
||||
"battlefield": [],
|
||||
"graveyard": [],
|
||||
"command_zone": [],
|
||||
"seat": seat,
|
||||
"life": 40,
|
||||
"cmdr_dmg": 0,
|
||||
"mana": 0,
|
||||
"cmdr_tax": 0,
|
||||
"creatures": 0,
|
||||
"lands": 0,
|
||||
"hand": [],
|
||||
"battlefield": [],
|
||||
"graveyard": [],
|
||||
"command_zone": [],
|
||||
}
|
||||
|
||||
|
||||
@@ -415,7 +347,7 @@ def encode_decisions_file(
|
||||
states = []
|
||||
raw = []
|
||||
|
||||
with open(jsonl_path, "r", encoding="utf-8") as f:
|
||||
with open(jsonl_path, "r") as f:
|
||||
for i, line in enumerate(f):
|
||||
if max_samples and i >= max_samples:
|
||||
break
|
||||
|
||||
+3
-27
@@ -16,7 +16,6 @@ Reuses `run_match`, `LearnedPolicy`, `HeuristicPolicy`,
|
||||
Usage:
|
||||
python -m ml.eval.elo_tracker
|
||||
python -m ml.eval.elo_tracker --episodes 50 --output data/elo_history.json
|
||||
python -m ml.eval.elo_tracker --top-n 10
|
||||
"""
|
||||
|
||||
import json
|
||||
@@ -130,7 +129,7 @@ class EloHistory:
|
||||
def _load(self):
|
||||
if os.path.exists(self.path):
|
||||
try:
|
||||
with open(self.path, "r", encoding="utf-8") as f:
|
||||
with open(self.path, "r") as f:
|
||||
data = json.load(f)
|
||||
self.entries = data.get("entries", [])
|
||||
except Exception:
|
||||
@@ -138,7 +137,7 @@ class EloHistory:
|
||||
|
||||
def save(self):
|
||||
os.makedirs(os.path.dirname(self.path) or ".", exist_ok=True)
|
||||
with open(self.path, "w", encoding="utf-8") as f:
|
||||
with open(self.path, "w") as f:
|
||||
json.dump({"entries": self.entries}, f, indent=2)
|
||||
|
||||
def append(self, generation: int, ratings: Dict[str, float]):
|
||||
@@ -286,17 +285,12 @@ def run_generation_tournament(
|
||||
episodes_per_matchup: int = 50,
|
||||
playstyle: str = "midrange",
|
||||
k_factor: float = DEFAULT_K,
|
||||
top_n: int = 10,
|
||||
) -> EloResult:
|
||||
"""
|
||||
Discover generation checkpoints and run an ELO tournament.
|
||||
|
||||
Looks for `gen-*/best_policy.pt` under checkpoint_dir.
|
||||
Also includes heuristic and random baselines.
|
||||
|
||||
Args:
|
||||
top_n: Only include the N most recent gen-* checkpoints.
|
||||
0 means include all (unbounded). Default is 10.
|
||||
"""
|
||||
policies: Dict[str, object] = {
|
||||
"heuristic": HeuristicPolicy(),
|
||||
@@ -305,20 +299,7 @@ def run_generation_tournament(
|
||||
|
||||
ckpt_path = Path(checkpoint_dir)
|
||||
if ckpt_path.exists():
|
||||
gen_dirs = sorted(ckpt_path.glob("gen-*"))
|
||||
|
||||
# Apply top_n cutoff — keep only the latest N generation dirs
|
||||
if top_n > 0 and len(gen_dirs) > top_n:
|
||||
skipped = len(gen_dirs) - top_n
|
||||
logger.info(
|
||||
"top_n=%d: skipping %d older generation(s), using %s … %s",
|
||||
top_n, skipped,
|
||||
gen_dirs[-top_n].name,
|
||||
gen_dirs[-1].name,
|
||||
)
|
||||
gen_dirs = gen_dirs[-top_n:]
|
||||
|
||||
for gen_dir in gen_dirs:
|
||||
for gen_dir in sorted(ckpt_path.glob("gen-*")):
|
||||
model_path = gen_dir / "best_policy.pt"
|
||||
if model_path.exists():
|
||||
name = gen_dir.name # e.g., "gen-001"
|
||||
@@ -382,10 +363,6 @@ def main():
|
||||
"--k-factor", type=float, default=32,
|
||||
help="ELO K-factor (default: 32)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--top-n", type=int, default=10,
|
||||
help="Only include the N most recent gen checkpoints (0 = all, default: 10)",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
@@ -396,7 +373,6 @@ def main():
|
||||
episodes_per_matchup=args.episodes,
|
||||
playstyle=args.playstyle,
|
||||
k_factor=args.k_factor,
|
||||
top_n=args.top_n,
|
||||
)
|
||||
|
||||
# Print results
|
||||
|
||||
@@ -1,548 +0,0 @@
|
||||
"""
|
||||
Commander AI Lab — Forge Evaluator (Issue #153)
|
||||
═════════════════════════════════════════════════
|
||||
Reusable eval harness that:
|
||||
1. Starts the policy inference server pointing at a given checkpoint
|
||||
2. Launches Forge games via MultiThreadBatchRunner with the policy
|
||||
server wired in as the AI decision-maker
|
||||
3. Collects per-game metrics and writes a JSON results file
|
||||
|
||||
Metrics collected per game:
|
||||
- won : bool
|
||||
- turns : int
|
||||
- life_final : int (winning seat final life)
|
||||
- life_delta : int (winner life - loser life at game end)
|
||||
- cmd_damage : int (commander damage dealt by policy seat)
|
||||
- illegal_acts : int (actions rejected by Forge — should be 0)
|
||||
- entropy_mean : float (mean policy entropy across decision steps)
|
||||
- inference_ms : float (mean per-step inference latency)
|
||||
|
||||
Usage:
|
||||
from ml.eval.forge_evaluator import ForgeEvaluator, EvalConfig
|
||||
cfg = EvalConfig(
|
||||
checkpoint_path="ml/models/checkpoints/forge-trained/final.pt",
|
||||
forge_jar="forge/forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar",
|
||||
deck_files=["decks/cedh_test_a.dck", "decks/cedh_test_b.dck"],
|
||||
num_games=200,
|
||||
num_threads=4,
|
||||
run_id="forge-trained",
|
||||
results_dir="results",
|
||||
)
|
||||
evaluator = ForgeEvaluator(cfg)
|
||||
summary = evaluator.run()
|
||||
print(summary)
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import uuid
|
||||
import subprocess
|
||||
from dataclasses import dataclass, field, asdict
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
import numpy as np
|
||||
|
||||
project_root = str(Path(__file__).parent.parent.parent)
|
||||
if project_root not in sys.path:
|
||||
sys.path.insert(0, project_root)
|
||||
|
||||
logger = logging.getLogger("ml.eval.forge_evaluator")
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────
|
||||
# Configuration
|
||||
# ─────────────────────────────────────────────
|
||||
|
||||
@dataclass
|
||||
class EvalConfig:
|
||||
"""All parameters needed for a single evaluation run."""
|
||||
|
||||
# Model
|
||||
checkpoint_path: str = "ml/models/checkpoints/best_policy.pt"
|
||||
embeddings_dir: str = "embeddings"
|
||||
device: str = None # None = auto-detect
|
||||
|
||||
# Forge
|
||||
forge_jar: str = "forge/forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar"
|
||||
forge_work_dir: str = "forge"
|
||||
java_path: str = "java"
|
||||
deck_files: List[str] = field(default_factory=list)
|
||||
deck_names: List[str] = field(default_factory=list)
|
||||
policy_server_url: str = "http://localhost:8091"
|
||||
clock_seconds: int = 120
|
||||
|
||||
# Run
|
||||
num_games: int = 200
|
||||
num_threads: int = 4
|
||||
seed: Optional[int] = None
|
||||
run_id: str = ""
|
||||
results_dir: str = "results"
|
||||
|
||||
# Inference
|
||||
greedy: bool = True
|
||||
temperature: float = 1.0
|
||||
|
||||
def __post_init__(self):
|
||||
if not self.run_id:
|
||||
self.run_id = str(uuid.uuid4())[:8]
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────
|
||||
# Per-game result
|
||||
# ─────────────────────────────────────────────
|
||||
|
||||
@dataclass
|
||||
class GameRecord:
|
||||
game_index: int
|
||||
won: bool
|
||||
turns: int
|
||||
life_final_policy: int # life total of policy seat at game end
|
||||
life_final_opponent: int
|
||||
life_delta: int # policy life - opponent life
|
||||
commander_damage_dealt: int
|
||||
illegal_actions: int
|
||||
entropy_mean: float # mean per-step policy entropy
|
||||
inference_ms_mean: float # mean per-step inference latency
|
||||
decision_steps: int # number of decision points in this game
|
||||
error: str = ""
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────
|
||||
# Eval summary
|
||||
# ─────────────────────────────────────────────
|
||||
|
||||
@dataclass
|
||||
class EvalSummary:
|
||||
run_id: str
|
||||
checkpoint_path: str
|
||||
num_games: int
|
||||
wins: int
|
||||
losses: int
|
||||
win_rate: float
|
||||
win_rate_ci95_low: float
|
||||
win_rate_ci95_high: float
|
||||
avg_turns: float
|
||||
avg_life_delta: float
|
||||
avg_commander_damage: float
|
||||
avg_illegal_actions: float
|
||||
avg_entropy: float
|
||||
avg_inference_ms: float
|
||||
total_decision_steps: int
|
||||
elapsed_wall_s: float
|
||||
games_per_sec: float
|
||||
error_count: int
|
||||
result_file: str
|
||||
|
||||
def __str__(self) -> str:
|
||||
return (
|
||||
f"EvalSummary run={self.run_id}\n"
|
||||
f" checkpoint : {self.checkpoint_path}\n"
|
||||
f" games : {self.num_games} ({self.wins}W / {self.losses}L)\n"
|
||||
f" win rate : {self.win_rate:.1%} "
|
||||
f"[{self.win_rate_ci95_low:.1%}, {self.win_rate_ci95_high:.1%}] 95% CI\n"
|
||||
f" avg turns : {self.avg_turns:.1f}\n"
|
||||
f" avg life Δ : {self.avg_life_delta:+.1f}\n"
|
||||
f" avg cmd dmg: {self.avg_commander_damage:.1f}\n"
|
||||
f" illegal : {self.avg_illegal_actions:.2f}/game\n"
|
||||
f" entropy : {self.avg_entropy:.4f}\n"
|
||||
f" latency : {self.avg_inference_ms:.1f} ms/step\n"
|
||||
f" throughput : {self.games_per_sec:.2f} games/sec\n"
|
||||
f" result file: {self.result_file}\n"
|
||||
)
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────
|
||||
# Main evaluator
|
||||
# ─────────────────────────────────────────────
|
||||
|
||||
class ForgeEvaluator:
|
||||
"""
|
||||
Orchestrates a full eval run:
|
||||
1. Spin up PolicyInferenceService on cfg.policy_server_url port
|
||||
2. Run cfg.num_games Forge games via MultiThreadBatchRunner
|
||||
3. Parse Forge JSONL output for per-game metrics
|
||||
4. Write results to {results_dir}/eval-{run_id}.json
|
||||
"""
|
||||
|
||||
def __init__(self, cfg: EvalConfig):
|
||||
self.cfg = cfg
|
||||
self._records: List[GameRecord] = []
|
||||
self._result_path = os.path.join(
|
||||
cfg.results_dir, f"eval-{cfg.run_id}.json"
|
||||
)
|
||||
|
||||
# ── Public entry point ───────────────────
|
||||
|
||||
def run(self) -> EvalSummary:
|
||||
"""Run the full evaluation. Returns an EvalSummary."""
|
||||
t0 = time.time()
|
||||
os.makedirs(self.cfg.results_dir, exist_ok=True)
|
||||
|
||||
logger.info("[Eval %s] Starting %d-game eval run", self.cfg.run_id, self.cfg.num_games)
|
||||
logger.info("[Eval %s] Checkpoint: %s", self.cfg.run_id, self.cfg.checkpoint_path)
|
||||
|
||||
# Load policy service
|
||||
service = self._load_policy_service()
|
||||
|
||||
# Run games — either via real Forge or synthetic fallback
|
||||
forge_available = (
|
||||
self.cfg.forge_jar
|
||||
and Path(self.cfg.forge_jar).exists()
|
||||
and len(self.cfg.deck_files) >= 3
|
||||
)
|
||||
|
||||
if forge_available:
|
||||
self._records = self._run_forge_games(service)
|
||||
else:
|
||||
logger.warning(
|
||||
"[Eval %s] Forge JAR or decks not found — running synthetic eval.",
|
||||
self.cfg.run_id
|
||||
)
|
||||
self._records = self._run_synthetic_eval(service)
|
||||
|
||||
elapsed = time.time() - t0
|
||||
summary = self._build_summary(elapsed)
|
||||
self._write_results(summary)
|
||||
logger.info("[Eval %s] Done. %s", self.cfg.run_id, summary)
|
||||
return summary
|
||||
|
||||
# ── Policy service setup ─────────────────
|
||||
|
||||
def _load_policy_service(self):
|
||||
try:
|
||||
from ml.serving.policy_server import PolicyInferenceService
|
||||
svc = PolicyInferenceService(
|
||||
checkpoint_dir=str(Path(self.cfg.checkpoint_path).parent),
|
||||
embeddings_dir=self.cfg.embeddings_dir,
|
||||
device=self.cfg.device,
|
||||
)
|
||||
svc.load()
|
||||
ok = svc.reload(self.cfg.checkpoint_path)
|
||||
if not ok:
|
||||
logger.warning("[Eval] Policy service load failed — entropy/latency metrics unavailable")
|
||||
return svc
|
||||
except Exception as e:
|
||||
logger.error("[Eval] Could not load PolicyInferenceService: %s", e)
|
||||
return None
|
||||
|
||||
# ── Real Forge game runner ───────────────
|
||||
|
||||
def _run_forge_games(self, service) -> List[GameRecord]:
|
||||
"""Launch LabCli Java subprocess with --ml-log, then parse JSONL output."""
|
||||
logger.info("[Eval %s] Launching Forge CLI subprocess for %d games", self.cfg.run_id, self.cfg.num_games)
|
||||
deck_args = []
|
||||
for i, df in enumerate(self.cfg.deck_files[:4]):
|
||||
deck_args.extend([f"--deck{i+1}", Path(df).stem])
|
||||
# Resolve Java 17
|
||||
java_exe = self.cfg.java_path
|
||||
for c in [r"C:\Program Files\Eclipse Adoptium\jdk-17.0.18.8-hotspot\bin\java.exe",
|
||||
r"C:\Program Files\Java\jdk-17\bin\java.exe"]:
|
||||
if Path(c).exists():
|
||||
java_exe = c
|
||||
break
|
||||
lab_jar = os.path.join(project_root, "target", "commander-ai-lab-1.0.0-SNAPSHOT.jar")
|
||||
if not Path(lab_jar).exists():
|
||||
logger.error("[Eval] Lab JAR not found at %s -- run mvn package -DskipTests first", lab_jar)
|
||||
return self._run_synthetic_eval(service)
|
||||
cmd = [
|
||||
java_exe, "-jar", lab_jar,
|
||||
"--forge-jar", self.cfg.forge_jar,
|
||||
"--forge-dir", self.cfg.forge_work_dir,
|
||||
*deck_args,
|
||||
"--games", str(self.cfg.num_games),
|
||||
"--threads", str(self.cfg.num_threads),
|
||||
"--clock", str(self.cfg.clock_seconds),
|
||||
"--ml-log",
|
||||
"--output", os.path.join(self.cfg.results_dir, f"eval-{self.cfg.run_id}-batch.json"),
|
||||
]
|
||||
if self.cfg.seed is not None:
|
||||
cmd.extend(["--seed", str(self.cfg.seed)])
|
||||
logger.info("[Eval %s] CMD: %s", self.cfg.run_id, " ".join(cmd))
|
||||
try:
|
||||
result = subprocess.run(cmd, capture_output=True, text=True,
|
||||
timeout=self.cfg.clock_seconds * self.cfg.num_games + 300)
|
||||
if result.returncode != 0:
|
||||
logger.warning("[Eval %s] Forge CLI exit %d: %s", self.cfg.run_id, result.returncode, result.stderr[:500])
|
||||
except (subprocess.TimeoutExpired, FileNotFoundError) as e:
|
||||
logger.error("[Eval %s] Forge CLI failed: %s -- falling back to synthetic", self.cfg.run_id, e)
|
||||
return self._run_synthetic_eval(service)
|
||||
return self._parse_batch_json(service)
|
||||
|
||||
def _parse_batch_json(self, service) -> List[GameRecord]:
|
||||
"""Parse the batch JSON output from LabCli Java subprocess."""
|
||||
batch_path = os.path.join(
|
||||
self.cfg.results_dir, f"eval-{self.cfg.run_id}-batch.json"
|
||||
)
|
||||
if not Path(batch_path).exists():
|
||||
logger.warning("[Eval] Batch JSON not found at %s — falling back to synthetic", batch_path)
|
||||
return self._run_synthetic_eval(service)
|
||||
with open(batch_path, encoding="utf-8") as f:
|
||||
batch = json.load(f)
|
||||
games_list = batch.get("games", [])
|
||||
if not games_list:
|
||||
logger.warning("[Eval] No games in batch JSON — falling back to synthetic")
|
||||
return self._run_synthetic_eval(service)
|
||||
records = []
|
||||
for idx, g in enumerate(games_list):
|
||||
won = g.get("winningSeat", -1) == 0
|
||||
turns = g.get("totalTurns", 0)
|
||||
pr = g.get("playerResults", [])
|
||||
lp = pr[0].get("finalLife", 40) if len(pr) > 0 else 40
|
||||
lo = pr[1].get("finalLife", 40) if len(pr) > 1 else 40
|
||||
records.append(GameRecord(
|
||||
game_index=idx,
|
||||
won=won,
|
||||
turns=turns,
|
||||
life_final_policy=lp,
|
||||
life_final_opponent=lo,
|
||||
life_delta=lp - lo,
|
||||
commander_damage_dealt=0,
|
||||
illegal_actions=0,
|
||||
entropy_mean=0.0,
|
||||
inference_ms_mean=0.0,
|
||||
decision_steps=turns,
|
||||
))
|
||||
if len(records) >= self.cfg.num_games:
|
||||
break
|
||||
logger.info("[Eval %s] Parsed %d games from batch JSON", self.cfg.run_id, len(records))
|
||||
return records
|
||||
|
||||
def _run_forge_via_subprocess(self, service) -> List[GameRecord]:
|
||||
"""Parse JSONL files produced by a separately-launched Forge batch."""
|
||||
records = []
|
||||
results_path = Path(self.cfg.results_dir)
|
||||
pattern = f"ml-decisions-forge-eval-{self.cfg.run_id}*.jsonl"
|
||||
|
||||
all_files = sorted(results_path.glob(pattern))
|
||||
if not all_files:
|
||||
logger.warning("[Eval] No JSONL files found matching %s — falling back to synthetic", pattern)
|
||||
return self._run_synthetic_eval(service)
|
||||
|
||||
from ml.training.forge_episode_generator import parse_forge_jsonl, snapshots_to_episode
|
||||
games_seen: Dict[str, list] = {}
|
||||
for f in all_files:
|
||||
for snap in parse_forge_jsonl(f):
|
||||
gid = snap.get("game_id", "g0")
|
||||
games_seen.setdefault(gid, []).append(snap)
|
||||
|
||||
for idx, (gid, snaps) in enumerate(games_seen.items()):
|
||||
records.append(self._snaps_to_record(idx, gid, snaps, service))
|
||||
if len(records) >= self.cfg.num_games:
|
||||
break
|
||||
|
||||
return records
|
||||
|
||||
def _java_results_to_records(self, java_results, service) -> List[GameRecord]:
|
||||
"""Convert Java GameResult objects to GameRecord."""
|
||||
records = []
|
||||
for jr in java_results:
|
||||
try:
|
||||
won = getattr(jr, "winningSeat", 0) == 0 # seat 0 = policy
|
||||
records.append(GameRecord(
|
||||
game_index=getattr(jr, "gameIndex", len(records)),
|
||||
won=won,
|
||||
turns=getattr(jr, "turns", 0),
|
||||
life_final_policy=getattr(jr, "lifeAtEnd", [40])[0] if hasattr(jr, "lifeAtEnd") else 40,
|
||||
life_final_opponent=getattr(jr, "lifeAtEnd", [40, 40])[1] if hasattr(jr, "lifeAtEnd") else 40,
|
||||
life_delta=0,
|
||||
commander_damage_dealt=0,
|
||||
illegal_actions=getattr(jr, "illegalActions", 0),
|
||||
entropy_mean=0.0,
|
||||
inference_ms_mean=0.0,
|
||||
decision_steps=0,
|
||||
))
|
||||
except Exception as e:
|
||||
logger.warning("[Eval] Skipping malformed GameResult: %s", e)
|
||||
return records
|
||||
|
||||
# ── Synthetic fallback ───────────────────
|
||||
|
||||
def _run_synthetic_eval(self, service) -> List[GameRecord]:
|
||||
"""Run synthetic games using self_play engine (no Forge required).
|
||||
|
||||
Used when Forge JAR is missing or deck files aren't configured.
|
||||
Metrics like illegal_actions and commander_damage are 0 in this mode.
|
||||
"""
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore", category=DeprecationWarning)
|
||||
|
||||
records = []
|
||||
try:
|
||||
from ml.training.self_play import (
|
||||
create_random_initial_state, apply_action, advance_turn,
|
||||
encode_state_simple, HeuristicPolicy
|
||||
)
|
||||
from ml.config.scope import NUM_ACTIONS, IDX_TO_ACTION
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
except ImportError as e:
|
||||
logger.error("[Eval] Cannot import self_play: %s", e)
|
||||
return []
|
||||
|
||||
opponent = HeuristicPolicy()
|
||||
agent_has_model = service is not None and service._loaded
|
||||
|
||||
for game_idx in range(self.cfg.num_games):
|
||||
state = create_random_initial_state()
|
||||
entropies, latencies = [], []
|
||||
steps = 0
|
||||
|
||||
while not state.game_over and steps < 60:
|
||||
sv = encode_state_simple(state, 0, "midrange")
|
||||
t0 = time.time()
|
||||
|
||||
if agent_has_model:
|
||||
snap = state.to_snapshot()
|
||||
result = service.predict(
|
||||
snap, playstyle="midrange",
|
||||
greedy=self.cfg.greedy,
|
||||
temperature=self.cfg.temperature,
|
||||
)
|
||||
action_idx = result.get("action_index", 7)
|
||||
probs_map = result.get("probabilities", {})
|
||||
probs = np.array(list(probs_map.values())) if probs_map else np.ones(NUM_ACTIONS) / NUM_ACTIONS
|
||||
else:
|
||||
action_idx = np.random.randint(NUM_ACTIONS)
|
||||
probs = np.ones(NUM_ACTIONS) / NUM_ACTIONS
|
||||
|
||||
latencies.append((time.time() - t0) * 1000)
|
||||
p = np.clip(probs, 1e-9, 1.0)
|
||||
p /= p.sum()
|
||||
entropies.append(float(-np.sum(p * np.log(p))))
|
||||
|
||||
state = apply_action(state, action_idx, 0)
|
||||
if not state.game_over:
|
||||
opp_sv = encode_state_simple(state, 1, "midrange")
|
||||
opp_a, _ = opponent.select_action(opp_sv)
|
||||
state = apply_action(state, opp_a, 1)
|
||||
if not state.game_over:
|
||||
state = advance_turn(state)
|
||||
steps += 1
|
||||
|
||||
won = state.winner == 0
|
||||
lp = state.players[0].life_total
|
||||
lo = state.players[1].life_total
|
||||
records.append(GameRecord(
|
||||
game_index=game_idx,
|
||||
won=won,
|
||||
turns=state.turn,
|
||||
life_final_policy=lp,
|
||||
life_final_opponent=lo,
|
||||
life_delta=lp - lo,
|
||||
commander_damage_dealt=0,
|
||||
illegal_actions=0,
|
||||
entropy_mean=float(np.mean(entropies)) if entropies else 0.0,
|
||||
inference_ms_mean=float(np.mean(latencies)) if latencies else 0.0,
|
||||
decision_steps=steps,
|
||||
))
|
||||
|
||||
return records
|
||||
|
||||
# ── JSONL snapshot → GameRecord ──────────
|
||||
|
||||
def _snaps_to_record(self, idx: int, gid: str, snaps: list, service) -> GameRecord:
|
||||
entropies, latencies = [], []
|
||||
won = False
|
||||
turns = len(snaps)
|
||||
cmd_dmg = 0
|
||||
illegal = 0
|
||||
lp, lo = 40, 40
|
||||
|
||||
for snap in snaps:
|
||||
if "outcome" in snap and snap == snaps[-1]:
|
||||
won = snap["outcome"].get("winner_seat", -1) == 0
|
||||
lp = snap["outcome"].get("life_remaining", 40)
|
||||
lo = snap["outcome"].get("opp_life_remaining", 0)
|
||||
cmd_dmg = snap["outcome"].get("commander_damage_dealt", 0)
|
||||
|
||||
if service and service._loaded and "state_vector" in snap:
|
||||
t0 = time.time()
|
||||
result = service.predict(snap, playstyle="midrange", greedy=self.cfg.greedy)
|
||||
latencies.append((time.time() - t0) * 1000)
|
||||
probs_map = result.get("probabilities", {})
|
||||
if probs_map:
|
||||
p = np.clip(list(probs_map.values()), 1e-9, 1.0)
|
||||
p /= p.sum()
|
||||
entropies.append(float(-np.sum(p * np.log(p))))
|
||||
|
||||
return GameRecord(
|
||||
game_index=idx,
|
||||
won=won,
|
||||
turns=turns,
|
||||
life_final_policy=lp,
|
||||
life_final_opponent=lo,
|
||||
life_delta=lp - lo,
|
||||
commander_damage_dealt=cmd_dmg,
|
||||
illegal_actions=illegal,
|
||||
entropy_mean=float(np.mean(entropies)) if entropies else 0.0,
|
||||
inference_ms_mean=float(np.mean(latencies)) if latencies else 0.0,
|
||||
decision_steps=len(snaps),
|
||||
)
|
||||
|
||||
# ── Summary builder ──────────────────────
|
||||
|
||||
def _build_summary(self, elapsed: float) -> EvalSummary:
|
||||
n = len(self._records)
|
||||
if n == 0:
|
||||
return EvalSummary(
|
||||
run_id=self.cfg.run_id,
|
||||
checkpoint_path=self.cfg.checkpoint_path,
|
||||
num_games=0, wins=0, losses=0,
|
||||
win_rate=0.0, win_rate_ci95_low=0.0, win_rate_ci95_high=0.0,
|
||||
avg_turns=0.0, avg_life_delta=0.0, avg_commander_damage=0.0,
|
||||
avg_illegal_actions=0.0, avg_entropy=0.0, avg_inference_ms=0.0,
|
||||
total_decision_steps=0, elapsed_wall_s=elapsed, games_per_sec=0.0,
|
||||
error_count=0, result_file=self._result_path,
|
||||
)
|
||||
|
||||
wins = sum(1 for r in self._records if r.won)
|
||||
losses = n - wins
|
||||
wr = wins / n
|
||||
|
||||
# Wilson score 95% CI
|
||||
z = 1.96
|
||||
denom = 1 + z**2 / n
|
||||
centre = (wr + z**2 / (2 * n)) / denom
|
||||
margin = (z * (wr * (1 - wr) / n + z**2 / (4 * n**2)) ** 0.5) / denom
|
||||
ci_lo = max(0.0, centre - margin)
|
||||
ci_hi = min(1.0, centre + margin)
|
||||
|
||||
errors = sum(1 for r in self._records if r.error)
|
||||
|
||||
return EvalSummary(
|
||||
run_id=self.cfg.run_id,
|
||||
checkpoint_path=self.cfg.checkpoint_path,
|
||||
num_games=n,
|
||||
wins=wins,
|
||||
losses=losses,
|
||||
win_rate=wr,
|
||||
win_rate_ci95_low=ci_lo,
|
||||
win_rate_ci95_high=ci_hi,
|
||||
avg_turns=float(np.mean([r.turns for r in self._records])),
|
||||
avg_life_delta=float(np.mean([r.life_delta for r in self._records])),
|
||||
avg_commander_damage=float(np.mean([r.commander_damage_dealt for r in self._records])),
|
||||
avg_illegal_actions=float(np.mean([r.illegal_actions for r in self._records])),
|
||||
avg_entropy=float(np.mean([r.entropy_mean for r in self._records])),
|
||||
avg_inference_ms=float(np.mean([r.inference_ms_mean for r in self._records])),
|
||||
total_decision_steps=sum(r.decision_steps for r in self._records),
|
||||
elapsed_wall_s=elapsed,
|
||||
games_per_sec=n / max(elapsed, 0.001),
|
||||
error_count=errors,
|
||||
result_file=self._result_path,
|
||||
)
|
||||
|
||||
# ── Result writer ────────────────────────
|
||||
|
||||
def _write_results(self, summary: EvalSummary) -> None:
|
||||
payload = {
|
||||
"summary": asdict(summary),
|
||||
"games": [asdict(r) for r in self._records],
|
||||
}
|
||||
os.makedirs(self.cfg.results_dir, exist_ok=True)
|
||||
with open(self._result_path, "w", encoding="utf-8") as f:
|
||||
json.dump(payload, f, indent=2)
|
||||
logger.info("[Eval %s] Results written to %s", self.cfg.run_id, self._result_path)
|
||||
@@ -1,548 +0,0 @@
|
||||
"""
|
||||
Commander AI Lab — Forge Evaluator (Issue #153)
|
||||
═════════════════════════════════════════════════
|
||||
Reusable eval harness that:
|
||||
1. Starts the policy inference server pointing at a given checkpoint
|
||||
2. Launches Forge games via MultiThreadBatchRunner with the policy
|
||||
server wired in as the AI decision-maker
|
||||
3. Collects per-game metrics and writes a JSON results file
|
||||
|
||||
Metrics collected per game:
|
||||
- won : bool
|
||||
- turns : int
|
||||
- life_final : int (winning seat final life)
|
||||
- life_delta : int (winner life - loser life at game end)
|
||||
- cmd_damage : int (commander damage dealt by policy seat)
|
||||
- illegal_acts : int (actions rejected by Forge — should be 0)
|
||||
- entropy_mean : float (mean policy entropy across decision steps)
|
||||
- inference_ms : float (mean per-step inference latency)
|
||||
|
||||
Usage:
|
||||
from ml.eval.forge_evaluator import ForgeEvaluator, EvalConfig
|
||||
cfg = EvalConfig(
|
||||
checkpoint_path="ml/models/checkpoints/forge-trained/final.pt",
|
||||
forge_jar="forge/forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar",
|
||||
deck_files=["decks/cedh_test_a.dck", "decks/cedh_test_b.dck"],
|
||||
num_games=200,
|
||||
num_threads=4,
|
||||
run_id="forge-trained",
|
||||
results_dir="results",
|
||||
)
|
||||
evaluator = ForgeEvaluator(cfg)
|
||||
summary = evaluator.run()
|
||||
print(summary)
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import uuid
|
||||
import subprocess
|
||||
from dataclasses import dataclass, field, asdict
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
import numpy as np
|
||||
|
||||
project_root = str(Path(__file__).parent.parent.parent)
|
||||
if project_root not in sys.path:
|
||||
sys.path.insert(0, project_root)
|
||||
|
||||
logger = logging.getLogger("ml.eval.forge_evaluator")
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────
|
||||
# Configuration
|
||||
# ─────────────────────────────────────────────
|
||||
|
||||
@dataclass
|
||||
class EvalConfig:
|
||||
"""All parameters needed for a single evaluation run."""
|
||||
|
||||
# Model
|
||||
checkpoint_path: str = "ml/models/checkpoints/best_policy.pt"
|
||||
embeddings_dir: str = "embeddings"
|
||||
device: str = None # None = auto-detect
|
||||
|
||||
# Forge
|
||||
forge_jar: str = "forge/forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar"
|
||||
forge_work_dir: str = "forge"
|
||||
java_path: str = "java"
|
||||
deck_files: List[str] = field(default_factory=list)
|
||||
deck_names: List[str] = field(default_factory=list)
|
||||
policy_server_url: str = "http://localhost:8091"
|
||||
clock_seconds: int = 120
|
||||
|
||||
# Run
|
||||
num_games: int = 200
|
||||
num_threads: int = 4
|
||||
seed: Optional[int] = None
|
||||
run_id: str = ""
|
||||
results_dir: str = "results"
|
||||
|
||||
# Inference
|
||||
greedy: bool = True
|
||||
temperature: float = 1.0
|
||||
|
||||
def __post_init__(self):
|
||||
if not self.run_id:
|
||||
self.run_id = str(uuid.uuid4())[:8]
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────
|
||||
# Per-game result
|
||||
# ─────────────────────────────────────────────
|
||||
|
||||
@dataclass
|
||||
class GameRecord:
|
||||
game_index: int
|
||||
won: bool
|
||||
turns: int
|
||||
life_final_policy: int # life total of policy seat at game end
|
||||
life_final_opponent: int
|
||||
life_delta: int # policy life - opponent life
|
||||
commander_damage_dealt: int
|
||||
illegal_actions: int
|
||||
entropy_mean: float # mean per-step policy entropy
|
||||
inference_ms_mean: float # mean per-step inference latency
|
||||
decision_steps: int # number of decision points in this game
|
||||
error: str = ""
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────
|
||||
# Eval summary
|
||||
# ─────────────────────────────────────────────
|
||||
|
||||
@dataclass
|
||||
class EvalSummary:
|
||||
run_id: str
|
||||
checkpoint_path: str
|
||||
num_games: int
|
||||
wins: int
|
||||
losses: int
|
||||
win_rate: float
|
||||
win_rate_ci95_low: float
|
||||
win_rate_ci95_high: float
|
||||
avg_turns: float
|
||||
avg_life_delta: float
|
||||
avg_commander_damage: float
|
||||
avg_illegal_actions: float
|
||||
avg_entropy: float
|
||||
avg_inference_ms: float
|
||||
total_decision_steps: int
|
||||
elapsed_wall_s: float
|
||||
games_per_sec: float
|
||||
error_count: int
|
||||
result_file: str
|
||||
|
||||
def __str__(self) -> str:
|
||||
return (
|
||||
f"EvalSummary run={self.run_id}\n"
|
||||
f" checkpoint : {self.checkpoint_path}\n"
|
||||
f" games : {self.num_games} ({self.wins}W / {self.losses}L)\n"
|
||||
f" win rate : {self.win_rate:.1%} "
|
||||
f"[{self.win_rate_ci95_low:.1%}, {self.win_rate_ci95_high:.1%}] 95% CI\n"
|
||||
f" avg turns : {self.avg_turns:.1f}\n"
|
||||
f" avg life Δ : {self.avg_life_delta:+.1f}\n"
|
||||
f" avg cmd dmg: {self.avg_commander_damage:.1f}\n"
|
||||
f" illegal : {self.avg_illegal_actions:.2f}/game\n"
|
||||
f" entropy : {self.avg_entropy:.4f}\n"
|
||||
f" latency : {self.avg_inference_ms:.1f} ms/step\n"
|
||||
f" throughput : {self.games_per_sec:.2f} games/sec\n"
|
||||
f" result file: {self.result_file}\n"
|
||||
)
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────
|
||||
# Main evaluator
|
||||
# ─────────────────────────────────────────────
|
||||
|
||||
class ForgeEvaluator:
|
||||
"""
|
||||
Orchestrates a full eval run:
|
||||
1. Spin up PolicyInferenceService on cfg.policy_server_url port
|
||||
2. Run cfg.num_games Forge games via MultiThreadBatchRunner
|
||||
3. Parse Forge JSONL output for per-game metrics
|
||||
4. Write results to {results_dir}/eval-{run_id}.json
|
||||
"""
|
||||
|
||||
def __init__(self, cfg: EvalConfig):
|
||||
self.cfg = cfg
|
||||
self._records: List[GameRecord] = []
|
||||
self._result_path = os.path.join(
|
||||
cfg.results_dir, f"eval-{cfg.run_id}.json"
|
||||
)
|
||||
|
||||
# ── Public entry point ───────────────────
|
||||
|
||||
def run(self) -> EvalSummary:
|
||||
"""Run the full evaluation. Returns an EvalSummary."""
|
||||
t0 = time.time()
|
||||
os.makedirs(self.cfg.results_dir, exist_ok=True)
|
||||
|
||||
logger.info("[Eval %s] Starting %d-game eval run", self.cfg.run_id, self.cfg.num_games)
|
||||
logger.info("[Eval %s] Checkpoint: %s", self.cfg.run_id, self.cfg.checkpoint_path)
|
||||
|
||||
# Load policy service
|
||||
service = self._load_policy_service()
|
||||
|
||||
# Run games — either via real Forge or synthetic fallback
|
||||
forge_available = (
|
||||
self.cfg.forge_jar
|
||||
and Path(self.cfg.forge_jar).exists()
|
||||
and len(self.cfg.deck_files) >= 3
|
||||
)
|
||||
|
||||
if forge_available:
|
||||
self._records = self._run_forge_games(service)
|
||||
else:
|
||||
logger.warning(
|
||||
"[Eval %s] Forge JAR or decks not found — running synthetic eval.",
|
||||
self.cfg.run_id
|
||||
)
|
||||
self._records = self._run_synthetic_eval(service)
|
||||
|
||||
elapsed = time.time() - t0
|
||||
summary = self._build_summary(elapsed)
|
||||
self._write_results(summary)
|
||||
logger.info("[Eval %s] Done. %s", self.cfg.run_id, summary)
|
||||
return summary
|
||||
|
||||
# ── Policy service setup ─────────────────
|
||||
|
||||
def _load_policy_service(self):
|
||||
try:
|
||||
from ml.serving.policy_server import PolicyInferenceService
|
||||
svc = PolicyInferenceService(
|
||||
checkpoint_dir=str(Path(self.cfg.checkpoint_path).parent),
|
||||
embeddings_dir=self.cfg.embeddings_dir,
|
||||
device=self.cfg.device,
|
||||
)
|
||||
svc.load()
|
||||
ok = svc.reload(self.cfg.checkpoint_path)
|
||||
if not ok:
|
||||
logger.warning("[Eval] Policy service load failed — entropy/latency metrics unavailable")
|
||||
return svc
|
||||
except Exception as e:
|
||||
logger.error("[Eval] Could not load PolicyInferenceService: %s", e)
|
||||
return None
|
||||
|
||||
# ── Real Forge game runner ───────────────
|
||||
|
||||
def _run_forge_games(self, service) -> List[GameRecord]:
|
||||
"""Launch LabCli Java subprocess with --ml-log, then parse JSONL output."""
|
||||
logger.info("[Eval %s] Launching Forge CLI subprocess for %d games", self.cfg.run_id, self.cfg.num_games)
|
||||
deck_args = []
|
||||
for i, df in enumerate(self.cfg.deck_files[:4]):
|
||||
deck_args.extend([f"--deck{i+1}", Path(df).stem])
|
||||
# Resolve Java 17
|
||||
java_exe = self.cfg.java_path
|
||||
for c in [r"C:\Program Files\Eclipse Adoptium\jdk-17.0.18.8-hotspot\bin\java.exe",
|
||||
r"C:\Program Files\Java\jdk-17\bin\java.exe"]:
|
||||
if Path(c).exists():
|
||||
java_exe = c
|
||||
break
|
||||
lab_jar = os.path.join(project_root, "target", "commander-ai-lab-1.0.0-SNAPSHOT.jar")
|
||||
if not Path(lab_jar).exists():
|
||||
logger.error("[Eval] Lab JAR not found at %s -- run mvn package -DskipTests first", lab_jar)
|
||||
return self._run_synthetic_eval(service)
|
||||
cmd = [
|
||||
java_exe, "-jar", lab_jar,
|
||||
"--forge-jar", self.cfg.forge_jar,
|
||||
"--forge-dir", self.cfg.forge_work_dir,
|
||||
*deck_args,
|
||||
"--games", str(self.cfg.num_games),
|
||||
"--threads", str(self.cfg.num_threads),
|
||||
"--clock", str(self.cfg.clock_seconds),
|
||||
"--ml-log",
|
||||
"--output", os.path.join(self.cfg.results_dir, f"eval-{self.cfg.run_id}-batch.json"),
|
||||
]
|
||||
if self.cfg.seed is not None:
|
||||
cmd.extend(["--seed", str(self.cfg.seed)])
|
||||
logger.info("[Eval %s] CMD: %s", self.cfg.run_id, " ".join(cmd))
|
||||
try:
|
||||
result = subprocess.run(cmd, capture_output=True, text=True,
|
||||
timeout=self.cfg.clock_seconds * self.cfg.num_games + 300)
|
||||
if result.returncode != 0:
|
||||
logger.warning("[Eval %s] Forge CLI exit %d: %s", self.cfg.run_id, result.returncode, result.stderr[:500])
|
||||
except (subprocess.TimeoutExpired, FileNotFoundError) as e:
|
||||
logger.error("[Eval %s] Forge CLI failed: %s -- falling back to synthetic", self.cfg.run_id, e)
|
||||
return self._run_synthetic_eval(service)
|
||||
return self._parse_batch_json(service)
|
||||
|
||||
def _parse_batch_json(self, service) -> List[GameRecord]:
|
||||
"""Parse the batch JSON output from LabCli Java subprocess."""
|
||||
batch_path = os.path.join(
|
||||
self.cfg.results_dir, f"eval-{self.cfg.run_id}-batch.json"
|
||||
)
|
||||
if not Path(batch_path).exists():
|
||||
logger.warning("[Eval] Batch JSON not found at %s — falling back to synthetic", batch_path)
|
||||
return self._run_synthetic_eval(service)
|
||||
with open(batch_path, encoding="utf-8") as f:
|
||||
batch = json.load(f)
|
||||
games_list = batch.get("games", [])
|
||||
if not games_list:
|
||||
logger.warning("[Eval] No games in batch JSON — falling back to synthetic")
|
||||
return self._run_synthetic_eval(service)
|
||||
records = []
|
||||
for idx, g in enumerate(games_list):
|
||||
won = g.get("winningSeat", -1) == 0
|
||||
turns = g.get("totalTurns", 0)
|
||||
pr = g.get("playerResults", [])
|
||||
lp = pr[0].get("finalLife", 40) if len(pr) > 0 else 40
|
||||
lo = pr[1].get("finalLife", 40) if len(pr) > 1 else 40
|
||||
records.append(GameRecord(
|
||||
game_index=idx,
|
||||
won=won,
|
||||
turns=turns,
|
||||
life_final_policy=lp,
|
||||
life_final_opponent=lo,
|
||||
life_delta=lp - lo,
|
||||
commander_damage_dealt=0,
|
||||
illegal_actions=0,
|
||||
entropy_mean=0.0,
|
||||
inference_ms_mean=0.0,
|
||||
decision_steps=turns,
|
||||
))
|
||||
if len(records) >= self.cfg.num_games:
|
||||
break
|
||||
logger.info("[Eval %s] Parsed %d games from batch JSON", self.cfg.run_id, len(records))
|
||||
return records
|
||||
|
||||
def _run_forge_via_subprocess(self, service) -> List[GameRecord]:
|
||||
"""Parse JSONL files produced by a separately-launched Forge batch."""
|
||||
records = []
|
||||
results_path = Path(self.cfg.results_dir)
|
||||
pattern = f"ml-decisions-forge-eval-{self.cfg.run_id}*.jsonl"
|
||||
|
||||
all_files = sorted(results_path.glob(pattern))
|
||||
if not all_files:
|
||||
logger.warning("[Eval] No JSONL files found matching %s — falling back to synthetic", pattern)
|
||||
return self._run_synthetic_eval(service)
|
||||
|
||||
from ml.training.forge_episode_generator import parse_forge_jsonl, snapshots_to_episode
|
||||
games_seen: Dict[str, list] = {}
|
||||
for f in all_files:
|
||||
for snap in parse_forge_jsonl(f):
|
||||
gid = snap.get("game_id", "g0")
|
||||
games_seen.setdefault(gid, []).append(snap)
|
||||
|
||||
for idx, (gid, snaps) in enumerate(games_seen.items()):
|
||||
records.append(self._snaps_to_record(idx, gid, snaps, service))
|
||||
if len(records) >= self.cfg.num_games:
|
||||
break
|
||||
|
||||
return records
|
||||
|
||||
def _java_results_to_records(self, java_results, service) -> List[GameRecord]:
|
||||
"""Convert Java GameResult objects to GameRecord."""
|
||||
records = []
|
||||
for jr in java_results:
|
||||
try:
|
||||
won = getattr(jr, "winningSeat", 0) == 0 # seat 0 = policy
|
||||
records.append(GameRecord(
|
||||
game_index=getattr(jr, "gameIndex", len(records)),
|
||||
won=won,
|
||||
turns=getattr(jr, "turns", 0),
|
||||
life_final_policy=getattr(jr, "lifeAtEnd", [40])[0] if hasattr(jr, "lifeAtEnd") else 40,
|
||||
life_final_opponent=getattr(jr, "lifeAtEnd", [40, 40])[1] if hasattr(jr, "lifeAtEnd") else 40,
|
||||
life_delta=0,
|
||||
commander_damage_dealt=0,
|
||||
illegal_actions=getattr(jr, "illegalActions", 0),
|
||||
entropy_mean=0.0,
|
||||
inference_ms_mean=0.0,
|
||||
decision_steps=0,
|
||||
))
|
||||
except Exception as e:
|
||||
logger.warning("[Eval] Skipping malformed GameResult: %s", e)
|
||||
return records
|
||||
|
||||
# ── Synthetic fallback ───────────────────
|
||||
|
||||
def _run_synthetic_eval(self, service) -> List[GameRecord]:
|
||||
"""Run synthetic games using self_play engine (no Forge required).
|
||||
|
||||
Used when Forge JAR is missing or deck files aren't configured.
|
||||
Metrics like illegal_actions and commander_damage are 0 in this mode.
|
||||
"""
|
||||
import warnings
|
||||
warnings.filterwarnings("ignore", category=DeprecationWarning)
|
||||
|
||||
records = []
|
||||
try:
|
||||
from ml.training.self_play import (
|
||||
create_random_initial_state, apply_action, advance_turn,
|
||||
encode_state_simple, HeuristicPolicy
|
||||
)
|
||||
from ml.config.scope import NUM_ACTIONS, IDX_TO_ACTION
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
except ImportError as e:
|
||||
logger.error("[Eval] Cannot import self_play: %s", e)
|
||||
return []
|
||||
|
||||
opponent = HeuristicPolicy()
|
||||
agent_has_model = service is not None and service._loaded
|
||||
|
||||
for game_idx in range(self.cfg.num_games):
|
||||
state = create_random_initial_state()
|
||||
entropies, latencies = [], []
|
||||
steps = 0
|
||||
|
||||
while not state.game_over and steps < 60:
|
||||
sv = encode_state_simple(state, 0, "midrange")
|
||||
t0 = time.time()
|
||||
|
||||
if agent_has_model:
|
||||
snap = state.to_snapshot()
|
||||
result = service.predict(
|
||||
snap, playstyle="midrange",
|
||||
greedy=self.cfg.greedy,
|
||||
temperature=self.cfg.temperature,
|
||||
)
|
||||
action_idx = result.get("action_index", 7)
|
||||
probs_map = result.get("probabilities", {})
|
||||
probs = np.array(list(probs_map.values())) if probs_map else np.ones(NUM_ACTIONS) / NUM_ACTIONS
|
||||
else:
|
||||
action_idx = np.random.randint(NUM_ACTIONS)
|
||||
probs = np.ones(NUM_ACTIONS) / NUM_ACTIONS
|
||||
|
||||
latencies.append((time.time() - t0) * 1000)
|
||||
p = np.clip(probs, 1e-9, 1.0)
|
||||
p /= p.sum()
|
||||
entropies.append(float(-np.sum(p * np.log(p))))
|
||||
|
||||
state = apply_action(state, action_idx, 0)
|
||||
if not state.game_over:
|
||||
opp_sv = encode_state_simple(state, 1, "midrange")
|
||||
opp_a, _ = opponent.select_action(opp_sv)
|
||||
state = apply_action(state, opp_a, 1)
|
||||
if not state.game_over:
|
||||
state = advance_turn(state)
|
||||
steps += 1
|
||||
|
||||
won = state.winner == 0
|
||||
lp = state.players[0].life_total
|
||||
lo = state.players[1].life_total
|
||||
records.append(GameRecord(
|
||||
game_index=game_idx,
|
||||
won=won,
|
||||
turns=state.turn,
|
||||
life_final_policy=lp,
|
||||
life_final_opponent=lo,
|
||||
life_delta=lp - lo,
|
||||
commander_damage_dealt=0,
|
||||
illegal_actions=0,
|
||||
entropy_mean=float(np.mean(entropies)) if entropies else 0.0,
|
||||
inference_ms_mean=float(np.mean(latencies)) if latencies else 0.0,
|
||||
decision_steps=steps,
|
||||
))
|
||||
|
||||
return records
|
||||
|
||||
# ── JSONL snapshot → GameRecord ──────────
|
||||
|
||||
def _snaps_to_record(self, idx: int, gid: str, snaps: list, service) -> GameRecord:
|
||||
entropies, latencies = [], []
|
||||
won = False
|
||||
turns = len(snaps)
|
||||
cmd_dmg = 0
|
||||
illegal = 0
|
||||
lp, lo = 40, 40
|
||||
|
||||
for snap in snaps:
|
||||
if "outcome" in snap and snap == snaps[-1]:
|
||||
won = snap["outcome"].get("winner_seat", -1) == 0
|
||||
lp = snap["outcome"].get("life_remaining", 40)
|
||||
lo = snap["outcome"].get("opp_life_remaining", 0)
|
||||
cmd_dmg = snap["outcome"].get("commander_damage_dealt", 0)
|
||||
|
||||
if service and service._loaded and "state_vector" in snap:
|
||||
t0 = time.time()
|
||||
result = service.predict(snap, playstyle="midrange", greedy=self.cfg.greedy)
|
||||
latencies.append((time.time() - t0) * 1000)
|
||||
probs_map = result.get("probabilities", {})
|
||||
if probs_map:
|
||||
p = np.clip(list(probs_map.values()), 1e-9, 1.0)
|
||||
p /= p.sum()
|
||||
entropies.append(float(-np.sum(p * np.log(p))))
|
||||
|
||||
return GameRecord(
|
||||
game_index=idx,
|
||||
won=won,
|
||||
turns=turns,
|
||||
life_final_policy=lp,
|
||||
life_final_opponent=lo,
|
||||
life_delta=lp - lo,
|
||||
commander_damage_dealt=cmd_dmg,
|
||||
illegal_actions=illegal,
|
||||
entropy_mean=float(np.mean(entropies)) if entropies else 0.0,
|
||||
inference_ms_mean=float(np.mean(latencies)) if latencies else 0.0,
|
||||
decision_steps=len(snaps),
|
||||
)
|
||||
|
||||
# ── Summary builder ──────────────────────
|
||||
|
||||
def _build_summary(self, elapsed: float) -> EvalSummary:
|
||||
n = len(self._records)
|
||||
if n == 0:
|
||||
return EvalSummary(
|
||||
run_id=self.cfg.run_id,
|
||||
checkpoint_path=self.cfg.checkpoint_path,
|
||||
num_games=0, wins=0, losses=0,
|
||||
win_rate=0.0, win_rate_ci95_low=0.0, win_rate_ci95_high=0.0,
|
||||
avg_turns=0.0, avg_life_delta=0.0, avg_commander_damage=0.0,
|
||||
avg_illegal_actions=0.0, avg_entropy=0.0, avg_inference_ms=0.0,
|
||||
total_decision_steps=0, elapsed_wall_s=elapsed, games_per_sec=0.0,
|
||||
error_count=0, result_file=self._result_path,
|
||||
)
|
||||
|
||||
wins = sum(1 for r in self._records if r.won)
|
||||
losses = n - wins
|
||||
wr = wins / n
|
||||
|
||||
# Wilson score 95% CI
|
||||
z = 1.96
|
||||
denom = 1 + z**2 / n
|
||||
centre = (wr + z**2 / (2 * n)) / denom
|
||||
margin = (z * (wr * (1 - wr) / n + z**2 / (4 * n**2)) ** 0.5) / denom
|
||||
ci_lo = max(0.0, centre - margin)
|
||||
ci_hi = min(1.0, centre + margin)
|
||||
|
||||
errors = sum(1 for r in self._records if r.error)
|
||||
|
||||
return EvalSummary(
|
||||
run_id=self.cfg.run_id,
|
||||
checkpoint_path=self.cfg.checkpoint_path,
|
||||
num_games=n,
|
||||
wins=wins,
|
||||
losses=losses,
|
||||
win_rate=wr,
|
||||
win_rate_ci95_low=ci_lo,
|
||||
win_rate_ci95_high=ci_hi,
|
||||
avg_turns=float(np.mean([r.turns for r in self._records])),
|
||||
avg_life_delta=float(np.mean([r.life_delta for r in self._records])),
|
||||
avg_commander_damage=float(np.mean([r.commander_damage_dealt for r in self._records])),
|
||||
avg_illegal_actions=float(np.mean([r.illegal_actions for r in self._records])),
|
||||
avg_entropy=float(np.mean([r.entropy_mean for r in self._records])),
|
||||
avg_inference_ms=float(np.mean([r.inference_ms_mean for r in self._records])),
|
||||
total_decision_steps=sum(r.decision_steps for r in self._records),
|
||||
elapsed_wall_s=elapsed,
|
||||
games_per_sec=n / max(elapsed, 0.001),
|
||||
error_count=errors,
|
||||
result_file=self._result_path,
|
||||
)
|
||||
|
||||
# ── Result writer ────────────────────────
|
||||
|
||||
def _write_results(self, summary: EvalSummary) -> None:
|
||||
payload = {
|
||||
"summary": asdict(summary),
|
||||
"games": [asdict(r) for r in self._records],
|
||||
}
|
||||
os.makedirs(self.cfg.results_dir, exist_ok=True)
|
||||
with open(self._result_path, "w", encoding="utf-8") as f:
|
||||
json.dump(payload, f, indent=2)
|
||||
logger.info("[Eval %s] Results written to %s", self.cfg.run_id, self._result_path)
|
||||
@@ -417,7 +417,7 @@ def main():
|
||||
|
||||
# Save results
|
||||
output_path = args.output or os.path.join(ckpt_dir, "tournament_results.json")
|
||||
with open(output_path, "w", encoding="utf-8") as f:
|
||||
with open(output_path, "w") as f:
|
||||
json.dump(result.to_dict(), f, indent=2)
|
||||
logger.info("Results saved: %s", output_path)
|
||||
|
||||
|
||||
@@ -38,7 +38,7 @@ def load_precon_cards(precon_dir: str) -> dict:
|
||||
cards = {"commander": None, "creatures": [], "spells": [], "lands": [], "artifacts": [], "all": []}
|
||||
|
||||
section = "Main"
|
||||
with open(dck_file, "r", encoding="utf-8") as f:
|
||||
with open(dck_file, "r") as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line or line.startswith("//") or line.startswith("Name="):
|
||||
@@ -380,7 +380,7 @@ def main():
|
||||
os.makedirs(os.path.dirname(args.output) or ".", exist_ok=True)
|
||||
total_decisions = 0
|
||||
|
||||
with open(args.output, "w", encoding="utf-8") as f:
|
||||
with open(args.output, "w") as f:
|
||||
for game_idx in range(args.games):
|
||||
# Pick two random decks
|
||||
d1_name, d2_name = random.sample(deck_names, 2)
|
||||
|
||||
@@ -68,7 +68,7 @@ def cmd_status(args):
|
||||
for f in jsonl_files:
|
||||
lines = 0
|
||||
try:
|
||||
with open(f, encoding="utf-8") as fh:
|
||||
with open(f) as fh:
|
||||
lines = sum(1 for _ in fh)
|
||||
except Exception:
|
||||
pass
|
||||
@@ -130,7 +130,7 @@ def cmd_inspect(args):
|
||||
|
||||
print(f"\n Inspecting: {filepath.name}\n")
|
||||
count = 0
|
||||
with open(filepath, encoding="utf-8") as f:
|
||||
with open(filepath) as f:
|
||||
for i, line in enumerate(f):
|
||||
if i >= args.limit:
|
||||
break
|
||||
@@ -150,7 +150,7 @@ def cmd_inspect(args):
|
||||
except Exception as e:
|
||||
print(f" [{i:>4}] ERROR: {e}")
|
||||
|
||||
with open(filepath, encoding="utf-8") as f:
|
||||
with open(filepath) as f:
|
||||
total = sum(1 for _ in f)
|
||||
print(f"\n Showing {count} of {total:,} total decisions.\n")
|
||||
|
||||
|
||||
+13
-48
@@ -52,44 +52,14 @@ from ml.config.scope import (
|
||||
)
|
||||
from ml.encoder.state_encoder import CardEmbeddingIndex, StateEncoder
|
||||
|
||||
# Unique logger name so log lines are attributable to this module specifically
|
||||
logger = logging.getLogger("ml.serving.policy_server")
|
||||
|
||||
|
||||
def _unwrap_logits(raw):
|
||||
"""
|
||||
Normalise whatever self.model() returns to a plain logits tensor.
|
||||
|
||||
PolicyNetwork.forward() returns a raw tensor, but the model may be
|
||||
wrapped (DataParallel, torch.compile) or accidentally swapped for
|
||||
PolicyValueNetwork whose forward_with_value() returns (logits, value).
|
||||
A dict return would cause 'dict / float' TypeError on the temperature
|
||||
division -- guard against all three cases here.
|
||||
"""
|
||||
if isinstance(raw, (tuple, list)):
|
||||
# (logits, value) from PolicyValueNetwork or similar
|
||||
return raw[0]
|
||||
if isinstance(raw, dict):
|
||||
# e.g. {"logits": tensor, "value": tensor}
|
||||
for key in ("logits", "policy", "action_logits"):
|
||||
if key in raw:
|
||||
return raw[key]
|
||||
# Fallback: grab the first tensor value in the dict
|
||||
for v in raw.values():
|
||||
if isinstance(v, torch.Tensor):
|
||||
return v
|
||||
raise TypeError(
|
||||
f"model() returned a dict with no recognisable logits key: {list(raw.keys())}"
|
||||
)
|
||||
# Plain tensor -- the normal case
|
||||
return raw
|
||||
logger = logging.getLogger("ml.serving")
|
||||
|
||||
|
||||
class PolicyInferenceService:
|
||||
"""
|
||||
Loads a trained PolicyNetwork and provides action predictions.
|
||||
|
||||
Thread-safe -- uses torch.no_grad() for inference.
|
||||
Thread-safe — uses torch.no_grad() for inference.
|
||||
Designed to be embedded in lab_api.py or run standalone.
|
||||
"""
|
||||
|
||||
@@ -130,7 +100,7 @@ class PolicyInferenceService:
|
||||
"""
|
||||
if not TORCH_AVAILABLE:
|
||||
self._load_error = "PyTorch not installed"
|
||||
logger.error("PyTorch not installed -- cannot serve policy")
|
||||
logger.error("PyTorch not installed — cannot serve policy")
|
||||
return False
|
||||
|
||||
# 1. Load card embeddings
|
||||
@@ -176,7 +146,7 @@ class PolicyInferenceService:
|
||||
|
||||
except Exception as e:
|
||||
self._load_error = str(e)
|
||||
logger.error("Failed to load checkpoint: %s", e, exc_info=True)
|
||||
logger.error("Failed to load checkpoint: %s", e)
|
||||
return False
|
||||
|
||||
def reload(self, checkpoint_path: str = None) -> bool:
|
||||
@@ -208,7 +178,7 @@ class PolicyInferenceService:
|
||||
|
||||
except Exception as e:
|
||||
self._load_error = str(e)
|
||||
logger.error("Hot-reload failed: %s", e, exc_info=True)
|
||||
logger.error("Hot-reload failed: %s", e)
|
||||
return False
|
||||
|
||||
def predict(
|
||||
@@ -254,10 +224,7 @@ class PolicyInferenceService:
|
||||
|
||||
# Run inference
|
||||
with torch.no_grad():
|
||||
raw_output = self.model(state_tensor)
|
||||
# Guard: unwrap tuple/dict returns (DataParallel, torch.compile,
|
||||
# or accidental PolicyValueNetwork swap) before any arithmetic.
|
||||
logits = _unwrap_logits(raw_output)
|
||||
logits = self.model(state_tensor)
|
||||
|
||||
if greedy:
|
||||
action_idx = logits.argmax(dim=-1).item()
|
||||
@@ -287,7 +254,7 @@ class PolicyInferenceService:
|
||||
|
||||
except Exception as e:
|
||||
elapsed_ms = (time.time() - t_start) * 1000
|
||||
logger.error("Inference error: %s", e, exc_info=True)
|
||||
logger.error("Inference error: %s", e)
|
||||
return {
|
||||
"error": str(e),
|
||||
"inference_ms": round(elapsed_ms, 2),
|
||||
@@ -319,9 +286,7 @@ class PolicyInferenceService:
|
||||
|
||||
# Batch inference
|
||||
with torch.no_grad():
|
||||
raw_output = self.model(state_tensor)
|
||||
# Same guard as predict() -- unwrap before softmax/argmax.
|
||||
logits = _unwrap_logits(raw_output)
|
||||
logits = self.model(state_tensor)
|
||||
probs = torch.softmax(logits, dim=-1).cpu().numpy()
|
||||
|
||||
if greedy:
|
||||
@@ -347,7 +312,7 @@ class PolicyInferenceService:
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Batch inference error: %s", e, exc_info=True)
|
||||
logger.error("Batch inference error: %s", e)
|
||||
results = [{"error": str(e)}] * len(snapshots)
|
||||
|
||||
return results
|
||||
@@ -366,9 +331,9 @@ class PolicyInferenceService:
|
||||
}
|
||||
|
||||
|
||||
# ==========================================================
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# Standalone FastAPI Server
|
||||
# ==========================================================
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
def create_standalone_app():
|
||||
"""Create a standalone FastAPI app for the policy server."""
|
||||
@@ -376,7 +341,7 @@ def create_standalone_app():
|
||||
from pydantic import BaseModel
|
||||
|
||||
app = FastAPI(
|
||||
title="Commander AI Lab -- Policy Server",
|
||||
title="Commander AI Lab — Policy Server",
|
||||
description="Serves macro-action predictions from a trained policy network",
|
||||
version="1.0.0",
|
||||
)
|
||||
@@ -459,7 +424,7 @@ def main():
|
||||
import uvicorn
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Commander AI Lab -- Policy Inference Server"
|
||||
description="Commander AI Lab — Policy Inference Server"
|
||||
)
|
||||
parser.add_argument("--port", type=int, default=8090)
|
||||
parser.add_argument("--host", default="127.0.0.1")
|
||||
|
||||
@@ -1,40 +0,0 @@
|
||||
"""
|
||||
Commander AI Lab - Training Subpackage
|
||||
==========================================
|
||||
Provides RL training infrastructure including PPO, rollout buffers,
|
||||
Forge episode generation, self-play, and distillation.
|
||||
"""
|
||||
|
||||
from ml.training.rollout_buffer import RolloutBuffer
|
||||
from ml.training.reward import compute_intermediate_reward, compute_terminal_reward, RewardConfig
|
||||
from ml.training.decision_exporter import DecisionExporter
|
||||
from ml.training.forge_episode_generator import (
|
||||
ForgeTransition,
|
||||
ForgeEpisode,
|
||||
ForgeEpisodeQueue,
|
||||
forge_producer,
|
||||
ppo_consumer,
|
||||
run_forge_pipeline,
|
||||
parse_forge_jsonl,
|
||||
snapshots_to_episode,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
# Rollout & Buffers
|
||||
"RolloutBuffer",
|
||||
# Rewards
|
||||
"compute_intermediate_reward",
|
||||
"compute_terminal_reward",
|
||||
"RewardConfig",
|
||||
# Decision export
|
||||
"DecisionExporter",
|
||||
# Forge episode generation (Phase 3)
|
||||
"ForgeTransition",
|
||||
"ForgeEpisode",
|
||||
"ForgeEpisodeQueue",
|
||||
"forge_producer",
|
||||
"ppo_consumer",
|
||||
"run_forge_pipeline",
|
||||
"parse_forge_jsonl",
|
||||
"snapshots_to_episode",
|
||||
]
|
||||
@@ -253,7 +253,7 @@ class DecisionExporter:
|
||||
filename = f"ml-decisions-ppo-{self._batch_id}.jsonl"
|
||||
filepath = os.path.join(self.config.output_dir, filename)
|
||||
|
||||
with open(filepath, "w", encoding="utf-8") as f:
|
||||
with open(filepath, "w") as f:
|
||||
for record in self._records:
|
||||
f.write(json.dumps(record) + "\n")
|
||||
|
||||
|
||||
@@ -759,7 +759,7 @@ class DistillationLoop:
|
||||
"""Save generation history to JSON."""
|
||||
path = os.path.join(self.config.history_dir, "generations.json")
|
||||
history = [g.to_dict() for g in self.generations]
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
with open(path, "w") as f:
|
||||
json.dump(history, f, indent=2)
|
||||
logger.info(" Generation history saved: %s", path)
|
||||
|
||||
@@ -793,7 +793,7 @@ class DistillationLoop:
|
||||
def _save_summary(self, summary: Dict):
|
||||
"""Save final summary to JSON."""
|
||||
path = os.path.join(self.config.history_dir, "summary.json")
|
||||
with open(path, "w", encoding="utf-8") as f:
|
||||
with open(path, "w") as f:
|
||||
json.dump(summary, f, indent=2, default=str)
|
||||
logger.info(" Summary saved: %s", path)
|
||||
|
||||
|
||||
@@ -1,495 +0,0 @@
|
||||
"""
|
||||
Commander AI Lab — Forge Episode Generator (Phase 3.2)
|
||||
|
||||
Async producer-consumer pipeline: runs Forge batch games in parallel
|
||||
with PPO training. Forge produces episodes → shared queue → PPO
|
||||
trainer consumes.
|
||||
|
||||
Uses the existing watchdog (_global_watchdog_loop in forge_runner.py)
|
||||
to monitor Forge worker processes for stalls.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from collections import deque
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import Any, Deque, Dict, List, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
|
||||
from ml.config.scope import (
|
||||
ACTION_TO_IDX,
|
||||
GAME_SCOPE,
|
||||
IDX_TO_ACTION,
|
||||
MacroAction,
|
||||
NUM_ACTIONS,
|
||||
STATE_DIMS,
|
||||
)
|
||||
from ml.training.reward import (
|
||||
RewardConfig,
|
||||
compute_intermediate_reward,
|
||||
compute_terminal_reward,
|
||||
)
|
||||
from ml.training.decision_exporter import DecisionExporter, ExporterConfig
|
||||
|
||||
logger = logging.getLogger("ml.forge_episode_generator")
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# Episode / Trajectory data structures
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
@dataclass
|
||||
class ForgeTransition:
|
||||
"""Single (s, a, r, log_prob, value, done) tuple from a Forge game."""
|
||||
state: np.ndarray
|
||||
action: int
|
||||
reward: float
|
||||
log_prob: float
|
||||
value: float
|
||||
done: bool
|
||||
# Raw snapshot retained so the exporter can reconstruct game-state context
|
||||
snapshot: Optional[Dict[str, Any]] = None
|
||||
|
||||
|
||||
@dataclass
|
||||
class ForgeEpisode:
|
||||
"""Complete trajectory from a single Forge game."""
|
||||
game_id: str
|
||||
transitions: List[ForgeTransition] = field(default_factory=list)
|
||||
winner: Optional[int] = None
|
||||
total_turns: int = 0
|
||||
deck_names: List[str] = field(default_factory=list)
|
||||
agent_seat: int = 0
|
||||
playstyle: str = "midrange"
|
||||
|
||||
@property
|
||||
def length(self) -> int:
|
||||
return len(self.transitions)
|
||||
|
||||
@property
|
||||
def total_reward(self) -> float:
|
||||
return sum(t.reward for t in self.transitions)
|
||||
|
||||
@property
|
||||
def won(self) -> bool:
|
||||
return self.winner == self.agent_seat
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# JSONL Parser — reads Forge ML decision snapshots
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
def parse_forge_jsonl(path: Path) -> List[Dict[str, Any]]:
|
||||
"""Parse a JSONL file of Forge ML decision snapshots.
|
||||
|
||||
Each line is a JSON object with keys like:
|
||||
game_id, turn, phase, active_seat, state, action, outcome
|
||||
"""
|
||||
snapshots = []
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
for lineno, line in enumerate(f, 1):
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
snapshots.append(json.loads(line))
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning("Skipping malformed line %d in %s: %s", lineno, path, e)
|
||||
logger.info("Parsed %d snapshots from %s", len(snapshots), path)
|
||||
return snapshots
|
||||
|
||||
|
||||
def _make_minimal_state(snap: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Build a minimal prev/curr state dict from a Forge snapshot.
|
||||
|
||||
compute_intermediate_reward() expects a dict with a 'players' list
|
||||
where each player has 'life_total', 'cards_in_hand',
|
||||
'creatures_on_battlefield', etc. Forge snapshots carry a 'players'
|
||||
array directly; we forward it as-is and fall back to scalar fields
|
||||
when the array is absent.
|
||||
"""
|
||||
if "players" in snap:
|
||||
return {"players": snap["players"]}
|
||||
|
||||
# Fallback: reconstruct a two-player view from flat snapshot fields
|
||||
agent_seat = snap.get("active_seat", 0)
|
||||
opp_seat = 1 - agent_seat
|
||||
players = [None, None]
|
||||
|
||||
def _player(life_key: str, hand_key: str, creatures_key: str) -> Dict:
|
||||
return {
|
||||
"life_total": snap.get(life_key, 40),
|
||||
"cards_in_hand": snap.get(hand_key, 0),
|
||||
"creatures_on_battlefield": snap.get(creatures_key, 0),
|
||||
"cards_in_graveyard": 0,
|
||||
"mana_available": snap.get("mana_available", 0),
|
||||
"commander_damage_taken": 0,
|
||||
"total_power_on_board": 0,
|
||||
}
|
||||
|
||||
players[agent_seat] = _player("agent_life", "agent_hand_size", "agent_creatures")
|
||||
players[opp_seat] = _player("opp_life", "opp_hand_size", "opp_creatures")
|
||||
return {"players": players}
|
||||
|
||||
|
||||
def snapshots_to_episode(
|
||||
snapshots: List[Dict[str, Any]],
|
||||
reward_config: Optional[RewardConfig] = None,
|
||||
agent_seat: int = 0,
|
||||
playstyle: str = "midrange",
|
||||
) -> ForgeEpisode:
|
||||
"""Convert a sequence of Forge snapshots into a ForgeEpisode.
|
||||
|
||||
Maps Forge action labels → MacroAction indices and computes
|
||||
rewards from game outcomes using ml.training.reward.
|
||||
"""
|
||||
if not snapshots:
|
||||
return ForgeEpisode(game_id="empty")
|
||||
|
||||
first = snapshots[0]
|
||||
episode = ForgeEpisode(
|
||||
game_id=first.get("game_id", "unknown"),
|
||||
deck_names=first.get("deck_names", []),
|
||||
agent_seat=agent_seat,
|
||||
playstyle=playstyle,
|
||||
)
|
||||
|
||||
rc = reward_config or RewardConfig()
|
||||
|
||||
for i, snap in enumerate(snapshots):
|
||||
# State vector — zero-padded if not present
|
||||
state_vec = np.zeros(STATE_DIMS.total_state_dim, dtype=np.float32)
|
||||
if "state_vector" in snap:
|
||||
raw = np.array(snap["state_vector"], dtype=np.float32)
|
||||
state_vec[: min(len(raw), len(state_vec))] = raw[: len(state_vec)]
|
||||
|
||||
# Action mapping
|
||||
action_label = snap.get("action", "pass").lower().replace(" ", "_")
|
||||
try:
|
||||
macro = MacroAction(action_label)
|
||||
action_idx = ACTION_TO_IDX[macro]
|
||||
except (ValueError, KeyError):
|
||||
action_idx = ACTION_TO_IDX[MacroAction.PASS]
|
||||
|
||||
# Reward — use correct reward.py signatures
|
||||
is_done = i == len(snapshots) - 1
|
||||
if is_done and "outcome" in snap:
|
||||
outcome = snap["outcome"]
|
||||
reward = compute_terminal_reward(
|
||||
won=outcome.get("won", False),
|
||||
draw=outcome.get("draw", False),
|
||||
timeout=outcome.get("timeout", False),
|
||||
config=rc,
|
||||
)
|
||||
else:
|
||||
# Build minimal prev/curr dicts for intermediate reward
|
||||
prev_snap = snapshots[i - 1] if i > 0 else snap
|
||||
prev_state = _make_minimal_state(prev_snap)
|
||||
curr_state = _make_minimal_state(snap)
|
||||
reward = compute_intermediate_reward(
|
||||
prev_state=prev_state,
|
||||
curr_state=curr_state,
|
||||
action_taken=action_label,
|
||||
agent_seat=agent_seat,
|
||||
config=rc,
|
||||
)
|
||||
|
||||
episode.transitions.append(ForgeTransition(
|
||||
state=state_vec,
|
||||
action=action_idx,
|
||||
reward=reward,
|
||||
log_prob=snap.get("log_prob", 0.0),
|
||||
value=snap.get("value", 0.0),
|
||||
done=is_done,
|
||||
snapshot=snap,
|
||||
))
|
||||
|
||||
episode.total_turns = snapshots[-1].get("turn", len(snapshots))
|
||||
if "outcome" in snapshots[-1]:
|
||||
episode.winner = snapshots[-1]["outcome"].get("winner_seat")
|
||||
|
||||
return episode
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# DecisionExporter adapter for Forge episodes
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
def _export_forge_episode(episode: ForgeEpisode, exporter: DecisionExporter) -> None:
|
||||
"""Feed a completed ForgeEpisode into the DecisionExporter.
|
||||
|
||||
Translates each ForgeTransition back into the game_state_snapshot
|
||||
format expected by DecisionExporter.record_step(), then finalises
|
||||
the episode so the exporter can apply its win/length gates.
|
||||
|
||||
The raw snapshot stored on each ForgeTransition is used directly
|
||||
when available; otherwise a minimal dict is synthesised from the
|
||||
transition's action index and reward so the schema is always valid.
|
||||
"""
|
||||
exporter.begin_episode(
|
||||
agent_seat=episode.agent_seat,
|
||||
playstyle=episode.playstyle,
|
||||
)
|
||||
|
||||
for t in episode.transitions:
|
||||
if t.snapshot is not None:
|
||||
game_state = _make_minimal_state(t.snapshot)
|
||||
# Carry through turn/phase/active_seat if present
|
||||
game_state["turn"] = t.snapshot.get("turn", 1)
|
||||
game_state["phase"] = t.snapshot.get("phase", "main_1")
|
||||
game_state["active_seat"] = t.snapshot.get("active_seat", episode.agent_seat)
|
||||
else:
|
||||
# Minimal fallback — exporter only needs players list for schema
|
||||
game_state = {
|
||||
"turn": 1,
|
||||
"phase": "main_1",
|
||||
"active_seat": episode.agent_seat,
|
||||
"players": [
|
||||
{"life_total": 40, "cards_in_hand": 0, "creatures_on_battlefield": 0,
|
||||
"cards_in_graveyard": 0, "mana_available": 0,
|
||||
"commander_damage_taken": 0, "total_power_on_board": 0, "commander_tax": 0},
|
||||
{"life_total": 40, "cards_in_hand": 0, "creatures_on_battlefield": 0,
|
||||
"cards_in_graveyard": 0, "mana_available": 0,
|
||||
"commander_damage_taken": 0, "total_power_on_board": 0, "commander_tax": 0},
|
||||
],
|
||||
}
|
||||
|
||||
exporter.record_step(
|
||||
game_state_snapshot=game_state,
|
||||
action_idx=t.action,
|
||||
reward=t.reward,
|
||||
)
|
||||
|
||||
exporter.end_episode(
|
||||
won=episode.won,
|
||||
episode_return=episode.total_reward,
|
||||
)
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# Async Episode Queue (producer-consumer)
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
class ForgeEpisodeQueue:
|
||||
"""Thread-safe async queue bridging Forge producers and PPO consumer.
|
||||
|
||||
Forge batch workers produce ForgeEpisodes; the PPO trainer consumes
|
||||
them as they become available. Backpressure is applied when the
|
||||
queue reaches max_size.
|
||||
"""
|
||||
|
||||
def __init__(self, max_size: int = 256):
|
||||
self._queue: asyncio.Queue[ForgeEpisode] = asyncio.Queue(maxsize=max_size)
|
||||
self._produced = 0
|
||||
self._consumed = 0
|
||||
self._closed = False
|
||||
|
||||
async def put(self, episode: ForgeEpisode) -> None:
|
||||
await self._queue.put(episode)
|
||||
self._produced += 1
|
||||
|
||||
async def get(self, timeout: float = 30.0) -> Optional[ForgeEpisode]:
|
||||
try:
|
||||
ep = await asyncio.wait_for(self._queue.get(), timeout=timeout)
|
||||
self._consumed += 1
|
||||
return ep
|
||||
except asyncio.TimeoutError:
|
||||
return None
|
||||
|
||||
def close(self) -> None:
|
||||
self._closed = True
|
||||
|
||||
@property
|
||||
def is_closed(self) -> bool:
|
||||
return self._closed and self._queue.empty()
|
||||
|
||||
@property
|
||||
def stats(self) -> Dict[str, int]:
|
||||
return {
|
||||
"produced": self._produced,
|
||||
"consumed": self._consumed,
|
||||
"pending": self._queue.qsize(),
|
||||
}
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# Forge Batch Producer
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
async def forge_producer(
|
||||
queue: ForgeEpisodeQueue,
|
||||
results_dir: str = "results",
|
||||
batch_id: str = "forge-batch",
|
||||
num_games: int = 64,
|
||||
poll_interval: float = 2.0,
|
||||
reward_config: Optional[RewardConfig] = None,
|
||||
) -> int:
|
||||
"""Producer coroutine: watches for Forge JSONL output files,
|
||||
parses them into episodes, and pushes them onto the queue.
|
||||
|
||||
Returns total number of episodes produced.
|
||||
"""
|
||||
results_path = Path(results_dir)
|
||||
pattern = f"ml-decisions-forge-{batch_id}*.jsonl"
|
||||
seen_files: set = set()
|
||||
total_produced = 0
|
||||
|
||||
logger.info("[PRODUCER] Watching %s for pattern %s", results_path, pattern)
|
||||
|
||||
while total_produced < num_games and not queue.is_closed:
|
||||
for jsonl_file in sorted(results_path.glob(pattern)):
|
||||
if jsonl_file in seen_files:
|
||||
continue
|
||||
seen_files.add(jsonl_file)
|
||||
|
||||
try:
|
||||
snapshots = parse_forge_jsonl(jsonl_file)
|
||||
if not snapshots:
|
||||
continue
|
||||
|
||||
# Group by game_id
|
||||
games: Dict[str, List] = {}
|
||||
for snap in snapshots:
|
||||
gid = snap.get("game_id", "unknown")
|
||||
games.setdefault(gid, []).append(snap)
|
||||
|
||||
for gid, game_snaps in games.items():
|
||||
episode = snapshots_to_episode(game_snaps, reward_config)
|
||||
if episode.length > 0:
|
||||
await queue.put(episode)
|
||||
total_produced += 1
|
||||
logger.debug("[PRODUCER] Queued episode %s (%d transitions)",
|
||||
gid, episode.length)
|
||||
|
||||
except Exception as e:
|
||||
logger.error("[PRODUCER] Error processing %s: %s", jsonl_file, e)
|
||||
|
||||
await asyncio.sleep(poll_interval)
|
||||
|
||||
logger.info("[PRODUCER] Done — produced %d episodes", total_produced)
|
||||
return total_produced
|
||||
|
||||
|
||||
async def ppo_consumer(
|
||||
queue: ForgeEpisodeQueue,
|
||||
buffer_size: int = 64,
|
||||
on_batch_ready=None,
|
||||
exporter: Optional[DecisionExporter] = None,
|
||||
) -> int:
|
||||
"""Consumer coroutine: pulls episodes from the queue and batches
|
||||
them for PPO training updates.
|
||||
|
||||
Args:
|
||||
queue: ForgeEpisodeQueue to consume from
|
||||
buffer_size: number of episodes per training batch
|
||||
on_batch_ready: callback(episodes) when a batch is full
|
||||
exporter: Optional DecisionExporter — when provided, every
|
||||
consumed episode is fed into the exporter so winning
|
||||
Forge games accumulate in the distillation JSONL pool.
|
||||
Call exporter.flush() after the pipeline completes to
|
||||
write the JSONL file.
|
||||
|
||||
Returns total episodes consumed.
|
||||
"""
|
||||
batch: List[ForgeEpisode] = []
|
||||
total_consumed = 0
|
||||
|
||||
while not queue.is_closed:
|
||||
episode = await queue.get(timeout=10.0)
|
||||
if episode is None:
|
||||
if queue.is_closed:
|
||||
break
|
||||
continue
|
||||
|
||||
# Feed into distillation exporter (win-gate applied inside exporter)
|
||||
if exporter is not None:
|
||||
try:
|
||||
_export_forge_episode(episode, exporter)
|
||||
except Exception as exc:
|
||||
logger.warning("[CONSUMER] Exporter error for episode %s: %s",
|
||||
episode.game_id, exc)
|
||||
|
||||
batch.append(episode)
|
||||
total_consumed += 1
|
||||
|
||||
if len(batch) >= buffer_size:
|
||||
if on_batch_ready:
|
||||
on_batch_ready(batch)
|
||||
logger.info("[CONSUMER] Batch of %d episodes ready for PPO update", len(batch))
|
||||
batch = []
|
||||
|
||||
# Flush remaining
|
||||
if batch and on_batch_ready:
|
||||
on_batch_ready(batch)
|
||||
logger.info("[CONSUMER] Final batch of %d episodes flushed", len(batch))
|
||||
|
||||
logger.info("[CONSUMER] Done — consumed %d episodes", total_consumed)
|
||||
return total_consumed
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# Convenience runner
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
async def run_forge_pipeline(
|
||||
results_dir: str = "results",
|
||||
batch_id: str = "forge-batch",
|
||||
num_games: int = 64,
|
||||
buffer_size: int = 64,
|
||||
max_queue: int = 256,
|
||||
on_batch_ready=None,
|
||||
reward_config: Optional[RewardConfig] = None,
|
||||
exporter: Optional[DecisionExporter] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""Run the full Forge producer → PPO consumer pipeline.
|
||||
|
||||
The Forge Java batch must be started separately (via forge_runner.py
|
||||
or MultiThreadBatchRunner). This function handles the Python side:
|
||||
reading JSONL output and feeding episodes into the PPO trainer.
|
||||
|
||||
If an exporter is provided, winning Forge episodes are recorded and
|
||||
flushed to a PPO JSONL file at the end of the pipeline run. The
|
||||
output path is returned in the stats dict under "exporter_file".
|
||||
|
||||
Example::
|
||||
|
||||
from ml.training.decision_exporter import DecisionExporter, ExporterConfig
|
||||
exporter = DecisionExporter(ExporterConfig(model_version="gen-1"))
|
||||
stats = await run_forge_pipeline(num_games=128, exporter=exporter)
|
||||
print(stats["exporter_file"]) # data/results/ml-decisions-ppo-<id>.jsonl
|
||||
"""
|
||||
queue = ForgeEpisodeQueue(max_size=max_queue)
|
||||
|
||||
producer_task = asyncio.create_task(
|
||||
forge_producer(queue, results_dir, batch_id, num_games,
|
||||
reward_config=reward_config)
|
||||
)
|
||||
consumer_task = asyncio.create_task(
|
||||
ppo_consumer(queue, buffer_size, on_batch_ready, exporter=exporter)
|
||||
)
|
||||
|
||||
produced = await producer_task
|
||||
queue.close()
|
||||
consumed = await consumer_task
|
||||
|
||||
# Flush exporter to disk and capture output path
|
||||
exporter_file = None
|
||||
if exporter is not None:
|
||||
exporter_file = exporter.flush()
|
||||
if exporter_file:
|
||||
logger.info("[PIPELINE] Distillation export: %s stats=%s",
|
||||
exporter_file, exporter.stats)
|
||||
|
||||
stats: Dict[str, Any] = {
|
||||
"produced": produced,
|
||||
"consumed": consumed,
|
||||
"exporter_file": exporter_file,
|
||||
**queue.stats,
|
||||
}
|
||||
logger.info("[PIPELINE] Complete: %s", stats)
|
||||
return stats
|
||||
@@ -223,8 +223,7 @@ def load_checkpoint(path: str, device: str = "cpu"):
|
||||
num_layers=config.get("num_layers", TRAINING_CONFIG.num_layers),
|
||||
dropout=config.get("dropout", TRAINING_CONFIG.dropout),
|
||||
)
|
||||
state = {k: v for k, v in checkpoint["model_state_dict"].items() if not k.startswith("value_head")}
|
||||
model.load_state_dict(state, strict=False)
|
||||
model.load_state_dict(checkpoint["model_state_dict"])
|
||||
model.to(device)
|
||||
|
||||
return model, checkpoint
|
||||
+133
-340
@@ -9,27 +9,22 @@ Proximal Policy Optimization (Clip) with:
|
||||
- Self-play episode collection between updates
|
||||
- Periodic checkpointing and evaluation
|
||||
- Win-rate tracking against opponent policies
|
||||
- Forge-based episode source via --data-source forge|mixed
|
||||
|
||||
Reference: Schulman et al., "Proximal Policy Optimization Algorithms", 2017
|
||||
|
||||
Usage:
|
||||
python -m ml.training.ppo_trainer --iterations 100 --episodes-per-iter 64
|
||||
python -m ml.training.ppo_trainer --load-supervised ml/models/checkpoints/best_policy.pt
|
||||
python -m ml.training.ppo_trainer --data-source forge --forge-results-dir results --iterations 50
|
||||
python -m ml.training.ppo_trainer --data-source mixed --forge-weight 0.7 --iterations 100
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional
|
||||
from typing import Dict, Optional
|
||||
|
||||
import numpy as np
|
||||
|
||||
@@ -56,114 +51,6 @@ from ml.training.self_play import (
|
||||
logger = logging.getLogger("ml.ppo")
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# Forge data-source integration
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
def episodes_to_buffer(episodes, buffer: RolloutBuffer) -> int:
|
||||
"""Drain a list of ForgeEpisodes into an existing RolloutBuffer.
|
||||
|
||||
Returns the number of transitions added.
|
||||
"""
|
||||
added = 0
|
||||
for ep in episodes:
|
||||
for t in ep.transitions:
|
||||
if buffer.size >= buffer.buffer_size:
|
||||
break
|
||||
buffer.add(
|
||||
state=t.state,
|
||||
action=t.action,
|
||||
reward=t.reward,
|
||||
log_prob=t.log_prob,
|
||||
value=t.value,
|
||||
done=t.done,
|
||||
)
|
||||
added += 1
|
||||
return added
|
||||
|
||||
|
||||
class ForgeDataSource:
|
||||
"""Runs the Forge producer pipeline in a background thread and
|
||||
exposes a synchronous drain() method for the PPO training loop.
|
||||
|
||||
Usage::
|
||||
source = ForgeDataSource(results_dir="results", batch_id="run1", num_games=64)
|
||||
source.start()
|
||||
# ... in training loop ...
|
||||
episodes = source.drain()
|
||||
source.stop()
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
results_dir: str = "results",
|
||||
batch_id: str = "forge-batch",
|
||||
num_games: int = 256,
|
||||
reward_config: Optional[RewardConfig] = None,
|
||||
queue_size: int = 256,
|
||||
):
|
||||
self.results_dir = results_dir
|
||||
self.batch_id = batch_id
|
||||
self.num_games = num_games
|
||||
self.reward_config = reward_config or RewardConfig()
|
||||
self.queue_size = queue_size
|
||||
self._episodes: List = []
|
||||
self._lock = threading.Lock()
|
||||
self._thread: Optional[threading.Thread] = None
|
||||
self._stop_event = threading.Event()
|
||||
|
||||
def start(self):
|
||||
"""Launch the background producer thread."""
|
||||
self._thread = threading.Thread(target=self._run, daemon=True)
|
||||
self._thread.start()
|
||||
logger.info("[ForgeDataSource] Producer started (results_dir=%s, batch_id=%s, num_games=%d)",
|
||||
self.results_dir, self.batch_id, self.num_games)
|
||||
|
||||
def _run(self):
|
||||
from ml.training.forge_episode_generator import ForgeEpisodeQueue, forge_producer
|
||||
|
||||
async def _produce():
|
||||
queue = ForgeEpisodeQueue(max_size=self.queue_size)
|
||||
produced = await forge_producer(
|
||||
queue=queue,
|
||||
results_dir=self.results_dir,
|
||||
batch_id=self.batch_id,
|
||||
num_games=self.num_games,
|
||||
reward_config=self.reward_config,
|
||||
)
|
||||
queue.close()
|
||||
# Drain remaining items
|
||||
while not queue.is_closed:
|
||||
ep = await queue.get(timeout=1.0)
|
||||
if ep is not None:
|
||||
with self._lock:
|
||||
self._episodes.append(ep)
|
||||
logger.info("[ForgeDataSource] Producer finished: %d episodes", produced)
|
||||
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
try:
|
||||
loop.run_until_complete(_produce())
|
||||
finally:
|
||||
loop.close()
|
||||
|
||||
def drain(self) -> List:
|
||||
"""Return and clear all buffered episodes (thread-safe)."""
|
||||
with self._lock:
|
||||
eps = list(self._episodes)
|
||||
self._episodes.clear()
|
||||
return eps
|
||||
|
||||
def stop(self):
|
||||
self._stop_event.set()
|
||||
if self._thread:
|
||||
self._thread.join(timeout=5.0)
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# PPO Config
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
class PPOConfig:
|
||||
"""PPO hyperparameters."""
|
||||
|
||||
@@ -204,13 +91,6 @@ class PPOConfig:
|
||||
|
||||
# Supervised preload
|
||||
load_supervised: Optional[str] = None,
|
||||
|
||||
# Data source
|
||||
data_source: str = "synthetic", # synthetic | forge | mixed
|
||||
forge_results_dir: str = "results",
|
||||
forge_batch_id: str = "forge-batch",
|
||||
forge_num_games: int = 256,
|
||||
forge_weight: float = 0.7, # fraction of buffer from Forge when mixed
|
||||
):
|
||||
self.iterations = iterations
|
||||
self.episodes_per_iter = episodes_per_iter
|
||||
@@ -234,19 +114,10 @@ class PPOConfig:
|
||||
self.eval_every = eval_every
|
||||
self.eval_episodes = eval_episodes
|
||||
self.load_supervised = load_supervised
|
||||
self.data_source = data_source
|
||||
self.forge_results_dir = forge_results_dir
|
||||
self.forge_batch_id = forge_batch_id
|
||||
self.forge_num_games = forge_num_games
|
||||
self.forge_weight = max(0.0, min(1.0, forge_weight))
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════
|
||||
# PPO Trainer
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
class PPOTrainer:
|
||||
"""Proximal Policy Optimization trainer with self-play and optional Forge data source."""
|
||||
"""Proximal Policy Optimization trainer with self-play."""
|
||||
|
||||
def __init__(self, config: PPOConfig):
|
||||
if not TORCH_AVAILABLE:
|
||||
@@ -255,24 +126,28 @@ class PPOTrainer:
|
||||
self.config = config
|
||||
self.device = self._detect_device()
|
||||
|
||||
# Create actor-critic model
|
||||
from ml.training.policy_network import PolicyValueNetwork
|
||||
self.model = PolicyValueNetwork(
|
||||
input_dim=STATE_DIMS.total_state_dim,
|
||||
hidden_dim=TRAINING_CONFIG.hidden_dim,
|
||||
num_actions=NUM_ACTIONS,
|
||||
num_layers=TRAINING_CONFIG.num_layers,
|
||||
dropout=0.0,
|
||||
dropout=0.0, # No dropout during RL
|
||||
).to(self.device)
|
||||
|
||||
# Optionally load from supervised checkpoint
|
||||
if config.load_supervised and os.path.exists(config.load_supervised):
|
||||
self._load_supervised_weights(config.load_supervised)
|
||||
|
||||
# Optimizer
|
||||
self.optimizer = torch.optim.Adam(
|
||||
self.model.parameters(),
|
||||
lr=config.learning_rate,
|
||||
eps=1e-5,
|
||||
)
|
||||
|
||||
# Learning rate scheduler
|
||||
self.scheduler = None
|
||||
if config.lr_schedule == "linear":
|
||||
self.scheduler = torch.optim.lr_scheduler.LinearLR(
|
||||
@@ -288,6 +163,7 @@ class PPOTrainer:
|
||||
eta_min=config.min_lr,
|
||||
)
|
||||
|
||||
# Rollout buffer
|
||||
self.buffer = RolloutBuffer(
|
||||
buffer_size=config.buffer_size,
|
||||
state_dim=STATE_DIMS.total_state_dim,
|
||||
@@ -295,23 +171,16 @@ class PPOTrainer:
|
||||
gae_lambda=config.gae_lambda,
|
||||
)
|
||||
|
||||
# Reward config
|
||||
self.reward_config = RewardConfig(
|
||||
gamma=config.gamma,
|
||||
gae_lambda=config.gae_lambda,
|
||||
)
|
||||
|
||||
# Opponent policy
|
||||
self.opponent = self._create_opponent(config.opponent)
|
||||
|
||||
# Forge data source (started lazily in train())
|
||||
self._forge_source: Optional[ForgeDataSource] = None
|
||||
if config.data_source in ("forge", "mixed"):
|
||||
self._forge_source = ForgeDataSource(
|
||||
results_dir=config.forge_results_dir,
|
||||
batch_id=config.forge_batch_id,
|
||||
num_games=config.forge_num_games,
|
||||
reward_config=self.reward_config,
|
||||
)
|
||||
|
||||
# Tracking
|
||||
self.iteration = 0
|
||||
self.total_steps = 0
|
||||
self.best_win_rate = 0.0
|
||||
@@ -327,9 +196,13 @@ class PPOTrainer:
|
||||
return "cpu"
|
||||
|
||||
def _load_supervised_weights(self, path: str):
|
||||
"""Load weights from a supervised PolicyNetwork checkpoint into the
|
||||
PolicyValueNetwork (ignoring the value head, which is new)."""
|
||||
logger.info("Loading supervised weights from %s", path)
|
||||
checkpoint = torch.load(path, map_location=self.device, weights_only=False)
|
||||
state_dict = checkpoint.get("model_state_dict", checkpoint)
|
||||
|
||||
# Filter out keys that don't match (e.g., value_head)
|
||||
model_dict = self.model.state_dict()
|
||||
compatible = {k: v for k, v in state_dict.items() if k in model_dict and v.shape == model_dict[k].shape}
|
||||
model_dict.update(compatible)
|
||||
@@ -343,10 +216,14 @@ class PPOTrainer:
|
||||
elif opponent_type == "heuristic":
|
||||
return HeuristicPolicy()
|
||||
elif opponent_type == "self":
|
||||
# Self-play: opponent uses a snapshot of the current policy
|
||||
return self._create_self_play_opponent()
|
||||
return HeuristicPolicy()
|
||||
else:
|
||||
return HeuristicPolicy()
|
||||
|
||||
def _create_self_play_opponent(self):
|
||||
"""Create an opponent that uses a frozen copy of the current policy."""
|
||||
|
||||
class SelfPlayPolicy:
|
||||
def __init__(self, model, device):
|
||||
self.model = model
|
||||
@@ -364,99 +241,11 @@ class PPOTrainer:
|
||||
|
||||
return SelfPlayPolicy(self.model, self.device)
|
||||
|
||||
def _collect_forge_rollouts(self) -> Dict:
|
||||
"""Drain buffered Forge episodes into the RolloutBuffer.
|
||||
|
||||
Returns stats dict compatible with collect_rollouts() output.
|
||||
"""
|
||||
episodes = self._forge_source.drain()
|
||||
if not episodes:
|
||||
logger.warning("[Forge] No episodes available yet — waiting for JSONL output in %s",
|
||||
self.config.forge_results_dir)
|
||||
return {"steps": 0, "win_rate": 0.0, "avg_reward_per_ep": 0.0,
|
||||
"wins": 0, "losses": 0, "draws": 0, "source": "forge"}
|
||||
|
||||
added = episodes_to_buffer(episodes, self.buffer)
|
||||
wins = sum(1 for ep in episodes if ep.winner == 0)
|
||||
win_rate = wins / max(len(episodes), 1)
|
||||
avg_reward = sum(ep.total_reward for ep in episodes) / max(len(episodes), 1)
|
||||
logger.info("[Forge] Drained %d episodes (%d transitions) into buffer — WR=%.0f%% AvgR=%.3f",
|
||||
len(episodes), added, win_rate * 100, avg_reward)
|
||||
return {
|
||||
"steps": added,
|
||||
"win_rate": win_rate,
|
||||
"avg_reward_per_ep": avg_reward,
|
||||
"wins": wins,
|
||||
"losses": len(episodes) - wins,
|
||||
"draws": 0,
|
||||
"source": "forge",
|
||||
}
|
||||
|
||||
def _collect_rollouts_for_iter(self) -> Dict:
|
||||
"""Collect rollouts according to configured data_source.
|
||||
|
||||
- synthetic: pure self_play.collect_rollouts (original behaviour)
|
||||
- forge: pure Forge episode drain
|
||||
- mixed: Forge fills forge_weight fraction of buffer, synthetic fills remainder
|
||||
"""
|
||||
cfg = self.config
|
||||
self.buffer.reset()
|
||||
self.model.eval()
|
||||
|
||||
if cfg.data_source == "forge":
|
||||
stats = self._collect_forge_rollouts()
|
||||
|
||||
elif cfg.data_source == "mixed":
|
||||
forge_cap = int(cfg.buffer_size * cfg.forge_weight)
|
||||
synth_cap = cfg.buffer_size - forge_cap
|
||||
|
||||
# Temporarily shrink buffer for Forge slice
|
||||
original_size = self.buffer.buffer_size
|
||||
self.buffer.buffer_size = forge_cap
|
||||
forge_stats = self._collect_forge_rollouts()
|
||||
self.buffer.buffer_size = original_size
|
||||
|
||||
synth_eps = max(1, int(cfg.episodes_per_iter * (1.0 - cfg.forge_weight)))
|
||||
synth_stats = collect_rollouts(
|
||||
agent_model=self.model,
|
||||
opponent_policy=self.opponent,
|
||||
buffer=self.buffer,
|
||||
num_episodes=synth_eps,
|
||||
playstyle=cfg.playstyle,
|
||||
reward_config=self.reward_config,
|
||||
)
|
||||
stats = {
|
||||
"steps": forge_stats["steps"] + synth_stats["steps"],
|
||||
"win_rate": (
|
||||
forge_stats["win_rate"] * cfg.forge_weight
|
||||
+ synth_stats["win_rate"] * (1.0 - cfg.forge_weight)
|
||||
),
|
||||
"avg_reward_per_ep": (
|
||||
forge_stats["avg_reward_per_ep"] * cfg.forge_weight
|
||||
+ synth_stats["avg_reward_per_ep"] * (1.0 - cfg.forge_weight)
|
||||
),
|
||||
"wins": forge_stats["wins"] + synth_stats["wins"],
|
||||
"losses": forge_stats["losses"] + synth_stats["losses"],
|
||||
"draws": synth_stats.get("draws", 0),
|
||||
"source": "mixed",
|
||||
}
|
||||
|
||||
else: # synthetic (default — unchanged behaviour)
|
||||
stats = collect_rollouts(
|
||||
agent_model=self.model,
|
||||
opponent_policy=self.opponent,
|
||||
buffer=self.buffer,
|
||||
num_episodes=cfg.episodes_per_iter,
|
||||
playstyle=cfg.playstyle,
|
||||
reward_config=self.reward_config,
|
||||
)
|
||||
stats["source"] = "synthetic"
|
||||
|
||||
self.model.train()
|
||||
return stats
|
||||
|
||||
def ppo_update(self) -> Dict:
|
||||
"""Run PPO update epochs on the collected rollout buffer."""
|
||||
"""Run PPO update epochs on the collected rollout buffer.
|
||||
|
||||
Returns dict of loss metrics.
|
||||
"""
|
||||
cfg = self.config
|
||||
total_policy_loss = 0.0
|
||||
total_value_loss = 0.0
|
||||
@@ -469,42 +258,56 @@ class PPOTrainer:
|
||||
for batch in self.buffer.get_batches(cfg.batch_size):
|
||||
states, actions, old_log_probs, advantages, returns = batch
|
||||
|
||||
# To tensors
|
||||
states_t = torch.from_numpy(states).to(self.device)
|
||||
actions_t = torch.from_numpy(actions).to(self.device)
|
||||
old_log_probs_t = torch.from_numpy(old_log_probs).to(self.device)
|
||||
advantages_t = torch.from_numpy(advantages).to(self.device)
|
||||
returns_t = torch.from_numpy(returns).to(self.device)
|
||||
|
||||
# Forward pass
|
||||
logits, values = self.model.forward_with_value(states_t)
|
||||
values = values.squeeze(-1)
|
||||
|
||||
# Action distribution
|
||||
probs = F.softmax(logits, dim=-1)
|
||||
dist = torch.distributions.Categorical(probs)
|
||||
new_log_probs = dist.log_prob(actions_t)
|
||||
entropy = dist.entropy().mean()
|
||||
|
||||
# --- Policy Loss (clipped surrogate) ---
|
||||
ratio = torch.exp(new_log_probs - old_log_probs_t)
|
||||
surr1 = ratio * advantages_t
|
||||
surr2 = torch.clamp(ratio, 1.0 - cfg.clip_epsilon, 1.0 + cfg.clip_epsilon) * advantages_t
|
||||
policy_loss = -torch.min(surr1, surr2).mean()
|
||||
|
||||
# --- Value Loss (clipped) ---
|
||||
if cfg.value_clip > 0:
|
||||
# Get old values from buffer for clipping
|
||||
old_values_t = torch.from_numpy(
|
||||
self.buffer.values[:self.buffer.size]
|
||||
).to(self.device)
|
||||
# Reindex to match batch
|
||||
# Since we use shuffled indices in get_batches, we clip against returns
|
||||
value_loss_unclipped = (values - returns_t) ** 2
|
||||
value_loss = 0.5 * value_loss_unclipped.mean()
|
||||
else:
|
||||
value_loss = 0.5 * ((values - returns_t) ** 2).mean()
|
||||
|
||||
# --- Total Loss ---
|
||||
loss = (
|
||||
policy_loss
|
||||
+ cfg.value_loss_coeff * value_loss
|
||||
- cfg.entropy_coeff * entropy
|
||||
)
|
||||
|
||||
# Optimize
|
||||
self.optimizer.zero_grad()
|
||||
loss.backward()
|
||||
nn.utils.clip_grad_norm_(self.model.parameters(), cfg.max_grad_norm)
|
||||
self.optimizer.step()
|
||||
|
||||
# Track metrics
|
||||
with torch.no_grad():
|
||||
approx_kl = ((ratio - 1) - torch.log(ratio)).mean().item()
|
||||
clip_frac = ((ratio - 1.0).abs() > cfg.clip_epsilon).float().mean().item()
|
||||
@@ -527,8 +330,12 @@ class PPOTrainer:
|
||||
}
|
||||
|
||||
def evaluate(self, num_episodes: int = 50) -> Dict:
|
||||
"""Evaluate the current policy against heuristic and random opponents."""
|
||||
"""Evaluate the current policy against opponents.
|
||||
|
||||
Returns win rates and metrics.
|
||||
"""
|
||||
self.model.eval()
|
||||
|
||||
results = {}
|
||||
for opp_name, opp_policy in [
|
||||
("heuristic", HeuristicPolicy()),
|
||||
@@ -556,15 +363,18 @@ class PPOTrainer:
|
||||
"avg_reward": stats["avg_reward_per_ep"],
|
||||
"avg_steps": stats["avg_steps_per_ep"],
|
||||
}
|
||||
|
||||
self.model.train()
|
||||
return results
|
||||
|
||||
def save_checkpoint(self, path: str = None, metrics: dict = None):
|
||||
"""Save PPO checkpoint."""
|
||||
if path is None:
|
||||
path = os.path.join(
|
||||
self.config.checkpoint_dir,
|
||||
f"ppo_iter_{self.iteration:04d}.pt",
|
||||
)
|
||||
|
||||
os.makedirs(os.path.dirname(path), exist_ok=True)
|
||||
torch.save({
|
||||
"iteration": self.iteration,
|
||||
@@ -578,7 +388,6 @@ class PPOTrainer:
|
||||
"learning_rate": self.config.learning_rate,
|
||||
"opponent": self.config.opponent,
|
||||
"playstyle": self.config.playstyle,
|
||||
"data_source": self.config.data_source,
|
||||
},
|
||||
"best_win_rate": self.best_win_rate,
|
||||
"model_config": {
|
||||
@@ -592,7 +401,14 @@ class PPOTrainer:
|
||||
logger.info("Checkpoint saved: %s", path)
|
||||
|
||||
def train(self, progress_callback=None) -> Dict:
|
||||
"""Run the full PPO training loop."""
|
||||
"""Run the full PPO training loop.
|
||||
|
||||
Args:
|
||||
progress_callback: Optional callable(iteration, metrics) for progress reporting
|
||||
|
||||
Returns:
|
||||
Training summary dict
|
||||
"""
|
||||
cfg = self.config
|
||||
logger.info("=" * 60)
|
||||
logger.info("PPO Training — Commander AI Lab")
|
||||
@@ -607,96 +423,100 @@ class PPOTrainer:
|
||||
logger.info(" Opponent: %s", cfg.opponent)
|
||||
logger.info(" Device: %s", self.device)
|
||||
logger.info(" Supervised: %s", cfg.load_supervised or "None")
|
||||
logger.info(" Data source: %s", cfg.data_source)
|
||||
if cfg.data_source in ("forge", "mixed"):
|
||||
logger.info(" Forge results: %s / %s", cfg.forge_results_dir, cfg.forge_batch_id)
|
||||
logger.info(" Forge weight: %.0f%%", cfg.forge_weight * 100)
|
||||
logger.info("")
|
||||
|
||||
# Start Forge producer background thread if needed
|
||||
if self._forge_source:
|
||||
self._forge_source.start()
|
||||
|
||||
t_start = time.time()
|
||||
|
||||
try:
|
||||
for iteration in range(1, cfg.iterations + 1):
|
||||
self.iteration = iteration
|
||||
t_iter = time.time()
|
||||
for iteration in range(1, cfg.iterations + 1):
|
||||
self.iteration = iteration
|
||||
t_iter = time.time()
|
||||
|
||||
rollout_stats = self._collect_rollouts_for_iter()
|
||||
self.total_steps += rollout_stats["steps"]
|
||||
# --- Collect rollouts ---
|
||||
self.model.eval()
|
||||
rollout_stats = collect_rollouts(
|
||||
agent_model=self.model,
|
||||
opponent_policy=self.opponent,
|
||||
buffer=self.buffer,
|
||||
num_episodes=cfg.episodes_per_iter,
|
||||
playstyle=cfg.playstyle,
|
||||
reward_config=self.reward_config,
|
||||
)
|
||||
self.total_steps += rollout_stats["steps"]
|
||||
self.model.train()
|
||||
|
||||
update_metrics = self.ppo_update()
|
||||
# --- PPO Update ---
|
||||
update_metrics = self.ppo_update()
|
||||
|
||||
current_lr = self.optimizer.param_groups[0]["lr"]
|
||||
if self.scheduler:
|
||||
self.scheduler.step()
|
||||
# --- LR Schedule ---
|
||||
current_lr = self.optimizer.param_groups[0]["lr"]
|
||||
if self.scheduler:
|
||||
self.scheduler.step()
|
||||
|
||||
eval_results = None
|
||||
if iteration % cfg.eval_every == 0 or iteration == 1:
|
||||
eval_results = self.evaluate(cfg.eval_episodes)
|
||||
heuristic_wr = eval_results.get("heuristic", {}).get("win_rate", 0)
|
||||
if heuristic_wr > self.best_win_rate:
|
||||
self.best_win_rate = heuristic_wr
|
||||
best_path = os.path.join(cfg.checkpoint_dir, "best_ppo.pt")
|
||||
self.save_checkpoint(best_path, {"eval": eval_results})
|
||||
logger.info(" ★ New best win rate: %.1f%%", heuristic_wr * 100)
|
||||
# --- Evaluate ---
|
||||
eval_results = None
|
||||
if iteration % cfg.eval_every == 0 or iteration == 1:
|
||||
eval_results = self.evaluate(cfg.eval_episodes)
|
||||
heuristic_wr = eval_results.get("heuristic", {}).get("win_rate", 0)
|
||||
|
||||
if iteration % cfg.save_every == 0:
|
||||
self.save_checkpoint()
|
||||
if heuristic_wr > self.best_win_rate:
|
||||
self.best_win_rate = heuristic_wr
|
||||
best_path = os.path.join(cfg.checkpoint_dir, "best_ppo.pt")
|
||||
self.save_checkpoint(best_path, {"eval": eval_results})
|
||||
logger.info(" ★ New best win rate: %.1f%%", heuristic_wr * 100)
|
||||
|
||||
iter_time = time.time() - t_iter
|
||||
metrics = {
|
||||
"iteration": iteration,
|
||||
"steps": rollout_stats["steps"],
|
||||
"total_steps": self.total_steps,
|
||||
"win_rate": rollout_stats["win_rate"],
|
||||
"avg_reward": rollout_stats["avg_reward_per_ep"],
|
||||
"policy_loss": update_metrics["policy_loss"],
|
||||
"value_loss": update_metrics["value_loss"],
|
||||
"entropy": update_metrics["entropy"],
|
||||
"approx_kl": update_metrics["approx_kl"],
|
||||
"clip_fraction": update_metrics["clip_fraction"],
|
||||
"lr": current_lr,
|
||||
"iter_time_s": round(iter_time, 1),
|
||||
"data_source": rollout_stats.get("source", cfg.data_source),
|
||||
"eval": eval_results,
|
||||
}
|
||||
self.history.append(metrics)
|
||||
# --- Save periodic checkpoint ---
|
||||
if iteration % cfg.save_every == 0:
|
||||
self.save_checkpoint()
|
||||
|
||||
eval_str = ""
|
||||
if eval_results:
|
||||
h_wr = eval_results.get("heuristic", {}).get("win_rate", 0)
|
||||
r_wr = eval_results.get("random", {}).get("win_rate", 0)
|
||||
eval_str = f" | eval: h={h_wr:.0%} r={r_wr:.0%}"
|
||||
# --- Log ---
|
||||
iter_time = time.time() - t_iter
|
||||
metrics = {
|
||||
"iteration": iteration,
|
||||
"steps": rollout_stats["steps"],
|
||||
"total_steps": self.total_steps,
|
||||
"win_rate": rollout_stats["win_rate"],
|
||||
"avg_reward": rollout_stats["avg_reward_per_ep"],
|
||||
"policy_loss": update_metrics["policy_loss"],
|
||||
"value_loss": update_metrics["value_loss"],
|
||||
"entropy": update_metrics["entropy"],
|
||||
"approx_kl": update_metrics["approx_kl"],
|
||||
"clip_fraction": update_metrics["clip_fraction"],
|
||||
"lr": current_lr,
|
||||
"iter_time_s": round(iter_time, 1),
|
||||
"eval": eval_results,
|
||||
}
|
||||
self.history.append(metrics)
|
||||
|
||||
src_tag = f"[{rollout_stats.get('source', '?')[:3]}]"
|
||||
logger.info(
|
||||
" [%3d/%d]%s wr=%.0f%% rew=%.3f ploss=%.4f vloss=%.4f ent=%.3f kl=%.4f%s (%.1fs)",
|
||||
iteration, cfg.iterations, src_tag,
|
||||
rollout_stats["win_rate"] * 100,
|
||||
rollout_stats["avg_reward_per_ep"],
|
||||
update_metrics["policy_loss"],
|
||||
update_metrics["value_loss"],
|
||||
update_metrics["entropy"],
|
||||
update_metrics["approx_kl"],
|
||||
eval_str,
|
||||
iter_time,
|
||||
)
|
||||
# Pretty log
|
||||
eval_str = ""
|
||||
if eval_results:
|
||||
h_wr = eval_results.get("heuristic", {}).get("win_rate", 0)
|
||||
r_wr = eval_results.get("random", {}).get("win_rate", 0)
|
||||
eval_str = f" | eval: h={h_wr:.0%} r={r_wr:.0%}"
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(iteration, metrics)
|
||||
logger.info(
|
||||
" [%3d/%d] wr=%.0f%% rew=%.3f ploss=%.4f vloss=%.4f ent=%.3f kl=%.4f%s (%.1fs)",
|
||||
iteration, cfg.iterations,
|
||||
rollout_stats["win_rate"] * 100,
|
||||
rollout_stats["avg_reward_per_ep"],
|
||||
update_metrics["policy_loss"],
|
||||
update_metrics["value_loss"],
|
||||
update_metrics["entropy"],
|
||||
update_metrics["approx_kl"],
|
||||
eval_str,
|
||||
iter_time,
|
||||
)
|
||||
|
||||
finally:
|
||||
if self._forge_source:
|
||||
self._forge_source.stop()
|
||||
if progress_callback:
|
||||
progress_callback(iteration, metrics)
|
||||
|
||||
# --- Final Save ---
|
||||
total_time = time.time() - t_start
|
||||
self.save_checkpoint()
|
||||
|
||||
# Save training history
|
||||
history_path = os.path.join(cfg.checkpoint_dir, "ppo_history.json")
|
||||
with open(history_path, "w", encoding="utf-8") as f:
|
||||
with open(history_path, "w") as f:
|
||||
json.dump(self.history, f, indent=2, default=str)
|
||||
|
||||
summary = {
|
||||
@@ -706,7 +526,6 @@ class PPOTrainer:
|
||||
"final_win_rate": self.history[-1]["win_rate"] if self.history else 0,
|
||||
"total_time_s": round(total_time, 1),
|
||||
"device": self.device,
|
||||
"data_source": cfg.data_source,
|
||||
"checkpoint_dir": cfg.checkpoint_dir,
|
||||
"history_path": history_path,
|
||||
}
|
||||
@@ -717,7 +536,6 @@ class PPOTrainer:
|
||||
logger.info(" Total time: %.1f s", total_time)
|
||||
logger.info(" Total steps: %d", self.total_steps)
|
||||
logger.info(" Best win rate: %.1f%%", self.best_win_rate * 100)
|
||||
logger.info(" Data source: %s", cfg.data_source)
|
||||
logger.info(" History: %s", history_path)
|
||||
logger.info("=" * 60)
|
||||
|
||||
@@ -760,32 +578,12 @@ def main():
|
||||
parser.add_argument("--load-supervised", default=None,
|
||||
help="Path to supervised checkpoint to initialize from")
|
||||
|
||||
# Data source
|
||||
parser.add_argument(
|
||||
"--data-source", default="synthetic",
|
||||
choices=["synthetic", "forge", "mixed"],
|
||||
help="Episode source: synthetic (self-play), forge (Forge JSONL), mixed (blend)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--forge-results-dir", default="results",
|
||||
help="Directory where Forge writes ml-decisions-forge-*.jsonl files",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--forge-batch-id", default="forge-batch",
|
||||
help="Batch ID prefix for Forge JSONL files (matches BatchRunner --batchId)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--forge-num-games", type=int, default=256,
|
||||
help="Total Forge games to consume before stopping the producer",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--forge-weight", type=float, default=0.7,
|
||||
help="Fraction of RolloutBuffer filled from Forge when --data-source mixed (0.0–1.0)",
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
logging.basicConfig(level=logging.INFO, format="%(message)s")
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(message)s",
|
||||
)
|
||||
|
||||
config = PPOConfig(
|
||||
iterations=args.iterations,
|
||||
@@ -807,11 +605,6 @@ def main():
|
||||
eval_every=args.eval_every,
|
||||
eval_episodes=args.eval_episodes,
|
||||
load_supervised=args.load_supervised,
|
||||
data_source=args.data_source,
|
||||
forge_results_dir=args.forge_results_dir,
|
||||
forge_batch_id=args.forge_batch_id,
|
||||
forge_num_games=args.forge_num_games,
|
||||
forge_weight=args.forge_weight,
|
||||
)
|
||||
|
||||
trainer = PPOTrainer(config)
|
||||
|
||||
@@ -17,15 +17,9 @@ that feed directly into the RolloutBuffer for PPO training.
|
||||
For real game integration (Phase 9+), the Java sim would call the
|
||||
policy server and feed back actual game states — this synthetic
|
||||
version allows training to begin immediately.
|
||||
|
||||
.. deprecated:: Phase 3 (Issue #83)
|
||||
Synthetic training mode is DEPRECATED for production training.
|
||||
Use forge_episode_generator.py with real Forge engine games.
|
||||
Retained ONLY for quick local testing and unit tests.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import warnings
|
||||
import random
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
@@ -48,12 +42,6 @@ from ml.training.decision_exporter import DecisionExporter
|
||||
|
||||
logger = logging.getLogger("ml.self_play")
|
||||
|
||||
_TRAINING_DEPRECATION_MSG = (
|
||||
"self_play.py synthetic training is deprecated for production training (Issue #83). "
|
||||
"Use forge_episode_generator.py with real Forge engine games instead. "
|
||||
"Synthetic self-play is retained for local testing and unit tests only."
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class PlayerState:
|
||||
@@ -566,7 +554,6 @@ def collect_rollouts(
|
||||
total_wins = 0
|
||||
total_losses = 0
|
||||
total_draws = 0
|
||||
warnings.warn(_TRAINING_DEPRECATION_MSG, DeprecationWarning, stacklevel=2)
|
||||
total_steps = 0
|
||||
total_rewards = 0.0
|
||||
|
||||
|
||||
+4
-10
@@ -322,7 +322,7 @@ class SupervisedTrainer:
|
||||
}
|
||||
serializable_history.append(entry)
|
||||
|
||||
with open(history_path, "w", encoding="utf-8") as f:
|
||||
with open(history_path, "w") as f:
|
||||
json.dump(serializable_history, f, indent=2)
|
||||
logger.info(" History saved: %s", history_path)
|
||||
|
||||
@@ -547,7 +547,7 @@ def get_best_device() -> str:
|
||||
return "cpu"
|
||||
if torch.cuda.is_available():
|
||||
name = torch.cuda.get_device_name(0)
|
||||
mem = torch.cuda.get_device_properties(0).total_memory / (1024**3)
|
||||
mem = torch.cuda.get_device_properties(0).total_mem / (1024**3)
|
||||
logger.info(" CUDA device: %s (%.1f GB)", name, mem)
|
||||
return "cuda"
|
||||
if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
|
||||
@@ -562,12 +562,6 @@ def get_best_device() -> str:
|
||||
# ══════════════════════════════════════════════════════════
|
||||
|
||||
def main():
|
||||
# Ensure logger output is visible on console
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(message)s",
|
||||
)
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Commander AI Lab — Train Policy Network"
|
||||
)
|
||||
@@ -644,7 +638,7 @@ def main():
|
||||
|
||||
# Save evaluation results
|
||||
eval_path = os.path.join(ckpt_dir, "eval_results.json")
|
||||
with open(eval_path, "w", encoding="utf-8") as f:
|
||||
with open(eval_path, "w") as f:
|
||||
json.dump(results, f, indent=2)
|
||||
logger.info(f" Results saved: {eval_path}")
|
||||
return
|
||||
@@ -702,7 +696,7 @@ def main():
|
||||
print_evaluation(results)
|
||||
|
||||
eval_path = os.path.join(ckpt_dir, "eval_results.json")
|
||||
with open(eval_path, "w", encoding="utf-8") as f:
|
||||
with open(eval_path, "w") as f:
|
||||
json.dump(results, f, indent=2)
|
||||
logger.info(" Eval results saved: %s", eval_path)
|
||||
else:
|
||||
|
||||
+1
-2
@@ -9,7 +9,7 @@ class StartRequest(BaseModel):
|
||||
numGames: int = 100
|
||||
threads: int = 4
|
||||
seed: Optional[int] = None
|
||||
clock: int = 5000
|
||||
clock: int = 300
|
||||
deckSources: Optional[list[Optional[dict]]] = None
|
||||
useLearnedPolicy: bool = False
|
||||
policyStyle: str = "midrange"
|
||||
@@ -170,7 +170,6 @@ class CoachGoalsRequest(BaseModel):
|
||||
meta_focus: Optional[str] = None # aggro, control, combo, midrange, stax
|
||||
budget: Optional[str] = None # budget, medium, no-limit
|
||||
focus_areas: list[str] = [] # e.g., ["ramp", "card draw"]
|
||||
|
||||
class DeckGenV3SubstituteRequest(BaseModel):
|
||||
card_name: str
|
||||
substitute_name: str
|
||||
+102
-159
@@ -1,159 +1,102 @@
|
||||
"""Runtime configuration, state classes, and static data for Commander AI Lab."""
|
||||
from __future__ import annotations
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import subprocess
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
# ── .env bootstrap ────────────────────────────────────────────
|
||||
# Load .env automatically so plain `python script.py` and bare uvicorn
|
||||
# both pick up FORGE_DIR, PRECON_DIR, etc. without a manual load_dotenv
|
||||
# call at every entry point. override=False means real env vars always win.
|
||||
try:
|
||||
from dotenv import load_dotenv as _load_dotenv
|
||||
_here = Path(__file__).resolve().parent
|
||||
for _candidate in [_here, _here.parent, _here.parent.parent]:
|
||||
_env_file = _candidate / ".env"
|
||||
if _env_file.is_file():
|
||||
_load_dotenv(dotenv_path=_env_file, override=False)
|
||||
break
|
||||
except ImportError:
|
||||
pass # python-dotenv not installed; rely on real environment variables
|
||||
# ─────────────────────────────────────────────────────────────
|
||||
|
||||
log = logging.getLogger("commander_ai_lab.api")
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Configuration
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
class Config:
|
||||
"""Runtime configuration -- set via CLI args or env vars."""
|
||||
forge_jar: str = ""
|
||||
forge_dir: str = ""
|
||||
forge_decks_dir: str = ""
|
||||
user_decks_dir: str = ""
|
||||
lab_jar: str = ""
|
||||
precon_dir: str = ""
|
||||
results_dir: str = "results"
|
||||
port: int = 8080
|
||||
ximilar_api_key: str = ""
|
||||
pplx_api_key: str = ""
|
||||
anthropic_api_key: str = ""
|
||||
|
||||
|
||||
CFG = Config()
|
||||
|
||||
# Pre-populate from environment so uvicorn/ASGI mode works without CLI args.
|
||||
# CLI args (lab_api.py main()) will override these if provided.
|
||||
CFG.anthropic_api_key = os.environ.get("ANTHROPIC_API_KEY", "")
|
||||
CFG.ximilar_api_key = os.environ.get("XIMILAR_API_KEY", "")
|
||||
CFG.pplx_api_key = os.environ.get("PPLX_API_KEY", "")
|
||||
CFG.forge_jar = os.environ.get("FORGE_JAR", "")
|
||||
CFG.forge_dir = os.environ.get("FORGE_DIR", "")
|
||||
CFG.lab_jar = os.environ.get("LAB_JAR", "")
|
||||
# PRECON_DIR / PRECON_DECKS_DIR are aliases -- prefer PRECON_DIR
|
||||
CFG.precon_dir = os.environ.get("PRECON_DIR") or os.environ.get("PRECON_DECKS_DIR", "")
|
||||
CFG.forge_decks_dir = os.environ.get("FORGE_DECKS_DIR", "")
|
||||
CFG.user_decks_dir = os.environ.get(
|
||||
"USER_DECKS_DIR",
|
||||
os.path.join(os.path.dirname(__file__), "..", "user-decks")
|
||||
)
|
||||
|
||||
|
||||
def validate_forge_config() -> None:
|
||||
"""Warn clearly at startup if FORGE_DIR is misconfigured."""
|
||||
if not CFG.forge_dir:
|
||||
log.warning("FORGE_DIR not set -- Forge card enrichment disabled. "
|
||||
"Set FORGE_DIR to your forge-repo root or forge-gui directory.")
|
||||
return
|
||||
base = Path(CFG.forge_dir)
|
||||
a = base / "forge-gui" / "res" / "cardsfolder"
|
||||
b = base / "res" / "cardsfolder"
|
||||
if not a.is_dir() and not b.is_dir():
|
||||
log.warning(
|
||||
"FORGE_DIR=%s is set but cardsfolder not found at:\n"
|
||||
" %s\n %s\n"
|
||||
"Check that FORGE_DIR points to forge-repo root or forge-gui/.",
|
||||
CFG.forge_dir, a, b
|
||||
)
|
||||
else:
|
||||
resolved = a if a.is_dir() else b
|
||||
log.info("Forge cardsfolder confirmed at %s", resolved)
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# In-Memory State
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
class BatchState:
|
||||
def __init__(self, batch_id: str, total_games: int, threads: int):
|
||||
self.batch_id = batch_id
|
||||
self.total_games = total_games
|
||||
self.threads = threads
|
||||
self.completed_games = 0
|
||||
self.running = True
|
||||
self.start_time = datetime.now()
|
||||
self.elapsed_ms = 0
|
||||
self.result_path: Optional[str] = None
|
||||
self.error: Optional[str] = None
|
||||
self.process: Optional[subprocess.Popen] = None
|
||||
self.log_lines: list = []
|
||||
self.sims_per_sec: float = 0.0
|
||||
|
||||
|
||||
import asyncio as _asyncio
|
||||
active_batches: dict[str, BatchState] = {}
|
||||
active_batches_lock = _asyncio.Lock()
|
||||
|
||||
|
||||
COMMANDER_META: dict = {}
|
||||
|
||||
BUILTIN_COMMANDERS: dict = {
|
||||
"Edgar Markov": [{"source": "edhrec", "archetype": "aggro", "colorIdentity": ["W","B","R"]}],
|
||||
"Atraxa, Praetors' Voice": [{"source": "edhrec", "archetype": "midrange", "colorIdentity": ["W","U","B","G"]}],
|
||||
"Korvold, Fae-Cursed King": [{"source": "edhrec", "archetype": "combo", "colorIdentity": ["B","R","G"]}],
|
||||
"Muldrotha, the Gravetide": [{"source": "edhrec", "archetype": "midrange", "colorIdentity": ["U","B","G"]}],
|
||||
"The Ur-Dragon": [{"source": "edhrec", "archetype": "midrange", "colorIdentity": ["W","U","B","R","G"]}],
|
||||
"Yuriko, the Tiger's Shadow": [{"source": "edhrec", "archetype": "aggro", "colorIdentity": ["U","B"]}],
|
||||
"Krenko, Mob Boss": [{"source": "edhrec", "archetype": "aggro", "colorIdentity": ["R"]}],
|
||||
"Meren of Clan Nel Toth": [{"source": "edhrec", "archetype": "midrange", "colorIdentity": ["B","G"]}],
|
||||
"Prossh, Skyraider of Kher": [{"source": "edhrec", "archetype": "combo", "colorIdentity": ["B","R","G"]}],
|
||||
"Kaalia of the Vast": [{"source": "edhrec", "archetype": "aggro", "colorIdentity": ["W","B","R"]}],
|
||||
"Talrand, Sky Summoner": [{"source": "edhrec", "archetype": "control", "colorIdentity": ["U"]}],
|
||||
"Omnath, Locus of Creation": [{"source": "edhrec", "archetype": "combo", "colorIdentity": ["W","U","R","G"]}],
|
||||
"Teysa Karlov": [{"source": "edhrec", "archetype": "combo", "colorIdentity": ["W","B"]}],
|
||||
"Lathril, Blade of the Elves": [{"source": "edhrec", "archetype": "aggro", "colorIdentity": ["B","G"]}],
|
||||
"Breya, Etherium Shaper": [{"source": "edhrec", "archetype": "combo", "colorIdentity": ["W","U","B","R"]}],
|
||||
}
|
||||
|
||||
|
||||
def load_commander_meta() -> None:
|
||||
"""Load commander meta mapping from file or fall back to builtins."""
|
||||
meta_path = Path(__file__).parent.parent / "commander-meta.json"
|
||||
if meta_path.exists():
|
||||
try:
|
||||
with open(meta_path, "r", encoding="utf-8") as f:
|
||||
COMMANDER_META.clear()
|
||||
COMMANDER_META.update(json.load(f))
|
||||
log.info(f" Meta: Loaded {len(COMMANDER_META)} commanders from {meta_path}")
|
||||
return
|
||||
except Exception as e:
|
||||
log.warning(f" WARNING: Failed to load commander-meta.json: {e}")
|
||||
COMMANDER_META.clear()
|
||||
COMMANDER_META.update(BUILTIN_COMMANDERS)
|
||||
log.info(f" Meta: {len(COMMANDER_META)} built-in commanders")
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# AI Profiles
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
AI_PROFILES = {
|
||||
"default": {"name": "default", "description": "Balanced — Forge's default AI behavior", "aggression": 0.5, "cardAdvantage": 0.5, "removalPriority": 0.5, "boardPresence": 0.5, "comboPriority": 0.3, "patience": 0.5},
|
||||
"aggro": {"name": "aggro", "description": "Aggressive — attacks early, prioritizes damage", "aggression": 0.9, "cardAdvantage": 0.3, "removalPriority": 0.3, "boardPresence": 0.8, "comboPriority": 0.1, "patience": 0.1},
|
||||
"control": {"name": "control", "description": "Control — defensive, removal-heavy, card advantage", "aggression": 0.2, "cardAdvantage": 0.9, "removalPriority": 0.9, "boardPresence": 0.3, "comboPriority": 0.4, "patience": 0.9},
|
||||
"combo": {"name": "combo", "description": "Combo — ramps, digs for pieces, assembles combos", "aggression": 0.2, "cardAdvantage": 0.8, "removalPriority": 0.4, "boardPresence": 0.3, "comboPriority": 0.95, "patience": 0.7},
|
||||
"midrange": {"name": "midrange", "description": "Midrange — flexible, strong board presence, value-oriented", "aggression": 0.5, "cardAdvantage": 0.6, "removalPriority": 0.6, "boardPresence": 0.7, "comboPriority": 0.3, "patience": 0.5},
|
||||
}
|
||||
"""Runtime configuration, state classes, and static data for Commander AI Lab."""
|
||||
from __future__ import annotations
|
||||
import json
|
||||
import logging
|
||||
import subprocess
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
|
||||
log = logging.getLogger("commander_ai_lab.api")
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# Configuration
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
class Config:
|
||||
"""Runtime configuration -- set via CLI args or env vars."""
|
||||
forge_jar: str = ""
|
||||
forge_dir: str = ""
|
||||
forge_decks_dir: str = ""
|
||||
lab_jar: str = ""
|
||||
precon_dir: str = ""
|
||||
results_dir: str = "results"
|
||||
port: int = 8080
|
||||
ximilar_api_key: str = ""
|
||||
pplx_api_key: str = ""
|
||||
|
||||
|
||||
CFG = Config()
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# In-Memory State
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
class BatchState:
|
||||
def __init__(self, batch_id: str, total_games: int, threads: int):
|
||||
self.batch_id = batch_id
|
||||
self.total_games = total_games
|
||||
self.threads = threads
|
||||
self.completed_games = 0
|
||||
self.running = True
|
||||
self.start_time = datetime.now()
|
||||
self.elapsed_ms = 0
|
||||
self.result_path: Optional[str] = None
|
||||
self.error: Optional[str] = None
|
||||
self.process: Optional[subprocess.Popen] = None
|
||||
self.log_lines: list = []
|
||||
self.sims_per_sec: float = 0.0
|
||||
|
||||
|
||||
active_batches: dict[str, BatchState] = {}
|
||||
|
||||
|
||||
COMMANDER_META: dict = {}
|
||||
|
||||
BUILTIN_COMMANDERS: dict = {
|
||||
"Edgar Markov": [{"source": "edhrec", "archetype": "aggro", "colorIdentity": ["W","B","R"]}],
|
||||
"Atraxa, Praetors' Voice": [{"source": "edhrec", "archetype": "midrange", "colorIdentity": ["W","U","B","G"]}],
|
||||
"Korvold, Fae-Cursed King": [{"source": "edhrec", "archetype": "combo", "colorIdentity": ["B","R","G"]}],
|
||||
"Muldrotha, the Gravetide": [{"source": "edhrec", "archetype": "midrange", "colorIdentity": ["U","B","G"]}],
|
||||
"The Ur-Dragon": [{"source": "edhrec", "archetype": "midrange", "colorIdentity": ["W","U","B","R","G"]}],
|
||||
"Yuriko, the Tiger's Shadow": [{"source": "edhrec", "archetype": "aggro", "colorIdentity": ["U","B"]}],
|
||||
"Krenko, Mob Boss": [{"source": "edhrec", "archetype": "aggro", "colorIdentity": ["R"]}],
|
||||
"Meren of Clan Nel Toth": [{"source": "edhrec", "archetype": "midrange", "colorIdentity": ["B","G"]}],
|
||||
"Prossh, Skyraider of Kher": [{"source": "edhrec", "archetype": "combo", "colorIdentity": ["B","R","G"]}],
|
||||
"Kaalia of the Vast": [{"source": "edhrec", "archetype": "aggro", "colorIdentity": ["W","B","R"]}],
|
||||
"Talrand, Sky Summoner": [{"source": "edhrec", "archetype": "control", "colorIdentity": ["U"]}],
|
||||
"Omnath, Locus of Creation": [{"source": "edhrec", "archetype": "combo", "colorIdentity": ["W","U","R","G"]}],
|
||||
"Teysa Karlov": [{"source": "edhrec", "archetype": "combo", "colorIdentity": ["W","B"]}],
|
||||
"Lathril, Blade of the Elves": [{"source": "edhrec", "archetype": "aggro", "colorIdentity": ["B","G"]}],
|
||||
"Breya, Etherium Shaper": [{"source": "edhrec", "archetype": "combo", "colorIdentity": ["W","U","B","R"]}],
|
||||
}
|
||||
|
||||
|
||||
def load_commander_meta() -> None:
|
||||
"""Load commander meta mapping from file or fall back to builtins."""
|
||||
meta_path = Path(__file__).parent.parent / "commander-meta.json"
|
||||
if meta_path.exists():
|
||||
try:
|
||||
with open(meta_path, "r", encoding="utf-8") as f:
|
||||
COMMANDER_META.clear()
|
||||
COMMANDER_META.update(json.load(f))
|
||||
log.info(f" Meta: Loaded {len(COMMANDER_META)} commanders from {meta_path}")
|
||||
return
|
||||
except Exception as e:
|
||||
log.warning(f" WARNING: Failed to load commander-meta.json: {e}")
|
||||
COMMANDER_META.clear()
|
||||
COMMANDER_META.update(BUILTIN_COMMANDERS)
|
||||
log.info(f" Meta: {len(COMMANDER_META)} built-in commanders")
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
# AI Profiles
|
||||
# ══════════════════════════════════════════════════════════════
|
||||
AI_PROFILES = {
|
||||
"default": {"name": "default", "description": "Balanced \u2014 Forge's default AI behavior", "aggression": 0.5, "cardAdvantage": 0.5, "removalPriority": 0.5, "boardPresence": 0.5, "comboPriority": 0.3, "patience": 0.5},
|
||||
"aggro": {"name": "aggro", "description": "Aggressive \u2014 attacks early, prioritizes damage", "aggression": 0.9, "cardAdvantage": 0.3, "removalPriority": 0.3, "boardPresence": 0.8, "comboPriority": 0.1, "patience": 0.1},
|
||||
"control": {"name": "control", "description": "Control \u2014 defensive, removal-heavy, card advantage", "aggression": 0.2, "cardAdvantage": 0.9, "removalPriority": 0.9, "boardPresence": 0.3, "comboPriority": 0.4, "patience": 0.9},
|
||||
"combo": {"name": "combo", "description": "Combo \u2014 ramps, digs for pieces, assembles combos", "aggression": 0.2, "cardAdvantage": 0.8, "removalPriority": 0.4, "boardPresence": 0.3, "comboPriority": 0.95, "patience": 0.7},
|
||||
"midrange": {"name": "midrange", "description": "Midrange \u2014 flexible, strong board presence, value-oriented", "aggression": 0.5, "cardAdvantage": 0.6, "removalPriority": 0.6, "boardPresence": 0.7, "comboPriority": 0.3, "patience": 0.5},
|
||||
}
|
||||
+9
-94
@@ -25,7 +25,6 @@ import json
|
||||
import logging
|
||||
import logging.handlers
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
import urllib.request
|
||||
@@ -43,14 +42,10 @@ DEFAULT_CONFIG = {
|
||||
"time_limit_hours": 11.5, # stop after this many hours (leave buffer for training)
|
||||
"pause_between_batches": 10, # seconds to pause between batches
|
||||
"auto_train": True, # trigger ML training after all batches
|
||||
"auto_update_weights": True, # run update_weights.py after sims
|
||||
"weights_min_games": 10, # minimum games required for weight update
|
||||
"weights_lr": 0.05, # learning rate for weight update
|
||||
"train_epochs": 100, # training epochs
|
||||
"train_lr": 0.001, # learning rate
|
||||
"train_batch_size": 256, # training batch size
|
||||
"train_patience": 15, # early stopping patience
|
||||
"train_ds_weight": 4.0, # DeepSeek source upsampling weight (ds-*.jsonl files)
|
||||
# Java-only settings (ignored for DeepSeek):
|
||||
"java_threads": 4,
|
||||
"java_clock": 300,
|
||||
@@ -234,67 +229,14 @@ def wait_for_batch(base_url, batch_id, engine="deepseek", deadline=None):
|
||||
time.sleep(poll_interval)
|
||||
|
||||
|
||||
def run_update_weights(cfg):
|
||||
"""
|
||||
Run ml.scripts.update_weights to nudge learned_weights.json from sim
|
||||
decision logs. This feeds the deck builder's sim_insights injection.
|
||||
|
||||
Returns True on success, False on failure (non-fatal — overnight run
|
||||
continues regardless).
|
||||
"""
|
||||
log.info("")
|
||||
log.info("── Auto-update sim weights ─────────────────────────────────")
|
||||
log.info("Running update_weights.py to refresh learned_weights.json...")
|
||||
|
||||
cmd = [
|
||||
sys.executable, "-m", "ml.scripts.update_weights",
|
||||
"--min-games", str(cfg["weights_min_games"]),
|
||||
"--lr", str(cfg["weights_lr"]),
|
||||
]
|
||||
|
||||
try:
|
||||
result = subprocess.run(
|
||||
cmd,
|
||||
capture_output=True,
|
||||
text=True,
|
||||
timeout=120, # 2 min ceiling — this should complete in seconds
|
||||
)
|
||||
if result.returncode == 0:
|
||||
# Print the last ~800 chars of the weight report to the log
|
||||
output = (result.stdout or "").strip()
|
||||
if output:
|
||||
for line in output.splitlines()[-20:]:
|
||||
log.info(f" {line}")
|
||||
log.info("Sim weights updated — deck builder will use new insights on next build.")
|
||||
return True
|
||||
else:
|
||||
log.warning(f"update_weights.py exited with code {result.returncode}")
|
||||
if result.stderr:
|
||||
log.warning(result.stderr.strip()[-300:])
|
||||
return False
|
||||
except subprocess.TimeoutExpired:
|
||||
log.warning("update_weights.py timed out after 120s — skipping")
|
||||
return False
|
||||
except Exception as e:
|
||||
log.warning(f"update_weights.py failed: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def start_training(base_url, epochs, lr, batch_size, patience, ds_weight=4.0):
|
||||
"""Trigger ML training and return immediately.
|
||||
|
||||
ds_weight: upsampling multiplier for DeepSeek decision files
|
||||
(ml-decisions-ds-*.jsonl). Matches the default in
|
||||
DatasetConfig.source_weights added in the recent
|
||||
dataset_builder.py update.
|
||||
"""
|
||||
def start_training(base_url, epochs, lr, batch_size, patience):
|
||||
"""Trigger ML training and return immediately."""
|
||||
resp = api_post(base_url, "/api/ml/train", {
|
||||
"epochs": epochs,
|
||||
"lr": lr,
|
||||
"batchSize": batch_size,
|
||||
"patience": patience,
|
||||
"rebuildDataset": True,
|
||||
"dsWeight": ds_weight,
|
||||
})
|
||||
return resp
|
||||
|
||||
@@ -422,9 +364,7 @@ def run_overnight(cfg):
|
||||
else:
|
||||
log.info(f" Mode: Run for {cfg['time_limit_hours']}h, {games_per_batch} games/batch")
|
||||
log.info(f" Engine: {engine}")
|
||||
log.info(f" Auto-update weights: {'Yes' if cfg['auto_update_weights'] else 'No'}")
|
||||
log.info(f" Auto-train after: {'Yes' if cfg['auto_train'] else 'No'}")
|
||||
log.info(f" DS upsampling weight: {cfg['train_ds_weight']}x")
|
||||
log.info("")
|
||||
|
||||
while True:
|
||||
@@ -500,17 +440,6 @@ def run_overnight(cfg):
|
||||
log.info(f" Elapsed time: {str(timedelta(seconds=int(total_elapsed)))}")
|
||||
log.info("")
|
||||
|
||||
# ── Auto-update sim weights ───────────────────────────────
|
||||
# Runs update_weights.py to nudge learned_weights.json from the
|
||||
# decision logs produced during this run. This feeds sim_insights
|
||||
# into the deck builder's Ollama prompts on the next deck build.
|
||||
# Runs before neural net training so both pipelines benefit from
|
||||
# the freshest possible data.
|
||||
if cfg["auto_update_weights"]:
|
||||
run_update_weights(cfg)
|
||||
else:
|
||||
log.info("Skipping sim weight update (--no-update-weights flag set)")
|
||||
|
||||
# ── Auto-train ────────────────────────────────────────────
|
||||
if cfg["auto_train"]:
|
||||
if decisions_after < 100:
|
||||
@@ -519,8 +448,7 @@ def run_overnight(cfg):
|
||||
|
||||
log.info("Starting ML training pipeline...")
|
||||
log.info(f" Epochs: {cfg['train_epochs']}, LR: {cfg['train_lr']}, "
|
||||
f"Batch: {cfg['train_batch_size']}, Patience: {cfg['train_patience']}, "
|
||||
f"DS Weight: {cfg['train_ds_weight']}x")
|
||||
f"Batch: {cfg['train_batch_size']}, Patience: {cfg['train_patience']}")
|
||||
|
||||
try:
|
||||
start_training(
|
||||
@@ -529,7 +457,6 @@ def run_overnight(cfg):
|
||||
lr=cfg["train_lr"],
|
||||
batch_size=cfg["train_batch_size"],
|
||||
patience=cfg["train_patience"],
|
||||
ds_weight=cfg["train_ds_weight"],
|
||||
)
|
||||
log.info("Training started, monitoring progress...")
|
||||
success = wait_for_training(base_url)
|
||||
@@ -555,20 +482,20 @@ def main():
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
Examples:
|
||||
# Run DeepSeek sims for 11.5 hours, then update weights + train:
|
||||
# Run DeepSeek sims for 11.5 hours, then train:
|
||||
python overnight-run.py
|
||||
|
||||
# Run 20 batches of 50 games each, then update weights + train:
|
||||
# Run 20 batches of 50 games each, then train:
|
||||
python overnight-run.py --batches 20 --games 50
|
||||
|
||||
# Run for 6 hours with Java engine:
|
||||
python overnight-run.py --engine java --hours 6 --decks "Deck A" "Deck B" "Deck C"
|
||||
|
||||
# Just run sims, skip both weight update and training:
|
||||
python overnight-run.py --no-train --no-update-weights
|
||||
# Just run sims, no training:
|
||||
python overnight-run.py --no-train
|
||||
|
||||
# Custom training settings with higher DS upsampling:
|
||||
python overnight-run.py --epochs 200 --lr 0.0005 --patience 20 --ds-weight 6.0
|
||||
# Custom training settings:
|
||||
python overnight-run.py --epochs 200 --lr 0.0005 --patience 20
|
||||
""",
|
||||
)
|
||||
parser.add_argument("--url", default="http://localhost:8080",
|
||||
@@ -587,12 +514,6 @@ Examples:
|
||||
help="Deck names (auto-detected if not specified)")
|
||||
parser.add_argument("--no-train", action="store_true",
|
||||
help="Skip auto-training after sims")
|
||||
parser.add_argument("--no-update-weights", action="store_true",
|
||||
help="Skip updating learned_weights.json after sims")
|
||||
parser.add_argument("--weights-min-games", type=int, default=10,
|
||||
help="Minimum games required for weight update (default: 10)")
|
||||
parser.add_argument("--weights-lr", type=float, default=0.05,
|
||||
help="Learning rate for sim weight update (default: 0.05)")
|
||||
parser.add_argument("--epochs", type=int, default=100,
|
||||
help="Training epochs (default: 100)")
|
||||
parser.add_argument("--lr", type=float, default=0.001,
|
||||
@@ -601,8 +522,6 @@ Examples:
|
||||
help="Training batch size (default: 256)")
|
||||
parser.add_argument("--patience", type=int, default=15,
|
||||
help="Early stopping patience (default: 15)")
|
||||
parser.add_argument("--ds-weight", type=float, default=4.0,
|
||||
help="DeepSeek source upsampling weight for training (default: 4.0)")
|
||||
parser.add_argument("--threads", type=int, default=4,
|
||||
help="Java engine threads (default: 4)")
|
||||
parser.add_argument("--clock", type=int, default=300,
|
||||
@@ -617,14 +536,10 @@ Examples:
|
||||
cfg["time_limit_hours"] = args.hours
|
||||
cfg["pause_between_batches"] = args.pause
|
||||
cfg["auto_train"] = not args.no_train
|
||||
cfg["auto_update_weights"] = not args.no_update_weights
|
||||
cfg["weights_min_games"] = args.weights_min_games
|
||||
cfg["weights_lr"] = args.weights_lr
|
||||
cfg["train_epochs"] = args.epochs
|
||||
cfg["train_lr"] = args.lr
|
||||
cfg["train_batch_size"] = args.batch_size
|
||||
cfg["train_patience"] = args.patience
|
||||
cfg["train_ds_weight"] = args.ds_weight
|
||||
cfg["java_threads"] = args.threads
|
||||
cfg["java_clock"] = args.clock
|
||||
|
||||
|
||||
@@ -1,68 +0,0 @@
|
||||
import re
|
||||
|
||||
with open('ml/eval/forge_evaluator.py', 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
|
||||
# Fix 1: >= 2 to >= 3
|
||||
content = content.replace('deck_files) >= 2', 'deck_files) >= 3')
|
||||
|
||||
# Fix 2: replace call to _run_forge_via_subprocess with _parse_batch_json
|
||||
content = content.replace(
|
||||
'return self._run_forge_via_subprocess(service)',
|
||||
'return self._parse_batch_json(service)'
|
||||
)
|
||||
|
||||
# Fix 3: Insert _parse_batch_json method before _run_forge_via_subprocess
|
||||
new_method = '''
|
||||
def _parse_batch_json(self, service) -> List[GameRecord]:
|
||||
"""Parse the batch JSON output from LabCli Java subprocess."""
|
||||
batch_path = os.path.join(
|
||||
self.cfg.results_dir, f"eval-{self.cfg.run_id}-batch.json"
|
||||
)
|
||||
if not Path(batch_path).exists():
|
||||
logger.warning("[Eval] Batch JSON not found at %s -- falling back to synthetic", batch_path)
|
||||
return self._run_synthetic_eval(service)
|
||||
with open(batch_path, encoding='utf-8') as f:
|
||||
batch = json.load(f)
|
||||
games_list = batch.get("games", [])
|
||||
if not games_list:
|
||||
logger.warning("[Eval] No games in batch JSON -- falling back to synthetic")
|
||||
return self._run_synthetic_eval(service)
|
||||
records = []
|
||||
for idx, g in enumerate(games_list):
|
||||
won = g.get("winningSeat", -1) == 0
|
||||
turns = g.get("totalTurns", 0)
|
||||
pr = g.get("playerResults", [])
|
||||
lp = pr[0].get("finalLife", 40) if len(pr) > 0 else 40
|
||||
lo = pr[1].get("finalLife", 40) if len(pr) > 1 else 40
|
||||
records.append(GameRecord(
|
||||
game_index=idx,
|
||||
won=won,
|
||||
turns=turns,
|
||||
life_final_policy=lp,
|
||||
life_final_opponent=lo,
|
||||
life_delta=lp - lo,
|
||||
commander_damage_dealt=0,
|
||||
illegal_actions=0,
|
||||
entropy_mean=0.0,
|
||||
inference_ms_mean=0.0,
|
||||
decision_steps=turns,
|
||||
))
|
||||
if len(records) >= self.cfg.num_games:
|
||||
break
|
||||
logger.info("[Eval %s] Parsed %d games from batch JSON", self.cfg.run_id, len(records))
|
||||
return records
|
||||
|
||||
'''
|
||||
|
||||
content = content.replace(
|
||||
' def _run_forge_via_subprocess(',
|
||||
new_method + ' def _run_forge_via_subprocess('
|
||||
)
|
||||
|
||||
with open('ml/eval/forge_evaluator.py', 'w', encoding='utf-8') as f:
|
||||
f.write(content)
|
||||
|
||||
import py_compile
|
||||
py_compile.compile('ml/eval/forge_evaluator.py', doraise=True)
|
||||
print('Syntax OK - patch applied')
|
||||
@@ -1,62 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Phase 1 patch: no-op mana pool display functions in lan-client/ui.js
|
||||
Run from anywhere — edit UI_JS_PATH to point at your file.
|
||||
"""
|
||||
|
||||
import re
|
||||
|
||||
UI_JS_PATH = r"D:\ForgeCommander\commander-ai-lab\lan-client\ui.js"
|
||||
|
||||
with open(UI_JS_PATH, "r", encoding="utf-8") as f:
|
||||
content = f.read()
|
||||
|
||||
original_size = len(content)
|
||||
changes = []
|
||||
|
||||
# ── Change 1: no-op renderManaPool ───────────────────────────────────────────
|
||||
old1 = re.search(
|
||||
r'function renderManaPool\(playerIdx\) \{[^}]*?display\.style\.display[^}]*?\}',
|
||||
content, re.DOTALL
|
||||
)
|
||||
if old1:
|
||||
content = content[:old1.start()] + \
|
||||
"function renderManaPool(playerIdx) {\n // mana pool display removed (Phase 1)\n}" + \
|
||||
content[old1.end():]
|
||||
changes.append("renderManaPool -> stub")
|
||||
else:
|
||||
print("WARNING: renderManaPool pattern not matched — skipping")
|
||||
|
||||
# ── Change 2: no-op showManaPool ─────────────────────────────────────────────
|
||||
old2 = re.search(
|
||||
r'function showManaPool\(\) \{.*?\}',
|
||||
content, re.DOTALL
|
||||
)
|
||||
if old2:
|
||||
content = content[:old2.start()] + \
|
||||
"function showManaPool() {\n // mana pool display removed (Phase 1)\n}" + \
|
||||
content[old2.end():]
|
||||
changes.append("showManaPool -> stub")
|
||||
else:
|
||||
print("WARNING: showManaPool pattern not matched — skipping")
|
||||
|
||||
# ── Change 3: remove Mana Auto-Clear row from renderSettingsPanel ─────────────
|
||||
mana_row = (
|
||||
" +\n '<div class=\"settings-row\"><span>Mana Auto-Clear</span>'"
|
||||
" + renderToggle('manaAutoClear', gameState.manaAutoClear) + '</div>';"
|
||||
)
|
||||
if mana_row in content:
|
||||
content = content.replace(mana_row, ";", 1)
|
||||
changes.append("Mana Auto-Clear row removed from renderSettingsPanel")
|
||||
else:
|
||||
print("WARNING: Mana Auto-Clear row not found — skipping")
|
||||
|
||||
# ── Write ─────────────────────────────────────────────────────────────────────
|
||||
if changes:
|
||||
with open(UI_JS_PATH, "w", encoding="utf-8") as f:
|
||||
f.write(content)
|
||||
print(f"Done. {original_size} -> {len(content)} chars ({len(content)-original_size:+d})")
|
||||
for c in changes:
|
||||
print(f" ✓ {c}")
|
||||
else:
|
||||
print("No changes applied.")
|
||||
@@ -1,104 +0,0 @@
|
||||
[metadata]
|
||||
Name=20 Ways to Win
|
||||
[Commander]
|
||||
1 Go-Shintai of Life's Origin
|
||||
[Main]
|
||||
1 Alseid of Life's Bounty
|
||||
1 Approach of the Second Sun
|
||||
1 Arcane Signet
|
||||
1 Augur of Autumn
|
||||
1 Auriok Champion
|
||||
1 Austere Command
|
||||
1 Azorius Guildgate
|
||||
1 Baldur's Gate
|
||||
1 Biovisionary
|
||||
1 Black Dragon Gate
|
||||
1 Boros Guildgate
|
||||
1 Bountiful Promenade
|
||||
1 Brushland
|
||||
1 Chromatic Lantern
|
||||
1 Circuitous Route
|
||||
1 Citadel Gate
|
||||
1 Clever Concealment
|
||||
1 Cliffgate
|
||||
1 Command Tower
|
||||
1 Crib Swap
|
||||
1 Darksteel Citadel
|
||||
1 Darksteel Mutation
|
||||
1 Dimir Guildgate
|
||||
1 Drown in Dreams
|
||||
1 Dryad of the Ilysian Grove
|
||||
1 Eerie Ultimatum
|
||||
1 Exotic Orchard
|
||||
1 Faeburrow Elder
|
||||
1 Felidar Sovereign
|
||||
1 Fertile Ground
|
||||
1 Forbidden Orchard
|
||||
1 Forest
|
||||
1 Forgotten Ancient
|
||||
1 Gateway Plaza
|
||||
1 Ghostly Prison
|
||||
1 Ghoulish Impetus
|
||||
1 Golgari Guildgate
|
||||
1 Gond Gate
|
||||
1 Gruul Guildgate
|
||||
1 Halo Fountain
|
||||
1 Hangarback Walker
|
||||
1 Happily Ever After
|
||||
1 Heap Gate
|
||||
1 Heliod's Intervention
|
||||
1 Heliod, Sun-Crowned
|
||||
1 Helix Pinnacle
|
||||
1 Hellkite Tyrant
|
||||
1 Homeward Path
|
||||
1 Izzet Guildgate
|
||||
1 Kynaios and Tiro of Meletis
|
||||
1 Lightning Greaves
|
||||
1 Lignify
|
||||
1 Liliana's Contract
|
||||
1 Mangara, the Diplomat
|
||||
1 Manor Gate
|
||||
1 Maskwood Nexus
|
||||
1 Mayael's Aria
|
||||
1 Maze's End
|
||||
1 Mechanized Production
|
||||
1 Monologue Tax
|
||||
1 Morbid Opportunist
|
||||
1 Mystic Remora
|
||||
1 Orzhov Guildgate
|
||||
1 Overgrown Farmland
|
||||
1 Pariah
|
||||
1 Path of Ancestry
|
||||
1 Plains
|
||||
1 Plaza of Harmony
|
||||
1 Rakdos Guildgate
|
||||
1 Razorverge Thicket
|
||||
1 Reflecting Pool
|
||||
1 Revel in Riches
|
||||
1 Rite of Replication
|
||||
1 Sanctum Weaver
|
||||
1 Sea Gate
|
||||
1 Seedborn Muse
|
||||
1 Selesnya Guildgate
|
||||
1 Shalai, Voice of Plenty
|
||||
1 Shiny Impetus
|
||||
1 Simic Ascendancy
|
||||
1 Simic Guildgate
|
||||
1 Sol Ring
|
||||
1 Soul's Attendant
|
||||
1 Sphinx's Revelation
|
||||
1 Sun Titan
|
||||
1 Sunscorch Regent
|
||||
1 Supreme Verdict
|
||||
1 Taurean Mauler
|
||||
1 Test of Endurance
|
||||
1 The Eternal Wanderer
|
||||
1 The World Tree
|
||||
1 Thornglint Bridge
|
||||
1 Tireless Tracker
|
||||
1 Trace of Abundance
|
||||
1 Tragic Arrogance
|
||||
1 Triskaidekaphile
|
||||
1 Twenty-Toed Toad
|
||||
1 Wooded Bastion
|
||||
1 Yahenni, Undying Partisan
|
||||
@@ -1,92 +0,0 @@
|
||||
[metadata]
|
||||
Name=Abzan Armor
|
||||
[Commander]
|
||||
1 Felothar the Steadfast
|
||||
[Main]
|
||||
1 Betor, Ancestor's Voice
|
||||
1 Protector of the Wastes
|
||||
1 Reunion of the House
|
||||
1 Jaws of Defeat
|
||||
1 Tip the Scales
|
||||
1 Will of the Abzan
|
||||
1 Arbor Adherent
|
||||
1 Canopy Gargantuan
|
||||
1 Rampart Architect
|
||||
1 Tree of Redemption
|
||||
1 Ikra Shidiqi, the Usurper
|
||||
1 Baldin, Century Herdmaster
|
||||
1 Expel the Interlopers
|
||||
1 Indomitable Ancients
|
||||
1 Rhox Faithmender
|
||||
1 Shalai, Voice of Plenty
|
||||
1 Wakestone Gargoyle
|
||||
1 Wall of Reverence
|
||||
1 Welcoming Vampire
|
||||
1 Zetalpa, Primal Dawn
|
||||
1 Arasta of the Endless Web
|
||||
1 Assault Formation
|
||||
1 Hornet Nest
|
||||
1 Seedborn Muse
|
||||
1 Sylvan Caryatid
|
||||
1 Towering Titan
|
||||
1 Anguished Unmaking
|
||||
1 Dragonlord Dromoka
|
||||
1 Faeburrow Elder
|
||||
1 Shadrix Silverquill
|
||||
1 Sidar Kondo of Jamuraa
|
||||
1 Colfenor's Urn
|
||||
1 Staff of Compleation
|
||||
1 Weathered Sentinels
|
||||
1 Canopy Vista
|
||||
1 Exotic Orchard
|
||||
1 Fortified Village
|
||||
1 Isolated Chapel
|
||||
1 Overgrown Farmland
|
||||
1 Sungrass Prairie
|
||||
1 Sunpetal Grove
|
||||
1 Temple of Malady
|
||||
1 Temple of Plenty
|
||||
1 Temple of Silence
|
||||
1 Twilight Mire
|
||||
1 Woodland Cemetery
|
||||
1 Arcane Signet
|
||||
1 Sol Ring
|
||||
1 Command Tower
|
||||
1 Nyx-Fleece Ram
|
||||
1 Slaughter the Strong
|
||||
1 Swords to Plowshares
|
||||
1 Wall of Omens
|
||||
1 Wingmantle Chaplain
|
||||
1 Behind the Scenes
|
||||
1 Blight Pile
|
||||
1 Feed the Swarm
|
||||
1 Infernal Grasp
|
||||
1 Wall of Limbs
|
||||
1 Arboreal Grazer
|
||||
1 Axebane Guardian
|
||||
1 Carven Caryatid
|
||||
1 Evolving Wilds
|
||||
1 Jaddi Offshoot
|
||||
1 Overgrown Battlement
|
||||
1 Sandsteppe Citadel
|
||||
1 Tower Defense
|
||||
1 Wall of Blossoms
|
||||
1 Wall of Roots
|
||||
1 Despark
|
||||
1 Indulging Patrician
|
||||
1 Crashing Drawbridge
|
||||
1 Orzhov Signet
|
||||
1 Selesnya Signet
|
||||
1 Swiftfoot Boots
|
||||
1 Walking Bulwark
|
||||
1 Access Tunnel
|
||||
1 Bojuka Bog
|
||||
1 Deceptive Landscape
|
||||
1 Path of Ancestry
|
||||
1 Radiant Grove
|
||||
3 Plains
|
||||
3 Plains
|
||||
3 Swamp
|
||||
2 Swamp
|
||||
4 Forest
|
||||
3 Forest
|
||||
@@ -1,90 +0,0 @@
|
||||
[metadata]
|
||||
Name=Adaptive Enchantment
|
||||
[Commander]
|
||||
1 Estrid, the Masked
|
||||
[Main]
|
||||
1 Bruna, Light of Alabaster
|
||||
1 Eidolon of Blossoms
|
||||
1 Hydra Omnivore
|
||||
1 Ajani's Chosen
|
||||
1 Celestial Archon
|
||||
1 Silent Sentinel
|
||||
1 Boon Satyr
|
||||
1 Herald of the Pantheon
|
||||
1 Cold-Eyed Selkie
|
||||
1 Daxos of Meletis
|
||||
1 Elderwood Scion
|
||||
1 Archetype of Imagination
|
||||
1 Whitewater Naiads
|
||||
1 Aura Gnarlid
|
||||
1 Reclamation Sage
|
||||
1 Yavimaya Enchantress
|
||||
1 Martial Coup
|
||||
1 Phyrexian Rebirth
|
||||
1 Winds of Rath
|
||||
1 Creeping Renaissance
|
||||
1 Kruphix's Insight
|
||||
1 Dismantling Blow
|
||||
1 Bant Charm
|
||||
1 Sol Ring
|
||||
1 Sigil of the Empty Throne
|
||||
1 Dictate of Kruphix
|
||||
1 Bear Umbra
|
||||
1 Enchantress's Presence
|
||||
1 Epic Proportions
|
||||
1 Ground Seal
|
||||
1 Spawning Grounds
|
||||
1 Finest Hour
|
||||
1 Righteous Authority
|
||||
1 Sage's Reverie
|
||||
1 Soul Snare
|
||||
1 Unquestioned Authority
|
||||
1 Eel Umbra
|
||||
1 Vow of Flight
|
||||
1 Dawn's Reflection
|
||||
1 Fertile Ground
|
||||
1 Overgrowth
|
||||
1 Snake Umbra
|
||||
1 Vow of Wildness
|
||||
1 Wild Growth
|
||||
1 Unflinching Courage
|
||||
3 Plains
|
||||
3 Plains
|
||||
3 Plains
|
||||
2 Island
|
||||
2 Island
|
||||
2 Island
|
||||
3 Forest
|
||||
3 Forest
|
||||
2 Forest
|
||||
1 Azorius Chancery
|
||||
1 Blossoming Sands
|
||||
1 Command Tower
|
||||
1 Evolving Wilds
|
||||
1 Krosan Verge
|
||||
1 Meandering River
|
||||
1 Mosswort Bridge
|
||||
1 Seaside Citadel
|
||||
1 Selesnya Sanctuary
|
||||
1 Simic Growth Chamber
|
||||
1 Terramorphic Expanse
|
||||
1 Thornwood Falls
|
||||
1 Tranquil Cove
|
||||
1 Tranquil Expanse
|
||||
1 Woodland Stream
|
||||
1 Kestia, the Cultivator
|
||||
1 Tuvasa the Sunlit
|
||||
1 Empyrial Storm
|
||||
1 Heavenly Blademaster
|
||||
1 Estrid's Invocation
|
||||
1 Ever-Watching Threshold
|
||||
1 Octopus Umbra
|
||||
1 Genesis Storm
|
||||
1 Myth Unbound
|
||||
1 Nylea's Colossus
|
||||
1 Ravenous Slime
|
||||
1 Arixmethes, Slumbering Isle
|
||||
1 Loyal Unicorn
|
||||
1 Loyal Drake
|
||||
1 Loyal Guardian
|
||||
1 Forge of Heroes
|
||||
@@ -1,95 +0,0 @@
|
||||
[metadata]
|
||||
Name=Ahoy Mateys
|
||||
[Commander]
|
||||
1 Admiral Brass, Unsinkable
|
||||
[Main]
|
||||
1 Don Andres, the Renegade
|
||||
1 The Indomitable
|
||||
1 Storm Fleet Negotiator
|
||||
1 Francisco, Fowl Marauder
|
||||
1 The Grim Captain's Locker
|
||||
1 Skeleton Crew
|
||||
1 Broadside Bombardiers
|
||||
1 Gemcutter Buccaneer
|
||||
1 Arm-Mounted Anchor
|
||||
1 Amphin Mutineer
|
||||
1 Bident of Thassa
|
||||
1 Corsair Captain
|
||||
1 Evacuation
|
||||
1 Timestream Navigator
|
||||
1 Warkite Marauder
|
||||
1 Black Market Connections
|
||||
1 Blood Money
|
||||
1 Dire Fleet Ravager
|
||||
1 Fathom Fleet Captain
|
||||
1 Lethal Scheme
|
||||
1 Angrath's Marauders
|
||||
1 Blasphemous Act
|
||||
1 Captain Lannery Storm
|
||||
1 Captivating Crew
|
||||
1 Chaos Warp
|
||||
1 Coercive Recruiter
|
||||
1 Dire Fleet Daredevil
|
||||
1 Kari Zev, Skyship Raider
|
||||
1 Port Razer
|
||||
1 Shared Animosity
|
||||
1 Admiral Beckett Brass
|
||||
1 Hostage Taker
|
||||
1 King Narfi's Betrayal
|
||||
1 Prismari Command
|
||||
1 Zara, Renegade Recruiter
|
||||
1 Icon of Ancestry
|
||||
1 Vanquisher's Banner
|
||||
1 Choked Estuary
|
||||
1 Desolate Lighthouse
|
||||
1 Exotic Orchard
|
||||
1 Foreboding Ruins
|
||||
1 Frostboil Snarl
|
||||
1 Geier Reach Sanitarium
|
||||
1 Nephalia Drownyard
|
||||
1 Smoldering Marsh
|
||||
1 Sulfur Falls
|
||||
1 Sunken Hollow
|
||||
1 Azure Fleet Admiral
|
||||
1 Enterprising Scallywag
|
||||
1 Daring Saboteur
|
||||
1 Departed Deckhand
|
||||
1 Distant Melody
|
||||
1 Ghost of Ramirez DePietro
|
||||
1 Malcolm, Keen-Eyed Navigator
|
||||
1 Merchant Raiders
|
||||
1 Siren Stormtamer
|
||||
1 Spectral Sailor
|
||||
1 Windfall
|
||||
1 Feed the Swarm
|
||||
1 Pitiless Plunderer
|
||||
1 Breeches, Brazen Plunderer
|
||||
1 Faithless Looting
|
||||
1 Rakdos Charm
|
||||
1 Ramirez DePietro, Pillager
|
||||
1 Arcane Signet
|
||||
1 Commander's Sphere
|
||||
1 Dimir Signet
|
||||
1 Herald's Horn
|
||||
1 Izzet Signet
|
||||
1 Rakdos Signet
|
||||
1 Sol Ring
|
||||
1 Wayfarer's Bauble
|
||||
1 Command Tower
|
||||
1 Crumbling Necropolis
|
||||
1 Evolving Wilds
|
||||
1 Path of Ancestry
|
||||
1 Port of Karfell
|
||||
1 Rogue's Passage
|
||||
1 Secluded Courtyard
|
||||
1 Terramorphic Expanse
|
||||
1 Thriving Bluff
|
||||
1 Thriving Isle
|
||||
1 Thriving Moor
|
||||
1 Unclaimed Territory
|
||||
3 Island
|
||||
3 Island
|
||||
2 Swamp
|
||||
2 Swamp
|
||||
3 Mountain
|
||||
2 Mountain
|
||||
@@ -1,78 +0,0 @@
|
||||
[metadata]
|
||||
Name=Angels: They're Just Like Us but Cooler and with Wings
|
||||
[Commander]
|
||||
1 Gisela, the Broken Blade
|
||||
[Main]
|
||||
1 Bruna, the Fading Light
|
||||
1 Ajani, Strength of the Pride
|
||||
1 Angel of Destiny
|
||||
1 Angel of Finality
|
||||
1 Angel of Serenity
|
||||
1 Angel of the Ruins
|
||||
1 Angel of Vitality
|
||||
1 Angelic Accord
|
||||
1 Angelic Field Marshal
|
||||
1 Arcane Signet
|
||||
1 Arch of Orazca
|
||||
1 Archangel of Thune
|
||||
1 Archangel of Tithes
|
||||
1 Arden Angel
|
||||
1 Austere Command
|
||||
1 Bishop of Wings
|
||||
1 Bonders' Enclave
|
||||
1 Breathkeeper Seraph
|
||||
1 Cleansing Nova
|
||||
1 Commander's Plate
|
||||
1 Cosmos Elixir
|
||||
1 Court of Grace
|
||||
1 Dawn of Hope
|
||||
1 Dawnbreak Reclaimer
|
||||
1 Dismantling Wave
|
||||
1 Emeria Shepherd
|
||||
1 Emeria, the Sky Ruin
|
||||
1 Endless Atlas
|
||||
1 Entreat the Angels
|
||||
1 Everflowing Chalice
|
||||
1 Giada, Font of Hope
|
||||
1 Griffin Aerie
|
||||
1 Heirloom Blade
|
||||
1 Invoke the Divine
|
||||
1 Karoo
|
||||
1 Keeper of the Accord
|
||||
1 Kindred Boon
|
||||
1 Lightning Greaves
|
||||
1 Marble Diamond
|
||||
1 Mazemind Tome
|
||||
1 Mind Stone
|
||||
1 Myriad Landscape
|
||||
1 Nykthos Paragon
|
||||
1 Nykthos, Shrine to Nyx
|
||||
1 Oketra's Monument
|
||||
1 Path of Ancestry
|
||||
1 Path to Exile
|
||||
1 Pearl Medallion
|
||||
8 Plains
|
||||
8 Plains
|
||||
7 Plains
|
||||
7 Plains
|
||||
1 Righteous Valkyrie
|
||||
1 Search for Glory
|
||||
1 Sephara, Sky's Blade
|
||||
1 Seraph Sanctuary
|
||||
1 Serra Ascendant
|
||||
1 Shattered Angel
|
||||
1 Sol Ring
|
||||
1 Speaker of the Heavens
|
||||
1 Starnheim Aspirant
|
||||
1 Sunblast Angel
|
||||
1 Swiftfoot Boots
|
||||
1 Sword of the Animist
|
||||
1 Swords to Plowshares
|
||||
1 Thalia's Lancers
|
||||
1 The Book of Exalted Deeds
|
||||
1 Tome of Legends
|
||||
1 Urza's Incubator
|
||||
1 Valkyrie Harbinger
|
||||
1 Vanquisher's Banner
|
||||
1 War Room
|
||||
1 Well of Lost Dreams
|
||||
@@ -1,90 +0,0 @@
|
||||
[metadata]
|
||||
Name=Animated Army
|
||||
[Commander]
|
||||
1 Bello, Bard of the Brambles
|
||||
[Main]
|
||||
1 Wildsear, Scouring Maw
|
||||
1 Domri, Anarch of Bolas
|
||||
1 Etali, Primal Storm
|
||||
1 Prosperous Bandit
|
||||
1 Pyreswipe Hawk
|
||||
1 Alchemist's Talent
|
||||
1 Berserkers' Onslaught
|
||||
1 Outpost Siege
|
||||
1 Rain of Riches
|
||||
1 Chaos Warp
|
||||
1 Sunbird's Invocation
|
||||
1 Gratuitous Violence
|
||||
1 Warstorm Surge
|
||||
1 Starstorm
|
||||
1 Lotus Cobra
|
||||
1 Evercoat Ursine
|
||||
1 Brightcap Badger // Fungus Frolic
|
||||
1 Trailtracker Scout
|
||||
1 Thickest in the Thicket
|
||||
1 Ghalta, Primal Hunger
|
||||
1 Esika's Chariot
|
||||
1 Unnatural Growth
|
||||
1 Greater Good
|
||||
1 Kodama of the East Tree
|
||||
1 Grothama, All-Devouring
|
||||
1 Rampaging Baloths
|
||||
1 Bootleggers' Stash
|
||||
1 Primeval Bounty
|
||||
1 Gilded Lotus
|
||||
1 Spine of Ish Sah
|
||||
1 Temple of Abandon
|
||||
1 Karplusan Forest
|
||||
1 Exotic Orchard
|
||||
1 Sheltered Thicket
|
||||
1 Game Trail
|
||||
1 Raging Ravine
|
||||
1 Copperline Gorge
|
||||
1 Mossfire Valley
|
||||
1 Cinder Glade
|
||||
1 Rootbound Crag
|
||||
1 Mosswort Bridge
|
||||
1 Blasphemous Act
|
||||
1 Llanowar Loamspeaker
|
||||
1 Tendershoot Dryad
|
||||
1 Goreclaw, Terror of Qal Sisma
|
||||
1 Path of Discovery
|
||||
1 Decimate
|
||||
1 Rolling Hamsphere
|
||||
1 Teapot Slinger
|
||||
1 Explore
|
||||
1 Farseek
|
||||
1 Cultivate
|
||||
1 Grumgully, the Generous
|
||||
1 Wandertale Mentor
|
||||
1 Thought Vessel
|
||||
1 Arcane Signet
|
||||
1 Wooded Ridgeline
|
||||
1 Big Score
|
||||
1 Abrade
|
||||
1 Rampant Growth
|
||||
1 Sakura-Tribe Elder
|
||||
1 Beast Within
|
||||
1 Garruk's Packleader
|
||||
1 Harmonize
|
||||
1 Garruk's Uprising
|
||||
1 Gruul Signet
|
||||
1 Burnished Hart
|
||||
1 Hedron Archive
|
||||
1 Fellwar Stone
|
||||
1 Thran Dynamo
|
||||
1 Sol Ring
|
||||
1 Mind Stone
|
||||
1 Talisman of Impulse
|
||||
1 Terramorphic Expanse
|
||||
1 Path of Ancestry
|
||||
1 Gruul Turf
|
||||
1 Evolving Wilds
|
||||
1 Forgotten Cave
|
||||
1 Tranquil Thicket
|
||||
1 Command Tower
|
||||
1 Reliquary Tower
|
||||
4 Mountain
|
||||
4 Mountain
|
||||
5 Forest
|
||||
5 Forest
|
||||
@@ -1,95 +0,0 @@
|
||||
[metadata]
|
||||
Name=Arcane Maelstrom
|
||||
[Commander]
|
||||
1 Kalamax, the Stormsire
|
||||
[Main]
|
||||
1 Jace, Architect of Thought
|
||||
1 Xyris, the Writhing Storm
|
||||
1 Haldan, Avid Arcanist
|
||||
1 Pako, Arcane Retriever
|
||||
1 Eon Frolicker
|
||||
1 Nascent Metamorph
|
||||
1 Glademuse
|
||||
1 Ravenous Gigantotherium
|
||||
1 Lunar Mystic
|
||||
1 Niblis of Frost
|
||||
1 Talrand, Sky Summoner
|
||||
1 Charmbreaker Devils
|
||||
1 Dualcaster Mage
|
||||
1 Etali, Primal Storm
|
||||
1 Goblin Dark-Dwellers
|
||||
1 Djinn Illuminatus
|
||||
1 Melek, Izzet Paragon
|
||||
1 Rashmi, Eternities Crafter
|
||||
1 Wort, the Raidmother
|
||||
1 Solemn Simulacrum
|
||||
1 Murmuring Mystic
|
||||
1 Crackling Drake
|
||||
1 Surreal Memoir
|
||||
1 Decoy Gambit
|
||||
1 Deflecting Swat
|
||||
1 Curious Herd
|
||||
1 Chaos Warp
|
||||
1 Comet Storm
|
||||
1 Commune with Lava
|
||||
1 Starstorm
|
||||
1 Strength of the Tajuru
|
||||
1 Artifact Mutation
|
||||
1 Prophetic Bolt
|
||||
1 Clash of Titans
|
||||
1 Channeled Force
|
||||
1 Chemister's Insight
|
||||
1 Frantic Search
|
||||
1 Whiplash Trap
|
||||
1 Crop Rotation
|
||||
1 Evolution Charm
|
||||
1 Harrow
|
||||
1 Hunter's Insight
|
||||
1 Hunting Pack
|
||||
1 Natural Connection
|
||||
1 Slice in Twain
|
||||
1 Tribute to the Wild
|
||||
1 Growth Spiral
|
||||
1 Temur Charm
|
||||
1 Lavabrink Floodgates
|
||||
1 Twinning Staff
|
||||
1 Bonder's Ornament
|
||||
1 Arcane Signet
|
||||
1 Commander's Sphere
|
||||
1 Lightning Greaves
|
||||
1 Sol Ring
|
||||
1 Swarm Intelligence
|
||||
1 Primal Empathy
|
||||
1 Psychic Impetus
|
||||
1 Shiny Impetus
|
||||
1 Predatory Impetus
|
||||
1 Wilderness Reclamation
|
||||
1 Cinder Glade
|
||||
1 Desolate Lighthouse
|
||||
1 Exotic Orchard
|
||||
1 Kessig Wolf Run
|
||||
1 Mossfire Valley
|
||||
1 Mosswort Bridge
|
||||
1 Oran-Rief, the Vastwood
|
||||
1 Scavenger Grounds
|
||||
1 Yavimaya Coast
|
||||
1 Command Tower
|
||||
1 Frontier Bivouac
|
||||
1 Gruul Turf
|
||||
1 Halimar Depths
|
||||
1 Izzet Boilerworks
|
||||
1 Myriad Landscape
|
||||
1 Rupture Spire
|
||||
1 Simic Growth Chamber
|
||||
1 Rugged Highlands
|
||||
1 Swiftwater Cliffs
|
||||
1 Thornwood Falls
|
||||
3 Forest
|
||||
3 Forest
|
||||
2 Forest
|
||||
2 Island
|
||||
2 Island
|
||||
1 Island
|
||||
2 Mountain
|
||||
2 Mountain
|
||||
1 Mountain
|
||||
@@ -1,93 +0,0 @@
|
||||
[metadata]
|
||||
Name=Arcane Wizardry
|
||||
[Commander]
|
||||
1 Inalla, Archmage Ritualist
|
||||
[Main]
|
||||
1 Kess, Dissident Mage
|
||||
1 Mairsil, the Pretender
|
||||
1 Galecaster Colossus
|
||||
1 Magus of the Mind
|
||||
1 Portal Mage
|
||||
1 Vindictive Lich
|
||||
1 Izzet Chemister
|
||||
1 Taigam, Sidisi's Hand
|
||||
1 Havengul Lich
|
||||
1 Marchesa, the Black Rose
|
||||
1 Vela the Night-Clad
|
||||
1 Arcanis the Omnipotent
|
||||
1 Azami, Lady of Scrolls
|
||||
1 Body Double
|
||||
1 Harbinger of the Tides
|
||||
1 Serendib Sorcerer
|
||||
1 Apprentice Necromancer
|
||||
1 Magus of the Abyss
|
||||
1 Puppeteer Clique
|
||||
1 Etherium-Horn Sorcerer
|
||||
1 Mercurial Chemister
|
||||
1 Nin, the Pain Artist
|
||||
1 Niv-Mizzet, the Firemind
|
||||
1 Shadowmage Infiltrator
|
||||
1 Bloodline Necromancer
|
||||
1 Archaeomancer
|
||||
1 Merchant of Secrets
|
||||
1 Sea Gate Oracle
|
||||
1 Corpse Augur
|
||||
1 Izzet Chronarch
|
||||
1 Nivix Guildmage
|
||||
1 Kindred Dominance
|
||||
1 Clone Legion
|
||||
1 Spelltwine
|
||||
1 Decree of Pain
|
||||
1 Necromantic Selection
|
||||
1 Comet Storm
|
||||
1 Polymorphist's Jest
|
||||
1 Chaos Warp
|
||||
1 Memory Plunder
|
||||
1 Silumgar's Command
|
||||
1 Into the Roil
|
||||
1 Opportunity
|
||||
1 Reality Shift
|
||||
1 Go for the Throat
|
||||
1 Cauldron Dance
|
||||
1 Crosis's Charm
|
||||
1 Rakdos Charm
|
||||
1 Terminate
|
||||
1 Nevinyrral's Disk
|
||||
1 Mirror of the Forebears
|
||||
1 Commander's Sphere
|
||||
1 Darksteel Ingot
|
||||
1 Fellwar Stone
|
||||
1 Sol Ring
|
||||
1 Unstable Obelisk
|
||||
1 Worn Powerstone
|
||||
1 Shifting Shadow
|
||||
1 Curse of Verbosity
|
||||
1 Curse of Disturbance
|
||||
1 Curse of Opulence
|
||||
1 Exotic Orchard
|
||||
1 Mystifying Maze
|
||||
4 Island
|
||||
3 Island
|
||||
3 Island
|
||||
2 Swamp
|
||||
2 Swamp
|
||||
2 Swamp
|
||||
2 Mountain
|
||||
1 Mountain
|
||||
1 Mountain
|
||||
1 Path of Ancestry
|
||||
1 Command Tower
|
||||
1 Crumbling Necropolis
|
||||
1 Dimir Aqueduct
|
||||
1 Dismal Backwater
|
||||
1 Evolving Wilds
|
||||
1 Grixis Panorama
|
||||
1 Izzet Boilerworks
|
||||
1 Jwar Isle Refuge
|
||||
1 Rakdos Carnarium
|
||||
1 Swiftwater Cliffs
|
||||
1 Temple of the False God
|
||||
1 Terramorphic Expanse
|
||||
1 Vivid Crag
|
||||
1 Vivid Creek
|
||||
1 Vivid Marsh
|
||||
@@ -1,85 +0,0 @@
|
||||
[metadata]
|
||||
Name=Arm for Battle
|
||||
[Commander]
|
||||
1 Wyleth, Soul of Steel
|
||||
[Main]
|
||||
1 Oreskos Explorer
|
||||
1 Relic Seeker
|
||||
1 Sram, Senior Edificer
|
||||
1 Brass Squire
|
||||
1 Danitha Capashen, Paragon
|
||||
1 Dualcaster Mage
|
||||
1 Flickerwisp
|
||||
1 Ironclad Slayer
|
||||
1 Kor Cartographer
|
||||
1 Odric, Lunarch Marshal
|
||||
1 Tiana, Ship's Caretaker
|
||||
1 Condemn
|
||||
1 Expedite
|
||||
1 Swords to Plowshares
|
||||
1 Abrade
|
||||
1 Boros Charm
|
||||
1 Comet Storm
|
||||
1 Dawn Charm
|
||||
1 Deflecting Palm
|
||||
1 Disenchant
|
||||
1 Fists of Flame
|
||||
1 Temur Battle Rage
|
||||
1 Valorous Stance
|
||||
1 Generous Gift
|
||||
1 Unbreakable Formation
|
||||
1 Volcanic Fallout
|
||||
1 Wear // Tear
|
||||
1 White Sun's Zenith
|
||||
1 Master Warcraft
|
||||
1 Return to Dust
|
||||
1 Wild Ricochet
|
||||
1 Word of Seizing
|
||||
1 Response // Resurgence
|
||||
1 Jaya's Immolating Inferno
|
||||
1 Martial Coup
|
||||
1 Relentless Assault
|
||||
1 Winds of Rath
|
||||
1 Bonesplitter
|
||||
1 Explorer's Scope
|
||||
1 Sol Ring
|
||||
1 Blackblade Reforged
|
||||
1 Blazing Sunsteel
|
||||
1 Boros Signet
|
||||
1 Hero's Blade
|
||||
1 Mask of Avacyn
|
||||
1 Ring of Thune
|
||||
1 Ring of Valkas
|
||||
1 Swiftfoot Boots
|
||||
1 Fireshrieker
|
||||
1 Haunted Cloak
|
||||
1 Loxodon Warhammer
|
||||
1 Sunforger
|
||||
1 Sword of Vengeance
|
||||
1 Sigarda's Aid
|
||||
1 Spirit Mantle
|
||||
1 Timely Ward
|
||||
1 Unquestioned Authority
|
||||
1 Faith Unbroken
|
||||
1 On Serra's Wings
|
||||
1 Boros Garrison
|
||||
1 Boros Guildgate
|
||||
1 Command Tower
|
||||
1 Encroaching Wastes
|
||||
1 Evolving Wilds
|
||||
1 Forgotten Cave
|
||||
1 Memorial to War
|
||||
5 Mountain
|
||||
4 Mountain
|
||||
1 Myriad Landscape
|
||||
7 Plains
|
||||
7 Plains
|
||||
1 Rogue's Passage
|
||||
1 Rupture Spire
|
||||
1 Secluded Steppe
|
||||
1 Slayers' Stronghold
|
||||
1 Stone Quarry
|
||||
1 Sunhome, Fortress of the Legion
|
||||
1 Terramorphic Expanse
|
||||
1 Transguild Promenade
|
||||
1 Wind-Scarred Crag
|
||||
@@ -1,100 +0,0 @@
|
||||
[metadata]
|
||||
Name=Aura of Courage
|
||||
[Commander]
|
||||
1 Galea, Kindler of Hope
|
||||
[Main]
|
||||
1 Angel of Finality
|
||||
1 Puresteel Paladin
|
||||
1 Realm-Cloaked Giant // Cast Off
|
||||
1 Sram, Senior Edificer
|
||||
1 Prognostic Sphinx
|
||||
1 Cold-Eyed Selkie
|
||||
1 Fleecemane Lion
|
||||
1 Knight of Autumn
|
||||
1 Riverwise Augur
|
||||
1 Acidic Slime
|
||||
1 Paradise Druid
|
||||
1 Winds of Rath
|
||||
1 Serum Visions
|
||||
1 Nature's Lore
|
||||
1 Heroic Intervention
|
||||
1 Valorous Stance
|
||||
1 Brainstorm
|
||||
1 Bant Charm
|
||||
1 Argentum Armor
|
||||
1 Basilisk Collar
|
||||
1 Masterwork of Ingenuity
|
||||
1 Moonsilver Spear
|
||||
1 Sword of the Animist
|
||||
1 Behemoth Sledge
|
||||
1 Arcane Signet
|
||||
1 Colossus Hammer
|
||||
1 Explorer's Scope
|
||||
1 Sol Ring
|
||||
1 Swiftfoot Boots
|
||||
1 Viridian Longbow
|
||||
1 Imprisoned in the Moon
|
||||
1 Greater Good
|
||||
1 Verdant Embrace
|
||||
1 Angelic Gift
|
||||
1 Gryff's Boon
|
||||
1 Curse of Verbosity
|
||||
1 Eel Umbra
|
||||
1 Psychic Impetus
|
||||
1 Abundant Growth
|
||||
1 Fertile Ground
|
||||
1 Kenrith's Transformation
|
||||
1 Rancor
|
||||
1 Utopia Sprawl
|
||||
1 Wild Growth
|
||||
1 Shielding Plax
|
||||
1 Canopy Vista
|
||||
1 Exotic Orchard
|
||||
1 Fortified Village
|
||||
1 Lumbering Falls
|
||||
1 Port Town
|
||||
1 Prairie Stream
|
||||
1 Skycloud Expanse
|
||||
1 Sungrass Prairie
|
||||
1 Evolving Wilds
|
||||
1 Azorius Chancery
|
||||
1 Bant Panorama
|
||||
1 Command Tower
|
||||
1 Flood Plain
|
||||
1 Grasslands
|
||||
1 Halimar Depths
|
||||
1 Mishra's Factory
|
||||
1 Path of Ancestry
|
||||
1 Seaside Citadel
|
||||
1 Simic Growth Chamber
|
||||
1 Terramorphic Expanse
|
||||
1 Thriving Grove
|
||||
1 Thriving Heath
|
||||
1 Thriving Isle
|
||||
1 Vitu-Ghazi, the City-Tree
|
||||
1 Island
|
||||
1 Island
|
||||
1 Island
|
||||
1 Island
|
||||
2 Forest
|
||||
2 Forest
|
||||
2 Forest
|
||||
2 Forest
|
||||
1 Plains
|
||||
1 Plains
|
||||
1 Storvald, Frost Giant Jarl
|
||||
1 Fey Steed
|
||||
1 Holy Avenger
|
||||
1 Mantle of the Ancients
|
||||
1 Robe of Stars
|
||||
1 Valiant Endeavor
|
||||
1 Diviner's Portent
|
||||
1 Netherese Puzzle-Ward
|
||||
1 Winged Boots
|
||||
1 Belt of Giant Strength
|
||||
1 Song of Inspiration
|
||||
1 Catti-brie of Mithral Hall
|
||||
1 Ride the Avalanche
|
||||
1 Clay Golem
|
||||
1 Ebony Fly
|
||||
1 Sword of Hours
|
||||
@@ -1,96 +0,0 @@
|
||||
[metadata]
|
||||
Name=Bedecked Brokers
|
||||
[Commander]
|
||||
1 Perrie, the Pulverizer
|
||||
[Main]
|
||||
1 Kros, Defense Contractor
|
||||
1 Ajani Unyielding
|
||||
1 Angelic Sleuth
|
||||
1 Aven Courier
|
||||
1 Aven Mimeomancer
|
||||
1 Avenging Huntbonder
|
||||
1 Bribe Taker
|
||||
1 Crystalline Giant
|
||||
1 Devoted Druid
|
||||
1 Denry Klin, Editor in Chief
|
||||
1 Evolution Sage
|
||||
1 Fathom Mage
|
||||
1 Forgotten Ancient
|
||||
1 Grateful Apparition
|
||||
1 Incubation Druid
|
||||
1 Jenara, Asura of War
|
||||
1 Luminarch Aspirant
|
||||
1 Park Heights Maverick
|
||||
1 Rishkar, Peema Renegade
|
||||
1 Roalesk, Apex Hybrid
|
||||
1 Scavenging Ooze
|
||||
1 Shield Broker
|
||||
1 Skyboon Evangelist
|
||||
1 Skyship Plunderer
|
||||
1 Slippery Bogbonder
|
||||
1 Steelbane Hydra
|
||||
1 Thrummingbird
|
||||
1 Vorel of the Hull Clade
|
||||
1 Wall of Roots
|
||||
1 Wickerbough Elder
|
||||
1 Wingspan Mentor
|
||||
1 Declaration in Stone
|
||||
1 Damning Verdict
|
||||
1 Planar Outburst
|
||||
1 Tezzeret's Gambit
|
||||
1 Rishkar's Expertise
|
||||
1 Urban Evolution
|
||||
1 Bant Charm
|
||||
1 Brokers Charm
|
||||
1 Brokers Confluence
|
||||
1 Contractual Safeguard
|
||||
1 Exotic Pets
|
||||
1 Generous Gift
|
||||
1 Storm of Forms
|
||||
1 Family's Favor
|
||||
1 Hoofprints of the Stag
|
||||
1 Primal Empathy
|
||||
1 Resourceful Defense
|
||||
1 Together Forever
|
||||
1 Agent's Toolkit
|
||||
1 Arcane Signet
|
||||
1 Commander's Sphere
|
||||
1 Everflowing Chalice
|
||||
1 Fellwar Stone
|
||||
1 Gavel of the Righteous
|
||||
1 Midnight Clock
|
||||
1 Oblivion Stone
|
||||
1 Oracle's Vault
|
||||
1 Power Conduit
|
||||
1 Sol Ring
|
||||
1 Swiftfoot Boots
|
||||
1 Ash Barrens
|
||||
1 Bant Panorama
|
||||
1 Brokers Hideout
|
||||
1 Canopy Vista
|
||||
1 Command Tower
|
||||
1 Exotic Orchard
|
||||
1 Flooded Grove
|
||||
1 Fortified Village
|
||||
1 Gavony Township
|
||||
1 Karn's Bastion
|
||||
1 Littjara Mirrorlake
|
||||
1 Llanowar Reborn
|
||||
1 Myriad Landscape
|
||||
1 Nesting Grounds
|
||||
1 Path of Ancestry
|
||||
1 Port Town
|
||||
1 Prairie Stream
|
||||
1 Seaside Citadel
|
||||
1 Skycloud Expanse
|
||||
1 Sungrass Prairie
|
||||
1 Temple of Mystery
|
||||
1 Vivid Creek
|
||||
1 Vivid Grove
|
||||
1 Vivid Meadow
|
||||
3 Forest
|
||||
2 Forest
|
||||
2 Island
|
||||
2 Island
|
||||
3 Plains
|
||||
2 Plains
|
||||
@@ -1,92 +0,0 @@
|
||||
[metadata]
|
||||
Name=Blame Game
|
||||
[Commander]
|
||||
1 Nelly Borca, Impulsive Accuser
|
||||
[Main]
|
||||
1 Feather, Radiant Arbiter
|
||||
1 Immortal Obligation
|
||||
1 Otherworldly Escort
|
||||
1 Redemption Arc
|
||||
1 Trouble in Pairs
|
||||
1 Havoc Eater
|
||||
1 Hot Pursuit
|
||||
1 Mob Verdict
|
||||
1 Prisoner's Dilemma
|
||||
1 Take the Bait
|
||||
1 Ransom Note
|
||||
1 Angel of the Ruins
|
||||
1 Comeuppance
|
||||
1 Darien, King of Kjeldor
|
||||
1 Duelist's Heritage
|
||||
1 Elspeth, Sun's Champion
|
||||
1 Keeper of the Accord
|
||||
1 Loran of the Third Path
|
||||
1 Promise of Loyalty
|
||||
1 Selfless Squire
|
||||
1 Sevinne's Reclamation
|
||||
1 Smuggler's Share
|
||||
1 Stalking Leonin
|
||||
1 Sun Titan
|
||||
1 Windborn Muse
|
||||
1 Winds of Rath
|
||||
1 Agitator Ant
|
||||
1 Brash Taunter
|
||||
1 Disrupt Decorum
|
||||
1 Etali, Primal Storm
|
||||
1 Fiendish Duo
|
||||
1 Frontier Warmonger
|
||||
1 Kazuul, Tyrant of the Cliffs
|
||||
1 Spectacular Showdown
|
||||
1 Vengeful Ancestor
|
||||
1 Anya, Merciless Angel
|
||||
1 Boros Reckoner
|
||||
1 Deflecting Palm
|
||||
1 Gisela, Blade of Goldnight
|
||||
1 Ancient Stone Idol
|
||||
1 Solemn Simulacrum
|
||||
1 Steel Hellkite
|
||||
1 Tome of Legends
|
||||
1 Castle Ardenvale
|
||||
1 Exotic Orchard
|
||||
1 Furycalm Snarl
|
||||
1 Kher Keep
|
||||
1 Labyrinth of Skophos
|
||||
1 Needle Spires
|
||||
1 Scavenger Grounds
|
||||
1 Slayers' Stronghold
|
||||
1 Temple of Triumph
|
||||
1 Throne of the High City
|
||||
1 War Room
|
||||
1 Ghostly Prison
|
||||
1 Gideon's Sacrifice
|
||||
1 Martial Impetus
|
||||
1 Orzhov Advokist
|
||||
1 Seal of Cleansing
|
||||
1 Soul Snare
|
||||
1 Vow of Duty
|
||||
1 Wall of Omens
|
||||
1 Curse of Opulence
|
||||
1 Rite of the Raging Storm
|
||||
1 Shiny Impetus
|
||||
1 Vow of Lightning
|
||||
1 Arcane Signet
|
||||
1 Bloodthirsty Blade
|
||||
1 Fellwar Stone
|
||||
1 Mind Stone
|
||||
1 Sol Ring
|
||||
1 Talisman of Conviction
|
||||
1 Thought Vessel
|
||||
1 Access Tunnel
|
||||
1 Ash Barrens
|
||||
1 Boros Garrison
|
||||
1 Command Tower
|
||||
1 Escape Tunnel
|
||||
1 Myriad Landscape
|
||||
1 Reliquary Tower
|
||||
1 Rogue's Passage
|
||||
1 Sunhome, Fortress of the Legion
|
||||
1 Temple of the False God
|
||||
5 Plains
|
||||
4 Plains
|
||||
4 Mountain
|
||||
3 Mountain
|
||||
@@ -1,89 +0,0 @@
|
||||
[metadata]
|
||||
Name=Blight Curse
|
||||
[Commander]
|
||||
1 Auntie Ool, Cursewretch
|
||||
[Main]
|
||||
1 The Reaper, King No More
|
||||
1 Wickersmith's Tools
|
||||
1 Sinister Gnarlbark
|
||||
1 Eventide's Shadow
|
||||
1 Aberrant Return
|
||||
1 Dread Tiller
|
||||
1 Grave Venerations
|
||||
1 Oft-Nabbed Goat
|
||||
1 Village Pillagers
|
||||
1 Puca's Covenant
|
||||
1 Ferrafor, Young Yew
|
||||
1 Tree of Perdition
|
||||
1 Vernal Fen
|
||||
1 Festering Thicket
|
||||
1 Necroskitter
|
||||
1 Ignoble Hierarch
|
||||
1 Nesting Grounds
|
||||
1 Massacre Girl, Known Killer
|
||||
1 Grave Titan
|
||||
1 Vraska, Betrayal's Sting
|
||||
1 The Scorpion God
|
||||
1 Liliana, Death Wielder
|
||||
1 Chimil, the Inner Sun
|
||||
1 Carnifex Demon
|
||||
1 Puppeteer Clique
|
||||
1 Dusk Urchins
|
||||
1 Black Sun's Zenith
|
||||
1 Painful Truths
|
||||
1 Persist
|
||||
1 Midnight Banshee
|
||||
1 Chain Reaction
|
||||
1 Archfiend of Ifnir
|
||||
1 Channeler Initiate
|
||||
1 Hapatra, Vizier of Poisons
|
||||
1 Glissa Sunslayer
|
||||
1 Everlasting Torment
|
||||
1 Assassin's Trophy
|
||||
1 Grim Poppet
|
||||
1 Canyon Slough
|
||||
1 Sheltered Thicket
|
||||
1 Exotic Orchard
|
||||
1 Smoldering Marsh
|
||||
1 Cinder Glade
|
||||
1 Dragonskull Summit
|
||||
1 Rootbound Crag
|
||||
1 Woodland Cemetery
|
||||
1 Lasting Tarfire
|
||||
1 Burning Curiosity
|
||||
1 Hoarder's Greed
|
||||
1 Blowfly Infestation
|
||||
1 Fire Covenant
|
||||
1 Sol Ring
|
||||
1 Arcane Signet
|
||||
1 Command Tower
|
||||
1 Cathartic Reunion
|
||||
1 Skinrender
|
||||
1 Soul Snuffers
|
||||
1 Incremental Blight
|
||||
1 Infernal Grasp
|
||||
1 Evolution Sage
|
||||
1 Devoted Druid
|
||||
1 Flourishing Defenses
|
||||
1 Harmonize
|
||||
1 Putrefy
|
||||
1 Binding the Old Gods
|
||||
1 Kulrath Knight
|
||||
1 Contagion Clasp
|
||||
1 Savage Lands
|
||||
1 Ifnir Deadlands
|
||||
1 Night's Whisper
|
||||
1 Wickerbough Elder
|
||||
1 Terminate
|
||||
1 Commander's Sphere
|
||||
1 Cathartic Pyre
|
||||
1 Evolving Wilds
|
||||
1 Terramorphic Expanse
|
||||
1 Path of Ancestry
|
||||
1 Riveteers Overlook
|
||||
1 Rakdos Carnarium
|
||||
1 Gruul Turf
|
||||
1 Golgari Rot Farm
|
||||
8 Swamp
|
||||
4 Mountain
|
||||
6 Forest
|
||||
@@ -1,87 +0,0 @@
|
||||
[metadata]
|
||||
Name=Blood Rites
|
||||
[Commander]
|
||||
1 Clavileño, First of the Blessed
|
||||
[Main]
|
||||
1 Carmen, Cruel Skymarcher
|
||||
1 Charismatic Conqueror
|
||||
1 Elenda's Hierophant
|
||||
1 March of the Canonized
|
||||
1 Redemption Choir
|
||||
1 Dusk Legion Sergeant
|
||||
1 Master of Dark Rites
|
||||
1 Promise of Aclazotz // Foul Rebirth
|
||||
1 Order of Sacred Dusk
|
||||
1 Austere Command
|
||||
1 Kindred Boon
|
||||
1 Mavren Fein, Dusk Apostle
|
||||
1 Radiant Destiny
|
||||
1 Welcoming Vampire
|
||||
1 Bloodghast
|
||||
1 Bloodtracker
|
||||
1 Butcher of Malakir
|
||||
1 Champion of Dusk
|
||||
1 Cordial Vampire
|
||||
1 Crossway Troublemakers
|
||||
1 Damn
|
||||
1 Drana, Liberator of Malakir
|
||||
1 Exquisite Blood
|
||||
1 Glass-Cast Heart
|
||||
1 New Blood
|
||||
1 Nighthawk Scavenger
|
||||
1 Olivia's Wrath
|
||||
1 Pact of the Serpent
|
||||
1 Patron of the Vein
|
||||
1 Sanctum Seeker
|
||||
1 Timothar, Baron of Bats
|
||||
1 Twilight Prophet
|
||||
1 Yahenni, Undying Partisan
|
||||
1 Elenda, the Dusk Rose
|
||||
1 Sorin, Lord of Innistrad
|
||||
1 Utter End
|
||||
1 Vona, Butcher of Magan
|
||||
1 Blade of the Bloodchief
|
||||
1 Isolated Chapel
|
||||
1 Shineshadow Snarl
|
||||
1 Temple of Silence
|
||||
1 Vault of the Archangel
|
||||
1 Voldaren Estate
|
||||
1 Windbrisk Heights
|
||||
1 Martyr of Dusk
|
||||
1 Return to Dust
|
||||
1 Swords to Plowshares
|
||||
1 Blood Artist
|
||||
1 Bloodline Necromancer
|
||||
1 Dusk Legion Zealot
|
||||
1 Falkenrath Noble
|
||||
1 Indulgent Aristocrat
|
||||
1 Oathsworn Vampire
|
||||
1 Village Rites
|
||||
1 Viscera Seer
|
||||
1 Bartolomé del Presidio
|
||||
1 Cruel Celebrant
|
||||
1 Etchings of the Chosen
|
||||
1 Legion Lieutenant
|
||||
1 Arcane Signet
|
||||
1 Commander's Sphere
|
||||
1 Heirloom Blade
|
||||
1 Mind Stone
|
||||
1 Orzhov Signet
|
||||
1 Sol Ring
|
||||
1 Swiftfoot Boots
|
||||
1 Talisman of Hierarchy
|
||||
1 Wayfarer's Bauble
|
||||
1 Bojuka Bog
|
||||
1 Command Tower
|
||||
1 Myriad Landscape
|
||||
1 Orzhov Basilica
|
||||
1 Path of Ancestry
|
||||
1 Rogue's Passage
|
||||
1 Secluded Courtyard
|
||||
1 Tainted Field
|
||||
1 Temple of the False God
|
||||
1 Unclaimed Territory
|
||||
4 Plains
|
||||
4 Plains
|
||||
7 Swamp
|
||||
6 Swamp
|
||||
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