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Author SHA1 Message Date
KoalaTrapLord 2c67222de1 fix(lobby): wire OnConfirm to POST /api/game/start, save game_id to PlayerPrefs
- OnConfirm() now calls ApiClient.PostGameStart() with 4-seat config
- On success: saves BattleSeats + GameId + WsUrl to PlayerPrefs,
  loads Battleground scene
- On failure: shows inline error label (red), re-enables Confirm button
- Added StartGameRequest/Response POCOs matching routes/game.py schema
- Added _errorLabel [SerializeField] for user-facing error feedback
2026-03-29 05:22:04 -04:00
KoalaTrapLord d8238ec11f feat(game): add routes/game.py + wire lab_api + fix GameWebSocketClient game_id URL
- routes/game.py: POST /api/game/start, POST /api/game/action (new)
- lab_api.py: import + register game_router
- GameWebSocketClient.cs: read game_id from PlayerPrefs, build
  ws://{host}/ws/game/{game_id} — fixes URL mismatch with ws_game.py
2026-03-29 05:17:48 -04:00
KoalaTrapLord 7aed64a660 fix: add PostGameAction to ApiClient + update TODO.md all issues complete
- ApiClient.cs: add PostGameAction() wrapper → POST /api/game/action
  (fixes compile error in HumanActionBar.cs)
- TODO.md: mark issues #145, #146, #147, #149, #150, #151 as ✅
  all 14 battleground issues now committed
2026-03-29 05:09:51 -04:00
KoalaTrapLord e3497a8f74 feat(#149): add GameWebSocketClient with reconnect, ping, and main-thread dispatch 2026-03-28 20:05:35 -04:00
KoalaTrapLord 6a655628c8 feat(#151): add Play Live button to SimulationController 2026-03-28 20:04:42 -04:00
KoalaTrapLord e3d4eb5785 feat(#147 #150 #151): add LobbySetupModal, ZoneViewerModal, EliminationHandler; add Play Live button to SimulationController 2026-03-28 20:03:43 -04:00
KoalaTrapLord abee551ab4 feat(#146): add StackZoneController, StackItemView, and ManaPoolDisplay 2026-03-28 20:02:23 -04:00
KoalaTrapLord 9fdb6d29fb chore: mark issues 1-8 as complete in task tracker 2026-03-28 19:58:00 -04:00
KoalaTrapLord 2c8c6c26bd feat(#144): add TokenModel POCO, TokenPanelController, and TokenRowView 2026-03-28 19:57:00 -04:00
KoalaTrapLord 9ffefd463c feat(#143): add CommanderZoneWidget with art crop, cast counter, and tax display 2026-03-28 19:56:24 -04:00
KoalaTrapLord 4c99957e76 feat(#142): add CardZone and CardView battlefield zone components 2026-03-28 19:56:09 -04:00
KoalaTrapLord e5e25a7553 feat(#141): add TurnOrderBar and TurnOrderPill 2026-03-28 19:55:40 -04:00
KoalaTrapLord 8d56feee80 feat(#140): add PhaseTrackerWidget and PhaseStepButton 2026-03-28 19:55:17 -04:00
KoalaTrapLord 837bb3d6f9 feat(#139): add LifeTrackerWidget, CommanderDamageRow, PoisonCounter 2026-03-28 19:54:56 -04:00
KoalaTrapLord 98b81ceab2 feat(#138): add BattlegroundController scene orchestrator 2026-03-28 19:54:24 -04:00
KoalaTrapLord fae04a9336 feat(#148): add GameStateManager singleton and GameState/SeatState POCOs 2026-03-28 19:54:01 -04:00
KoalaTrapLord 8fd155a925 feat(#143): add LoadArtCrop and LoadCardRaw (RawImage) to ImageCache 2026-03-28 19:53:25 -04:00
KoalaTrapLord 34ce9bfbf7 feat(#142): add [NonSerialized] battleground runtime state to CardModel 2026-03-28 19:53:09 -04:00
KoalaTrapLord 07010a71ed chore: add battleground UI task tracker (2026-03-28) 2026-03-28 19:50:06 -04:00
KoalaTrapLord ac536999eb Update lab_api.pyfeat: wire validate_forge_config into lifespan startup 2026-03-28 14:48:43 -04:00
KoalaTrapLord 2e43511d87 feat: add validate_forge_config() startup check
Validates FORGE_DIR at startup with clear warning if cardsfolder not found.
Checks both forge-gui/res/cardsfolder and res/cardsfolder layouts.
2026-03-28 14:34:50 -04:00
KoalaTrapLord 1bebe38e66 fix: forge_card_loader path resolution + reset_forge_cache helper
- _get_cards_folder() now tries both forge-gui/res/cardsfolder and res/cardsfolder
  so FORGE_DIR can point to either the repo root or the forge-gui subdirectory
- Add reset_forge_cache() to allow hot-reloading without server restart
2026-03-28 14:31:42 -04:00
KoalaTrapLord eca1653024 fix: backfill basic lands on card_entries to guarantee 99 cards
The previous backfill operated on seen_names dict which silently
ignored basics already present (dict keys are unique). Now backfills
directly on card_entries list after Scryfall fetch, incrementing
quantity on existing basics or appending new CardEntry objects.
Distributes shortfall evenly across commander color identity basics.
2026-03-28 00:26:42 -04:00
KoalaTrapLord 08f1cb7581 fix(deckgen): backfill basic lands after cross-category dedup to reach 99 cards 2026-03-28 00:10:52 -04:00
KoalaTrapLord 9d9e8675a9 fix: restore missing # on line 27 section banner in rules.py (SyntaxError on import) 2026-03-27 23:36:02 -04:00
KoalaTrapLord 64526b7bee fix: exclude lands from cmc_buckets in _build_deck_report_from_db (matches deck_service behaviour) 2026-03-27 20:38:49 -04:00
KoalaTrapLord e9732d292f fix: expand _land_kw, hoist to module level, guard has_real_data for cmc=0 lands after Forge lookup 2026-03-27 20:36:22 -04:00
KoalaTrapLord fa5349ea7c fix: basic land name fallback in _build_deck_report_from_db when type_line is missing from collection join 2026-03-27 20:32:15 -04:00
KoalaTrapLord 1c491bc2b0 fix: add Land role detection to _detect_card_roles (fixes basics showing as Unknown) 2026-03-27 20:28:35 -04:00
KoalaTrapLord 81fe5108d2 fix: coach_apply rollback on partial write, 4-space reindent; ws_game hoist inline imports 2026-03-27 20:24:55 -04:00
KoalaTrapLord 61fa37dc48 fix: lifespan indent error, get_running_loop, remove double rag init, register policy routes 2026-03-27 20:22:42 -04:00
KoalaTrapLord 0118950939 fix: add anthropic_api_key to Config with env-var default (fixes AttributeError in ASGI mode) 2026-03-27 20:20:45 -04:00
KoalaTrapLord 22cf23b24d fix: correct query_cards kwarg names in coach_service (n= -> n_results=, colors= -> color_identity=) 2026-03-27 20:17:14 -04:00
KoalaTrapLord 0010b0f394 refactor: RAG improvements — shared card_text, Chroma fallback, staleness monitor (fixes #133, #134, #135) (#136)
* refactor: add shared build_card_text() utility

* refactor: unify _build_document via shared card_text + add is_chroma_available/check_staleness (fixes #133, #134, #135)

* refactor: delegate embeddings._build_card_text to shared utility

* feat: add background RAG staleness monitor to lab_api lifespan (fixes #135)
2026-03-27 16:46:33 -04:00
KoalaTrapLord d0cdafa66a feat(rag): Phase 3 — inject RAG cards into coach prompts (#132)
* feat(rag): add RAG card injection to prompt_template.py

* feat(rag): wire RAG query into coach_service.py

* test(rag): add Phase 3 RAG injection tests
2026-03-27 15:20:49 -04:00
KoalaTrapLord bcf558418a fix(rag): squash all RAG/ChromaDB bugs #123–#130
Fixes critical API contract mismatch, batch_size wiring, lifespan init, failure tracking, build_meta persistence, mana_cost in embeddings, pyproject.toml deps, and adds full test suite.

Closes #123, #124, #125, #126, #127, #128, #129, #130
2026-03-27 13:24:41 -04:00
KoalaTrapLord 61ac349445 fix: re-encode requirements.txt as UTF-8 (fixes pip install failure) 2026-03-27 11:14:34 -04:00
KoalaTrapLord 06ce5cc91a fix(rules): add nonbasic land guard before creature fallback (fixes #118) (#122) 2026-03-27 10:22:05 -04:00
KoalaTrapLord fc2e9d88d5 feat(rag): wire ensure_rag_store into main() startup (#121) 2026-03-27 09:13:03 -04:00
KoalaTrapLord 4b0cd34eda feat(rag): Phase 2 — ChromaDB vector index over bulk card DB (#120)
* feat(rag): Phase 2 — ChromaDB vector index over bulk card DB

* feat(rag): add routes/rag.py with build, search, status endpoints

* Update lab_api.pyfeat(rag): register rag_router in lab_api.py, remove old inline rag_status

* Update requirements.txtfeat(rag): add chromadb>=0.5.0 to requirements.txtfeat(rag): add chromadb to requirements.txt
2026-03-27 08:39:02 -04:00
KoalaTrapLord 83502ce726 feat: Phase 3/5 deep analysis + Quick Digest endpoint (issue #108) (#119)
* feat(coach): Phase 3/5 JSON schema + QUICK_DIGEST prompt (issue #108)

- Append removalCoverage, rampQuality, drawEngineProfile, winConditions,
  antiSynergyFlags, podPresence, tempoAssessment, metaMatchups to schema
- Add 8 new RULES for Phase 3/5 analysis
- Add QUICK_DIGEST_SYSTEM_PROMPT constant
- Add build_quick_system_prompt() function

* feat(coach): Phase 3/5 JSON parsing + run_quick_digest() (issue #108)

* feat(routes): add POST /api/coach/decks/{deck_id}/quick endpoint (issue #108)

* feat(coach): Phase 3/5 parsing + run_quick_digest() (issue #108)

* feat(coach): Phase 3/5 parsing + run_quick_digest() (issue #108)

- Import RemovalCoverage, RampQuality, DrawEngineProfile, WinCondition,
  AntiSynergyFlag, PodPresence, TempoAssessment, MetaMatchup
- Add Phase 3/5 blocks to _populate_session_from_json()
- Add run_quick_digest() method

* feat(ui): Top Priority card + Quick/Deep buttons in coach.html (issue #108)

* feat(coach): Phase 3/5 parsing + run_quick_digest() (issue #108)

* feat(ui): wire Quick Digest button + displayTopPriority() (issue #108)
2026-03-27 07:51:01 -04:00
KoalaTrapLord 8df5900ff9 feat(rag): Phase 1 — Scryfall bulk offline card database (SQLite FTS5) (#117)
* feat(rag): add scryfall_bulk.py — offline SQLite FTS5 card database (RAG Phase 1)

* feat(rag): wire ensure_bulk_db() into startup and add /api/rag/status endpoint (RAG Phase 1)

* fix: migrate on_event to lifespan handler, fix /api/rag/status route ordering
2026-03-27 07:16:55 -04:00
KoalaTrapLord 864cefb8d1 feat(coach): Phase 5 - add PodPresence, TempoAssessment, MetaMatchup models + isQuickDigest flag (#108) 2026-03-27 04:22:23 -04:00
KoalaTrapLord 6c03e56794 fix(#116): dual card_records fallback JOIN, card_type/card_category fields, simulator land detection
- deck_service._compute_deck_analysis: add LEFT JOIN card_records with COALESCE on all fields
- deck_service: add _classify_card_category() with oracle-text keyword matching
- deckbuilder.GET /api/decks/{id}/cards: add card_records fallback JOIN + COALESCE, emit card_type/card_category per card
- deepseek._fetch_deck_card_data: add card_records fallback JOIN so lands get correct type_line before enrich_card()

Closes #116
2026-03-27 04:10:17 -04:00
KoalaTrapLord 4b92a5f364 Update models.pyfeat(coach): Phase 3 - add RemovalCoverage, RampQuality, DrawEngineProfile, WinCondition, AntiSynergyFlag models (#108) 2026-03-27 04:01:25 -04:00
KoalaTrapLord da0193eb11 Update coach_service.pyfeat(coach): Phase 2 - parse upgradePriority, commanderDependency, mulliganAnalysis in coach_service (#108)feat(coach): Phase 2 - parse new fields in coach_service (#108) 2026-03-27 03:45:32 -04:00
KoalaTrapLord 71e0db4985 feat(coach): Phase 2 - add upgradePriority, commanderDependency, mulliganAnalysis to prompt schema (#108) 2026-03-27 03:32:03 -04:00
KoalaTrapLord 825946ae75 fix(deckgen): Phase 1 - non-destructive smart substitution + 99-card guarantee (#111)
- Add SubstitutionMethod enum to track how each card was resolved
- smart_substitute() now always returns a valid card string (never None/drops)
  fallback chain: exact -> fuzzy -> functional analog -> original kept -> basic land
- apply_substitutions() loop no longer silently drops cards on failure
- _pad_to_99() circuit breaker added before BuildResult validation
- Pre-validation count logging per category for fast regression diagnosis
- Startup assertion: sum(SLOT_BUDGET) == 99 raises on app boot if misconfigured
- Prompt injection: slot budget + exact card count requirement sent to LLM
- run_substitution() anthropic fallback failure now pads with basic land instead of leaving slot missing
- SubstitutionBatchResult gains padded_slots field (> 0 = circuit breaker fired)
2026-03-27 03:27:52 -04:00
KoalaTrapLord bd4df0c263 Update models.pyfeat(coach): Phase 2 - add UpgradePriority, CommanderDependency, MulliganAnalysis models (#108) 2026-03-27 02:52:01 -04:00
KoalaTrapLord 113b5c63f0 docs: rewrite README to reflect full current feature set (#115) 2026-03-27 02:51:12 -04:00
KoalaTrapLord 3f6d71ecbc fix(coach): switch all Anthropic calls to streaming to prevent 10-min timeout error (closes #109)
- anthropic_client.py: all 4 methods (chat_structured, chat_plain, achat_structured, achat_plain) now use .messages.stream() context manager
- coach_service.py: inline aclient.messages.create() replaced with async streaming via aclient.messages.stream()
- _parse_response() updated to accept pre-extracted text string (since stream gives us text directly)
- check_status() left as non-streaming (max_tokens=10, safe from the 10min rule)
2026-03-27 02:36:22 -04:00
KoalaTrapLord 4246555228 feat: auto-run update_weights.py after overnight sims (#114)
Completes the sim → weights → deck builder feedback loop.

- Add run_update_weights() to overnight-run.py
- Add auto_update_weights config key (default True)
- Add --no-update-weights, --weights-min-games, --weights-lr CLI flags
- Runs before neural net training so both pipelines get fresh data
2026-03-27 02:35:08 -04:00
KoalaTrapLord 8dd9623417 feat: inject simulation-learned weights into deck builder LLM prompts (#113)
Closes #112

- Add sim_insights.py to translate learned_weights.json into LLM prompt text
- Add sim_context param to suggest_cards() and enforce_deck_ratios() in ollama_client.py
- Wire get_sim_insights() into build_deck pipeline (Steps 4 & 6)
- Fully backwards compatible — no-op until real sim data exists
2026-03-27 02:27:42 -04:00
KoalaTrapLord 817b71f69d feat(ui): add tip-icon tooltips to distillation, simulator, coach, deckgenerator pages
- distillation.html: tooltips on all 10 params + ELO Ratings header
- simulator.html: tooltips on Opponent, Games, Record logs, API Endpoint, Model, Temp, Timeout
- coach.html: tooltips on Power Level, Meta Focus, Budget, Focus Areas, Similar Cards Results
- deckgenerator.html: tooltips on Target Bracket, Use my collection, Smart Substitution, AI Model
- All pages now share tooltip styles from global.css (no per-page duplication needed)
2026-03-27 02:06:58 -04:00
KoalaTrapLord e71cac9dd1 feat(ui): add tooltips to all pages — distillation, simulator, coach, deckgenerator
- Move .tip-icon tooltip CSS from training.css into global.css so all pages share it
- distillation.html: 8 param tooltips (Max Generations, Convergence Window, Supervised Epochs, Supervised LR, PPO Iterations, PPO Episodes/Iter, PPO LR, Min Win Rate Gate, Opponent, Playstyle) + ELO Ratings header
- simulator.html: tooltips on Opponent, Games, Record turn logs, API Endpoint, Model, Temp, Timeout
- coach.html: tooltips on Target Power Level, Meta Focus, Budget, Focus Areas, Find Similar Cards Results
- deckgenerator.html: tooltips on Target Bracket, Use my collection, Smart Substitution, AI Model
2026-03-27 01:57:37 -04:00
KoalaTrapLord cb38081a8b fix: add top_n cutoff to ELO tournament (default top 10 generations) 2026-03-27 01:48:10 -04:00
KoalaTrapLord cd9d2eebd8 feat(coach): Phase 1 - pass richer card stats to Claude prompt (#108)
- _format_underperformers: add synergyScore, clunkinessScore, avgTurnCast, keptInOpeningHandRate
- _format_top_cards: add synergyScore and avgTurnCast
- _format_matchups: append perArchetypeWinRates block from DeckMeta
- USER_PROMPT_TEMPLATE: add ### Archetype Win Rates section placeholder
2026-03-27 01:32:51 -04:00
KoalaTrapLord e6f18d4a61 feat(training): add intro banner and parameter tooltips to ML Training page 2026-03-27 00:18:21 -04:00
KoalaTrapLord 4e90337f54 feat: extend card hover preview to all card names in coach UI 2026-03-26 23:48:27 -04:00
KoalaTrapLord a01a70f725 Use DEFAULT_MAX_TOKENS config for Anthropic max_tokens 2026-03-26 22:50:10 -04:00
KoalaTrapLord 4da58681a4 Enhance coach prompt for more detailed reports 2026-03-26 22:41:23 -04:00
KoalaTrapLord 224b4b5d5b feat: increase coach detail limits (MAX_UNDERPERFORMERS=8, MAX_TOKENS=16384) 2026-03-26 22:33:18 -04:00
KoalaTrapLord 1a34f00574 Update .env.examplefix: correct model ID in .env.example (claude-opus-4-0 -> claude-opus-4) 2026-03-26 15:50:53 -04:00
KoalaTrapLord 8ec7db1612 fix: correct malformed string literals in config.py model defaults 2026-03-26 15:42:34 -04:00
KoalaTrapLord e0a75e2d04 fix: correct Anthropic model ID from claude-opus-4-0 to claude-opus-4 2026-03-26 15:36:23 -04:00
KoalaTrapLord 15bd6f42bb fix: resolve all ruff linting errors across coach module 2026-03-26 15:30:27 -04:00
KoalaTrapLord 0aa57ab7d2 fix: remove duplicate resp token on line 284 in run_coaching_session (#105) 2026-03-26 14:23:52 -04:00
KoalaTrapLord 9f5eb4a99f fix: add missing resp= assignment on line 284 (#105) 2026-03-26 14:11:35 -04:00
KoalaTrapLord a9f18f53da fix: correct indentation of try/except/else block in run_coaching_session (#105) 2026-03-26 14:06:27 -04:00
KoalaTrapLord c9e932ef43 fix: correct try: block indentation in run_coaching_session (#105) 2026-03-26 13:44:33 -04:00
KoalaTrapLord cb99388e3c fix: correct indentation of total_steps in collect_rollouts 2026-03-26 13:14:34 -04:00
KoalaTrapLord 2e316548e7 fix: repair resp= assignment and _re.sub typo in coach_service.py (#105) 2026-03-26 13:11:55 -04:00
KoalaTrapLord b46e1e0877 fix: remove stray string literal in init_coach_service (#105) 2026-03-26 12:31:52 -04:00
KoalaTrapLord e17b877dcc fix: repair syntax errors in coach_status and init_coach_service (#105) 2026-03-26 12:18:07 -04:00
KoalaTrapLord 6ce21908d3 Update coach.py 2026-03-26 11:53:39 -04:00
KoalaTrapLord 15495483f3 fix: remove _coach_init_error reference from status endpoint (#105) 2026-03-26 11:36:33 -04:00
KoalaTrapLord 540fbbf1b3 fix: remove broken _coach_init_error lines from coach.py (#105) 2026-03-26 10:52:41 -04:00
KoalaTrapLord a5cc7736b4 feat: update thread dropdown to 1,2,4,8,16,24,32 2026-03-25 23:15:38 -04:00
KoalaTrapLord 8c6248cf84 feat: increase max threads from 16 to 32 2026-03-25 21:37:21 -04:00
KoalaTrapLord 22d63fd15e Update coach.py 2026-03-25 21:29:24 -04:00
KoalaTrapLord 2dc0ba2d25 Update coach.py 2026-03-25 21:18:27 -04:00
KoalaTrapLord 8f180820f9 fix: add missing _DEFAULT_ORIGINS definition in lab_api.py (#105) 2026-03-25 21:00:34 -04:00
KoalaTrapLord b283418879 fix: correct indentation in _call_anthropic_api else-block (#105) 2026-03-25 20:43:11 -04:00
KoalaTrapLord 61be42ea6d fix: resolve IndentationError on line 882 in routes/deckgen.py (#105) 2026-03-25 20:18:19 -04:00
KoalaTrapLord 3906b93c6a feat: add WebSocket support for Unity game state streaming (#107)
* feat: add ws/ package for WebSocket game state streaming

* feat: add WebSocket ConnectionManager with per-game broadcasting

* feat: add GameStateTracker with deep-diff delta engine

* feat: add WebSocket route with delta push and REST bridge endpoints

* feat: register ws_game_router in lab_api.py
2026-03-25 19:51:15 -04:00
KoalaTrapLord af2a71d1a5 fix: eliminate event-loop-blocking sync calls in async FastAPI routes (#106)
* feat: add async DB wrapper and eliminate event-loop blocking

* fix: rewrite llm_client.py with native async httpx - removes urlopen and run_in_executor

* fix: rewrite routes/coach.py - replace all sync DB/HTTP with async (aiosqlite + httpx)

* fix: add async DB shutdown hook to lab_api.py

* feat: add aiosqlite>=0.19.0 to requirements
2026-03-25 19:17:41 -04:00
KoalaTrapLord cf7c872e12 feat: update test mock from pplx_client to anthropic_client (#105) 2026-03-25 16:43:11 -04:00
KoalaTrapLord c9227dac70 Update substitution_prompt.pyfeat: update substitution_prompt.py docstring from Perplexity to Claude Opus (#105) 2026-03-25 16:41:47 -04:00
KoalaTrapLord 34b1ea2e30 feat: update deck_gen_user.py docstring from Perplexity to Claude Opus (#105) 2026-03-25 16:41:24 -04:00
KoalaTrapLord 1c6454dce5 feat: update substitution_schema.py source field from perplexity to anthropic (#105) 2026-03-25 16:39:16 -04:00
KoalaTrapLord 464dd732d5 feat: update deck_schema.py docstring from Perplexity to Claude Opus (#105) 2026-03-25 16:36:22 -04:00
KoalaTrapLord f481243c4e feat: replace Perplexity AI with Claude Opus in deckgenerator.html (#105) 2026-03-25 16:33:00 -04:00
KoalaTrapLord f49b6cbea5 feat: replace Perplexity AI spinner text with Claude Opus in deckbuilder.html (#105) 2026-03-25 16:30:00 -04:00
KoalaTrapLord 71f316a220 feat: update status label from Perplexity to Claude Opus in coach.html (#105) 2026-03-25 16:26:31 -04:00
KoalaTrapLord 9556f9d7d0 feat: replace Perplexity with Anthropic Claude in lab_api.py (#105) 2026-03-25 15:18:14 -04:00
KoalaTrapLord 9de0965c78 feat: replace Perplexity with Anthropic Claude in routes/deckgen.py (#105) 2026-03-25 15:09:46 -04:00
KoalaTrapLord 38bfa51d85 feat: replace Perplexity with Anthropic Claude in routes/coach.py (#105) 2026-03-25 14:41:34 -04:00
KoalaTrapLord c58f561df4 feat: replace PerplexityClient with AnthropicClient in deck_generator.py (#105) 2026-03-25 14:34:18 -04:00
KoalaTrapLord 6cf1e58018 feat: replace Perplexity with Anthropic Claude in coach_service.py (#105) 2026-03-25 14:30:40 -04:00
KoalaTrapLord 1382d25e46 feat: update clients __init__.py comment to reference Anthropic (#105) 2026-03-25 12:25:56 -04:00
KoalaTrapLord d8f7105471 feat: replace PPLX_API_KEY with ANTHROPIC_API_KEY in install script (#105) 2026-03-25 12:25:01 -04:00
KoalaTrapLord 071088715f feat: replace PPLX_API_KEY with ANTHROPIC_API_KEY in start-lab.bat (#105) 2026-03-25 12:23:43 -04:00
KoalaTrapLord 95429d9f35 feat: replace PPLX_API_KEY with ANTHROPIC_API_KEY in .env.example (#105) 2026-03-25 12:21:40 -04:00
KoalaTrapLord 55379a98ef feat: replace Perplexity config with Anthropic Claude settings (#105) 2026-03-25 12:20:33 -04:00
KoalaTrapLord 173dee6ccc feat: add anthropic>=0.40.0 and openai>=1.0.0 to requirements (#105) 2026-03-25 12:17:52 -04:00
KoalaTrapLord 34dee24399 feat: add AnthropicClient as drop-in replacement for PerplexityClient (closes #105 phase 1) 2026-03-25 12:16:14 -04:00
KoalaTrapLord 88f1022d0e fix: deduplicate card names globally before Scryfall batch lookup
enforce_deck_ratios can return the same card name under multiple
categories (e.g. Teferi's Protection in both protection + synergy).
all_card_names had no cross-category dedup, so get_cards_by_names
returned duplicate CardEntry objects that then failed the singleton
validator in CommanderDeck.

Fix: build all_card_names with dict.fromkeys() across all categories
so each name appears exactly once. Also deduplicate after Scryfall
lookup by name as a second safety net.
2026-03-25 11:23:06 -04:00
KoalaTrapLord 737863c55d fix: .env.example default COACH_PROVIDER to perplexity 2026-03-25 10:43:42 -04:00
KoalaTrapLord e710287c35 fix: run_coaching_session respects COACH_PROVIDER for Perplexity API
The coaching pipeline in coach_service.py was always calling self.llm (Ollama) regardless of COACH_PROVIDER setting. Now branches on COACH_PROVIDER == 'perplexity' to call the Perplexity API directly via httpx, matching the pattern used in routes/coach.py coach_chat endpoint.
2026-03-25 10:42:49 -04:00
KoalaTrapLord 2e3c1ec0f1 fix: startup logs and DeckBuilderAdapter respect COACH_PROVIDER/DECK_GEN_PROVIDER instead of hardcoding Ollama 2026-03-25 10:34:04 -04:00
KoalaTrapLord 21f032d32e fix: set DECK_GEN_PROVIDER default to perplexity and DECK_GEN_MODEL to sonar-pro 2026-03-25 10:29:54 -04:00
KoalaTrapLord 2881309d41 fix: set COACH_PROVIDER default to perplexity, remove stale DeepSeek comment 2026-03-25 10:13:58 -04:00
KoalaTrapLord e162d50608 feat: bulk quantity edit for collection table 2026-03-25 10:12:03 -04:00
KoalaTrapLord 49bab7fb0a fix: set FORGE_DIR to repo root so cardsfolder path resolves correctly 2026-03-25 10:09:10 -04:00
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# Get yours at: https://app.ximilar.com/
XIMILAR_API_KEY=your-ximilar-api-key-here
# Perplexity API Key (AI deck research & generation)
# Get yours at: https://www.perplexity.ai/settings/api
PPLX_API_KEY=your-perplexity-api-key-here
# Perplexity model for deck research & generation (sonar / sonar-pro / sonar-deep-research)
PPLX_MODEL=sonar-pro
# Coach chat provider: "local" (Ollama) or "perplexity" (Perplexity API)
COACH_PROVIDER=local
# Anthropic API Key (AI deck generation & coaching with Claude Opus)
# Get yours at: https://console.anthropic.com/settings/keys
ANTHROPIC_API_KEY=your-anthropic-api-key-here
# Anthropic model for deck generation and coaching (Claude Opus recommended)
ANTHROPIC_MODEL=claude-opus-4-20250514
# Coach chat provider: "local" (Ollama) or "anthropic" (Claude API)
COACH_PROVIDER=anthropic
# CORS allowed origins (comma-separated)
# Default: http://localhost:5173,http://localhost:8080
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# Commander AI Lab
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.
A full-stack MTG Commander AI research platform. Simulate hundreds of games overnight, train ML models on the results, and let simulation data automatically shape the AI deck builder — all from a web UI or CLI.
## What It Does
| Layer | What it is |
|---|---|
| **Monte Carlo Engine** | Pure-Python headless sim — runs thousands of games in minutes, no JVM needed |
| **Forge Engine** | Java/Forge rules-complete sim for ground-truth validation |
| **Overnight Runner** | Unattended batch runner — runs sims, updates sim weights, trains neural net |
| **ML Pipeline** | Outcome-weighted RL nudges card scoring weights; neural net trains on decision logs |
| **Deck Builder** | 7-step LLM pipeline (EDHrec → Scryfall → Ollama) now injected with sim-learned insights |
| **AI Coach** | LLM-powered coaching sessions (Anthropic/Ollama) with streaming support |
| **Web UI** | React + FastAPI dashboard for decks, sims, training, and deck building |
## Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ Web UI (React/TypeScript — frontend/) │
│ Deck Builder · Sim Dashboard · Training · Coach │
└────────────────────────┬────────────────────────────────────────┘
│ HTTP/REST + WebSocket
┌────────────────────────┴────────────────────────────────────────┐
│ FastAPI Server (lab_api.py / routes/ — port 8080) │
│ /api/lab/* /api/ml/* /api/coach/* /api/deck-builder/* │
└────────┬──────────────────┬──────────────────┬──────────────────┘
│ │ │
Java subprocess Python sim engine Ollama (local LLM)
(Forge rules) (Monte Carlo) qwen2.5:7b + others
│ │
┌────────┴───────┐ ┌───────┴────────────────────────────────────┐
│ Forge Engine │ │ src/commander_ai_lab/ │
│ (rules- │ │ sim/ — engine, models, rules │
│ complete) │ │ deck_builder/ — 7-step LLM pipeline │
└────────────────┘ │ coach/ — streaming LLM coach │
└────────────────────────────────────────────┘
```
## 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`
**Prerequisites:** Java 17+, Python 3.10+, Maven 3.8+, [Forge](https://github.com/Card-Forge/forge) built at `D:\ForgeCommander\forge-repo`
### 1. Build Forge (one-time)
```powershell
# 1. Build Forge (one-time)
cd D:\ForgeCommander\forge-repo
mvn package -pl forge-gui-desktop "-DskipTests" "-Drevision=2.0.12-SNAPSHOT" -am
```
### 2. Build Commander AI Lab
```powershell
# 2. Build Commander AI Lab
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
```powershell
pip install fastapi uvicorn
```
# 3. Install Python deps
pip install -e .
### 4. Launch the Web UI
**Option A — One-click launcher:**
```powershell
# 4. Launch
.\start-lab.bat
# → open http://localhost:8080
```
Edit the paths at the top of `start-lab.bat` if needed.
**Option B — Manual launch:**
## Overnight AFK Runner
Runs batch sims unattended, then updates card weights and trains the neural net:
```powershell
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
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
```
Open `http://localhost:8080` — select 3 decks, set game count, click **Run AI Sims**.
**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
## Sim → Deck Builder Feedback Loop
Simulation outcomes automatically improve deck recommendations:
```
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
```
When active, `BuildResult.sources_consulted` includes `"sim_weights"`. No-op until real sim data exists.
## Deck Builder Pipeline
The `POST /api/deck-builder/build` endpoint runs 7 steps:
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 ← **sim insights injected here**
5. Filter by color identity, collection, ban list
6. Ollama enforces ratios (trim/expand to 99) ← **sim insights injected here**
7. Assemble final deck
## ML Pipeline
### 5. CLI-only usage (no web server)
```powershell
cd D:\ForgeCommander\forge-repo\forge-gui
# Update sim card weights from decision logs (runs automatically in overnight-run.py)
python -m ml.scripts.update_weights --min-games 10 --lr 0.05 --dry-run
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
# Reset weights to defaults
python -m ml.scripts.update_weights --reset
# Generate synthetic training data
python ml/scripts/generate_synthetic_data.py
```
## Scaling Tests
Learned weights are clamped to `[-20.0, 20.0]` and stored in `src/commander_ai_lab/sim/learned_weights.json`.
### 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
## Python Simulator API
```python
from commander_ai_lab.lab.experiments import build_deck, run_simulation
deck = build_deck(["Sol Ring", "Cultivate", "Lightning Bolt"] + ["Forest"] * 36)
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}")
```
### 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.
The Monte Carlo engine models: combat keywords (flying, trample, deathtouch, lifelink, menace, reach, haste), summoning sickness, tapped/untap state, commander zone, multiplayer attack targeting, and board wipes. It does **not** model the stack, instants, or continuous type-changing effects.
## API Reference
| Endpoint | Method | Description |
|---|---|---|
| `/api/lab/decks` | GET | List available Commander decks |
| `/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 |
### POST /api/lab/start
```json
{
"decks": ["Edgar Markov", "Grimgrin", "Xyris"],
"numGames": 100,
"threads": 4,
"seed": null,
"clock": 120
}
```
### GET /api/lab/status response
```json
{
"batchId": "abc123",
"running": true,
"completed": 42,
"total": 100,
"threads": 4,
"elapsedMs": 156000,
"error": null
}
```
## Python Simulator (Monte Carlo Engine)
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.
### Location
```
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()
```
### Install as editable package
```powershell
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.
### 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
# 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%}")
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.
| `/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/ml/train` | POST | Start neural net training |
| `/api/ml/train/status` | GET | Poll training progress |
| `/api/coach/chat` | POST | LLM coaching session (streaming) |
| `/api/deck-builder/build` | POST | Build a 99-card Commander deck |
## Project Structure
```
commander-ai-lab/
├── lab_api.py # Python FastAPI server (web UI backend)
├── overnight-run.py # AFK batch runner
├── start-lab.bat # Windows one-click launcher
├── 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
├── lab_api.py # FastAPI server entry point
├── frontend/ # React + TypeScript web UI
├── ui/ # Legacy static HTML UI
├── src/commander_ai_lab/
│ ├── sim/ # Monte Carlo engine (engine, models, rules)
│ ├── deck_builder/ # LLM deck builder pipeline
│ │ ├── api/ # Scryfall, EDHrec, Ollama, sim_insights
│ │ ├── core/ # Models, rules engine, assembler
│ │ └── pipeline/ # build_deck.py (7-step orchestrator)
│ └── coach/ # LLM coaching (Anthropic + Ollama)
├── ml/
│ ├── scripts/ # update_weights.py, ml_cli.py
│ ├── training/ # Neural net trainer
│ ├── encoder/ # Feature encoding
│ └── models/ # Saved model checkpoints
├── scanner/ # Card OCR pipeline
├── logs/decisions/ # ML decision logs (gitignored)
└── src/main/java/commanderailab/ # Java/Forge batch runner
```
## Output Format
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
}
}
```
## 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
- 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
- **Monte Carlo engine** — no stack, no instants, no continuous type changes (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 (quiet mode only reports losers)
- **Deck builder** — Ollama suggestions are only as good as the local model; `qwen2.5:7b` works well
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# Coach clients — Perplexity, LM Studio
# Coach clients — Anthropic Claude, LM Studio
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"""
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
+260 -53
View File
@@ -18,12 +18,16 @@ from typing import Optional, List, Dict
from .config import (
DECK_REPORTS_DIR, COACH_SESSIONS_DIR,
MAX_UNDERPERFORMERS, MAX_CANDIDATES_PER_UNDERPERFORMER,
COACH_PROVIDER, PPLX_MODEL,
UNDERPERFORMER_IMPACT_THRESHOLD, ensure_dirs,
COACH_PROVIDER, ANTHROPIC_API_KEY, ANTHROPIC_MODEL,
UNDERPERFORMER_IMPACT_THRESHOLD, DEFAULT_MAX_TOKENS, 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
@@ -61,23 +65,19 @@ 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()):
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") as f:
data = json.load(f)
@@ -92,16 +92,14 @@ 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."""
# 1. Write JSON file (backward compat)
COACH_SESSIONS_DIR.mkdir(parents=True, exist_ok=True)
path = COACH_SESSIONS_DIR / f"{session.sessionId}.json"
with open(path, "w") as f:
f.write(session.model_dump_json(indent=2))
# 2. Write to SQLite
if _DB_AVAILABLE:
try:
_db_save_session({
@@ -124,10 +122,8 @@ class CoachService:
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."""
# Try SQLite first
if _DB_AVAILABLE:
try:
row = _db_load_session(session_id)
@@ -150,7 +146,6 @@ class CoachService:
)
except Exception as e:
logger.warning("SQLite load failed for %s: %s", session_id, e)
# Fallback to disk
path = COACH_SESSIONS_DIR / f"{session_id}.json"
if not path.exists():
return None
@@ -162,10 +157,8 @@ class CoachService:
logger.error("Failed to load session %s: %s", session_id, e)
return None
def list_sessions(self, deck_id: str = None) -> List[dict]:
"""List coaching sessions from SQLite, falling back to disk."""
# Try SQLite first
if _DB_AVAILABLE:
try:
rows = _db_list_sessions(deck_id)
@@ -173,7 +166,6 @@ class CoachService:
return rows
except Exception as e:
logger.warning("SQLite list_sessions failed: %s", e)
# Fallback to disk
if not COACH_SESSIONS_DIR.exists():
return []
sessions = []
@@ -195,10 +187,11 @@ class CoachService:
continue
return sessions
# ── Main Coaching Pipeline ───────────────────────────────
# ── Main Coaching Pipeline ─────────────────────────────────
async def run_coaching_session(self, deck_id: str,
async def run_coaching_session(
self,
deck_id: str,
goals: Optional[CoachGoals] = None,
fallback_report: Optional[DeckReport] = None,
) -> CoachSession:
@@ -208,11 +201,10 @@ class CoachService:
2. Identify underperformers
3. Find replacement candidates via embeddings
4. Build prompt
5. Call LLM
5. Call LLM (streaming for Anthropic to avoid 10-min SDK timeout)
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:
@@ -224,7 +216,6 @@ 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
@@ -232,25 +223,18 @@ 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 [])
@@ -268,18 +252,61 @@ class CoachService:
candidates[card_name] = [m.to_dict() for m in matches]
else:
logger.warning("Embeddings not loaded — skipping candidate search")
# Phase 3: RAG vector search for additional card context
rag_cards: List[dict] = []
try:
from services.rag_store import query_cards
query_text = f"{report.commander} {' '.join(report.colorIdentity)} commander deck"
rag_cards = query_cards(query_text, n_results=15, color_identity=report.colorIdentity)
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)
# 4. Build prompts
system_prompt = build_system_prompt(report, goals)
user_prompt = build_user_prompt(report, candidates)
user_prompt = build_user_prompt(report, candidates, rag_cards=rag_cards)
# 5. Call LLM
logger.info("Calling LLM for deck: %s", deck_id)
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)
# 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,
@@ -293,27 +320,111 @@ class CoachService:
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 3: light RAG query for quick digest too
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) # RAG-enriched quick digest # 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(
@@ -322,7 +433,6 @@ class CoachService:
replacementOptions=cut_data.get("replacementOptions", []),
))
# Suggested adds
for add_data in data.get("suggestedAdds", []):
if isinstance(add_data, dict):
session.suggestedAdds.append(SuggestedAdd(
@@ -332,24 +442,125 @@ class CoachService:
synergyWith=add_data.get("synergyWith", []),
))
# ── Status Check ───────────────────────────────────────────
# Phase 2
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", ""),
)
# Phase 3: Removal coverage
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"]
})
# Phase 3: Ramp quality
rq = data.get("rampQuality")
if isinstance(rq, dict):
session.rampQuality = RampQuality(**{
k: rq.get(k, "") for k in ["manaRocks", "landFetch", "manaDorks", "costReducers", "fragility", "canReachFourByTurnThree"]
})
# Phase 3: Draw engine profile
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", ""),
)
# Phase 3: Win conditions
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", ""),
))
# Phase 3: Anti-synergy flags
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", ""),
))
# Phase 5: Pod presence
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", ""),
)
# Phase 5: Tempo assessment
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", ""),
)
# Phase 5: Meta matchups
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 ─────────────────────────────────────────
def get_status(self) -> CoachStatus:
"""Check the health of all coach subsystems."""
status = CoachStatus()
# Check LLM / Perplexity provider
if COACH_PROVIDER == "perplexity":
pplx_key = os.environ.get("PPLX_API_KEY", "")
if pplx_key:
if COACH_PROVIDER == "anthropic":
anthropic_key = ANTHROPIC_API_KEY
if anthropic_key:
status.llmConnected = True
status.llmModel = f"Perplexity ({PPLX_MODEL})"
status.llmModels = [PPLX_MODEL]
status.llmModel = f"Claude Opus ({ANTHROPIC_MODEL})"
status.llmModels = [ANTHROPIC_MODEL]
else:
status.llmConnected = False
status.llmModel = None
status.llmModels = []
status.error = "Perplexity API key not configured. Set PPLX_API_KEY env var."
status.error = "Anthropic API key not configured. Set ANTHROPIC_API_KEY env var."
else:
llm_status = self.llm.check_connection()
status.llmConnected = llm_status.get("connected", False)
@@ -358,11 +569,7 @@ class CoachService:
if not status.llmConnected:
status.error = llm_status.get("error", "Local LLM 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 -29
View File
@@ -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.
"""
@@ -8,61 +8,60 @@ All paths are relative to the commander-ai-lab root directory.
import os
from pathlib import Path
# ── Base Paths ──────────────────────────────────────────────
# Resolve relative to wherever the lab is installed
# ── Base Paths ───────────────────────────────────────────────────────
LAB_ROOT = Path(os.environ.get("COMMANDER_LAB_ROOT", Path(__file__).parent.parent))
# ── Ollama (OpenAI-compatible API) ──────────────────────
# ── Ollama (OpenAI-compatible API) ────────────────────────────
LLM_URL = os.environ.get("LLM_URL", "http://localhost: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 = 5
MAX_UNDERPERFORMERS = 4
MAX_CANDIDATES_PER_UNDERPERFORMER = 8
MAX_UNDERPERFORMERS = 8
UNDERPERFORMER_IMPACT_THRESHOLD = -0.05 # impactScore below this = underperformer
# ── LLM Generation Settings (Coach — Ollama) ──────────────
# ── LLM Generation Settings (Coach — Ollama fallback) ─────────────
DEFAULT_TEMPERATURE = 0.7
# DeepSeek-R1 uses <think> reasoning tokens before the JSON output,
# so we need a generous limit to avoid truncation
DEFAULT_MAX_TOKENS = 8192
DEFAULT_MAX_TOKENS = 16384
# ── 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")
# Perplexity cloud model (sonar / sonar-pro / sonar-deep-research)
PPLX_MODEL = os.environ.get("PPLX_MODEL", "sonar-pro")
# Coach chat provider: "local" (Ollama) or "perplexity" (Perplexity API)
COACH_PROVIDER = os.environ.get("COACH_PROVIDER", "local")
# ── 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")
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 Perplexity fallback
# ── Smart Substitution Settings ───────────────────────────────────
# Minimum embedding similarity to accept a substitute without Anthropic 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 (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"
# 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"
# ── 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)
+3 -11
View File
@@ -21,7 +21,6 @@ import json
import logging
import re
import os
import time
from pathlib import Path
from typing import List, Optional, Tuple
from urllib.request import urlopen, Request
@@ -29,6 +28,7 @@ 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")
@@ -115,15 +115,8 @@ def _fetch_scryfall_card(card_name: str) -> Optional[dict]:
def _build_card_text(card: dict) -> str:
"""Build the text string to embed, matching minimaxir/mtg-embeddings format."""
parts = [card["name"]]
if card.get("mana_cost"):
parts.append(card["mana_cost"])
if card.get("type_line"):
parts.append(card["type_line"])
if card.get("oracle_text"):
parts.append(card["oracle_text"])
return " | ".join(parts)
"""Delegate to shared build_card_text() -- see services/card_text.py."""
return build_card_text(card)
class CardMatch:
@@ -331,7 +324,6 @@ class MTGEmbeddingIndex:
d = np.load(str(CUSTOM_CACHE_PATH), allow_pickle=True)
for n in d["names"]:
existing_names.add(n.lower())
existing = dict(d)
except Exception:
pass
+112 -115
View File
@@ -1,22 +1,25 @@
"""
Commander AI Lab — Ollama Client
Commander AI Lab — Ollama LLM Client
════════════════════════════════════
Async HTTP client targeting Ollama's OpenAI-compatible
/v1/chat/completions endpoint. Includes retry logic for
non-JSON responses and configurable timeout.
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.
"""
from __future__ import annotations
import json
import logging
import asyncio
from typing import Optional
from urllib.request import urlopen, Request
from urllib.error import URLError
import httpx
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")
@@ -24,9 +27,15 @@ 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
@@ -36,7 +45,7 @@ class LLMResponse:
class LLMClient:
"""
Client for Ollama's OpenAI-compatible API.
Fully async client for Ollama's OpenAI-compatible API.
Usage:
client = LLMClient()
@@ -45,19 +54,25 @@ 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 = None # actual model name from /models endpoint
self._resolved_model: Optional[str] = None
def _resolve_model(self) -> str:
# ── Model resolution ───────────────────────────────────────
async 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 = self.check_connection()
status = await self.check_connection()
if status.get("connected") and status.get("active_model"):
self._resolved_model = status["active_model"]
logger.info("Resolved Ollama model: %s", self._resolved_model)
@@ -66,11 +81,18 @@ class LLMClient:
pass
return self.model
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 = {
# ── 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 = {
"model": model_name,
"messages": [
{"role": "system", "content": system_prompt},
@@ -79,32 +101,27 @@ 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"}
return body
# ── Response parsing ──────────────────────────────────────
@staticmethod
def _strip_think_tags(text: str) -> str:
"""Remove DeepSeek-R1 <think>...</think> reasoning blocks from LLM output."""
"""Remove DeepSeek-R1 <think>...</think> reasoning blocks."""
import re
# Remove all <think>...</think> blocks (greedy, handles multiline)
cleaned = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
return cleaned.strip()
return re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL).strip()
@staticmethod
def _extract_json_object(text: str) -> Optional[dict]:
"""Find and parse the first complete JSON object in text.
Uses brace-counting to handle nested objects reliably."""
"""Find and parse the first complete JSON object via 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:
@@ -124,66 +141,56 @@ 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 # we're already past this brace
depth = 1
return None
return None
def _parse_response(self, raw: dict) -> LLMResponse:
"""Extract content and usage from OpenAI-format response.
Handles DeepSeek-R1 <think> tags, markdown fences, and raw JSON."""
"""Extract content and usage from an OpenAI-format response."""
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)
# Step 2: Try direct JSON parse on cleaned content
parsed = None
parsed: Optional[dict] = None
# 1. Direct JSON parse
try:
parsed = json.loads(cleaned)
except (json.JSONDecodeError, TypeError):
pass
# Step 3: Try extracting from markdown fences
# 2. Markdown fences
if parsed is None and "```json" in cleaned:
try:
start = cleaned.index("```json") + 7
end = cleaned.index("```", start)
parsed = json.loads(cleaned[start:end].strip())
s = cleaned.index("```json") + 7
e = cleaned.index("```", s)
parsed = json.loads(cleaned[s:e].strip())
except (json.JSONDecodeError, ValueError, IndexError):
pass
if parsed is None and "```" in cleaned:
try:
start = cleaned.index("```") + 3
end = cleaned.index("```", start)
parsed = json.loads(cleaned[start:end].strip())
s = cleaned.index("```") + 3
e = cleaned.index("```", s)
parsed = json.loads(cleaned[s:e].strip())
except (json.JSONDecodeError, ValueError, IndexError):
pass
# Step 4: Brute-force — find the first valid JSON object in the text
# 3. Brace-counting extraction
if parsed is None:
parsed = self._extract_json_object(cleaned)
# Step 5: If still nothing, try on the original content (in case
# think-tag stripping was too aggressive)
# 4. Fallback on original content
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,
@@ -192,87 +199,77 @@ class LLMClient:
model=raw.get("model", self.model),
)
def chat_sync(self, system_prompt: str, user_prompt: str,
temperature: float = None, max_tokens: int = None,
force_json: bool = True) -> LLMResponse:
# ── Public API ─────────────────────────────────────────────
async def chat(
self,
system_prompt: str,
user_prompt: str,
temperature: float = None,
max_tokens: int = None,
force_json: bool = True,
) -> LLMResponse:
"""
Synchronous chat call to Ollama.
Retries up to LLM_MAX_RETRIES on failure or non-JSON response.
Async chat call to Ollama. Retries up to LLM_MAX_RETRIES.
Never blocks the event loop — uses httpx.AsyncClient throughout.
"""
temp = temperature if temperature is not None else DEFAULT_TEMPERATURE
tokens = max_tokens if max_tokens is not None else DEFAULT_MAX_TOKENS
body = self._build_request_body(system_prompt, user_prompt,
temp, tokens, force_json)
last_error = None
body = await self._build_request_body(
system_prompt, user_prompt, temp, tokens, force_json
)
last_error: Optional[Exception] = None
for attempt in range(1, LLM_MAX_RETRIES + 1):
try:
logger.info("LLM call attempt %d/%d", attempt, LLM_MAX_RETRIES)
req = Request(
async with httpx.AsyncClient(
timeout=httpx.Timeout(float(self.timeout))
) as client:
resp = await client.post(
f"{self.base_url}/chat/completions",
data=json.dumps(body).encode("utf-8"),
json=body,
headers={"Content-Type": "application/json"},
method="POST",
)
with urlopen(req, timeout=self.timeout) as resp:
raw = json.loads(resp.read().decode("utf-8"))
resp.raise_for_status()
raw = resp.json()
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 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
import asyncio
await asyncio.sleep(2 * attempt)
raise ConnectionError(
f"LLM call failed after {LLM_MAX_RETRIES} attempts: {last_error}"
)
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)
)
def check_connection(self) -> dict:
"""
Check if Ollama is reachable. Returns status dict.
"""
async def check_connection(self) -> dict:
"""Check if Ollama is reachable. Returns status dict."""
try:
req = Request(
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"},
method="GET",
)
with urlopen(req, timeout=5) as resp:
data = json.loads(resp.read().decode("utf-8"))
resp.raise_for_status()
data = resp.json()
models = data.get("data", [])
model_ids = [m.get("id", "unknown") for m in models]
return {
+140 -17
View File
@@ -4,12 +4,9 @@ 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)
# ══════════════════════════════════════════════════════════════
@@ -17,16 +14,16 @@ from pydantic import BaseModel, Field
class CardPerformance(BaseModel):
"""Per-card aggregated stats from simulation data."""
name: str
drawnRate: float = 0.0 # Fraction of games where card was drawn
castRate: float = 0.0 # Fraction of games where drawn AND cast
drawnRate: float = 0.0
castRate: float = 0.0
keptInOpeningHandRate: 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)
deadCardRate: float = 0.0
impactScore: float = 0.0
synergyScore: float = 0.0
clunkinessScore: float = 0.0
avgTurnCast: Optional[float] = None
avgDamageDealt: float = 0.0
tags: List[str] = Field(default_factory=list) # ramp, draw, removal, etc.
tags: List[str] = Field(default_factory=list)
class MatchupRecord(BaseModel):
@@ -48,7 +45,7 @@ class DeckMeta(BaseModel):
class DeckStructure(BaseModel):
"""Deck composition breakdown."""
landCount: int = 0
curveBuckets: List[int] = Field(default_factory=lambda: [0]*8) # CMC 0-7+
curveBuckets: List[int] = Field(default_factory=lambda: [0]*8)
cardTypeCounts: Dict[str, int] = Field(default_factory=dict)
functionalCounts: Dict[str, int] = Field(default_factory=dict)
@@ -57,7 +54,7 @@ class ComboRecord(BaseModel):
"""Known combo with performance data."""
cardNames: List[str]
winRateWhenAssembled: float = 0.0
assemblyRate: float = 0.0 # Fraction of games where all pieces drawn
assemblyRate: float = 0.0
class DeckReport(BaseModel):
@@ -84,10 +81,10 @@ class DeckReport(BaseModel):
class CoachGoals(BaseModel):
"""User-specified coaching goals."""
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"]
targetPowerLevel: Optional[int] = None
metaFocus: Optional[str] = None
budget: Optional[str] = None
focusAreas: List[str] = Field(default_factory=list)
class CoachRequest(BaseModel):
@@ -107,12 +104,120 @@ class SuggestedCut(BaseModel):
class SuggestedAdd(BaseModel):
"""A card the coach suggests adding."""
cardName: str
role: str = "" # ramp, draw, removal, finisher, etc.
role: str = ""
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.
@@ -131,6 +236,24 @@ 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):
+236 -30
View File
@@ -6,7 +6,6 @@ Injects deck report data, underperformer analysis, and
candidate replacement cards into a structured template.
"""
import json
from typing import List, Dict, Optional
from .models import DeckReport, CoachGoals, CardPerformance
@@ -27,40 +26,186 @@ 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 3-5 DIFFERENT cards to cut and 3-5 DIFFERENT cards to add.
- IMPORTANT: Suggest 5-8 DIFFERENT cards to cut and 5-8 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.
- 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": "2-3 sentence overview of the deck's strengths and weaknesses",
"summary": "4-6 sentence detailed overview of the deck's strengths, weaknesses, key synergies, and strategic identity. Reference specific win rates and simulation data.",
"suggestedCuts": [
{{
"cardName": "Card To Remove",
"reason": "Why this card underperforms",
"replacementOptions": ["Better Card 1", "Better Card 2"]
"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"
}}
],
"suggestedAdds": [
{{
"cardName": "Card To Add",
"role": "ramp|draw|removal|threat|utility|combo|protection",
"reason": "Why this card improves the deck",
"synergyWith": ["Card In Deck 1", "Card In Deck 2"]
"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"
}}
],
"heuristicHints": [
"Concise strategic tip based on data"
"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"
}}
],
"manaBaseAdvice": "Specific mana base improvement suggestion or null",
"rawTextExplanation": "Detailed paragraph explaining the analysis and reasoning"
"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"
],
"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 (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"
}}
]
}}
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}.
{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 Builder
@@ -82,24 +227,29 @@ Avg Game Length: {avg_game_length:.1f} turns
### 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."""
Based on this simulation data, provide your coaching suggestions."""
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.")
@@ -126,28 +276,38 @@ def _format_curve(buckets: List[int]) -> str:
def _format_top_cards(cards: List[CardPerformance], top_n: int = 10) -> str:
"""Format the best performing cards."""
"""Format the best performing cards with extended stats."""
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 ""
lines.append(f"- {c.name}: impact={c.impactScore:.3f}, "
f"castRate={c.castRate:.1%}, drawnRate={c.drawnRate:.1%}{tags}")
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}{turn_str}{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 their stats."""
"""Format underperforming cards with full available 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:
lines.append(f"- {c.name}: impact={c.impactScore:.3f}, "
turn_str = f", avgTurnCast={c.avgTurnCast:.1f}" if c.avgTurnCast is not None else ""
lines.append(
f"- {c.name}: impact={c.impactScore:.3f}, "
f"deadCardRate={c.deadCardRate:.1%}, "
f"castRate={c.castRate:.1%}, "
f"clunkiness={c.clunkinessScore:.3f}")
f"clunkiness={c.clunkinessScore:.3f}, "
f"synergy={c.synergyScore:.3f}, "
f"keptInOpeningHand={c.keptInOpeningHandRate:.1%}"
f"{turn_str}"
)
else:
lines.append(f"- {name}: (no detailed stats)")
return "\n".join(lines) if lines else "No clear underperformers identified."
@@ -156,12 +316,13 @@ def _format_underperformers(cards: List[CardPerformance],
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. "
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).")
"(ramp, draw, removal, threats, utility)."
)
lines = []
for underperformer, replacements in list(candidates.items())[:MAX_UNDERPERFORMERS]:
lines.append(f"\nReplacements for '{underperformer}':")
@@ -178,15 +339,46 @@ def _format_candidates(candidates: Dict[str, List[dict]]) -> str:
lines.append("cards NOT in this list if they better address the deck's weaknesses.")
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 matchup data."""
"""Format per-opponent 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)")
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."""
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]):
lines.append(f"- vs {archetype}: {wr:.1%}")
return "\n".join(lines)
@@ -197,8 +389,10 @@ 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)
@@ -217,16 +411,26 @@ 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) -> str:
candidates: Dict[str, List[dict]] = None,
rag_cards: 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,
@@ -239,9 +443,11 @@ def build_user_prompt(report: DeckReport,
type_summary=", ".join(type_parts),
role_summary=", ".join(role_parts),
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),
)
+11
View File
@@ -1,6 +1,9 @@
"""
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.
@@ -9,6 +12,8 @@ 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
@@ -16,6 +21,12 @@ 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
+43 -14
View File
@@ -1,11 +1,33 @@
"""
Commander AI Lab — Deck Generation User Prompt Builder
═══════════════════════════════════════════════════════
Constructs the user message for Perplexity deck generation
from a DeckGenV3Request.
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
"""
from typing import List, Optional
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)
def build_user_prompt(
@@ -18,6 +40,7 @@ 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.
@@ -26,18 +49,19 @@ 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 (e.g. "zombie tokens")
strategy: User-specified strategy focus
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 = [
f"Build a complete 100-card Commander deck.",
f"",
"Build a complete 100-card Commander deck.",
"",
f"COMMANDER: {commander}",
]
@@ -60,16 +84,21 @@ def build_user_prompt(
lines.append(f"OMIT LIST (do NOT include these cards): {', '.join(omit_cards)}")
if collection_summary:
lines.append(f"")
lines.append("")
lines.append(collection_summary)
lines.append(f"")
lines.append(f"STRONGLY PREFER cards from the collection when they fit the strategy.")
lines.append("")
lines.append("STRONGLY PREFER cards from the collection when they fit the strategy.")
else:
lines.append(f"")
lines.append(f"No collection data available — suggest the best cards regardless of ownership.")
lines.append("")
lines.append("No collection data available — suggest the best cards regardless of ownership.")
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.")
# 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.")
return "\n".join(lines)
+1 -1
View File
@@ -1,7 +1,7 @@
"""
Commander AI Lab — Substitution Prompt Templates
═════════════════════════════════════════════════
Prompts for the Perplexity fallback substitution pass.
Prompts for the Claude Opus substitution pass.
"""
+2 -2
View File
@@ -1,7 +1,7 @@
"""
Commander AI Lab — Deck Generation JSON Schemas
═════════════════════════════════════════════════
Pydantic models and JSON Schema dicts for Perplexity
Pydantic models and JSON Schema dicts for Claude Opus
structured output deck generation.
"""
@@ -58,7 +58,7 @@ class GeneratedDeckList(BaseModel):
# ══════════════════════════════════════════════════════════════
# JSON Schema dicts (for Perplexity response_format)
# JSON Schema dicts (for Claude Opus response_format)
# ══════════════════════════════════════════════════════════════
DECK_CARD_SCHEMA = {
+20 -4
View File
@@ -3,6 +3,10 @@ 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
@@ -18,7 +22,7 @@ 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 'perplexity'")
source: str = Field(default="embedding", description="How it was found: 'embedding' or 'anthropic'")
role_overlap: List[str] = Field(default_factory=list, description="Shared functional roles")
cmc_delta: float = Field(default=0.0, description="CMC difference from original")
@@ -28,6 +32,7 @@ class DeckCardWithStatus(BaseModel):
class Config:
extra = "ignore"
name: str
count: int = 1
category: str = ""
@@ -41,9 +46,15 @@ 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 if this is a substitution")
original_card: Optional[str] = Field(
default=None,
description="Original card name before substitution (None if not substituted)"
)
alternatives: List[CardAlternative] = Field(default_factory=list)
selected_substitute: Optional[str] = Field(default=None, description="User-selected substitute name")
selected_substitute: Optional[str] = Field(
default=None,
description="The substitute card name chosen for this slot"
)
class SubstitutionRequest(BaseModel):
@@ -61,11 +72,16 @@ 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 Perplexity fallback substitution)
# JSON Schema dicts (for LLM fallback substitution)
# ══════════════════════════════════════════════════════════════
SUBSTITUTION_RESPONSE_SCHEMA = {
+401 -117
View File
@@ -1,19 +1,28 @@
"""
Commander AI Lab — V3 Deck Generator Service
═════════════════════════════════════════════
Generates Commander decks using Perplexity Sonar
Generates Commander decks using Anthropic Claude
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.perplexity_client import PerplexityClient
from ..clients.anthropic_client import AnthropicClient
from ..schemas.deck_schema import DECK_LIST_SCHEMA, GeneratedDeckList
from ..schemas.substitution_schema import (
CardAlternative, DeckCardWithStatus, SubstitutionBatchResult,
@@ -26,35 +35,261 @@ 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_PPLX_FALLBACK,
SUBSTITUTION_MODEL, SUBSTITUTION_USE_ANTHROPIC_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 Perplexity structured output.
V3 Deck Generator using Anthropic Claude structured output.
Pipeline:
1. Resolve commander via Scryfall
2. Build collection summary
3. Call Perplexity with structured JSON schema
3. Call Anthropic Claude with structured JSON schema
4. Validate card names via Scryfall
5. Check ownership against collection DB
6. Run Smart Substitution (embedding + Perplexity fallback)
7. Return finalized deck with substitution data
6. Run Smart Substitution (non-destructive)
7. _pad_to_99 circuit breaker
8. Pre-validation count logging
9. Return finalized deck — guaranteed 99 cards
"""
def __init__(self, pplx_client: PerplexityClient, db_conn_factory,
def __init__(self, anthropic_client: AnthropicClient, db_conn_factory,
embedding_index=None):
"""
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.anthropic = anthropic_client
self.db_conn_factory = db_conn_factory
self.embedding_index = embedding_index
@@ -77,9 +312,12 @@ 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": "",
@@ -88,6 +326,7 @@ class DeckGeneratorV3:
"mana_cost": "",
"image_url": "",
"oracle_text": "",
"scryfall_found": False,
}
# ── Step 2: Build Collection Summary ─────────────────────
@@ -110,16 +349,10 @@ 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 = ""
@@ -137,7 +370,6 @@ class DeckGeneratorV3:
if not filtered:
return ""
# Group by card type
groups = {}
for row in filtered:
type_line = row["type_line"] or ""
@@ -159,14 +391,12 @@ 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]:
@@ -176,7 +406,7 @@ class DeckGeneratorV3:
return "\n".join(lines)
# ── Step 3: Generate Deck via Perplexity ─────────────────
# ── Step 3: Generate Deck via Anthropic Claude ─────────────────
def generate_deck(
self,
commander_name: str,
@@ -193,24 +423,33 @@ class DeckGeneratorV3:
Returns dict with:
- commander: dict with Scryfall data
- raw_deck: parsed GeneratedDeckList from Perplexity
- cards: list of DeckCardWithStatus (after ownership check)
- raw_deck: parsed GeneratedDeckList from Anthropic Claude
- cards: list of DeckCardWithStatus (after ownership check + substitution)
- 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"]
logger.info("Commander: %s, CI: %s", cmdr["name"], color_identity)
scryfall_found = cmdr.get("scryfall_found", True)
logger.info("Commander: %s, CI: %s, Scryfall: %s",
cmdr["name"], color_identity, scryfall_found)
# Step 2: Collection summary
collection_block = ""
if use_collection:
if use_collection and scryfall_found:
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
# Step 3: Build prompts (with slot budget injected)
user_prompt = build_user_prompt(
commander=cmdr["name"],
commander_type=cmdr["type_line"],
@@ -221,13 +460,14 @@ class DeckGeneratorV3:
budget_mode=budget_mode,
omit_cards=omit_cards,
collection_summary=collection_block,
slot_budget=SLOT_BUDGET,
)
# Step 4: Call Perplexity with structured output
# Step 4: Call LLM with structured output
use_model = model or DECK_GEN_MODEL
logger.info("Calling Perplexity (%s) for deck generation...", use_model)
logger.info("Calling LLM (%s) for deck generation...", use_model)
response = self.pplx.chat_structured(
response = self.anthropic.chat_structured(
system_prompt=SYSTEM_PROMPT,
user_prompt=user_prompt,
json_schema=DECK_LIST_SCHEMA,
@@ -239,26 +479,29 @@ class DeckGeneratorV3:
if not response.ok:
raise ValueError(
f"Perplexity returned invalid response: {response.content[:300]}"
f"LLM returned invalid response: {response.content[:300]}"
)
raw_deck = response.parsed_json
card_count = len(raw_deck.get("cards", []))
truncated = card_count < 90 # Commander decks should have ~100 cards
logger.info("Deck generated: %d cards, bracket %d%s",
truncated = card_count < 90
logger.info(
"Deck generated: %d cards, bracket %s%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 "")
(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 "",
)
# Step 5: Check ownership and build enriched card list
# Step 5: Check ownership
cards_with_status = self._check_ownership(raw_deck.get("cards", []))
# Step 5b: Fill basic lands to hit exactly 100 cards
# Step 5b: Fill basic lands to reach target_total=100
cards_with_status = self._fill_basic_lands(cards_with_status, color_identity)
# Step 5c: Secondary safety-net deduplication
seen = {}
deduped = []
# Step 5c: Secondary deduplication
seen: dict[str, int] = {}
deduped: List[DeckCardWithStatus] = []
for c in cards_with_status:
key = c.name.lower()
if key in seen:
@@ -269,7 +512,27 @@ class DeckGeneratorV3:
deduped.append(c)
cards_with_status = deduped
# Step 6: Compute stats
# 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
stats = self._compute_stats(cards_with_status)
return {
@@ -283,6 +546,12 @@ 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,
@@ -296,13 +565,11 @@ class DeckGeneratorV3:
def _check_ownership(self, cards: List[dict]) -> List[DeckCardWithStatus]:
"""
Cross-reference generated cards against the collection DB.
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.
Deduplicates by name (case-insensitive).
"""
conn = self.db_conn_factory()
enriched = []
seen_names: dict[str, int] = {} # name_lower → index in enriched
enriched: List[DeckCardWithStatus] = []
seen_names: dict[str, int] = {}
for card in cards:
name = card.get("name", "")
@@ -314,10 +581,9 @@ 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 (LLM returned it twice)", name)
logger.warning("Duplicate card '%s' merged", 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",
@@ -348,11 +614,8 @@ class DeckGeneratorV3:
return enriched
# ── Step 5b: Basic Land Fill ──────────────────────────────
_COLOR_TO_BASIC = {
'W': 'Plains', 'U': 'Island', 'B': 'Swamp',
'R': 'Mountain', 'G': 'Forest',
}
_BASIC_NAMES = set(_COLOR_TO_BASIC.values()) | {'Wastes'}
_COLOR_TO_BASIC = _COLOR_TO_BASIC
_BASIC_NAMES = _BASIC_NAMES
def _fill_basic_lands(
self,
@@ -374,7 +637,7 @@ 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)
@@ -385,20 +648,20 @@ 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))
return non_basics
# ── Step 6: Smart Substitution ───────────────────────────
# ── Step 6: Smart Substitution (legacy wrapper for route compatibility) ──
def run_substitution(
self,
cards: List[DeckCardWithStatus],
@@ -408,15 +671,12 @@ class DeckGeneratorV3:
"""
Run the Smart Substitution Engine on missing cards.
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.
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.
"""
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]
@@ -431,47 +691,63 @@ class DeckGeneratorV3:
logger.info("Substitution: %d missing / %d owned", len(missing), len(owned))
# Build list of owned card names for substitution pool
# Build owned 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]
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}
# 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 = []
# a) Embedding pass for missing cards
needs_anthropic_fallback: List[DeckCardWithStatus] = []
for card in missing:
embedding_alts = self._embedding_substitution(
card, color_identity, available_pool
card, color_identity, list(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"
# 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()]
card.owned = True
available_pool.discard(card.selected_substitute.lower())
else:
needs_pplx_fallback.append(card)
needs_anthropic_fallback.append(card)
# b) Perplexity fallback for low-confidence matches
if needs_pplx_fallback and SUBSTITUTION_USE_PPLX_FALLBACK:
self._pplx_substitution(
needs_pplx_fallback, available_pool,
# b) Anthropic Claude fallback
if needs_anthropic_fallback and SUBSTITUTION_USE_ANTHROPIC_FALLBACK:
self._anthropic_substitution(
needs_anthropic_fallback, list(available_pool),
commander, strategy
)
# Count results
# 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
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),
@@ -490,39 +766,37 @@ class DeckGeneratorV3:
if not self.embedding_index or not self.embedding_index.loaded:
return []
# Use the embeddings search to find similar cards
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, # Over-fetch, then filter to owned
top_n=SUBSTITUTION_MAX_ALTERNATIVES * 3,
)
except Exception as e:
logger.warning("Embedding search failed for '%s': %s", card.name, e)
return []
# Filter to only cards in the available pool
pool_lower = set(n.lower() for n in available_pool)
pool_lower = {n.lower() for n in available_pool}
alternatives = []
for match in matches:
if match.name.lower() in pool_lower:
# 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 []
role_overlap = list(set(card.role_tags) & set(getattr(match, "role_tags", [])))
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=match.mana_value - (card.estimated_price_usd or 0),
cmc_delta=getattr(match, "mana_value", 0) - (card.estimated_price_usd or 0),
))
if len(alternatives) >= SUBSTITUTION_MAX_ALTERNATIVES:
break
return alternatives
def _pplx_substitution(
def _anthropic_substitution(
self,
missing_cards: List[DeckCardWithStatus],
available_pool: List[str],
@@ -530,13 +804,14 @@ class DeckGeneratorV3:
strategy: str,
):
"""
Batch Perplexity fallback for cards with low embedding confidence.
Mutates the cards in-place with substitution data.
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.
"""
if not available_pool:
logger.warning("[anthropic_substitution] No available pool — skipping")
return
# Build the request
missing_dicts = [
{
"name": c.name,
@@ -549,7 +824,7 @@ class DeckGeneratorV3:
user_prompt = build_substitution_prompt(
missing_cards=missing_dicts,
allowed_cards=available_pool[:200], # Limit to avoid token overflow
allowed_cards=available_pool[:200],
commander=commander.get("name", ""),
color_identity=commander.get("color_identity", []),
strategy=strategy,
@@ -557,7 +832,7 @@ class DeckGeneratorV3:
try:
from ..schemas.substitution_schema import SUBSTITUTION_RESPONSE_SCHEMA
response = self.pplx.chat_structured(
response = self.anthropic.chat_structured(
system_prompt=SUBSTITUTION_SYSTEM,
user_prompt=user_prompt,
json_schema=SUBSTITUTION_RESPONSE_SCHEMA,
@@ -568,13 +843,16 @@ class DeckGeneratorV3:
)
if not response.ok:
logger.warning("Perplexity substitution fallback failed — non-JSON response")
logger.warning(
"[anthropic_substitution] LLM returned non-JSON — "
"%d cards will fall through to basic land fallback",
len(missing_cards),
)
return
subs = response.parsed_json.get("substitutions", [])
logger.info("Perplexity suggested %d substitutions", len(subs))
logger.info("[anthropic_substitution] Received %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()
@@ -583,32 +861,38 @@ class DeckGeneratorV3:
if orig in card_by_name and substitute:
card = card_by_name[orig]
# Add as top alternative
pplx_alt = CardAlternative(
anthropic_alt = CardAlternative(
name=substitute,
similarity_score=0.80, # Nominal score for Perplexity suggestions
similarity_score=0.80,
reason=reason,
source="perplexity",
source="anthropic",
role_overlap=sub.get("role_overlap", []),
)
# Insert at front
card.alternatives.insert(0, pplx_alt)
card.alternatives.insert(0, anthropic_alt)
card.selected_substitute = substitute
card.status = "substituted"
card.owned = True
card.original_card = card.name
except Exception as e:
logger.error("Perplexity substitution fallback error: %s", 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
# ── Stats ────────────────────────────────────────────────
@staticmethod
def _compute_stats(cards: List[DeckCardWithStatus]) -> dict:
"""Compute deck statistics from the enriched card list."""
by_category = {}
by_status = {"owned": 0, "substituted": 0, "missing": 0}
by_category: dict[str, int] = {}
by_status: dict[str, int] = {"owned": 0, "substituted": 0, "missing": 0}
total_price = 0.0
owned_price = 0.0
missing_price = 0.0
role_counts = {}
role_counts: dict[str, int] = {}
for card in cards:
cat = card.category or "Other"
+1 -1
View File
@@ -137,7 +137,7 @@ if (-not (Test-Path $envFile)) {
# Uncomment and fill in the values you need:
# XIMILAR_API_KEY=your_ximilar_key_here
# PPLX_API_KEY=your_perplexity_key_here
# ANTHROPIC_API_KEY=your_anthropic_api_key_here
# FORGE_JAR=C:\Path\To\forge-gui-desktop.jar
# FORGE_DIR=C:\Path\To\Forge
# LAB_PORT=8080
+106 -67
View File
@@ -3,9 +3,13 @@
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:
@@ -22,7 +26,7 @@ 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
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
@@ -32,20 +36,86 @@ 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.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")
# ── CORS origins (env-configurable) ────────────────────────────
_DEFAULT_ORIGINS = "http://localhost:5173,http://localhost:8080"
_DEFAULT_ORIGINS = "http://localhost:5173,http://localhost:3000,http://localhost:8080"
_allowed = os.environ.get("ALLOWED_ORIGINS", _DEFAULT_ORIGINS)
allowed_origins = [o.strip() for o in _allowed.split(",") if o.strip()]
# ── App ────────────────────────────────────────────────────────
app = FastAPI(title="Commander AI Lab API", version="3.0.0")
# ── 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()
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."""
import time as _time
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"):
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 routes: register if ML policy model is available
try:
from ml.serving.policy_server import PolicyInferenceService
from routes.policy import register_policy_routes
_policy_svc = PolicyInferenceService()
if _policy_svc.load():
register_policy_routes(application, _policy_svc)
log.info("Policy routes registered (/api/policy/*)")
else:
log.warning("Policy model not loaded — /api/policy/* routes inactive")
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)
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.add_middleware(
CORSMiddleware,
allow_origins=allowed_origins,
@@ -60,33 +130,29 @@ app.include_router(precon_router)
app.include_router(import_router)
app.include_router(lab_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
# ── 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"}
@app.on_event("startup")
async def _on_startup():
"""Ensure DB and precon index are ready (supports uvicorn --reload)."""
# Ensure precon_dir is resolved even when started via uvicorn directly
if not CFG.precon_dir:
CFG.precon_dir = _resolve_precon_dir()
init_collection_db()
if not PRECON_INDEX:
download_precon_database()
if not COMMANDER_META:
load_commander_meta()
# ── Static UI ──────────────────────────────────────────────────
_legacy_ui_dir = Path(__file__).parent / "ui"
_spa_dir = Path(__file__).parent / "frontend" / "commander-ai-lab-ui" / "dist"
# ── Static UI (MUST come after all @app.get routes) ──────────────────────────────────────────
from pathlib import Path as _Path
_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")
@@ -94,6 +160,7 @@ 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
@@ -102,7 +169,8 @@ elif _spa_dir.exists():
return FileResponse(str(requested_file))
return FileResponse(str(_spa_dir / "index.html"))
# ── Startup ────────────────────────────────────────────────────
# ── Argument parsing + auto-detection helpers ────────────────────────────────────────────────────────
def _parse_args():
p = argparse.ArgumentParser(description="Commander AI Lab API Server")
p.add_argument("--forge-jar", default=os.environ.get("FORGE_JAR", ""))
@@ -112,10 +180,11 @@ def _parse_args():
p.add_argument("--precon-dir", default=os.environ.get("PRECON_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("--pplx-key", default=os.environ.get("PPLX_API_KEY", ""))
p.add_argument("--anthropic-key", default=os.environ.get("ANTHROPIC_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":
@@ -133,38 +202,24 @@ def _resolve_forge_decks_dir() -> str:
return str(candidate)
return ""
def _resolve_forge_dir() -> str:
"""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.
"""
"""Auto-detect the Forge installation directory."""
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",
@@ -174,11 +229,8 @@ def _resolve_forge_dir() -> str:
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:
@@ -186,32 +238,25 @@ 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" / "forge-gui-desktop" / "target",
]
# 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 [
@@ -225,6 +270,7 @@ 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():
@@ -239,36 +285,25 @@ def _resolve_lab_jar() -> str:
return str(jars[0])
return ""
def _resolve_precon_dir() -> str:
"""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.
"""
"""Auto-detect the precon-decks directory."""
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)
@@ -280,7 +315,7 @@ def main():
CFG.precon_dir = args.precon_dir or _resolve_precon_dir()
CFG.port = args.port
CFG.ximilar_api_key = args.ximilar_key
CFG.pplx_api_key = args.pplx_key
CFG.anthropic_api_key = args.anthropic_key
from services.forge_runner import get_java17
j17 = get_java17()
@@ -294,7 +329,7 @@ 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" Perplexity: {'configured' if CFG.pplx_api_key else 'NOT SET'}")
log.info(f" Anthropic: {'configured' if CFG.anthropic_api_key else 'NOT SET'}")
log.info(f" Java 17: {j17}")
load_commander_meta()
@@ -302,8 +337,12 @@ 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()
+25 -1
View File
@@ -16,6 +16,7 @@ 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
@@ -285,12 +286,17 @@ 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(),
@@ -299,7 +305,20 @@ def run_generation_tournament(
ckpt_path = Path(checkpoint_dir)
if ckpt_path.exists():
for gen_dir in sorted(ckpt_path.glob("gen-*")):
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:
model_path = gen_dir / "best_policy.pt"
if model_path.exists():
name = gen_dir.name # e.g., "gen-001"
@@ -363,6 +382,10 @@ 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()
@@ -373,6 +396,7 @@ def main():
episodes_per_matchup=args.episodes,
playstyle=args.playstyle,
k_factor=args.k_factor,
top_n=args.top_n,
)
# Print results
+29
View File
@@ -2,6 +2,7 @@
from __future__ import annotations
import json
import logging
import os
import subprocess
from datetime import datetime
from pathlib import Path
@@ -25,9 +26,35 @@ class Config:
port: int = 8080
ximilar_api_key: str = ""
pplx_api_key: str = ""
anthropic_api_key: str = ""
CFG = Config()
# Pre-populate from env so ASGI mode (uvicorn lab_api:app) works without main()
CFG.anthropic_api_key = os.environ.get("ANTHROPIC_API_KEY", "")
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)
CFG.ximilar_api_key = os.environ.get("XIMILAR_API_KEY", "")
CFG.pplx_api_key = os.environ.get("PPLX_API_KEY", "")
# ══════════════════════════════════════════════════════════════
@@ -49,7 +76,9 @@ class BatchState:
self.sims_per_sec: float = 0.0
import asyncio as _asyncio
active_batches: dict[str, BatchState] = {}
active_batches_lock = _asyncio.Lock()
COMMANDER_META: dict = {}
+75 -4
View File
@@ -25,6 +25,7 @@ import json
import logging
import logging.handlers
import os
import subprocess
import sys
import time
import urllib.request
@@ -42,6 +43,9 @@ 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
@@ -229,6 +233,52 @@ 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):
"""Trigger ML training and return immediately."""
resp = api_post(base_url, "/api/ml/train", {
@@ -364,6 +414,7 @@ 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("")
@@ -440,6 +491,17 @@ 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:
@@ -482,17 +544,17 @@ def main():
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
# Run DeepSeek sims for 11.5 hours, then train:
# Run DeepSeek sims for 11.5 hours, then update weights + train:
python overnight-run.py
# Run 20 batches of 50 games each, then train:
# Run 20 batches of 50 games each, then update weights + 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, no training:
python overnight-run.py --no-train
# Just run sims, skip both weight update and training:
python overnight-run.py --no-train --no-update-weights
# Custom training settings:
python overnight-run.py --epochs 200 --lr 0.0005 --patience 20
@@ -514,6 +576,12 @@ 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,
@@ -536,6 +604,9 @@ 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
+22
View File
@@ -25,6 +25,28 @@ dev = [
"httpx>=0.24.0",
"ruff>=0.1.0",
]
# RAG Phase 2: ChromaDB vector store + Ollama embedding support
# Install with: pip install ".[rag]"
rag = [
"chromadb>=0.5.0",
]
# Local coach embeddings (sentence-transformers, no Ollama required)
# Install with: pip install ".[coach-embeddings]"
coach-embeddings = [
"sentence-transformers>=3.0.0",
"torch>=2.0.0",
"pandas>=1.5.0",
"pyarrow>=12.0.0",
]
# Full local AI stack (RAG + coach embeddings)
# Install with: pip install ".[all]"
all = [
"chromadb>=0.5.0",
"sentence-transformers>=3.0.0",
"torch>=2.0.0",
"pandas>=1.5.0",
"pyarrow>=12.0.0",
]
[tool.setuptools.packages.find]
where = ["src"]
+8
View File
@@ -7,6 +7,11 @@ uvicorn[standard]>=0.23.0
pydantic>=2.0.0
httpx>=0.24.0
websockets>=11.0
openai>=1.0.0
aiosqlite>=0.19.0
# Anthropic Claude API
anthropic>=0.40.0
# Deck Builder dependencies
requests>=2.31.0
@@ -15,5 +20,8 @@ ollama>=0.2.0
scrython>=2.0.0
# Embeddings
# RAG vector store
chromadb>=0.5.0
sentence-transformers>=3.0.0
torch>=2.0.0
+183 -324
View File
@@ -1,11 +1,12 @@
"""
routes/coach.py
===============
================
LLM-powered deck coaching endpoints:
GET /api/coach/status
GET /api/coach/decks
GET /api/coach/decks/{deck_id}/report
POST /api/coach/decks/{deck_id}
POST /api/coach/decks/{deck_id}/quick
GET /api/coach/sessions
GET /api/coach/sessions/{session_id}
POST /api/coach/embeddings/download
@@ -19,6 +20,9 @@ Globals:
_coach_service, _coach_embeddings, _coach_llm
_deck_gen_v3, _deck_gen_v3_error
init_coach_service(), _build_deck_report_from_db()
Async-safety: every DB and HTTP call in this module uses
async helpers so nothing blocks the FastAPI event loop.
"""
from __future__ import annotations
@@ -34,27 +38,34 @@ from fastapi.responses import StreamingResponse
from models.requests import CoachRequestBody, CoachChatMessage, CoachChatRequest, CoachApplyRequest, CoachGoalsRequest
from models.state import CFG
from services.database import _get_db_conn
from services.async_db import (
async_get_db, async_fetchall, async_fetchone,
async_execute, async_execute_commit, async_commit,
)
from services.deck_service import _compute_deck_analysis
from services.logging import log_coach, log_deckgen
import httpx
from coach.config import COACH_PROVIDER, PPLX_MODEL
from coach.config import COACH_PROVIDER, ANTHROPIC_API_KEY, ANTHROPIC_MODEL, DECK_GEN_PROVIDER, DECK_GEN_MODEL
LLM_TIMEOUT = 120 # seconds
router = APIRouter(tags=["coach"])
# ══════════════════════════════════════════════════════════════
# Coach Service (LLM-powered deck coaching)
# ══════════════════════════════════════════════════════════════
# Global coach instances (initialized at startup)
_coach_service = None
_coach_embeddings = None
_coach_llm = None
_deck_gen_v3 = None # V3 Deck Generator (Perplexity structured output)
_deck_gen_v3_error = None # Error message if V3 init failed
_deck_gen_v3 = None
_deck_gen_v3_error = None
# Known basic land names whose type_line may be absent when the card was added
# via plain-text import and has no matching collection_entries row yet.
_BASIC_LAND_NAMES: frozenset[str] = frozenset({
"plains", "island", "swamp", "mountain", "forest", "wastes",
"snow-covered plains", "snow-covered island", "snow-covered swamp",
"snow-covered mountain", "snow-covered forest",
})
def init_coach_service():
@@ -65,41 +76,38 @@ def init_coach_service():
from coach.embeddings import MTGEmbeddingIndex
from coach.coach_service import CoachService
from coach.config import ensure_dirs
from services.database import _get_db_conn
ensure_dirs()
_coach_llm = LLMClient()
_coach_embeddings = MTGEmbeddingIndex()
# Try to load embeddings (non-blocking — download happens on first use)
try:
from coach.config import EMBEDDINGS_NPZ
if EMBEDDINGS_NPZ.exists():
_coach_embeddings.load()
log_coach.info(f" Coach: Embeddings loaded ({_coach_embeddings.card_count} cards)")
else:
log_coach.warning(" Coach: Embeddings not yet downloaded (will download on first use)")
log_coach.warning(" Coach: Embeddings not yet downloaded")
except Exception as e:
log_coach.error(f" Coach: Embeddings load failed: {e}")
_coach_service = CoachService(_coach_llm, _coach_embeddings)
# Check LLM connection
llm_status = _coach_llm.check_connection()
if COACH_PROVIDER == "anthropic":
log_coach.info(f" Coach LLM: Anthropic Claude ({ANTHROPIC_MODEL})")
else:
llm_status = _coach_llm.check_connection_sync()
if llm_status.get("connected"):
log_coach.info(f" Coach LLM: Connected ({llm_status.get('active_model', 'unknown')})")
else:
log_coach.warning(f" Coach LLM: Not connected (start Ollama on localhost:11434)")
# Initialize V3 Deck Generator (Ollama-based)
log_coach.warning(" Coach LLM: Not connected (start Ollama on localhost:11434)")
_deck_gen_v3_error = None
try:
from src.commander_ai_lab.deck_builder.adapter import DeckBuilderAdapter
_deck_gen_v3 = DeckBuilderAdapter(
db_conn_factory=_get_db_conn,
model="gpt-oss:20b",
)
log_deckgen.info(" Deck Gen V3: Initialized (Ollama DeckBuilderAdapter)")
if DECK_GEN_PROVIDER == "anthropic":
_deck_gen_v3 = DeckBuilderAdapter(db_conn_factory=_get_db_conn, model=DECK_GEN_MODEL)
log_deckgen.info(f" Deck Gen V3: Initialized (Anthropic Claude {DECK_GEN_MODEL})")
else:
from coach.config import LLM_MODEL
_deck_gen_v3 = DeckBuilderAdapter(db_conn_factory=_get_db_conn, model=LLM_MODEL)
log_deckgen.info(f" Deck Gen V3: Initialized (Ollama {LLM_MODEL})")
except ImportError as e:
_deck_gen_v3_error = f"Missing dependency: {e}"
log_deckgen.error(f" Deck Gen V3: {_deck_gen_v3_error}")
@@ -108,65 +116,50 @@ def init_coach_service():
_deck_gen_v3_error = str(e)
log_deckgen.error(f" Deck Gen V3: Failed to initialize: {e}")
_deck_gen_v3 = None
except Exception as e:
log_coach.error(f" Coach: Failed to initialize: {e}\n{traceback.format_exc()}")
log_coach.error(f" Coach: Failed to initialize: {e}\n")
_coach_service = None
@router.get("/api/coach/status")
async def coach_status():
"""Check coach subsystem health."""
if _coach_service is None:
return {"llmConnected": False, "embeddingsLoaded": False,
"embeddingCards": 0, "deckReportsAvailable": 0,
"error": "Coach service not initialized"}
return {"llmConnected": False, "embeddingsLoaded": False, "embeddingCards": 0, "deckReportsAvailable": 0, "error": "Coach service not initialized"}
status = _coach_service.get_status()
return status.model_dump()
@router.get("/api/coach/decks")
async def coach_list_decks():
"""List all decks available for coaching (from deck builder DB)."""
conn = _get_db_conn()
rows = conn.execute(
"""List all decks available for coaching (async DB)."""
rows = await async_fetchall(
"""SELECT d.id as deck_id, d.name as deck_name, d.commander_name,
COUNT(dc.id) as card_count
FROM decks d
LEFT JOIN deck_cards dc ON dc.deck_id = d.id
GROUP BY d.id
ORDER BY d.name"""
).fetchall()
# Also get report availability from coach service
FROM decks d LEFT JOIN deck_cards dc ON dc.deck_id = d.id
GROUP BY d.id ORDER BY d.name"""
)
report_ids = set()
if _coach_service:
report_ids = set(_coach_service.list_deck_reports())
decks = []
for r in rows:
deck_name = r["deck_name"]
# Check if a report exists (by deck name slug match)
has_report = any(
deck_name.lower().replace(" ", "-") == rid.lower() or
deck_name.lower() == rid.lower()
deck_name.lower().replace(" ", "-") == rid.lower()
or deck_name.lower() == rid.lower()
for rid in report_ids
)
decks.append({
"deck_id": r["deck_id"],
"deck_name": r["deck_name"],
"deck_id": r["deck_id"], "deck_name": r["deck_name"],
"commander": r["commander_name"] or "",
"card_count": r["card_count"],
"has_report": has_report,
"report_count": 1 if has_report else 0,
"last_report_date": None,
"card_count": r["card_count"], "has_report": has_report,
"report_count": 1 if has_report else 0, "last_report_date": None,
})
return decks
@router.get("/api/coach/decks/{deck_id}/report")
async def coach_get_report(deck_id: str):
"""Get the latest DeckReport for a deck."""
if _coach_service is None:
raise HTTPException(500, "Coach service not initialized")
report = _coach_service.load_deck_report(deck_id)
@@ -177,17 +170,13 @@ async def coach_get_report(deck_id: str):
@router.post("/api/coach/decks/{deck_id}")
async def coach_run_session(deck_id: str, body: CoachRequestBody = None):
"""Trigger a coaching session for a deck."""
if _coach_service is None:
raise HTTPException(500, "Coach service not initialized")
# Load embeddings on first use if not loaded
if _coach_embeddings and not _coach_embeddings.loaded:
try:
_coach_embeddings.load(force_download=True)
except Exception as e:
log_coach.error(f" Coach: Embeddings download failed: {e}")
goals = None
if body and body.goals:
from coach.models import CoachGoals
@@ -195,14 +184,11 @@ async def coach_run_session(deck_id: str, body: CoachRequestBody = None):
goals = CoachGoals(**body.goals)
except Exception:
goals = None
# Build a fallback DeckReport from the DB if no simulation report exists
fallback_report = None
try:
fallback_report = _build_deck_report_from_db(deck_id)
fallback_report = await _build_deck_report_from_db(deck_id)
except Exception as e:
log_coach.error(f" Coach: Fallback report build failed for '{deck_id}': {e}")
try:
session = await _coach_service.run_coaching_session(deck_id, goals, fallback_report=fallback_report)
return session.model_dump()
@@ -214,74 +200,80 @@ async def coach_run_session(deck_id: str, body: CoachRequestBody = None):
raise HTTPException(500, f"Coach session failed: {e}")
def _build_deck_report_from_db(deck_slug: str):
"""
Build a lightweight DeckReport from the deck builder DB when no
simulation report exists. Allows the coach to analyze deck composition
even without simulation data.
"""
from coach.models import DeckReport, CardPerformance, DeckStructure
conn = _get_db_conn()
@router.post("/api/coach/decks/{deck_id}/quick")
async def coach_run_quick_digest(deck_id: str, body: CoachRequestBody = None):
"""Fast ~5s quick digest: summary, top 3 upgrades, commander dependency."""
if _coach_service is None:
raise HTTPException(500, "Coach service not initialized")
goals = None
if body and body.goals:
from coach.models import CoachGoals
try:
goals = CoachGoals(**body.goals)
except Exception:
goals = None
fallback_report = None
try:
fallback_report = await _build_deck_report_from_db(deck_id)
except Exception as e:
log_coach.error(f" Coach: Quick fallback failed for '{deck_id}': {e}")
try:
session = await _coach_service.run_quick_digest(deck_id, goals, fallback_report=fallback_report)
return session.model_dump()
except ValueError as e:
raise HTTPException(404, str(e))
except ConnectionError as e:
raise HTTPException(503, f"LLM connection failed: {e}")
except Exception as e:
raise HTTPException(500, f"Quick digest failed: {e}")
# --- Input validation ---
async def _build_deck_report_from_db(deck_slug: str):
"""Build a lightweight DeckReport from DB (async). Allows coaching without simulation data."""
from coach.models import DeckReport, CardPerformance, DeckStructure
import re
if not deck_slug or len(deck_slug) > 200:
return None
deck_slug = re.sub(r'[^a-zA-Z0-9 _-]', '', deck_slug)
if not deck_slug:
return None
# Find the deck by parameterized query instead of full-table scan
slug_lower = deck_slug.lower()
slug_hyphenated = slug_lower.replace(' ', '-')
clean_slug = re.sub(r'[^a-z0-9]+', '-', slug_lower).strip('-')
if slug_lower.isdigit():
# Numeric slug -- match by ID directly
matched_deck = conn.execute(
matched_deck = await async_fetchone(
"SELECT id, name, commander_name, color_identity FROM decks WHERE id = ?",
(int(slug_lower),)
).fetchone()
)
else:
# Try exact name match, hyphenated match, or clean-slug match
matched_deck = conn.execute(
matched_deck = await async_fetchone(
"""SELECT id, name, commander_name, color_identity FROM decks
WHERE LOWER(name) = ?
OR LOWER(REPLACE(name, ' ', '-')) = ?
OR LOWER(REPLACE(name, ' ', '-')) = ?
LIMIT 1""",
WHERE LOWER(name) = ? OR LOWER(REPLACE(name, ' ', '-')) = ?
OR LOWER(REPLACE(name, ' ', '-')) = ? LIMIT 1""",
(slug_lower, slug_hyphenated, clean_slug)
).fetchone()
)
if matched_deck is None:
return None
deck_id = matched_deck["id"]
deck_name = matched_deck["name"]
commander = matched_deck["commander_name"] or ""
# Parse color identity
ci_raw = matched_deck["color_identity"] or "[]"
try:
color_identity = json.loads(ci_raw) if isinstance(ci_raw, str) else ci_raw
except Exception:
color_identity = []
# Load all cards in this deck
card_rows = conn.execute(
card_rows = await async_fetchall(
"""SELECT dc.card_name, dc.quantity, dc.is_commander, dc.role_tag,
ce.type_line, ce.cmc, ce.oracle_text
FROM deck_cards dc
LEFT JOIN collection_entries ce ON LOWER(dc.card_name) = LOWER(ce.name)
WHERE dc.deck_id = ?""",
(deck_id,)
).fetchall()
)
cards = []
type_counts = {}
cmc_buckets = [0] * 8
land_count = 0
for cr in card_rows:
card_name = cr["card_name"] or ""
type_line = cr["type_line"] or ""
@@ -289,7 +281,12 @@ def _build_deck_report_from_db(deck_slug: str):
qty = cr["quantity"] or 1
role_tag = cr["role_tag"] or ""
# Build tags from type_line and role_tag
# Fallback for cards added via plain-text import that have no
# collection_entries row yet (type_line comes back empty from the LEFT JOIN).
# Basic lands are identified by name so their land_count is never lost.
if not type_line and card_name.lower() in _BASIC_LAND_NAMES:
type_line = "Basic Land"
tags = []
if role_tag:
tags.append(role_tag)
@@ -308,42 +305,24 @@ def _build_deck_report_from_db(deck_slug: str):
tags.append("enchantment")
elif "Planeswalker" in type_line:
tags.append("planeswalker")
# Type count
for t in ["Creature", "Instant", "Sorcery", "Artifact", "Enchantment", "Planeswalker", "Land"]:
if t in type_line:
type_counts[t] = type_counts.get(t, 0) + qty
# CMC bucket
# Mirrors deck_service._compute_deck_analysis(): lands are excluded from
# the mana curve so cmc=0 lands don't inflate the free-spell bucket and
# mislead the LLM coach into thinking the deck has many 0-cost spells.
bucket = min(int(cmc), 7)
if "Land" not in type_line:
cmc_buckets[bucket] += qty
# CardPerformance with zeroed-out sim stats
cards.append(CardPerformance(
name=card_name,
drawnRate=0.0,
castRate=0.0,
impactScore=0.0,
tags=tags,
))
slug = deck_slug
cards.append(CardPerformance(name=card_name, drawnRate=0.0, castRate=0.0, impactScore=0.0, tags=tags))
return DeckReport(
deckId=slug,
commander=commander,
colorIdentity=color_identity,
cards=cards,
structure=DeckStructure(
landCount=land_count,
curveBuckets=cmc_buckets,
cardTypeCounts=type_counts,
),
deckId=deck_slug, commander=commander, colorIdentity=color_identity,
cards=cards, structure=DeckStructure(landCount=land_count, curveBuckets=cmc_buckets, cardTypeCounts=type_counts),
)
@router.get("/api/coach/sessions")
async def coach_list_sessions(deck_id: str = None):
"""List all coaching sessions, optionally filtered by deck."""
if _coach_service is None:
raise HTTPException(500, "Coach service not initialized")
return {"sessions": _coach_service.list_sessions(deck_id)}
@@ -351,7 +330,6 @@ async def coach_list_sessions(deck_id: str = None):
@router.get("/api/coach/sessions/{session_id}")
async def coach_get_session(session_id: str):
"""Get a specific coaching session."""
if _coach_service is None:
raise HTTPException(500, "Coach service not initialized")
session = _coach_service.load_session(session_id)
@@ -362,52 +340,39 @@ async def coach_get_session(session_id: str):
@router.post("/api/coach/embeddings/download")
async def coach_download_embeddings():
"""Download and convert MTG card embeddings from HuggingFace."""
if _coach_embeddings is None:
raise HTTPException(500, "Coach service not initialized")
try:
_coach_embeddings.load(force_download=True)
return {
"success": True,
"cards": _coach_embeddings.card_count,
"message": f"Loaded {_coach_embeddings.card_count} card embeddings"
}
return {"success": True, "cards": _coach_embeddings.card_count,
"message": f"Loaded {_coach_embeddings.card_count} card embeddings"}
except Exception as e:
raise HTTPException(500, f"Failed to download embeddings: {e}")
@router.get("/api/coach/embeddings/search")
async def coach_search_similar(card: str, colors: str = None, top_n: int = 10):
"""Search for similar cards by name."""
if _coach_embeddings is None or not _coach_embeddings.loaded:
raise HTTPException(503, "Embeddings not loaded")
color_filter = list(colors.upper()) if colors else None
matches = _coach_embeddings.search_similar(
query_card=card, color_filter=color_filter, top_n=top_n
)
matches = _coach_embeddings.search_similar(query_card=card, color_filter=color_filter, top_n=top_n)
return {"query": card, "matches": [m.to_dict() for m in matches]}
@router.post("/api/coach/chat")
async def coach_chat(body: CoachChatRequest):
"""Multi-turn coaching chat. Sends conversation history to LLM and returns response."""
"""Multi-turn coaching chat. Fully async - no event loop blocking."""
if _coach_service is None:
raise HTTPException(500, "Coach service not initialized")
# Load deck report for context
report = _coach_service.load_deck_report(body.deck_id)
# Build system prompt with report context
from coach.prompt_template import build_system_prompt
from coach.models import CoachGoals
goals = None
if body.goals:
try:
goals = CoachGoals(**body.goals)
except Exception:
goals = None
system_prompt = ""
if report:
system_prompt = build_system_prompt(report, goals)
@@ -417,239 +382,145 @@ async def coach_chat(body: CoachChatRequest):
"Answer questions about deck building, strategy, card choices, and game theory. "
"Be specific and actionable in your advice."
)
# Build messages array for LLM
messages = [{"role": "system", "content": system_prompt}]
for msg in body.messages:
messages.append({"role": msg.get("role", "user"), "content": msg.get("content", "")})
# ── Perplexity provider path ──────────────────────────────
if COACH_PROVIDER == 'perplexity':
if not CFG.pplx_api_key:
raise HTTPException(400, 'Perplexity API key not configured. Set PPLX_API_KEY env var.')
pplx_payload = {
'model': PPLX_MODEL,
'messages': messages,
'max_tokens': 4096,
'temperature': 0.7,
}
pplx_headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {CFG.pplx_api_key}',
}
# ── Anthropic Claude provider path (async httpx) ──
if COACH_PROVIDER == 'anthropic':
if not ANTHROPIC_API_KEY:
raise HTTPException(400, 'Anthropic API key not configured.')
import anthropic
aclient = anthropic.AsyncAnthropic(api_key=ANTHROPIC_API_KEY)
if body.stream:
async def pplx_stream():
pplx_payload['stream'] = True
async with httpx.AsyncClient(timeout=120.0) as client:
async with client.stream('POST', 'https://api.perplexity.ai/chat/completions',
json=pplx_payload, headers=pplx_headers) as resp:
async for line in resp.aiter_lines():
if line.startswith('data: '):
data = line[6:]
if data == '[DONE]':
yield 'data: [DONE]\n\n'
break
try:
chunk = json.loads(data)
content = chunk.get('choices', [{}])[0].get('delta', {}).get('content', '')
if content:
yield f'data: {json.dumps({"content": content})}\n\n'
except json.JSONDecodeError:
continue
return StreamingResponse(pplx_stream(), media_type='text/event-stream')
async def anthropic_stream():
async with aclient.messages.stream(
model=ANTHROPIC_MODEL, system=system_prompt,
messages=[{"role": m["role"], "content": m["content"]} for m in messages if m["role"] != "system"],
max_tokens=4096, temperature=0.7,
) as stream:
async for text in stream.text_stream:
yield f"data: {json.dumps({'content': text})}\n\n"
yield "data: [DONE]\n\n"
return StreamingResponse(anthropic_stream(), media_type="text/event-stream")
else:
try:
async with httpx.AsyncClient(timeout=120.0) as client:
resp = await client.post(
'https://api.perplexity.ai/chat/completions',
json=pplx_payload, headers=pplx_headers,
resp = await aclient.messages.create(
model=ANTHROPIC_MODEL, system=system_prompt,
messages=[{"role": m["role"], "content": m["content"]} for m in messages if m["role"] != "system"],
max_tokens=4096, temperature=0.7,
)
resp.raise_for_status()
raw = resp.json()
content = raw.get('choices', [{}])[0].get('message', {}).get('content', '')
usage = raw.get('usage', {})
content = resp.content[0].text if resp.content else ""
usage = resp.usage
return {
'content': content,
'model': raw.get('model', ''),
'prompt_tokens': usage.get('prompt_tokens', 0),
'completion_tokens': usage.get('completion_tokens', 0),
"content": content, "model": resp.model,
"prompt_tokens": usage.input_tokens if usage else 0,
"completion_tokens": usage.output_tokens if usage else 0,
}
except httpx.HTTPStatusError as e:
raise HTTPException(502, f'Perplexity API returned {e.response.status_code}')
except httpx.RequestError as e:
raise HTTPException(502, f'Perplexity API call failed: {e}')
# Call LLM with multi-turn messages
except Exception as e:
raise HTTPException(502, f"Anthropic API call failed: {e}")
# ── Local Ollama provider path (async httpx) ──
try:
import json
from urllib.request import urlopen, Request as UrlRequest
model_name = _coach_llm._resolve_model()
llm_body = {
"model": model_name,
"messages": messages,
"temperature": 0.7,
"max_tokens": 4096,
}
model_name = await _coach_llm._resolve_model()
llm_body = {"model": model_name, "messages": messages, "temperature": 0.7, "max_tokens": 4096}
if body.stream:
# SSE streaming response
llm_body["stream"] = True
async def generate():
import asyncio
def _stream():
req = UrlRequest(
f"{_coach_llm.base_url}/chat/completions",
data=json.dumps(llm_body).encode("utf-8"),
headers={"Content-Type": "application/json"},
method="POST",
)
import http.client
import urllib.parse
parsed = urllib.parse.urlparse(f"{_coach_llm.base_url}/chat/completions")
conn = http.client.HTTPConnection(parsed.hostname, parsed.port, timeout=LLM_TIMEOUT)
conn.request("POST", parsed.path, body=json.dumps(llm_body).encode("utf-8"),
headers={"Content-Type": "application/json"})
resp = conn.getresponse()
chunks = []
while True:
line = resp.readline()
if not line:
break
line = line.decode("utf-8").strip()
async with httpx.AsyncClient(timeout=httpx.Timeout(float(LLM_TIMEOUT))) as client:
async with client.stream("POST", f"{_coach_llm.base_url}/chat/completions",
json=llm_body, headers={"Content-Type": "application/json"}) as resp:
async for line in resp.aiter_lines():
if line.startswith("data: "):
data = line[6:]
if data == "[DONE]":
chunks.append("[DONE]")
break
chunks.append(data)
conn.close()
return chunks
loop = asyncio.get_event_loop()
chunks = await loop.run_in_executor(None, _stream)
for chunk_str in chunks:
if chunk_str == "[DONE]":
yield f"data: [DONE]\n\n"
yield "data: [DONE]\n\n"
break
try:
chunk = json.loads(chunk_str)
delta = chunk.get("choices", [{}])[0].get("delta", {})
content = delta.get("content", "")
chunk = json.loads(data)
content = chunk.get("choices", [{}])[0].get("delta", {}).get("content", "")
if content:
# Strip think tags from streaming chunks
yield f"data: {json.dumps({'content': content})}\n\n"
except json.JSONDecodeError:
continue
return StreamingResponse(generate(), media_type="text/event-stream")
else:
# Non-streaming: regular call
req = UrlRequest(
async with httpx.AsyncClient(timeout=httpx.Timeout(float(LLM_TIMEOUT))) as client:
resp = await client.post(
f"{_coach_llm.base_url}/chat/completions",
data=json.dumps(llm_body).encode("utf-8"),
headers={"Content-Type": "application/json"},
method="POST",
json=llm_body, headers={"Content-Type": "application/json"},
)
import asyncio
loop = asyncio.get_event_loop()
def _call():
with urlopen(req, timeout=LLM_TIMEOUT) as resp:
return json.loads(resp.read().decode("utf-8"))
raw = await loop.run_in_executor(None, _call)
resp.raise_for_status()
raw = resp.json()
content = raw.get("choices", [{}])[0].get("message", {}).get("content", "")
# Strip think tags
content = _coach_llm._strip_think_tags(content)
usage = raw.get("usage", {})
return {
"content": content,
"model": raw.get("model", ""),
"content": content, "model": raw.get("model", ""),
"prompt_tokens": usage.get("prompt_tokens", 0),
"completion_tokens": usage.get("completion_tokens", 0),
}
except Exception as e:
raise HTTPException(500, f"Chat failed: {e}")
@router.post("/api/coach/apply")
async def coach_apply_suggestions(body: CoachApplyRequest):
"""Apply accepted coaching suggestions to a deck — remove cuts, add adds."""
conn = _get_db_conn()
# Verify deck exists
deck = conn.execute("SELECT * FROM decks WHERE id = ?", (body.deck_id,)).fetchone()
"""Apply accepted coaching suggestions to a deck (async DB + async HTTP)."""
db = await async_get_db()
deck = await async_fetchone("SELECT * FROM decks WHERE id = ?", (body.deck_id,))
if not deck:
raise HTTPException(404, f"Deck {body.deck_id} not found")
results = {"cuts": [], "adds": [], "errors": []}
# Process cuts: remove cards by name
try:
for card_name in body.accepted_cuts:
row = conn.execute(
row = await async_fetchone(
"SELECT id FROM deck_cards WHERE deck_id = ? AND card_name = ? LIMIT 1",
(body.deck_id, card_name)
).fetchone()
)
if row:
conn.execute("DELETE FROM deck_cards WHERE id = ?", (row["id"],))
await async_execute("DELETE FROM deck_cards WHERE id = ?", (row["id"],))
results["cuts"].append({"name": card_name, "status": "removed"})
else:
# Try case-insensitive
row = conn.execute(
row = await async_fetchone(
"SELECT id, card_name FROM deck_cards WHERE deck_id = ? AND LOWER(card_name) = LOWER(?) LIMIT 1",
(body.deck_id, card_name)
).fetchone()
)
if row:
conn.execute("DELETE FROM deck_cards WHERE id = ?", (row["id"],))
await async_execute("DELETE FROM deck_cards WHERE id = ?", (row["id"],))
results["cuts"].append({"name": row["card_name"], "status": "removed"})
else:
results["errors"].append({"name": card_name, "error": "Card not found in deck"})
# Process adds: look up in collection first, then Scryfall
for card_name in body.accepted_adds:
# Try to find in collection
ce = conn.execute(
ce = await async_fetchone(
"SELECT scryfall_id, name FROM collection_entries WHERE LOWER(name) = LOWER(?) LIMIT 1",
(card_name,)
).fetchone()
)
scryfall_id = None
resolved_name = card_name
if ce:
scryfall_id = ce["scryfall_id"]
resolved_name = ce["name"]
else:
# Try Scryfall API lookup
try:
import urllib.parse
from urllib.request import urlopen, Request as UrlRequest
encoded = urllib.parse.quote(card_name)
scry_req = UrlRequest(
async with httpx.AsyncClient(timeout=10.0) as client:
resp = await client.get(
f"https://api.scryfall.com/cards/named?fuzzy={encoded}",
headers={"User-Agent": "commander-ai-lab/1.0"}
)
with urlopen(scry_req, timeout=10) as resp:
card_data = json.loads(resp.read().decode("utf-8"))
resp.raise_for_status()
card_data = resp.json()
scryfall_id = card_data.get("id")
resolved_name = card_data.get("name", card_name)
except Exception:
pass
if scryfall_id:
# Check if already in deck
existing = conn.execute(
existing = await async_fetchone(
"SELECT id FROM deck_cards WHERE deck_id = ? AND scryfall_id = ?",
(body.deck_id, scryfall_id)
).fetchone()
)
if not existing:
conn.execute(
await async_execute(
"INSERT INTO deck_cards (deck_id, scryfall_id, card_name, quantity) VALUES (?, ?, ?, 1)",
(body.deck_id, scryfall_id, resolved_name)
)
@@ -658,10 +529,14 @@ async def coach_apply_suggestions(body: CoachApplyRequest):
results["adds"].append({"name": resolved_name, "status": "already_in_deck"})
else:
results["errors"].append({"name": card_name, "error": "Could not resolve card"})
conn.execute("UPDATE decks SET updated_at = datetime('now') WHERE id = ?", (body.deck_id,))
conn.commit()
await async_execute("UPDATE decks SET updated_at = datetime('now') WHERE id = ?", (body.deck_id,))
await async_commit()
except Exception as e:
try:
await async_execute("ROLLBACK")
except Exception:
pass
raise HTTPException(500, f"Apply failed and was rolled back: {e}")
results["total_cuts"] = len(results["cuts"])
results["total_adds"] = len([a for a in results["adds"] if a["status"] == "added"])
return results
@@ -669,49 +544,35 @@ async def coach_apply_suggestions(body: CoachApplyRequest):
@router.get("/api/coach/cards-like")
async def coach_cards_like(card: str, colors: str = None, top_n: int = 10):
"""Find cards similar to a given card using embeddings. UI-friendly version."""
"""Find similar cards using embeddings (async DB for ownership check)."""
if _coach_embeddings is None or not _coach_embeddings.loaded:
raise HTTPException(503, "Embeddings not loaded")
color_filter = list(colors.upper()) if colors else None
matches = _coach_embeddings.search_similar(
query_card=card, color_filter=color_filter, top_n=top_n
)
conn = _get_db_conn()
matches = _coach_embeddings.search_similar(query_card=card, color_filter=color_filter, top_n=top_n)
results = []
for m in matches:
# Check if card is in collection
owned = conn.execute(
owned = await async_fetchone(
"SELECT SUM(quantity) as qty FROM collection_entries WHERE LOWER(name) = LOWER(?)",
(m.name,)
).fetchone()
)
owned_qty = (owned["qty"] or 0) if owned else 0
# Get image URL and price
ce = conn.execute(
ce = await async_fetchone(
"SELECT scryfall_id, tcg_price FROM collection_entries WHERE LOWER(name) = LOWER(?) LIMIT 1",
(m.name,)
).fetchone()
)
results.append({
"name": m.name,
"similarity": round(m.similarity, 4),
"types": m.types,
"mana_value": m.mana_value,
"mana_cost": m.mana_cost,
"text": m.text[:200] if m.text else "",
"name": m.name, "similarity": round(m.similarity, 4),
"types": m.types, "mana_value": m.mana_value,
"mana_cost": m.mana_cost, "text": m.text[:200] if m.text else "",
"owned_qty": owned_qty,
"image_url": f"https://api.scryfall.com/cards/{ce['scryfall_id']}?format=image&version=normal" if ce and ce["scryfall_id"] else None,
"tcg_price": ce["tcg_price"] if ce else None,
})
return {"query": card, "results": results}
@router.post("/api/coach/reports/generate")
async def coach_generate_reports():
"""Rebuild all deck reports from batch result JSONs in results/."""
try:
from coach.report_generator import generate_deck_reports
lab_root = Path(__file__).parent.parent
@@ -719,9 +580,7 @@ async def coach_generate_reports():
reports_dir = str(lab_root / "deck-reports")
updated = generate_deck_reports(results_dir, reports_dir)
return {
"status": "ok",
"decksUpdated": updated,
"count": len(updated),
"status": "ok", "decksUpdated": updated, "count": len(updated),
"message": f"Generated reports for {len(updated)} decks" if updated else "No batch results found",
}
except Exception as e:
+5
View File
@@ -406,6 +406,11 @@ async def update_collection_card(cardId: int, body: dict):
if finish_val not in ("NORMAL", "FOIL", "ETCHED"):
raise HTTPException(400, "finish must be NORMAL, FOIL, or ETCHED")
updates["finish"] = finish_val
if "quantity" in body:
qty_val = int(body["quantity"])
if qty_val < 0:
raise HTTPException(400, "quantity must be >= 0")
updates["quantity"] = qty_val
if not updates:
return _add_image_url(_row_to_dict(row))
+21 -51
View File
@@ -45,7 +45,7 @@ from services.card_analysis import _detect_card_roles
from services.database import _get_db_conn, _row_to_dict, _add_image_url
from services.deck_service import (
_get_deck_or_404, _compute_deck_analysis, _check_ratio_limit,
_classify_card_type, _to_edhrec_slug, _TYPE_TARGETS,
_classify_card_type, _classify_card_category, _to_edhrec_slug, _TYPE_TARGETS,
_save_profile_to_dck,
)
from services.import_helpers import (
@@ -121,7 +121,6 @@ async def get_deck(deck_id: int):
deck["cards"] = [dict(r) for r in card_rows]
deck["total_cards"] = sum(r["quantity"] for r in card_rows)
deck["card_slots"] = len(card_rows)
# Composition summary by type
analysis = _compute_deck_analysis(deck_id)
deck["composition"] = analysis["counts_by_type"]
return deck
@@ -130,7 +129,7 @@ async def get_deck(deck_id: int):
@router.put("/api/decks/{deck_id}")
async def update_deck(deck_id: int, req: UpdateDeckRequest):
"""Update deck metadata."""
_get_deck_or_404(deck_id) # ensure exists
_get_deck_or_404(deck_id)
conn = _get_db_conn()
updates = {}
@@ -158,7 +157,7 @@ async def update_deck(deck_id: int, req: UpdateDeckRequest):
@router.delete("/api/decks/{deck_id}")
async def delete_deck(deck_id: int):
"""Delete a deck and all its cards (cascade)."""
_get_deck_or_404(deck_id) # ensure exists
_get_deck_or_404(deck_id)
conn = _get_db_conn()
conn.execute("DELETE FROM deck_cards WHERE deck_id = ?", (deck_id,))
conn.execute("DELETE FROM decks WHERE id = ?", (deck_id,))
@@ -181,7 +180,7 @@ async def delete_all_decks():
@router.get("/api/decks/{deck_id}/cards")
async def get_deck_cards(deck_id: int):
"""Get all cards in a deck with joined collection data."""
"""Get all cards in a deck with joined collection and card_records data."""
_get_deck_or_404(deck_id)
conn = _get_db_conn()
rows = conn.execute(
@@ -189,7 +188,12 @@ async def get_deck_cards(deck_id: int):
SELECT
dc.id, dc.deck_id, dc.scryfall_id, dc.card_name, dc.quantity,
dc.is_commander, dc.role_tag,
ce.type_line, ce.cmc, ce.mana_cost, ce.color_identity, ce.oracle_text, ce.keywords,
COALESCE(ce.type_line, cr.type_line, '') as type_line,
COALESCE(ce.cmc, cr.cmc, 0) as cmc,
COALESCE(ce.mana_cost, '') as mana_cost,
COALESCE(ce.color_identity, cr.color_identity, '[]') as color_identity,
COALESCE(ce.oracle_text, cr.oracle_text, '') as oracle_text,
COALESCE(ce.keywords, cr.keywords, '[]') as keywords,
ce.tcg_price, ce.quantity AS owned_qty, ce.is_legendary,
ce.salt_score, ce.is_game_changer
FROM deck_cards dc
@@ -200,6 +204,11 @@ async def get_deck_cards(deck_id: int):
FROM collection_entries
GROUP BY scryfall_id
) ce ON ce.scryfall_id = dc.scryfall_id
LEFT JOIN (
SELECT name, type_line, cmc, color_identity, oracle_text, keywords
FROM card_records
GROUP BY name
) cr ON cr.name = dc.card_name
WHERE dc.deck_id = ?
ORDER BY dc.is_commander DESC, dc.card_name ASC
""",
@@ -208,7 +217,6 @@ async def get_deck_cards(deck_id: int):
cards = []
for row in rows:
d = dict(row)
# Parse JSON fields
for f in ("color_identity", "keywords"):
if isinstance(d.get(f), str):
try:
@@ -219,6 +227,8 @@ async def get_deck_cards(deck_id: int):
f"https://api.scryfall.com/cards/{d['scryfall_id']}?format=image&version=normal"
if d.get("scryfall_id") else None
)
d["card_type"] = _classify_card_type(d.get("type_line", ""))
d["card_category"] = _classify_card_category(d.get("type_line", ""), d.get("oracle_text", ""))
cards.append(d)
return {"cards": cards, "total": len(cards)}
@@ -229,14 +239,12 @@ async def add_deck_card(deck_id: int, req: AddDeckCardRequest):
_get_deck_or_404(deck_id)
conn = _get_db_conn()
# Look up card_name from collection_entries by scryfall_id, fall back to request
ce_row = conn.execute(
"SELECT name FROM collection_entries WHERE scryfall_id = ? LIMIT 1",
(req.scryfall_id,)
).fetchone()
card_name = ce_row["name"] if ce_row else (req.card_name or "")
# Check if this scryfall_id is already in the deck
existing = conn.execute(
"SELECT id, quantity FROM deck_cards WHERE deck_id = ? AND scryfall_id = ?",
(deck_id, req.scryfall_id)
@@ -266,7 +274,6 @@ async def add_deck_card(deck_id: int, req: AddDeckCardRequest):
conn.commit()
row = conn.execute("SELECT * FROM deck_cards WHERE id = ?", (cur.lastrowid,)).fetchone()
# Update deck updated_at
conn.execute("UPDATE decks SET updated_at = datetime('now') WHERE id = ?", (deck_id,))
conn.commit()
return dict(row)
@@ -341,28 +348,23 @@ async def recommend_from_collection(deck_id: int, max_results: int = 20, roles:
deck_color_identity = deck.get("color_identity", [])
conn = _get_db_conn()
# Run analysis to find shortfalls
analysis = _compute_deck_analysis(deck_id)
deltas = analysis["deltas"]
deltas = analysis.get("deltas", {})
counts_by_type = analysis["counts_by_type"]
# Find types that are below their target midpoint (shortfall)
shortfall_types = set()
for t, delta in deltas.items():
lo, hi = _TYPE_TARGETS.get(t, [0, 0])
if counts_by_type.get(t, 0) < lo:
shortfall_types.add(t)
# Get scryfall_ids already in deck to exclude
in_deck_ids = set(
r["scryfall_id"] for r in
conn.execute("SELECT scryfall_id FROM deck_cards WHERE deck_id = ?", (deck_id,)).fetchall()
)
# Parse role filter
role_filter = [r.strip() for r in roles.split(",") if r.strip()] if roles else []
# Query collection for candidate cards
coll_rows = conn.execute(
"SELECT * FROM collection_entries WHERE quantity > 0"
).fetchall()
@@ -372,11 +374,9 @@ async def recommend_from_collection(deck_id: int, max_results: int = 20, roles:
card = _row_to_dict(row)
card_id = card.get("scryfall_id", "")
# Skip cards already in deck
if card_id in in_deck_ids:
continue
# Color identity check: all card colors must be in deck colors
card_ci = card.get("color_identity", [])
if isinstance(card_ci, str):
try:
@@ -393,22 +393,17 @@ async def recommend_from_collection(deck_id: int, max_results: int = 20, roles:
card_type = _classify_card_type(type_line)
card_roles = _detect_card_roles(oracle_text, type_line, keywords)
# Role filter
if role_filter and not any(r in card_roles for r in role_filter):
continue
# Scoring
score = 0
# +3 if card type is in shortfall
if card_type in shortfall_types:
score += 3
# +2 per matching role
for r in card_roles:
if r in ("Ramp", "Draw", "Removal", "BoardWipe"):
score += 2
else:
score += 1
# +1 for lower CMC (curve fit)
cmc = float(card.get("cmc", 0))
if cmc <= 3:
score += 1
@@ -429,7 +424,6 @@ async def recommend_from_collection(deck_id: int, max_results: int = 20, roles:
scored.sort(key=lambda x: x["score"], reverse=True)
# Group by type
grouped = {}
for card in scored[:max_results]:
ct = card["card_type"]
@@ -456,7 +450,6 @@ async def deck_edh_recs(deck_id: int, only_owned: bool = False, max_results: int
if not commander_name:
raise HTTPException(400, "Deck has no commander_name set. Update the deck first.")
# Cache check
cache_key = f"edhrec:avg:{_to_edhrec_slug(commander_name)}"
cached_profile = _edhrec_cache_get(cache_key)
if cached_profile is None:
@@ -471,13 +464,11 @@ async def deck_edh_recs(deck_id: int, only_owned: bool = False, max_results: int
conn = _get_db_conn()
# Get cards already in deck
in_deck_names = set(
r["card_name"].lower() for r in
conn.execute("SELECT card_name FROM deck_cards WHERE deck_id = ?", (deck_id,)).fetchall()
)
# Build collection lookup: name (lower) -> {owned, qty, scryfall_id}
coll_rows = conn.execute(
"SELECT name, quantity, scryfall_id FROM collection_entries"
).fetchall()
@@ -488,7 +479,6 @@ async def deck_edh_recs(deck_id: int, only_owned: bool = False, max_results: int
if not existing or r["quantity"] > existing["qty"]:
coll_map[key] = {"owned": True, "qty": r["quantity"], "scryfall_id": r["scryfall_id"]}
# Also look up card details from collection_entries for type/role
coll_details = {}
for r in conn.execute("SELECT name, type_line, oracle_text, keywords, scryfall_id FROM collection_entries").fetchall():
key = r["name"].lower()
@@ -498,7 +488,6 @@ async def deck_edh_recs(deck_id: int, only_owned: bool = False, max_results: int
results = []
for card_name in mainboard:
name_lower = card_name.lower()
# Skip cards already in deck
if name_lower in in_deck_names:
continue
@@ -507,7 +496,6 @@ async def deck_edh_recs(deck_id: int, only_owned: bool = False, max_results: int
if only_owned and not owned_info["owned"]:
continue
# Get type/role from collection if available
details = coll_details.get(name_lower, {})
type_line = details.get("type_line", "")
oracle_text = details.get("oracle_text", "")
@@ -520,7 +508,7 @@ async def deck_edh_recs(deck_id: int, only_owned: bool = False, max_results: int
"type_line": type_line,
"role": card_roles[0] if card_roles else "Other",
"roles": card_roles,
"inclusion_pct": None, # EDHREC average doesn't expose % directly in this path
"inclusion_pct": None,
"synergy_score": None,
"owned": owned_info["owned"],
"owned_qty": owned_info["qty"],
@@ -569,7 +557,6 @@ async def bulk_add_cards(deck_id: int, req: BulkAddRequest):
details.append({"scryfall_id": scryfall_id, "status": "skipped", "reason": "missing scryfall_id"})
continue
# Look up card name and type from collection
ce_row = conn.execute(
"SELECT name, type_line, oracle_text, keywords FROM collection_entries WHERE scryfall_id = ? LIMIT 1",
(scryfall_id,)
@@ -589,7 +576,6 @@ async def bulk_add_cards(deck_id: int, req: BulkAddRequest):
})
continue
# Upsert
existing = conn.execute(
"SELECT id, quantity FROM deck_cards WHERE deck_id = ? AND scryfall_id = ?",
(deck_id, scryfall_id)
@@ -631,7 +617,6 @@ async def bulk_add_recommended(deck_id: int, req: BulkAddRecommendedRequest):
candidates = []
if source == "collection":
# Reuse the recommend_from_collection logic
deck = _get_deck_or_404(deck_id)
deck_color_identity = deck.get("color_identity", [])
analysis = _compute_deck_analysis(deck_id)
@@ -691,7 +676,7 @@ async def bulk_add_recommended(deck_id: int, req: BulkAddRecommendedRequest):
if req.only_owned and not coll_info:
continue
if not coll_info:
continue # can't add without scryfall_id
continue
scryfall_id = coll_info.get("scryfall_id", "")
if not scryfall_id:
continue
@@ -706,7 +691,6 @@ async def bulk_add_recommended(deck_id: int, req: BulkAddRecommendedRequest):
else:
raise HTTPException(400, f"Unknown source '{source}'. Use 'collection' or 'edhrec'.")
# Now bulk-add candidates
added = 0
skipped = 0
details = []
@@ -770,7 +754,6 @@ async def export_deck_to_sim(deck_id: int):
if not card_rows:
raise HTTPException(400, "Deck has no cards to export")
# Build .dck content
lines = ["[metadata]"]
deck_name = deck.get("name", f"Deck {deck_id}")
lines.append(f"Name={deck_name}")
@@ -795,7 +778,6 @@ async def export_deck_to_sim(deck_id: int):
content = "\n".join(lines)
# Determine save directory
safe_name = re.sub(r"[^a-zA-Z0-9 _-]", "", deck_name).replace(" ", "_").strip()
if not safe_name:
safe_name = f"deck_{deck_id}"
@@ -835,14 +817,13 @@ def _parse_decklist_text(text: str) -> dict:
"""
commanders = []
cards = []
section = 'main' # default section
section = 'main'
for raw_line in text.splitlines():
line = raw_line.strip()
if not line or line.startswith('//') or line.startswith('#'):
continue
# Section headers
low = line.lower()
if low.startswith('[commander'):
section = 'commander'
@@ -854,14 +835,12 @@ def _parse_decklist_text(text: str) -> dict:
section = 'skip'
continue
if low.startswith('['):
# Unknown section, treat as main
section = 'main'
continue
if section == 'skip':
continue
# Parse quantity + name
m = re.match(r'^(\d+)\s*[xX]?\s+(.+)$', line)
if m:
qty = int(m.group(1))
@@ -870,7 +849,6 @@ def _parse_decklist_text(text: str) -> dict:
qty = 1
name = line
# Strip set codes like "(M21)" or "(M21) 123" from MTGA exports
name = re.sub(r'\s*\([A-Z0-9]+\)\s*\d*\s*$', '', name).strip()
if not name:
@@ -912,7 +890,6 @@ async def import_decklist(deck_id: int, request: FastAPIRequest):
conn = _get_db_conn()
# Optionally clear existing cards
if clear_first:
conn.execute('DELETE FROM deck_cards WHERE deck_id = ?', (deck_id,))
conn.commit()
@@ -926,7 +903,6 @@ async def import_decklist(deck_id: int, request: FastAPIRequest):
qty = entry['qty']
is_cmd = entry['is_commander']
# Look up on Scryfall
sf = _scryfall_fuzzy_lookup(name)
if not sf:
failed.append(name)
@@ -936,7 +912,6 @@ async def import_decklist(deck_id: int, request: FastAPIRequest):
scryfall_id = sf.get('id', '')
resolved_name = sf.get('name', name)
# Check if already in deck
existing = conn.execute(
'SELECT id, quantity FROM deck_cards WHERE deck_id = ? AND scryfall_id = ?',
(deck_id, scryfall_id)
@@ -956,11 +931,9 @@ async def import_decklist(deck_id: int, request: FastAPIRequest):
added += 1
results.append({'name': resolved_name, 'qty': qty, 'status': 'added', 'isCommander': is_cmd})
# Update commander on deck record if we found one
cmd_entries = [r for r in results if r.get('isCommander')]
if cmd_entries:
cmd_name = cmd_entries[0]['name']
# Look up scryfall_id from the deck_cards we just inserted
cmd_row = conn.execute(
'SELECT scryfall_id FROM deck_cards WHERE deck_id = ? AND is_commander = 1 LIMIT 1',
(deck_id,)
@@ -1007,7 +980,6 @@ async def import_decklist_new(request: FastAPIRequest):
if not all_entries:
return JSONResponse({'error': 'No cards found in decklist'}, status_code=400)
# Auto-name from first commander if no name given
if not deck_name:
cmd = parsed['commanders'][0]['name'] if parsed['commanders'] else None
deck_name = cmd or 'Imported Deck'
@@ -1020,7 +992,6 @@ async def import_decklist_new(request: FastAPIRequest):
conn.commit()
new_deck_id = cur.lastrowid
# Set commander on the deck if we have one
first_cmd_scryfall = None
first_cmd_name = None
@@ -1051,7 +1022,6 @@ async def import_decklist_new(request: FastAPIRequest):
conn.commit()
added += 1
# Update commander on deck record
if first_cmd_scryfall:
conn.execute(
'UPDATE decks SET commander_name = ?, commander_scryfall_id = ? WHERE id = ?',
+43 -35
View File
@@ -1,14 +1,14 @@
"""
routes/deckgen.py
=================
Deck generation endpoints (classic, Perplexity V1, V3):
Deck generation endpoints (classic, Anthropic Claude V1, V3):
GET /api/deck-generator/config
POST /api/deck-generator/preview
POST /api/deck-generator/commit
GET /api/deck-generator/commander-search
POST /api/deck-research
POST /api/deck-generate
GET /api/pplx/status
GET /api/anthropic/status
GET /api/deck/v3/status
POST /api/deck/v3/generate
POST /api/deck/v3/commit
@@ -724,7 +724,7 @@ async def deck_generator_commander_search(q: str = ""):
# ══════════════════════════════════════════════════════════════
# Perplexity API — AI Deck Research & Generation
# Anthropic Claude API — AI Deck Research & Generation
# ══════════════════════════════════════════════════════════════
@@ -847,54 +847,62 @@ def _build_collection_summary(color_identity: list[str] | None = None) -> dict:
}
async def _call_pplx_api(messages: list[dict], max_tokens: int = 4096, temperature: float = 0.2) -> str:
"""Call Perplexity API chat/completions endpoint (non-blocking).
async def _call_anthropic_api(messages: list[dict], max_tokens: int = 4096, temperature: float = 0.2) -> str:
"""Call Anthropic Claude API messages endpoint (non-blocking).
Uses httpx.AsyncClient so the uvicorn event loop is never stalled.
Returns the assistant message content.
"""
from coach.config import DECK_GEN_PROVIDER, DECK_GEN_BASE_URL, DECK_GEN_MODEL, PPLX_MODEL
from coach.config import DECK_GEN_PROVIDER, DECK_GEN_BASE_URL, DECK_GEN_MODEL, ANTHROPIC_MODEL
if DECK_GEN_PROVIDER == 'local':
base_url = DECK_GEN_BASE_URL
model = DECK_GEN_MODEL
headers = {'Content-Type': 'application/json'}
else:
if not CFG.pplx_api_key:
raise HTTPException(400, 'Perplexity API key not configured. Set PPLX_API_KEY env var or --pplx-key.')
base_url = 'https://api.perplexity.ai'
model = PPLX_MODEL
if not CFG.anthropic_api_key:
raise HTTPException(400, 'Anthropic Claude API key not configured. Set anthropic_api_key env var or --anthropic-key.')
base_url = 'https://api.anthropic.com'
model = ANTHROPIC_MODEL
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {CFG.pplx_api_key}',
'x-api-key': CFG.anthropic_api_key, 'anthropic-version': '2023-06-01',
}
# Separate system messages for Anthropic API format
system_msgs = [m['content'] for m in messages if m.get('role') == 'system']
user_msgs = [m for m in messages if m.get('role') != 'system']
system_text = '\n'.join(system_msgs) if system_msgs else None
payload = {
'model': model,
'messages': messages,
'messages': user_msgs,
'max_tokens': max_tokens,
'temperature': temperature,
}
if system_text:
payload['system'] = system_text
try:
async with httpx.AsyncClient(timeout=120.0) as client:
resp = await client.post(
f'{base_url}/chat/completions',
f'{base_url}/v1/messages',
json=payload,
headers=headers,
)
resp.raise_for_status()
data = resp.json()
except httpx.HTTPStatusError as e:
raise HTTPException(502, f'Perplexity API returned {e.response.status_code}: {e.response.text[:200]}')
raise HTTPException(502, f'Anthropic Claude API returned {e.response.status_code}: {e.response.text[:200]}')
except httpx.RequestError as e:
raise HTTPException(502, f'Perplexity API call failed: {e}')
raise HTTPException(502, f'Anthropic Claude API call failed: {e}')
choices = data.get('choices', [])
if not choices:
raise HTTPException(502, 'Empty response from Perplexity API')
content = choices[0].get('message', {}).get('content', '')
content_blocks = data.get('content', [])
if not content_blocks:
raise HTTPException(502, 'Empty response from Anthropic Claude API')
content = content_blocks[0].get('text', '')
usage = data.get('usage', {})
log_pplx.debug(
f'tokens: prompt={usage.get("prompt_tokens", "?")}, '
f'completion={usage.get("completion_tokens", "?")}, '
f'tokens: prompt={usage.get("input_tokens", "?")}, '
f'completion={usage.get("output_tokens", "?")}, '
f'model={data.get("model", "?")}'
)
return content
@@ -1032,7 +1040,7 @@ def _fill_basic_lands(cards: list[dict], color_identity: list[str] | None = None
@router.post('/api/deck-research')
async def deck_research(req: DeckResearchRequest):
"""Analyze an existing deck using Perplexity AI and suggest improvements."""
"""Analyze an existing deck using Anthropic Claude and suggest improvements."""
conn = _get_db_conn()
# Load deck info
@@ -1157,7 +1165,7 @@ DECKLIST ({len(card_rows)} cards):
Analyze EVERYTHING: the deck's identity, strategy, archetype, bracket level (1-4 per Commander Rules Committee), ALL synergy packages between cards, ALL win conditions, game plan by phase (early/mid/late), mana base health, every role gap. Suggest specific cuts with severity and specific adds with priority and synergy tags. For adds, prioritize cards from the COLLECTION SUMMARY when available."""
content = await _call_pplx_api([
content = await _call_anthropic_api([
{'role': 'system', 'content': system_msg},
{'role': 'user', 'content': user_msg},
], max_tokens=8192)
@@ -1187,7 +1195,7 @@ Analyze EVERYTHING: the deck's identity, strategy, archetype, bracket level (1-4
@router.post('/api/deck-generate')
async def deck_generate_ai(req: DeckGenerateAIRequest):
"""Generate a full 100-card Commander deck using Perplexity AI."""
"""Generate a full 100-card Commander deck using Anthropic Claude."""
commander = req.commander.strip()
if not commander:
raise HTTPException(400, 'Commander name is required')
@@ -1267,7 +1275,7 @@ COLOR IDENTITY: {', '.join(color_identity) if color_identity else 'Unknown'}
Build the deck as JSON. Prioritize collection cards when they fit the strategy."""
content = await _call_pplx_api([
content = await _call_anthropic_api([
{'role': 'system', 'content': system_msg},
{'role': 'user', 'content': user_msg},
], max_tokens=8192, temperature=0.3)
@@ -1294,16 +1302,16 @@ Build the deck as JSON. Prioritize collection cards when they fit the strategy."
return result
@router.get('/api/pplx/status')
async def pplx_status():
"""Check if Perplexity API is configured."""
@router.get('/api/anthropic/status')
async def anthropic_status():
"""Check if Anthropic Claude API is configured."""
return {
'configured': bool(CFG.pplx_api_key),
'configured': bool(CFG.anthropic_api_key),
}
# ══════════════════════════════════════════════════════════════
# V3 Deck Generator (Perplexity Structured Output)
# V3 Deck Generator (Anthropic Claude Structured Output)
# ══════════════════════════════════════════════════════════════
@router.get('/api/deck/v3/status')
@@ -1311,8 +1319,8 @@ async def deck_gen_v3_status():
"""Check V3 deck generator status."""
return {
'initialized': _coach._deck_gen_v3 is not None,
'pplx_configured': bool(CFG.pplx_api_key),
'model': getattr(_coach._deck_gen_v3, 'model', getattr(getattr(_coach._deck_gen_v3, 'pplx', None), 'model', 'ollama/gpt-oss:20b')) if _coach._deck_gen_v3 else None,
'anthropic_configured': bool(CFG.anthropic_api_key),
'model': getattr(_coach._deck_gen_v3, 'model', getattr(getattr(_coach._deck_gen_v3, 'anthropic_client', None), 'model', 'ollama/gpt-oss:20b')) if _coach._deck_gen_v3 else None,
'embeddings_loaded': False,
'embedding_cards': 0,
'error': _coach._deck_gen_v3_error,
@@ -1322,14 +1330,14 @@ async def deck_gen_v3_status():
@router.post('/api/deck/v3/generate')
async def deck_gen_v3_generate(req: DeckGenV3Request):
"""
V3 Deck Generation — Perplexity structured output with Smart Substitution.
V3 Deck Generation — Anthropic Claude Structured Output with Smart Substitution.
Pipeline:
1. Resolve commander via Scryfall
2. Build collection summary from DB
3. Call Perplexity Sonar with JSON schema enforcement
3. Call Anthropic Claude with JSON schema enforcement
4. Cross-reference cards with collection for ownership
5. Run Smart Substitution (embedding + Perplexity fallback)
5. Run Smart Substitution (embedding + Anthropic fallback)
6. Return complete deck with substitution data
"""
if _coach._deck_gen_v3 is None:
+19 -60
View File
@@ -37,11 +37,9 @@ logger = logging.getLogger(__name__)
router = APIRouter(tags=["deepseek"])
# In-memory store for simulation runs
_sim_runs = {} # sim_id -> { status, result, error }
_sim_lock = _threading.Lock()
# ── Validator singleton ──
_validator_brain: ValidatorBrain | None = None
_validator_lock = _threading.Lock()
@@ -61,15 +59,11 @@ def _get_validator_brain() -> ValidatorBrain | None:
_validator_brain = ValidatorBrain(cfg)
return _validator_brain
# ══════════════════════════════════════════════════════════════
# Shared Helpers (extracted to deduplicate sim threads — #42)
# ══════════════════════════════════════════════════════════════
_SRC_PATH_ADDED = False
def _ensure_src_path():
"""Add project src/ to sys.path once."""
global _SRC_PATH_ADDED
if not _SRC_PATH_ADDED:
src_dir = str(Path(__file__).resolve().parent.parent / 'src')
@@ -126,7 +120,6 @@ def _run_game_loop(engine, deck_a, deck_b, deck_name, opponent_name,
for i in range(num_games):
kw = dict(run_kwargs)
# DeepSeek engine needs per-game IDs
if 'game_id_prefix' in kw:
prefix = kw.pop('game_id_prefix')
kw['game_id'] = f'{prefix}-g{i+1}'
@@ -134,7 +127,6 @@ def _run_game_loop(engine, deck_a, deck_b, deck_name, opponent_name,
name_b=opponent_name, **kw)
game_data = result.to_dict()
game_data['gameNumber'] = i + 1
# Phase 8: Surface player_explanation from validation in turn log
for turn_entry in game_data.get('log', []):
v = turn_entry.get('validation')
if isinstance(v, dict) and v.get('player_explanation'):
@@ -178,7 +170,6 @@ def _run_game_loop(engine, deck_a, deck_b, deck_name, opponent_name,
def _build_summary(stats: dict, deck_name: str, opponent_name: str,
num_games: int, elapsed: float, **extra) -> dict:
"""Build the JSON-serialisable summary dict from accumulated stats."""
n = num_games
summary = {
'deckName': deck_name,
@@ -208,7 +199,6 @@ def _finish_sim(sim_id, summary, game_results, engine=None):
_sim_runs[sim_id]['status'] = 'complete'
_sim_runs[sim_id]['result'] = {'summary': summary, 'games': game_results}
# --- Persist ml-decision-*.json for coach / training dashboard ---
try:
deck_name = summary.get('deckName', 'Unknown')
opponent_name = summary.get('opponentName', 'Training Deck')
@@ -240,7 +230,6 @@ def _finish_sim(sim_id, summary, game_results, engine=None):
json.dump(batch_data, f, indent=2, default=str)
logger.info(f'Saved batch results to {batch_path}')
# Auto-generate deck report for training dashboard / coach
try:
from coach.report_generator import generate_deck_reports
reports_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'deck-reports')
@@ -254,9 +243,6 @@ def _finish_sim(sim_id, summary, game_results, engine=None):
except Exception as e:
logger.error(f'Failed to persist sim results: {e}')
# --- Flush ML decisions JSONL for training pipeline ---
# NOTE: DeepSeek thread flushes manually before calling _finish_sim(engine=None)
# to avoid a double-flush that would produce an empty .jsonl file.
if engine is not None and hasattr(engine, 'flush_ml_decisions'):
ml_decisions = engine.flush_ml_decisions()
if ml_decisions:
@@ -269,18 +255,13 @@ def _finish_sim(sim_id, summary, game_results, engine=None):
def _fail_sim(sim_id, error):
"""Mark a sim run as errored."""
with _sim_lock:
_sim_runs[sim_id]['status'] = 'error'
_sim_runs[sim_id]['error'] = str(error)
# ══════════════════════════════════════════════════════════════
# Background Sim Threads (now thin wrappers around shared helpers)
# ══════════════════════════════════════════════════════════════
def _run_sim_thread(sim_id: str, decklist: list, num_games: int,
deck_name: str, record_logs: bool):
"""Background thread: basic Monte Carlo sim from a raw decklist."""
try:
_ensure_src_path()
from commander_ai_lab.sim.engine import GameEngine
@@ -307,7 +288,6 @@ def _run_sim_thread(sim_id: str, decklist: list, num_games: int,
def _run_sim_thread_v2(sim_id: str, card_data: list[dict], num_games: int,
deck_name: str, record_logs: bool):
"""Background thread: sim with full card data from the DB."""
try:
_ensure_src_path()
from commander_ai_lab.sim.engine import GameEngine
@@ -335,7 +315,6 @@ def _run_sim_thread_v2(sim_id: str, card_data: list[dict], num_games: int,
def _run_sim_thread_deepseek(sim_id: str, card_data: list[dict],
num_games: int, deck_name: str,
record_logs: bool):
"""Background thread: sim using DeepSeek AI opponent."""
try:
_ensure_src_path()
from commander_ai_lab.sim.deepseek_engine import DeepSeekGameEngine
@@ -351,7 +330,7 @@ def _run_sim_thread_deepseek(sim_id: str, card_data: list[dict],
if brain and not brain._connected:
brain.check_connection()
validator = _get_validator_brain() # None if disabled
validator = _get_validator_brain()
engine = DeepSeekGameEngine(
brain=brain, ai_player_index=1,
max_turns=25, record_log=record_logs, ml_log=True,
@@ -363,8 +342,6 @@ def _run_sim_thread_deepseek(sim_id: str, card_data: list[dict],
game_id_prefix=f'ds-sim-{sim_id[:8]}',
archetype='midrange')
# Flush ML decisions once here — pass engine=None to _finish_sim
# to prevent a second flush that would produce an empty .jsonl file.
ml_decisions = engine.flush_ml_decisions()
if ml_decisions and brain and brain._connected:
ml_path = os.path.join('results',
@@ -387,21 +364,15 @@ def _run_sim_thread_deepseek(sim_id: str, card_data: list[dict],
summary = _build_summary(
stats, deck_name, 'DeepSeek AI', num_games, elapsed,
opponentType='deepseek', deepseekStats=ds_stats)
# engine=None: ML decisions already flushed above — do not flush again
_finish_sim(sim_id, summary, game_results, engine=None)
except Exception as e:
_fail_sim(sim_id, e)
traceback.print_exc()
# ══════════════════════════════════════════════════════════════
# N-Player Sim Thread
# ══════════════════════════════════════════════════════════════
def _run_sim_thread_n_player(sim_id: str, decks_data: list[list[dict]],
deck_names: list[str], num_games: int,
record_logs: bool, engine_type: str):
"""Background thread: N-player (2-4) batch simulation."""
try:
_ensure_src_path()
from commander_ai_lab.sim.engine import GameEngine
@@ -423,7 +394,6 @@ def _run_sim_thread_n_player(sim_id: str, decks_data: list[list[dict]],
else:
engine = GameEngine(max_turns=25, record_log=record_logs, ml_log=True)
# Per-seat accumulators
seat_wins = [0] * n_players
seat_turns = [0] * n_players
seat_damage_dealt = [0] * n_players
@@ -493,13 +463,7 @@ def _run_sim_thread_n_player(sim_id: str, decks_data: list[list[dict]],
traceback.print_exc()
# ══════════════════════════════════════════════════════════════
# Simulation API Endpoints
# ══════════════════════════════════════════════════════════════
def _create_sim_run(num_games, deck_name):
"""Create a queued sim run entry and return its ID."""
sim_id = str(_uuid.uuid4())[:8]
with _sim_lock:
_sim_runs[sim_id] = {
@@ -521,31 +485,42 @@ def _fetch_deck_card_data(deck_id):
deck_name = row[0]
cur.execute("""
SELECT dc.card_name, dc.quantity,
ce.type_line, ce.cmc, ce.power, ce.toughness,
ce.oracle_text, ce.keywords, ce.mana_cost
COALESCE(ce.type_line, cr.type_line, '') as type_line,
COALESCE(ce.cmc, cr.cmc, 0) as cmc,
ce.power, ce.toughness,
COALESCE(ce.oracle_text, cr.oracle_text, '') as oracle_text,
COALESCE(ce.keywords, cr.keywords, '') as keywords,
COALESCE(ce.mana_cost, '') as mana_cost
FROM deck_cards dc
LEFT JOIN (
SELECT scryfall_id, type_line, cmc, power, toughness,
oracle_text, keywords, mana_cost
FROM collection_entries GROUP BY scryfall_id
) ce ON ce.scryfall_id = dc.scryfall_id
LEFT JOIN (
SELECT name, type_line, cmc, oracle_text, keywords
FROM card_records GROUP BY name
) cr ON cr.name = dc.card_name
WHERE dc.deck_id = ?
""", (deck_id,))
card_data = []
for r in cur.fetchall():
for _ in range(r[1] or 1):
card_data.append({
'name': r[0], 'type_line': r[2] or '',
'cmc': r[3] or 0, 'power': r[4] or '',
'toughness': r[5] or '', 'oracle_text': r[6] or '',
'keywords': r[7] or '', 'mana_cost': r[8] or '',
'name': r[0],
'type_line': r[2] or '',
'cmc': r[3] or 0,
'power': r[4] or '',
'toughness': r[5] or '',
'oracle_text': r[6] or '',
'keywords': r[7] or '',
'mana_cost': r[8] or '',
})
return deck_name, card_data
@router.post('/api/sim/run')
async def sim_run(request: FastAPIRequest):
"""Start a Monte Carlo simulation with the Python engine."""
body = await request.json()
decklist = body.get('decklist', [])
num_games = body.get('numGames', 10)
@@ -566,7 +541,6 @@ async def sim_run(request: FastAPIRequest):
@router.get('/api/sim/status')
async def sim_status(simId: str):
"""Poll simulation progress."""
with _sim_lock:
run = _sim_runs.get(simId)
if not run:
@@ -580,7 +554,6 @@ async def sim_status(simId: str):
@router.get('/api/sim/result')
async def sim_result(simId: str):
"""Get completed simulation results."""
with _sim_lock:
run = _sim_runs.get(simId)
if not run:
@@ -593,7 +566,6 @@ async def sim_result(simId: str):
@router.post('/api/sim/run-from-deck')
async def sim_run_from_deck(request: FastAPIRequest):
"""Start simulation using a deck from the Deck Builder (by deck ID)."""
body = await request.json()
deck_id = body.get('deckId')
num_games = body.get('numGames', 10)
@@ -618,7 +590,6 @@ async def sim_run_from_deck(request: FastAPIRequest):
@router.post('/api/sim/run-deepseek')
async def sim_run_deepseek(request: FastAPIRequest):
"""Start simulation using DeepSeek AI as the opponent brain."""
body = await request.json()
deck_id = body.get('deckId')
num_games = body.get('numGames', 5)
@@ -647,7 +618,6 @@ async def sim_run_deepseek(request: FastAPIRequest):
@router.post('/api/sim/run-n-player')
async def sim_run_n_player(request: FastAPIRequest):
"""Start an N-player (2-4) batch simulation."""
body = await request.json()
deck_ids = body.get('deckIds', [])
num_games = body.get('numGames', 10)
@@ -681,14 +651,8 @@ async def sim_run_n_player(request: FastAPIRequest):
})
# ══════════════════════════════════════════════════════════════
# DeepSeek AI Brain Endpoints
# ══════════════════════════════════════════════════════════════
@router.post('/api/deepseek/connect')
async def deepseek_connect(request: FastAPIRequest):
"""Test connection to the DeepSeek LLM endpoint and auto-detect model."""
body = await request.json() if await request.body() else {}
api_base = body.get('apiBase', None)
model = body.get('model', None)
@@ -714,7 +678,6 @@ async def deepseek_connect(request: FastAPIRequest):
@router.get('/api/deepseek/status')
async def deepseek_status():
"""Get DeepSeek brain status and performance stats."""
brain = _get_deepseek_brain()
if brain is None:
return JSONResponse({'connected': False, 'error': 'Brain not initialized'})
@@ -723,7 +686,6 @@ async def deepseek_status():
@router.post('/api/deepseek/configure')
async def deepseek_configure(request: FastAPIRequest):
"""Update DeepSeek configuration at runtime."""
body = await request.json()
brain = _get_deepseek_brain()
if brain is None:
@@ -764,7 +726,6 @@ async def deepseek_configure(request: FastAPIRequest):
@router.get('/api/deepseek/logs')
async def deepseek_logs():
"""Get decision log stats and flush pending entries."""
brain = _get_deepseek_brain()
if brain is None:
return JSONResponse({'error': 'Brain not initialized'}, status_code=500)
@@ -799,7 +760,6 @@ async def deepseek_logs():
@router.get("/api/validator/status")
async def validator_status():
"""Get DeepSeek-R1-14B validator brain status."""
v = _get_validator_brain()
if v is None:
return JSONResponse({"enabled": False,
@@ -809,7 +769,6 @@ async def validator_status():
@router.post("/api/validator/configure")
async def validator_configure(request: FastAPIRequest):
"""Update validator configuration at runtime."""
body = await request.json()
v = _get_validator_brain()
if v is None:
+209
View File
@@ -0,0 +1,209 @@
"""Live game session routes.
Endpoints:
POST /api/game/start — create a new live game session (called by LobbySetupModal)
POST /api/game/action — submit a human player action (called by HumanActionBar)
"""
from __future__ import annotations
import logging
import uuid
from typing import List, Optional
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
log = logging.getLogger("routes.game")
router = APIRouter(tags=["game"])
# ───────────────────────────────────────────────────────────────
# Request / Response models
# ───────────────────────────────────────────────────────────────
class SeatConfig(BaseModel):
seat_index: int = 0
seat_type: str = "AI" # "Human" | "AI Aggro" | "AI Control" | "AI Combo" | "AI Political"
deck_name: str = ""
player_name: str = ""
class StartGameRequest(BaseModel):
seats: List[SeatConfig] = Field(default_factory=list)
class StartGameResponse(BaseModel):
game_id: str
status: str = "started"
ws_url: str
class GameActionRequest(BaseModel):
game_id: str
seat: int = 0
action: str # "pass_priority" | "end_turn" | "declare_attackers" |
# "declare_blockers" | "select_target"
target_id: Optional[str] = None
extra: dict = Field(default_factory=dict)
class GameActionResponse(BaseModel):
status: str = "ok"
game_id: str
action: str
# ───────────────────────────────────────────────────────────────
# In-memory session store (replace with DB / Redis for persistence)
# ───────────────────────────────────────────────────────────────
# { game_id: { "seats": [...], "status": "active" | "ended", "pending_actions": [...] } }
_sessions: dict[str, dict] = {}
# ───────────────────────────────────────────────────────────────
# POST /api/game/start
# ───────────────────────────────────────────────────────────────
@router.post("/api/game/start", response_model=StartGameResponse)
async def start_game(req: StartGameRequest):
"""Create a new live game session.
Called by Unity's LobbySetupModal after the player configures seats.
Returns a game_id that Unity uses to connect to the WebSocket at
/ws/game/{game_id}.
TODO: Wire Forge engine launch here once the IPC layer is ready.
For now, creates a session record and returns the game_id so Unity
can connect and receive state pushes from the Forge IPC client.
"""
if not req.seats:
raise HTTPException(status_code=400, detail="At least one seat is required.")
game_id = uuid.uuid4().hex
_sessions[game_id] = {
"game_id": game_id,
"seats": [s.dict() for s in req.seats],
"status": "active",
"pending_actions": [],
}
log.info(
"Game session created: game_id=%s seats=%d",
game_id,
len(req.seats),
)
return StartGameResponse(
game_id=game_id,
status="started",
ws_url=f"/ws/game/{game_id}",
)
# ───────────────────────────────────────────────────────────────
# POST /api/game/action
# ───────────────────────────────────────────────────────────────
@router.post("/api/game/action", response_model=GameActionResponse)
async def game_action(req: GameActionRequest):
"""Submit a human player action during a live game.
Called by Unity's HumanActionBar when seat 0 (human) has priority.
The action is queued in the session's pending_actions list, where
the Forge IPC polling loop will pick it up.
Valid actions:
pass_priority — pass priority to the next player
end_turn — end the current turn
declare_attackers — declare attacking creatures
declare_blockers — declare blocking creatures
select_target — select a target (requires target_id)
"""
valid_actions = {
"pass_priority", "end_turn",
"declare_attackers", "declare_blockers",
"select_target",
}
if req.action not in valid_actions:
raise HTTPException(
status_code=400,
detail=f"Unknown action '{req.action}'. Valid: {sorted(valid_actions)}",
)
if req.action == "select_target" and not req.target_id:
raise HTTPException(
status_code=400,
detail="select_target requires a target_id.",
)
session = _sessions.get(req.game_id)
if session is None:
raise HTTPException(
status_code=404,
detail=f"Game session '{req.game_id}' not found.",
)
if session["status"] != "active":
raise HTTPException(
status_code=409,
detail=f"Game session '{req.game_id}' is not active (status={session['status']}).",
)
action_record = {
"seat": req.seat,
"action": req.action,
"target_id": req.target_id,
"extra": req.extra,
}
session["pending_actions"].append(action_record)
log.info(
"Action queued: game_id=%s seat=%d action=%s",
req.game_id, req.seat, req.action,
)
return GameActionResponse(
status="ok",
game_id=req.game_id,
action=req.action,
)
# ───────────────────────────────────────────────────────────────
# GET /api/game/{game_id}/actions (Forge IPC polling endpoint)
# ───────────────────────────────────────────────────────────────
@router.get("/api/game/{game_id}/actions")
async def poll_actions(game_id: str):
"""Drain and return all pending human actions for a game session.
Called by the Forge IPC polling loop to consume human player decisions.
Clears the queue on each call (consume-once semantics).
"""
session = _sessions.get(game_id)
if session is None:
raise HTTPException(status_code=404, detail=f"Game session '{game_id}' not found.")
actions = session["pending_actions"].copy()
session["pending_actions"].clear()
return {"game_id": game_id, "actions": actions}
# ───────────────────────────────────────────────────────────────
# GET /api/game/sessions (debug)
# ───────────────────────────────────────────────────────────────
@router.get("/api/game/sessions")
async def list_sessions():
"""List active game sessions (debug endpoint)."""
return {
"count": len(_sessions),
"sessions": [
{"game_id": gid, "status": s["status"], "seats": len(s["seats"])}
for gid, s in _sessions.items()
],
}
+2 -2
View File
@@ -66,8 +66,8 @@ async def start_batch(req: StartRequest, background_tasks: BackgroundTasks):
raise HTTPException(400, f"All {len(req.decks)} deck slots must be filled")
if req.numGames < 1 or req.numGames > 10000:
raise HTTPException(400, "numGames must be 1-10000")
if req.threads < 1 or req.threads > 16:
raise HTTPException(400, "threads must be 1-16")
if req.threads < 1 or req.threads > 32:
raise HTTPException(400, "threads must be 1-32")
if not os.path.exists(CFG.forge_jar):
raise HTTPException(500, f"Forge JAR not found: {CFG.forge_jar}")
+15 -6
View File
@@ -1097,7 +1097,7 @@ _elo_tournament_state = _EloTournamentState()
_elo_tournament_lock = threading.Lock()
def _run_elo_tournament_pipeline(episodes: int, playstyle: str):
def _run_elo_tournament_pipeline(episodes: int, playstyle: str, top_n: int):
"""Run ELO tournament in a background thread."""
try:
import sys
@@ -1108,7 +1108,7 @@ def _run_elo_tournament_pipeline(episodes: int, playstyle: str):
with _elo_tournament_lock:
_elo_tournament_state.running = True
_elo_tournament_state.phase = "running"
_elo_tournament_state.message = "Running ELO tournament..."
_elo_tournament_state.message = f"Running ELO tournament (top {top_n} generations)..."
_elo_tournament_state.error = None
_elo_tournament_state.result = None
@@ -1121,6 +1121,7 @@ def _run_elo_tournament_pipeline(episodes: int, playstyle: str):
checkpoint_dir=ckpt_dir,
episodes_per_matchup=episodes,
playstyle=playstyle,
top_n=top_n,
)
# Save to ELO history
@@ -1155,29 +1156,37 @@ def _run_elo_tournament_pipeline(episodes: int, playstyle: str):
@router.post("/api/ml/elo/tournament")
async def ml_start_elo_tournament(request: FastAPIRequest):
"""Start an ELO-rated tournament across all generation checkpoints."""
"""Start an ELO-rated tournament across generation checkpoints.
Body params:
episodes (int): Games per matchup pair (default 50)
playstyle (str): Game playstyle (default midrange)
topN (int): Max number of most-recent gen checkpoints to include
(default 10, 0 = unlimited)
"""
if _elo_tournament_state.running:
raise HTTPException(409, "ELO tournament already in progress")
body = await request.json() if await request.body() else {}
episodes = body.get("episodes", 50)
playstyle = body.get("playstyle", "midrange")
top_n = body.get("topN", 10)
with _elo_tournament_lock:
_elo_tournament_state.running = True
_elo_tournament_state.phase = "starting"
_elo_tournament_state.message = "Initializing ELO tournament..."
_elo_tournament_state.message = f"Initializing ELO tournament (top {top_n} generations)..."
_elo_tournament_state.result = None
_elo_tournament_state.error = None
thread = threading.Thread(
target=_run_elo_tournament_pipeline,
args=(episodes, playstyle),
args=(episodes, playstyle, top_n),
daemon=True,
)
thread.start()
return {"status": "started", "episodes": episodes}
return {"status": "started", "episodes": episodes, "top_n": top_n}
@router.get("/api/ml/elo/tournament/status")
+121
View File
@@ -0,0 +1,121 @@
"""
routes/rag.py
=============
RAG (Retrieval-Augmented Generation) endpoints for semantic card search.
Endpoints:
POST /api/rag/build — build / rebuild the ChromaDB vector index
POST /api/rag/search — semantic search over indexed cards
GET /api/rag/status — index health & stats
"""
from __future__ import annotations
import logging
from typing import List, Optional
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
log = logging.getLogger("commander_ai_lab.routes.rag")
router = APIRouter(tags=["rag"])
# ── Request / Response models ────────────────────────────────────────────────
class RAGSearchRequest(BaseModel):
query: str = Field(..., description="Natural-language card search query")
color_identity: Optional[List[str]] = Field(
None,
description="Optional color identity filter, e.g. ['W','U','B']",
)
top_k: int = Field(10, ge=1, le=50, description="Number of results to return")
class RAGCardResult(BaseModel):
name: str
type_line: str = ""
oracle_text: str = ""
color_identity: str = ""
score: float = 0.0
class RAGSearchResponse(BaseModel):
query: str
results: List[RAGCardResult]
total: int
class RAGBuildRequest(BaseModel):
force: bool = Field(False, description="Force full rebuild even if index exists")
batch_size: int = Field(500, ge=50, le=5000)
# ── Endpoints ────────────────────────────────────────────────────────────────
@router.post("/api/rag/build")
async def rag_build(body: RAGBuildRequest = RAGBuildRequest()):
"""
Build or rebuild the ChromaDB vector index from the Scryfall bulk DB.
Runs synchronously (may take 1-5 min on first build).
"""
try:
from services.rag_store import build_index
result = build_index(force=body.force, batch_size=body.batch_size)
return {"status": "ok", **result}
except FileNotFoundError as exc:
raise HTTPException(status_code=404, detail=str(exc))
except Exception as exc:
log.exception("RAG build failed")
raise HTTPException(status_code=500, detail=str(exc))
@router.post("/api/rag/search")
async def rag_search(body: RAGSearchRequest):
"""
Semantic search over the ChromaDB card index.
Returns ranked card results with relevance scores.
"""
try:
from services.rag_store import query_cards
raw_results = query_cards(
query=body.query,
n_results=body.top_k,
color_identity=body.color_identity,
)
results = [
RAGCardResult(
name=r.get("name", ""),
type_line=r.get("type_line", ""),
oracle_text=r.get("oracle_text", ""),
color_identity=r.get("color_identity", ""),
score=round(1.0 - r.get("distance", 0.0), 4),
)
for r in raw_results
]
return RAGSearchResponse(
query=body.query,
results=results,
total=len(results),
)
except FileNotFoundError as exc:
raise HTTPException(
status_code=404,
detail=f"Index not built yet. Call POST /api/rag/build first. {exc}",
)
except Exception as exc:
log.exception("RAG search failed")
raise HTTPException(status_code=500, detail=str(exc))
@router.get("/api/rag/status")
async def rag_status_detail():
"""
Detailed RAG index status including card count and age.
"""
try:
from services.rag_store import get_rag_stats
return {"status": "ok", **get_rag_stats()}
except Exception as exc:
log.exception("RAG status check failed")
return {"status": "error", "error": str(exc)}
+176
View File
@@ -0,0 +1,176 @@
"""WebSocket route for real-time game state streaming.
Endpoints:
WS /ws/game/{game_id} — Stream state deltas to Unity
POST /api/ws/game/{game_id}/push — Push a state update (called by Forge IPC)
POST /api/ws/game/{game_id}/decision — Push AI decision event
POST /api/ws/game/{game_id}/end — Signal game over
GET /api/ws/stats — Connection statistics
Unity connects to the WebSocket endpoint and receives:
1. An initial full snapshot on first state push
2. Subsequent delta-only messages as state changes
3. Decision events when the AI acts
4. A game_over event when the game ends
"""
from __future__ import annotations
import copy
import logging
import time
import uuid
from typing import Optional
from fastapi import APIRouter, WebSocket, WebSocketDisconnect
from pydantic import BaseModel, Field
from ws.connection_manager import game_manager
from ws.game_state import get_tracker, remove_tracker
log = logging.getLogger("routes.ws_game")
router = APIRouter(tags=["websocket"])
# ────────────────────────────────────────────────────────────
# WebSocket endpoint — Unity connects here
# ────────────────────────────────────────────────────────────
@router.websocket("/ws/game/{game_id}")
async def game_ws(websocket: WebSocket, game_id: str):
"""WebSocket endpoint for Unity game-state streaming.
Query params:
client_id — optional unique identifier for this client
The connection stays open for the duration of the game.
The server pushes messages; the client can send:
{"type": "ping"} — keep-alive
{"type": "request_snapshot"} — request a full re-sync
"""
client_id = (
websocket.query_params.get("client_id")
or f"unity-{uuid.uuid4().hex[:8]}"
)
client = await game_manager.connect(websocket, game_id, client_id)
# If the tracker already has a baseline, send the current state
tracker = get_tracker(game_id)
if tracker.has_baseline:
snapshot_msg = tracker.update(tracker._last_snapshot)
await websocket.send_json(snapshot_msg)
try:
while True:
data = await websocket.receive_json()
msg_type = data.get("type", "")
if msg_type == "ping":
await websocket.send_json({"type": "pong"})
elif msg_type == "request_snapshot":
if tracker.has_baseline:
snap = copy.deepcopy(tracker._last_snapshot)
tracker._seq += 1
await websocket.send_json({
"type": "snapshot",
"seq": tracker._seq,
"game_id": game_id,
"timestamp": time.time(),
"state": snap,
})
else:
await websocket.send_json({
"type": "error",
"message": "No game state available yet",
})
except WebSocketDisconnect:
pass
except Exception as e:
log.warning("WS error game=%s client=%s: %s", game_id, client_id, e)
finally:
await game_manager.disconnect(game_id, client_id)
# ────────────────────────────────────────────────────────────
# REST endpoints — called by Forge IPC / policy server
# ────────────────────────────────────────────────────────────
class PushStateRequest(BaseModel):
"""Game state snapshot pushed from Forge."""
game_id: str = ""
turn: int = 0
phase: str = ""
active_player: int = 0
players: list = Field(default_factory=list)
stack: list = Field(default_factory=list)
legal_actions: list = Field(default_factory=list)
class DecisionEvent(BaseModel):
"""AI decision event pushed after policy inference."""
action: str
action_index: int = 0
confidence: float = 0.0
probabilities: dict = Field(default_factory=dict)
inference_ms: float = 0.0
class GameOverEvent(BaseModel):
"""Game-over event."""
winner_seat: int = -1
reason: str = ""
turns_played: int = 0
@router.post("/api/ws/game/{game_id}/push")
async def push_game_state(game_id: str, req: PushStateRequest):
"""Push a game state update to all connected Unity clients."""
tracker = get_tracker(game_id)
snapshot = req.dict()
delta_msg = tracker.update(snapshot)
sent = await game_manager.broadcast_to_game(game_id, delta_msg)
return {
"status": "ok",
"type": delta_msg["type"],
"seq": delta_msg["seq"],
"clients_notified": sent,
"changes": len(delta_msg.get("changes", [])),
}
@router.post("/api/ws/game/{game_id}/decision")
async def push_decision(game_id: str, event: DecisionEvent):
"""Push an AI decision event to connected Unity clients."""
tracker = get_tracker(game_id)
msg = tracker.make_decision_event(
action=event.action,
action_index=event.action_index,
confidence=event.confidence,
probabilities=event.probabilities,
inference_ms=event.inference_ms,
)
sent = await game_manager.broadcast_to_game(game_id, msg)
return {"status": "ok", "clients_notified": sent}
@router.post("/api/ws/game/{game_id}/end")
async def push_game_over(game_id: str, event: GameOverEvent):
"""Signal game over to all connected clients and clean up."""
tracker = get_tracker(game_id)
msg = tracker.make_game_over_event(
winner_seat=event.winner_seat,
reason=event.reason,
turns_played=event.turns_played,
)
sent = await game_manager.broadcast_to_game(game_id, msg)
remove_tracker(game_id)
return {"status": "ok", "clients_notified": sent}
@router.get("/api/ws/stats")
async def ws_stats():
"""Return WebSocket connection statistics."""
return game_manager.stats()
+220
View File
@@ -0,0 +1,220 @@
"""Async SQLite wrapper for FastAPI — eliminates event-loop blocking.
Drop-in async replacements for the sync helpers in services/database.py.
Uses aiosqlite under the hood so every DB call yields back to the
event loop instead of freezing it.
Usage in route handlers:
from services.async_db import async_get_db, async_execute, async_fetchall
rows = await async_fetchall("SELECT * FROM decks")
row = await async_fetchone("SELECT * FROM decks WHERE id = ?", (1,))
await async_execute("INSERT INTO ...", params)
await async_execute_commit("DELETE FROM ...", params)
"""
from __future__ import annotations
import json
import logging
from pathlib import Path
from typing import Any, Optional
import aiosqlite
log = logging.getLogger("commander_ai_lab.async_db")
# Re-use the same DB path as the sync module
COLLECTION_DB_PATH = Path(__file__).parent.parent / "collection.db"
# Module-level connection (lazy-init, one per process)
_aconn: Optional[aiosqlite.Connection] = None
async def async_get_db() -> aiosqlite.Connection:
"""Return (or create) the module-level async SQLite connection."""
global _aconn
if _aconn is None:
_aconn = await aiosqlite.connect(str(COLLECTION_DB_PATH))
_aconn.row_factory = aiosqlite.Row
await _aconn.execute("PRAGMA journal_mode=WAL")
log.info("Async SQLite connection opened: %s", COLLECTION_DB_PATH)
return _aconn
async def async_close_db() -> None:
"""Close the module-level connection (call on shutdown)."""
global _aconn
if _aconn is not None:
await _aconn.close()
_aconn = None
log.info("Async SQLite connection closed.")
# ── Convenience helpers ─────────────────────────────────────────
async def async_fetchall(
sql: str, params: tuple = (), *, conn: aiosqlite.Connection = None
) -> list[dict]:
"""Execute a SELECT and return all rows as dicts."""
db = conn or await async_get_db()
async with db.execute(sql, params) as cursor:
columns = [d[0] for d in cursor.description]
rows = await cursor.fetchall()
return [dict(zip(columns, row)) for row in rows]
async def async_fetchone(
sql: str, params: tuple = (), *, conn: aiosqlite.Connection = None
) -> Optional[dict]:
"""Execute a SELECT and return one row as a dict (or None)."""
db = conn or await async_get_db()
async with db.execute(sql, params) as cursor:
columns = [d[0] for d in cursor.description]
row = await cursor.fetchone()
return dict(zip(columns, row)) if row else None
async def async_execute(
sql: str, params: tuple = (), *, conn: aiosqlite.Connection = None
) -> int:
"""Execute a write statement. Returns lastrowid."""
db = conn or await async_get_db()
cursor = await db.execute(sql, params)
return cursor.lastrowid
async def async_execute_commit(
sql: str, params: tuple = (), *, conn: aiosqlite.Connection = None
) -> int:
"""Execute a write statement and commit. Returns lastrowid."""
db = conn or await async_get_db()
cursor = await db.execute(sql, params)
await db.commit()
return cursor.lastrowid
async def async_executemany_commit(
sql: str, params_seq, *, conn: aiosqlite.Connection = None
) -> None:
"""Execute many and commit."""
db = conn or await async_get_db()
await db.executemany(sql, params_seq)
await db.commit()
async def async_commit(*, conn: aiosqlite.Connection = None) -> None:
"""Commit the current transaction."""
db = conn or await async_get_db()
await db.commit()
# ── JSON field helpers (mirrors database._row_to_dict) ──────────
_JSON_FIELDS = ("category", "color_identity", "subtypes", "keywords")
def row_to_dict_parsed(row: dict) -> dict:
"""Parse JSON string columns into Python lists (same as database._row_to_dict)."""
if row is None:
return None
d = dict(row)
for f in _JSON_FIELDS:
if f in d and isinstance(d[f], str):
try:
d[f] = json.loads(d[f])
except (json.JSONDecodeError, TypeError):
d[f] = []
return d
# ── Coach session async helpers ─────────────────────────────────
async def async_save_coach_session(session_data: dict) -> int:
"""Insert or update a coach session. Returns row id."""
db = await async_get_db()
existing = await async_fetchone(
"SELECT id FROM coach_sessions WHERE session_id = ?",
(session_data["session_id"],),
)
if existing:
await db.execute(
"""UPDATE coach_sessions SET
deck_id=?, timestamp=?, model_used=?, prompt_tokens=?,
completion_tokens=?, summary=?, goals_json=?, cuts_json=?,
adds_json=?, heuristic_hints_json=?, mana_base_advice=?,
raw_text=?
WHERE session_id=?""",
(
session_data.get("deck_id", ""),
session_data.get("timestamp", ""),
session_data.get("model_used", ""),
session_data.get("prompt_tokens", 0),
session_data.get("completion_tokens", 0),
session_data.get("summary", ""),
session_data.get("goals_json", "{}"),
session_data.get("cuts_json", "[]"),
session_data.get("adds_json", "[]"),
session_data.get("heuristic_hints_json", "[]"),
session_data.get("mana_base_advice", ""),
session_data.get("raw_text", ""),
session_data["session_id"],
),
)
await db.commit()
return existing["id"]
else:
cursor = await db.execute(
"""INSERT INTO coach_sessions
(session_id, deck_id, timestamp, model_used, prompt_tokens,
completion_tokens, summary, goals_json, cuts_json, adds_json,
heuristic_hints_json, mana_base_advice, raw_text)
VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?)""",
(
session_data["session_id"],
session_data.get("deck_id", ""),
session_data.get("timestamp", ""),
session_data.get("model_used", ""),
session_data.get("prompt_tokens", 0),
session_data.get("completion_tokens", 0),
session_data.get("summary", ""),
session_data.get("goals_json", "{}"),
session_data.get("cuts_json", "[]"),
session_data.get("adds_json", "[]"),
session_data.get("heuristic_hints_json", "[]"),
session_data.get("mana_base_advice", ""),
session_data.get("raw_text", ""),
),
)
await db.commit()
return cursor.lastrowid
async def async_load_coach_session(session_id: str) -> Optional[dict]:
"""Load a single coach session by session_id."""
return await async_fetchone(
"SELECT * FROM coach_sessions WHERE session_id = ?", (session_id,)
)
async def async_list_coach_sessions(deck_id: str = None) -> list[dict]:
"""List coach sessions, optionally filtered by deck_id."""
if deck_id:
return await async_fetchall(
"SELECT session_id, deck_id, timestamp, summary, created_at "
"FROM coach_sessions WHERE deck_id = ? ORDER BY timestamp DESC",
(deck_id,),
)
return await async_fetchall(
"SELECT session_id, deck_id, timestamp, summary, created_at "
"FROM coach_sessions ORDER BY timestamp DESC"
)
async def async_delete_coach_session(session_id: str) -> bool:
"""Delete a coach session. Returns True if a row was deleted."""
db = await async_get_db()
cursor = await db.execute(
"DELETE FROM coach_sessions WHERE session_id = ?", (session_id,)
)
await db.commit()
return cursor.rowcount > 0
+8
View File
@@ -15,6 +15,14 @@ def _detect_card_roles(oracle_text: str, type_line: str, keywords) -> list:
kw_raw = []
kw = " ".join(str(k).lower() for k in kw_raw)
# Land check must come first — basic lands have empty oracle_text so every
# other check below would silently miss them, leaving them as Unknown role.
if "land" in tl:
roles.append("Land")
if not ot:
# Basic lands have no oracle text; no other role applies.
return roles
if ("add {" in ot or "add one mana" in ot
or ("search your library for a" in ot and "land" in ot)
or "treasure token" in ot or "add mana" in ot):
+37
View File
@@ -0,0 +1,37 @@
"""
services/card_text.py
=====================
Shared utility for building card text representations.
Both services/rag_store.py and coach/embeddings.py need to produce
identical text strings for embedding so that vector similarity is
consistent across both systems. This module provides a single
canonical implementation.
Format: Name | Mana Cost | Type | Oracle Text
(fields that are empty/missing are omitted)
"""
from __future__ import annotations
def build_card_text(card: dict) -> str:
"""Build the canonical text representation of a card for embedding.
Args:
card: Dict with keys 'name', 'mana_cost', 'type_line', 'oracle_text'.
Missing keys are treated as empty strings.
Returns:
Pipe-separated string like 'Sol Ring | {1} | Artifact | {T}: Add {C}{C}.'
"""
parts = [card.get("name", "") or ""]
mana_cost = card.get("mana_cost", "") or ""
if mana_cost:
parts.append(mana_cost)
type_line = card.get("type_line", "") or ""
if type_line:
parts.append(type_line)
oracle = card.get("oracle_text", "") or ""
if oracle:
parts.append(oracle)
return " | ".join(parts)
+33 -2
View File
@@ -34,6 +34,30 @@ def _classify_card_type(type_line: str) -> str:
return "Other"
_CATEGORY_MAP = {
"Ramp": ["search your library for a", "add one mana", "add {c}", "{t}: add"],
"Removal": ["destroy target", "exile target", "counter target spell"],
"Draw": ["draw a card", "draw two", "draw three", "draw cards"],
"Board Wipe": ["destroy all", "exile all", "all creatures get -"],
"Protection": ["hexproof", "indestructible", "shroud", "ward"],
"Tutor": ["search your library"],
}
def _classify_card_category(type_line: str, oracle_text: str) -> str:
"""Assign a functional category based on type_line and oracle text."""
card_type = _classify_card_type(type_line)
if card_type == "Land":
return "Land"
text = (oracle_text or "").lower()
for cat, phrases in _CATEGORY_MAP.items():
if any(p in text for p in phrases):
return cat
if card_type == "Creature":
return "Creature"
return card_type
def _get_deck_or_404(deck_id: int):
conn = _get_db_conn()
row = conn.execute("SELECT * FROM decks WHERE id = ?", (deck_id,)).fetchone()
@@ -51,12 +75,20 @@ def _compute_deck_analysis(deck_id: int) -> dict:
conn = _get_db_conn()
rows = conn.execute("""
SELECT dc.id, dc.scryfall_id, dc.card_name, dc.quantity, dc.is_commander, dc.role_tag,
ce.type_line, ce.oracle_text, ce.keywords, ce.cmc, ce.color_identity
COALESCE(ce.type_line, cr.type_line, '') as type_line,
COALESCE(ce.oracle_text, cr.oracle_text, '') as oracle_text,
COALESCE(ce.keywords, cr.keywords, '[]') as keywords,
COALESCE(ce.cmc, cr.cmc, 0) as cmc,
COALESCE(ce.color_identity, cr.color_identity, '[]') as color_identity
FROM deck_cards dc
LEFT JOIN (
SELECT scryfall_id, type_line, oracle_text, keywords, cmc, color_identity
FROM collection_entries GROUP BY scryfall_id
) ce ON ce.scryfall_id = dc.scryfall_id
LEFT JOIN (
SELECT name, type_line, oracle_text, keywords, cmc, color_identity
FROM card_records GROUP BY name
) cr ON cr.name = dc.card_name
WHERE dc.deck_id = ?
""", (deck_id,)).fetchall()
counts_by_type = {t: 0 for t in _TYPE_PRIORITY}
@@ -91,7 +123,6 @@ def _compute_deck_analysis(deck_id: int) -> dict:
"counts_by_type": counts_by_type,
"mana_curve": mana_curve,
"color_pips": color_pips,
"total_cards": total_cards,
"roles": roles_count,
}
+558
View File
@@ -0,0 +1,558 @@
"""
services/rag_store.py
=====================
ChromaDB-backed semantic vector store over the Scryfall bulk card database.
Reads all cards from the Phase 1 SQLite bulk DB (services/scryfall_bulk.py),
embeds their oracle text using Ollama's nomic-embed-text model, and stores
the vectors in a persistent ChromaDB collection on disk.
This is RAG Phase 2. Phase 3 will query this store to inject relevant card
oracle text into the LLM prompt before calling the coach.
Public API:
ensure_rag_store() -> None (build if missing / stale)
build_index(force, batch_size) -> dict (explicit build, returns stats)
query_cards(text, n, colors) -> list[dict]
get_rag_stats() -> dict
Data path: ./data/rag_chroma/
Embedding: nomic-embed-text via Ollama (localhost:11434)
"""
from __future__ import annotations
import json
import logging
import os
import time
import threading
from services.card_text import build_card_text
from pathlib import Path
from typing import Optional
logger = logging.getLogger("commander_ai_lab.rag_store")
# ── Config ────────────────────────────────────────────────────────────────
RAG_CHROMA_DIR = Path(__file__).parent.parent / "data" / "rag_chroma"
BUILD_META_PATH = RAG_CHROMA_DIR / "build_meta.json"
OLLAMA_BASE_URL = os.environ.get("OLLAMA_BASE_URL", "http://localhost:11434")
EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "nomic-embed-text")
COLLECTION_NAME = "mtg_oracle"
DEFAULT_BATCH_SIZE = 500
_rag_lock = threading.Lock()
_rag_ready = False
# Module-level singletons to avoid re-creating client/collection on every call
_chroma_client = None
_chroma_collection = None
# ── Helpers ────────────────────────────────────────────────────────────────
def _build_document(card: dict) -> str:
"""Delegate to shared build_card_text() -- see services/card_text.py."""
return build_card_text(card)
def _build_metadata(card: dict) -> dict:
"""Build ChromaDB metadata dict for filtering."""
ci_raw = card.get("color_identity", "[]")
if isinstance(ci_raw, str):
try:
ci_list = json.loads(ci_raw)
except (json.JSONDecodeError, TypeError):
ci_list = []
else:
ci_list = ci_raw or []
return {
"name": card.get("name", ""),
"type_line": card.get("type_line", ""),
"cmc": float(card.get("cmc") or 0),
"color_identity": ",".join(sorted(ci_list)),
"rarity": card.get("rarity", ""),
"mana_cost": card.get("mana_cost", ""),
}
def _get_chromadb_client():
"""Return the module-level persistent ChromaDB client (singleton)."""
global _chroma_client
if _chroma_client is not None:
return _chroma_client
try:
import chromadb
except ImportError:
raise ImportError(
"chromadb is required for RAG Phase 2. "
"Install with: pip install 'commander-ai-lab[rag]'"
)
RAG_CHROMA_DIR.mkdir(parents=True, exist_ok=True)
_chroma_client = chromadb.PersistentClient(path=str(RAG_CHROMA_DIR))
return _chroma_client
def _get_ollama_embedding_fn():
"""Return a ChromaDB-compatible embedding function using Ollama."""
try:
from chromadb.utils.embedding_functions import OllamaEmbeddingFunction
except ImportError:
raise ImportError(
"chromadb >= 0.4.0 with Ollama support is required. "
"Install with: pip install 'commander-ai-lab[rag]'"
)
return OllamaEmbeddingFunction(
url=f"{OLLAMA_BASE_URL}/api/embeddings",
model_name=EMBEDDING_MODEL,
)
def _get_collection():
"""Return the ChromaDB collection singleton, creating it if needed."""
global _chroma_collection
if _chroma_collection is not None:
return _chroma_collection
client = _get_chromadb_client()
embed_fn = _get_ollama_embedding_fn()
_chroma_collection = client.get_or_create_collection(
name=COLLECTION_NAME,
embedding_function=embed_fn,
metadata={"hnsw:space": "cosine"},
)
return _chroma_collection
def _invalidate_collection_singleton() -> None:
"""Reset the cached collection after a rebuild so the next call re-opens it."""
global _chroma_collection
_chroma_collection = None
def _read_build_meta() -> dict:
"""Read the sidecar build_meta.json, returning {} if missing or corrupt."""
try:
if BUILD_META_PATH.exists():
return json.loads(BUILD_META_PATH.read_text(encoding="utf-8"))
except Exception as exc:
logger.warning("Could not read build_meta.json: %s", exc)
return {}
def _write_build_meta(card_count: int) -> None:
"""Persist build timestamp and card count to sidecar JSON file."""
try:
RAG_CHROMA_DIR.mkdir(parents=True, exist_ok=True)
BUILD_META_PATH.write_text(
json.dumps({"built_at": time.time(), "card_count": card_count}),
encoding="utf-8",
)
except Exception as exc:
logger.warning("Could not write build_meta.json: %s", exc)
def _collection_is_current(collection, bulk_card_count: int) -> bool:
"""Check if the ChromaDB collection is reasonably up-to-date.
Uses sidecar build_meta.json for the age check (survives restarts),
and compares card count within a 5% tolerance.
"""
chroma_count = collection.count()
if chroma_count == 0:
return False
# Age check via sidecar file (not collection.metadata which doesn't persist)
meta = _read_build_meta()
built_at = meta.get("built_at", 0)
if built_at:
age_days = (time.time() - float(built_at)) / 86400
if age_days > 14:
logger.info("RAG collection older than 14 days (%0.1f) — rebuilding.", age_days)
return False
ratio = chroma_count / max(bulk_card_count, 1)
if ratio < 0.95 or ratio > 1.05:
logger.info(
"RAG collection count mismatch: chroma=%d, bulk=%d — rebuilding.",
chroma_count, bulk_card_count,
)
return False
return True
# ── Build pipeline ────────────────────────────────────────────────────────────────
def _build_collection_from_bulk(batch_size: int = DEFAULT_BATCH_SIZE) -> dict:
"""Read all cards from the bulk SQLite DB and insert into ChromaDB.
Args:
batch_size: Number of cards to embed and insert per ChromaDB call.
Returns:
Dict with keys: indexed (int), failed (int).
Raises:
RuntimeError: If more than 1% of cards failed to insert.
"""
from services.scryfall_bulk import get_bulk_db
conn = get_bulk_db()
cursor = conn.execute(
"SELECT name, oracle_text, type_line, mana_cost, cmc, "
"color_identity, rarity, scryfall_id FROM cards "
"WHERE oracle_text != '' "
"ORDER BY edhrec_rank ASC"
)
all_rows = cursor.fetchall()
total_rows = len(all_rows)
client = _get_chromadb_client()
embed_fn = _get_ollama_embedding_fn()
# Drop and recreate collection to ensure a clean build
try:
client.delete_collection(COLLECTION_NAME)
except Exception:
pass
_invalidate_collection_singleton()
collection = client.create_collection(
name=COLLECTION_NAME,
embedding_function=embed_fn,
metadata={"hnsw:space": "cosine"},
)
inserted = 0
failed_cards = 0
failed_batches = 0
batch_ids: list[str] = []
batch_docs: list[str] = []
batch_metas: list[dict] = []
for row in all_rows:
card = dict(row)
doc = _build_document(card)
if not doc.strip():
continue
card_id = card.get("scryfall_id", "")
if not card_id:
continue
batch_ids.append(card_id)
batch_docs.append(doc)
batch_metas.append(_build_metadata(card))
if len(batch_ids) >= batch_size:
try:
collection.add(
ids=batch_ids,
documents=batch_docs,
metadatas=batch_metas,
)
inserted += len(batch_ids)
if inserted % 2000 == 0:
logger.info(" RAG indexed %d / %d cards...", inserted, total_rows)
except Exception as exc:
failed_cards += len(batch_ids)
failed_batches += 1
logger.warning(
"RAG batch insert failed (batch #%d, %d cards lost): %s",
failed_batches, len(batch_ids), exc,
)
batch_ids.clear()
batch_docs.clear()
batch_metas.clear()
# Final partial batch
if batch_ids:
try:
collection.add(
ids=batch_ids,
documents=batch_docs,
metadatas=batch_metas,
)
inserted += len(batch_ids)
except Exception as exc:
failed_cards += len(batch_ids)
failed_batches += 1
logger.warning("RAG final batch insert failed: %s", exc)
logger.info(
"RAG vector store complete: %d indexed, %d failed (%d batches).",
inserted, failed_cards, failed_batches,
)
# Abort if failure rate exceeds 1% — delete partial collection
if total_rows > 0 and (failed_cards / total_rows) > 0.01:
logger.error(
"RAG build failure rate %.1f%% exceeds 1%% threshold — deleting partial collection.",
100.0 * failed_cards / total_rows,
)
try:
client.delete_collection(COLLECTION_NAME)
_invalidate_collection_singleton()
except Exception:
pass
raise RuntimeError(
f"RAG build aborted: {failed_cards}/{total_rows} cards failed to insert. "
"Check Ollama connectivity and retry."
)
_write_build_meta(card_count=inserted)
# Invalidate singleton so next _get_collection() picks up the new collection
_invalidate_collection_singleton()
return {"indexed": inserted, "failed": failed_cards}
# ── Public API ────────────────────────────────────────────────────────────────
def build_index(force: bool = False, batch_size: int = DEFAULT_BATCH_SIZE) -> dict:
"""Explicitly build or rebuild the ChromaDB vector index.
This is the function called by POST /api/rag/build.
Unlike ensure_rag_store(), this always runs (no _rag_ready short-circuit)
and returns stats so the API can surface them to the caller.
Args:
force: If True, rebuild even if the index appears current.
batch_size: Cards per ChromaDB insert batch (default 500).
Returns:
Dict with keys: indexed (int), failed (int).
"""
global _rag_ready
with _rag_lock:
if not force:
try:
from services.scryfall_bulk import get_stats as bulk_stats
stats = bulk_stats()
bulk_count = stats.get("card_count", 0)
collection = _get_collection()
if _collection_is_current(collection, bulk_count):
logger.info("RAG index is current — skipping rebuild (use force=True to override).")
return {"indexed": collection.count(), "failed": 0, "skipped": True}
except Exception:
pass
result = _build_collection_from_bulk(batch_size=batch_size)
_rag_ready = True
return result
def ensure_rag_store() -> None:
"""Build the ChromaDB vector store if it is missing or stale.
Safe to call at startup — no-ops quickly if the store is current.
Requires:
- Phase 1 bulk DB to be populated (services/scryfall_bulk)
- Ollama running with nomic-embed-text pulled
"""
global _rag_ready
with _rag_lock:
if _rag_ready:
return
try:
from services.scryfall_bulk import get_stats as bulk_stats
stats = bulk_stats()
bulk_count = stats.get("card_count", 0)
if bulk_count == 0:
logger.warning(
"Scryfall bulk DB is empty — skipping RAG store build. "
"Run ensure_bulk_db() first."
)
return
collection = _get_collection()
if _collection_is_current(collection, bulk_count):
logger.info(
"RAG vector store is current (%d cards).",
collection.count(),
)
_rag_ready = True
return
logger.info(
"Building RAG vector store from %d bulk cards "
"(this may take several minutes on first run)...",
bulk_count,
)
_build_collection_from_bulk()
_rag_ready = True
except ImportError as exc:
logger.warning("RAG store unavailable: %s", exc)
except Exception as exc:
logger.error("RAG store build failed: %s", exc)
def query_cards(
query: str,
n_results: int = 8,
color_identity: Optional[list[str]] = None,
type_filter: Optional[str] = None,
) -> list[dict]:
"""Semantic search over card oracle text.
Args:
query: Natural language query (e.g. "draw cards when creatures die").
n_results: Maximum number of results.
color_identity: If provided, only return cards whose color identity
is a subset (e.g. ["W", "U", "B"]).
type_filter: Optional type_line substring filter (e.g. "Creature").
Applied as a post-query Python filter for ChromaDB compatibility.
Returns:
List of dicts with keys: name, type_line, oracle_text, cmc,
color_identity, mana_cost, distance.
"""
if not _rag_ready:
logger.warning("RAG store not ready — returning empty results.")
return []
try:
collection = _get_collection()
except Exception as exc:
logger.error("Failed to get RAG collection: %s", exc)
return []
try:
# Fetch extra results to account for post-query color/type filtering
fetch_n = n_results * 4 if (color_identity or type_filter) else n_results
results = collection.query(
query_texts=[query],
n_results=min(fetch_n, 200),
include=["documents", "metadatas", "distances"],
)
except Exception as exc:
logger.error("RAG query failed: %s", exc)
return []
if not results or not results.get("ids") or not results["ids"][0]:
return []
cards: list[dict] = []
ids = results["ids"][0]
docs = results["documents"][0] if results.get("documents") else [""] * len(ids)
metas = results["metadatas"][0] if results.get("metadatas") else [{}] * len(ids)
dists = results["distances"][0] if results.get("distances") else [0.0] * len(ids)
for i, card_id in enumerate(ids):
meta = metas[i] if i < len(metas) else {}
doc = docs[i] if i < len(docs) else ""
# Color identity post-filter (Python-side for ChromaDB compat)
if color_identity:
card_ci_str = meta.get("color_identity", "")
card_ci = set(c for c in card_ci_str.split(",") if c) if card_ci_str else set()
deck_ci = set(color_identity)
if not card_ci.issubset(deck_ci):
continue
# Type post-filter
if type_filter:
card_type = meta.get("type_line", "")
if type_filter.lower() not in card_type.lower():
continue
# Document format: Name | Mana Cost | Type | Oracle Text
parts = doc.split(" | ", 3)
oracle = parts[3] if len(parts) > 3 else (parts[-1] if parts else "")
cards.append({
"name": meta.get("name", ""),
"type_line": meta.get("type_line", ""),
"oracle_text": oracle,
"cmc": meta.get("cmc", 0),
"color_identity": meta.get("color_identity", ""),
"mana_cost": meta.get("mana_cost", ""),
"distance": round(dists[i], 4) if i < len(dists) else 0.0,
})
if len(cards) >= n_results:
break
return cards
def get_rag_stats() -> dict:
"""Return metadata about the RAG vector store state."""
result = {
"ready": _rag_ready,
"chroma_dir": str(RAG_CHROMA_DIR),
"embedding_model": EMBEDDING_MODEL,
"ollama_url": OLLAMA_BASE_URL,
"card_count": 0,
"built_at": None,
"age_days": None,
}
try:
collection = _get_collection()
result["card_count"] = collection.count()
except Exception:
pass
# Read age from sidecar file (persists across restarts)
meta = _read_build_meta()
built_at = meta.get("built_at")
if built_at:
result["built_at"] = built_at
try:
age = (time.time() - float(built_at)) / 86400
result["age_days"] = round(age, 1)
except (ValueError, TypeError):
pass
return result
def is_chroma_available() -> bool:
"""Check whether ChromaDB + Ollama are reachable without side effects."""
try:
_get_chromadb_client()
except Exception:
return False
try:
import urllib.request
req = urllib.request.Request(
f"{OLLAMA_BASE_URL}/api/tags",
headers={"User-Agent": "commander-ai-lab/1.0"},
)
with urllib.request.urlopen(req, timeout=3):
pass
except Exception:
return False
return True
def check_staleness() -> dict:
"""Return staleness info so a background scheduler can trigger rebuilds.
Returns dict with keys:
stale (bool): True if the index should be rebuilt.
reason (str): Human-readable explanation.
age_days (float | None): Age of the current build in days.
"""
meta = _read_build_meta()
built_at = meta.get("built_at")
if not built_at:
return {"stale": True, "reason": "no build metadata found", "age_days": None}
try:
age_days = (time.time() - float(built_at)) / 86400
except (ValueError, TypeError):
return {"stale": True, "reason": "corrupt build timestamp", "age_days": None}
if age_days > 14:
return {"stale": True, "reason": f"index is {age_days:.1f} days old (>14)", "age_days": round(age_days, 1)}
try:
from services.scryfall_bulk import get_stats as bulk_stats
bulk_count = bulk_stats().get("card_count", 0)
collection = _get_collection()
chroma_count = collection.count()
ratio = chroma_count / max(bulk_count, 1)
if ratio < 0.95 or ratio > 1.05:
return {
"stale": True,
"reason": f"card count drift: chroma={chroma_count}, bulk={bulk_count}",
"age_days": round(age_days, 1),
}
except Exception as exc:
logger.warning("check_staleness card count check failed: %s", exc)
return {"stale": False, "reason": "index is current", "age_days": round(age_days, 1)}
+372
View File
@@ -0,0 +1,372 @@
"""
services/scryfall_bulk.py
=========================
Downloads and indexes the full Scryfall `default_cards` bulk export into a
local SQLite database with FTS5 full-text search over card names and oracle
text. This replaces per-card live Scryfall API calls for the RAG pipeline.
Usage (one-time or nightly):
from services.scryfall_bulk import ensure_bulk_db
ensure_bulk_db() # call at startup; no-ops if data is fresh
Public API:
ensure_bulk_db() -> None (download if stale)
lookup_card(name) -> dict | None
search_oracle(query, colors, limit) -> list[dict]
get_bulk_db() -> sqlite3.Connection
Data path: ./data/scryfall_bulk.db
Re-download: every SCRYFALL_BULK_STALENESS_DAYS days (default 7)
"""
from __future__ import annotations
import json
import logging
import os
import sqlite3
import threading
import time
from pathlib import Path
from urllib.request import Request, urlopen
logger = logging.getLogger("commander_ai_lab.scryfall_bulk")
# ── Config ──────────────────────────────────────────────────────────────────
BULK_DB_PATH = Path(__file__).parent.parent / "data" / "scryfall_bulk.db"
BULK_META_URL = "https://api.scryfall.com/bulk-data"
SCRYFALL_BULK_STALENESS_DAYS = int(
os.environ.get("SCRYFALL_BULK_STALENESS_DAYS", "7")
)
_API_HEADERS = {"User-Agent": "CommanderAILab/3.0", "Accept": "application/json"}
# ── Module-level singletons ──────────────────────────────────────────────────
_bulk_conn: sqlite3.Connection | None = None
_bulk_lock = threading.Lock()
# ── DB init ──────────────────────────────────────────────────────────────────
def _init_db(conn: sqlite3.Connection) -> None:
"""Create tables and FTS5 virtual table on first run."""
conn.execute("PRAGMA journal_mode=WAL")
conn.execute("PRAGMA synchronous=NORMAL")
conn.executescript("""
CREATE TABLE IF NOT EXISTS cards (
scryfall_id TEXT PRIMARY KEY,
name TEXT NOT NULL,
oracle_text TEXT DEFAULT '',
type_line TEXT DEFAULT '',
mana_cost TEXT DEFAULT '',
cmc REAL DEFAULT 0,
color_identity TEXT DEFAULT '[]',
keywords TEXT DEFAULT '[]',
power TEXT DEFAULT '',
toughness TEXT DEFAULT '',
rarity TEXT DEFAULT '',
edhrec_rank INTEGER DEFAULT 0,
legalities TEXT DEFAULT '{}',
rulings_uri TEXT DEFAULT '',
set_code TEXT DEFAULT '',
updated_at TEXT DEFAULT (datetime('now'))
);
CREATE VIRTUAL TABLE IF NOT EXISTS cards_fts USING fts5(
name,
oracle_text,
content='cards',
content_rowid='rowid'
);
CREATE TABLE IF NOT EXISTS meta (
key TEXT PRIMARY KEY,
value TEXT NOT NULL
);
""")
conn.commit()
# ── Staleness check ───────────────────────────────────────────────────────────
def _is_stale(conn: sqlite3.Connection) -> bool:
row = conn.execute(
"SELECT value FROM meta WHERE key = 'last_download'"
).fetchone()
if not row:
return True
try:
age_days = (time.time() - float(row[0])) / 86400
return age_days > SCRYFALL_BULK_STALENESS_DAYS
except (ValueError, TypeError):
return True
# ── Download + index ──────────────────────────────────────────────────────────
def _get_bulk_download_url() -> str:
"""Fetch the current default_cards download URL from the Scryfall manifest."""
req = Request(BULK_META_URL, headers=_API_HEADERS)
with urlopen(req, timeout=15) as resp:
meta = json.loads(resp.read())
for entry in meta.get("data", []):
if entry.get("type") == "default_cards":
return entry["download_uri"]
raise RuntimeError("Could not find default_cards entry in Scryfall bulk-data manifest")
def _download_and_index(conn: sqlite3.Connection) -> int:
"""
Download the full default_cards JSON from Scryfall and index into SQLite.
Replaces all existing rows. Returns number of cards indexed.
"""
logger.info("Fetching Scryfall bulk-data manifest...")
url = _get_bulk_download_url()
logger.info("Downloading Scryfall default_cards bulk export (~100 MB)...")
req = Request(url, headers=_API_HEADERS)
with urlopen(req, timeout=300) as resp:
raw_bytes = resp.read()
cards_json: list[dict] = json.loads(raw_bytes)
logger.info(f"Received {len(cards_json)} card objects from Scryfall.")
# Wipe existing data for a clean rebuild
conn.execute("DELETE FROM cards")
conn.execute("DELETE FROM cards_fts")
conn.commit()
BATCH_SIZE = 1000
inserted = 0
batch: list[tuple] = []
_SKIP_LAYOUTS = {"token", "emblem", "art_series", "double_faced_token"}
for card in cards_json:
if card.get("layout") in _SKIP_LAYOUTS:
continue
# Handle double-faced / meld cards: concatenate face oracle texts
oracle = card.get("oracle_text", "")
if not oracle and card.get("card_faces"):
oracle = "\n//\n".join(
f.get("oracle_text", "")
for f in card["card_faces"]
if f.get("oracle_text")
)
mana_cost = card.get("mana_cost", "")
if not mana_cost and card.get("card_faces"):
mana_cost = card["card_faces"][0].get("mana_cost", "")
batch.append((
card.get("id", ""),
card.get("name", ""),
oracle,
card.get("type_line", ""),
mana_cost,
float(card.get("cmc") or 0),
json.dumps(card.get("color_identity", [])),
json.dumps(card.get("keywords", [])),
card.get("power", "") or "",
card.get("toughness", "") or "",
card.get("rarity", ""),
int(card.get("edhrec_rank") or 0),
json.dumps(card.get("legalities", {})),
card.get("rulings_uri", "") or "",
card.get("set", ""),
))
if len(batch) >= BATCH_SIZE:
_flush_batch(conn, batch)
inserted += len(batch)
batch.clear()
logger.debug(f" Indexed {inserted} cards...")
if batch:
_flush_batch(conn, batch)
inserted += len(batch)
# Rebuild FTS5 index from cards table
conn.execute("INSERT INTO cards_fts(cards_fts) VALUES('rebuild')")
# Record download timestamp
conn.execute(
"INSERT OR REPLACE INTO meta (key, value) VALUES ('last_download', ?)",
(str(time.time()),)
)
conn.execute(
"INSERT OR REPLACE INTO meta (key, value) VALUES ('card_count', ?)",
(str(inserted),)
)
conn.commit()
logger.info(f"Scryfall bulk index complete: {inserted} cards stored.")
return inserted
def _flush_batch(conn: sqlite3.Connection, batch: list[tuple]) -> None:
conn.executemany(
"""
INSERT OR REPLACE INTO cards
(scryfall_id, name, oracle_text, type_line, mana_cost, cmc,
color_identity, keywords, power, toughness, rarity,
edhrec_rank, legalities, rulings_uri, set_code)
VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)
""",
batch,
)
conn.commit()
# ── Public API ────────────────────────────────────────────────────────────────
def get_bulk_db() -> sqlite3.Connection:
"""
Return the module-level SQLite connection, creating it on first call.
Thread-safe via _bulk_lock.
"""
global _bulk_conn
with _bulk_lock:
if _bulk_conn is None:
BULK_DB_PATH.parent.mkdir(parents=True, exist_ok=True)
_bulk_conn = sqlite3.connect(
str(BULK_DB_PATH), check_same_thread=False
)
_bulk_conn.row_factory = sqlite3.Row
_init_db(_bulk_conn)
return _bulk_conn
def ensure_bulk_db() -> None:
"""
Call at startup. Downloads and indexes the full Scryfall card set if the
local DB is missing or older than SCRYFALL_BULK_STALENESS_DAYS.
No-ops if data is already fresh.
"""
conn = get_bulk_db()
if _is_stale(conn):
logger.info(
"Scryfall bulk DB is stale or missing — downloading now "
"(~100 MB, one-time per week)..."
)
try:
_download_and_index(conn)
except Exception as exc:
logger.error(
f"Scryfall bulk download failed: {exc}. "
"Continuing without bulk data — per-card Scryfall cache will be used."
)
else:
row = conn.execute(
"SELECT value FROM meta WHERE key = 'card_count'"
).fetchone()
count = row[0] if row else "unknown"
logger.info(f"Scryfall bulk DB is fresh ({count} cards cached).")
def lookup_card(name: str) -> dict | None:
"""
Exact case-insensitive card name lookup.
Returns a dict with all columns from the `cards` table, or None if not
found. Prefers the most-recently-printed printing (lowest edhrec_rank).
"""
conn = get_bulk_db()
row = conn.execute(
"""
SELECT * FROM cards
WHERE LOWER(name) = LOWER(?)
ORDER BY edhrec_rank ASC
LIMIT 1
""",
(name,),
).fetchone()
return dict(row) if row else None
def search_oracle(
query: str,
color_identity: list[str] | None = None,
type_filter: str | None = None,
limit: int = 10,
) -> list[dict]:
"""
Full-text search over card names and oracle text using SQLite FTS5.
Args:
query: Natural language or keyword query string.
color_identity: If provided, restrict results to cards whose color
identity is a subset of this list (e.g. ['W', 'U']).
type_filter: Optional type-line substring filter (e.g. 'Creature').
limit: Maximum number of results to return.
Returns:
List of card dicts ordered by FTS5 relevance rank.
"""
conn = get_bulk_db()
# Build FTS5 query: wrap each token in quotes to allow phrase matching
tokens = [t.strip() for t in query.split() if t.strip()]
if not tokens:
return []
fts_query = " OR ".join(f'"{t}"' for t in tokens)
# Over-fetch to allow post-filtering by color/type
fetch_limit = max(limit * 5, 50)
rows = conn.execute(
"""
SELECT c.*
FROM cards c
JOIN cards_fts fts ON fts.rowid = c.rowid
WHERE cards_fts MATCH ?
ORDER BY rank
LIMIT ?
""",
(fts_query, fetch_limit),
).fetchall()
results: list[dict] = []
for row in rows:
card = dict(row)
# Color identity filter: card's CI must be a subset of deck's CI
if color_identity:
try:
card_ci = set(json.loads(card.get("color_identity") or "[]"))
except (json.JSONDecodeError, TypeError):
card_ci = set()
if not card_ci.issubset(set(color_identity)):
continue
# Optional type-line filter
if type_filter:
if type_filter.lower() not in (card.get("type_line") or "").lower():
continue
results.append(card)
if len(results) >= limit:
break
return results
def get_stats() -> dict:
"""Return metadata about the current bulk DB state."""
conn = get_bulk_db()
rows = conn.execute("SELECT key, value FROM meta").fetchall()
meta = {r["key"]: r["value"] for r in rows}
card_count = conn.execute("SELECT COUNT(*) FROM cards").fetchone()[0]
stale = _is_stale(conn)
last_dl = meta.get("last_download")
age_days: float | None = None
if last_dl:
try:
age_days = round((time.time() - float(last_dl)) / 86400, 1)
except (ValueError, TypeError):
pass
return {
"card_count": card_count,
"stale": stale,
"age_days": age_days,
"db_path": str(BULK_DB_PATH),
"staleness_threshold_days": SCRYFALL_BULK_STALENESS_DAYS,
}
@@ -102,7 +102,7 @@ def chat(
return response.choices[0].message.content or ""
# ── 1. Analyze synergies ─────────────────────────────────────────
# ── 1. Analyze synergies ─────────────────────────────────────────────
def analyze_synergies(
commander_name: str,
@@ -145,7 +145,7 @@ Cards to evaluate:
return []
# ── 2. Suggest cards for a category ──────────────────────────────
# ── 2. Suggest cards for a category ────────────────────────────────
def suggest_cards(
commander_name: str,
@@ -154,13 +154,23 @@ def suggest_cards(
count: int = 10,
exclude: Optional[List[str]] = None,
strategy_notes: Optional[str] = None,
sim_context: str = "",
) -> List[str]:
"""
Ask the model to suggest cards for a specific deck category.
Parameters
----------
sim_context:
Optional natural-language insight string from get_sim_insights().
When non-empty, appended to the prompt so the LLM can factor in
statistically successful card traits from simulation data.
Returns a list of card names.
"""
exclude_str = f"\nDo NOT suggest these cards: {json.dumps(exclude)}" if exclude else ""
strategy = f"\nStrategy notes: {strategy_notes}" if strategy_notes else ""
sim_block = f"\n\n{sim_context}" if sim_context else ""
prompt = f"""You are a Magic: The Gathering Commander deck-building expert.
@@ -168,7 +178,7 @@ Commander: {commander_name}
Color identity: {', '.join(color_identity)}
Category: {category}
{strategy}
{exclude_str}
{exclude_str}{sim_block}
Suggest exactly {count} cards for the {category} category that work well with this commander.
Only suggest cards that are legal in Commander and match the color identity.
@@ -200,7 +210,7 @@ Return ONLY a JSON array of card name strings, like: ["Card A", "Card B", ...]""
return []
# ── 3. Filter by color identity ──────────────────────────────────
# ── 3. Filter by color identity ────────────────────────────────────
def filter_color_identity(
card_names: List[str],
@@ -242,17 +252,28 @@ Cards:
return card_names
# ── 4. Enforce deck ratios ───────────────────────────────────────
# ── 4. Enforce deck ratios ───────────────────────────────────────────
def enforce_deck_ratios(
cards_by_category: Dict[str, List[str]],
target_ratios: Dict[str, int],
commander_name: str,
sim_context: str = "",
) -> Dict[str, List[str]]:
"""
Ask the model to trim/expand categories to hit target counts.
Parameters
----------
sim_context:
Optional natural-language insight string from get_sim_insights().
When non-empty, appended to the prompt so the LLM preferentially
retains cards matching statistically high-value traits when trimming.
Returns adjusted dict of category -> card names.
"""
sim_block = f"\n\n{sim_context}" if sim_context else ""
prompt = f"""You are a Magic: The Gathering Commander deck-building expert.
Commander: {commander_name}
@@ -265,7 +286,7 @@ Target card counts per category:
Adjust each category to match the target count. Remove the weakest cards from
over-filled categories. For under-filled categories, suggest additional cards.
The total across all categories must be exactly 99.
The total across all categories must be exactly 99.{sim_block}
Return a JSON object with the same category keys, each mapping to an array of card name strings."""
@@ -284,7 +305,7 @@ Return a JSON object with the same category keys, each mapping to an array of ca
return cards_by_category
# ── 5. Assemble final deck JSON ──────────────────────────────────
# ── 5. Assemble final deck JSON ─────────────────────────────────────────
def assemble_deck_json(
commander_name: str,
@@ -0,0 +1,124 @@
"""
sim_insights.py
===============
Translates learned simulation weights (learned_weights.json) into concise
natural-language insight strings that can be injected into Ollama prompts.
This bridges the gap between the Monte Carlo engine's outcome-weighted
reinforcement learning and the LLM-driven deck builder: keywords that
statistically correlate with wins are surfaced to Ollama so it can
prioritize those card traits when suggesting and trimming cards.
Usage::
from commander_ai_lab.deck_builder.api.sim_insights import get_sim_insights
context = get_sim_insights() # returns "" if no meaningful learning yet
if context:
prompt += f"\n\n{context}"
The function is intentionally safe to call when learned_weights.json does
not yet exist (load_weights() falls back to AI_DEFAULT_WEIGHTS silently),
and returns an empty string when no weight has drifted far enough from the
default to be worth mentioning — so existing prompt behaviour is unchanged
until there is real simulation data.
"""
from __future__ import annotations
import logging
from typing import Optional
from commander_ai_lab.sim.rules import AI_DEFAULT_WEIGHTS, load_weights
logger = logging.getLogger(__name__)
# Maps weight keys → human-readable MTG concepts shown to the LLM.
# Only keys the LLM can meaningfully act on are included.
_KEY_TO_CONCEPT: dict[str, str] = {
"kw_flying": "flying (evasion)",
"kw_haste": "haste",
"kw_deathtouch": "deathtouch",
"kw_indestructible": "indestructible",
"kw_doubleStrike": "double strike",
"kw_hexproof": "hexproof",
"kw_trample": "trample",
"kw_lifelink": "lifelink",
"kw_menace": "menace",
"kw_ward": "ward",
"kw_annihilator": "annihilator",
"kw_cascade": "cascade",
"spell_ramp": "mana ramp spells",
"spell_destroy": "targeted removal",
"spell_draw": "card draw",
"spell_counter": "counterspells and board control",
"trig_etb": "enters-the-battlefield triggers",
"trig_dies": "death triggers",
"trig_attack": "attack triggers",
"card_ptBonus": "high power/toughness creatures",
}
def get_sim_insights(
top_n: int = 6,
threshold_delta: float = 0.05,
weights_path: Optional[str] = None,
) -> str:
"""
Return a concise insight paragraph for injecting into LLM prompts.
Parameters
----------
top_n:
Maximum number of concepts to mention (avoids bloating the prompt).
threshold_delta:
Minimum absolute drift from the default weight before a concept
is considered meaningful enough to report. 0.05 means a weight
must have moved by at least 5% of its default before it matters.
weights_path:
Optional explicit path to learned_weights.json. If None, the
default location (``src/.../sim/learned_weights.json``) is used.
Returns
-------
str
A multi-line insight string ready to append to an LLM prompt, or
an empty string if no meaningful learning is available yet.
"""
try:
learned = load_weights(weights_path)
except Exception as exc:
logger.warning("sim_insights: could not load weights: %s", exc)
return ""
deltas: dict[str, float] = {}
for key, concept in _KEY_TO_CONCEPT.items():
default_val = AI_DEFAULT_WEIGHTS.get(key, 0.0)
learned_val = learned.get(key, default_val)
delta = learned_val - default_val
if abs(delta) >= threshold_delta:
deltas[concept] = delta
if not deltas:
# No meaningful drift yet — don't inject noise into prompts
return ""
# Sort by magnitude, take top_n
sorted_items = sorted(deltas.items(), key=lambda x: -abs(x[1]))[:top_n]
boosted = [c for c, d in sorted_items if d > 0]
penalized = [c for c, d in sorted_items if d < 0]
lines = [
"[Simulation Insights] Data from thousands of Commander games in this lab suggests:",
]
if boosted:
lines.append(f" - PRIORITIZE cards with: {', '.join(boosted)}")
if penalized:
lines.append(f" - DEPRIORITIZE cards with: {', '.join(penalized)}")
lines.append(
"Use this to guide your selections where multiple options are otherwise comparable."
)
result = "\n".join(lines)
logger.debug("sim_insights: injecting %d concept(s) into prompt", len(sorted_items))
return result
@@ -13,6 +13,7 @@ from typing import Dict, List, Optional
from ..api import edhrec, scryfall
from ..api import ollama_client as ollama
from ..api.sim_insights import get_sim_insights
from ..core.collection_filter import filter_names_by_collection
from ..core.models import BuildRequest, BuildResult, CardEntry, CommanderDeck, DeckRatios
from ..core.rules_engine import check_ban_list, filter_by_color_identity, validate_deck
@@ -37,7 +38,7 @@ def build_deck(request: BuildRequest) -> BuildResult:
warnings: List[str] = []
sources: List[str] = []
# ── Step 1: Resolve commander ────────────────────────────────
# ── Step 1: Resolve commander ────────────────────────────────────────────
logger.info(f"Step 1: Resolving commander '{request.commander_name}'")
commander = scryfall.search_commander(request.commander_name)
if not commander:
@@ -50,7 +51,17 @@ def build_deck(request: BuildRequest) -> BuildResult:
logger.info(f"Commander: {commander.name}, CI: {ci_list}")
sources.append("scryfall")
# ── Step 2: Fetch EDHrec recommendations ─────────────────────
# Load simulation insights once; injected into Ollama prompts in Steps 4 & 6.
# Returns "" when learned_weights.json doesn't exist or has no meaningful drift
# yet — so behaviour is identical to before until real sim data is available.
sim_context = get_sim_insights()
if sim_context:
logger.info("Sim insights available — injecting into Ollama prompts")
sources.append("sim_weights")
else:
logger.debug("No sim insights available (run update_weights.py after overnight sims)")
# ── Step 2: Fetch EDHrec recommendations ─────────────────────────────
logger.info(f"Step 2: Fetching EDHrec data for '{commander.name}'")
edhrec_cards = edhrec.get_recommended_cards(commander.name)
if edhrec_cards:
@@ -59,7 +70,7 @@ def build_deck(request: BuildRequest) -> BuildResult:
else:
warnings.append("EDHrec returned no data — relying on Scryfall + Ollama")
# ── Step 3: Fetch Scryfall candidates by category ────────────
# ── Step 3: Fetch Scryfall candidates by category ──────────────────────
logger.info("Step 3: Fetching Scryfall candidates by category")
scryfall_candidates: Dict[str, List[CardEntry]] = {
"ramp": scryfall.search_ramp(commander_ci),
@@ -70,7 +81,7 @@ def build_deck(request: BuildRequest) -> BuildResult:
for cat, cards in scryfall_candidates.items():
logger.info(f" Scryfall {cat}: {len(cards)} candidates")
# ── Step 4: Ollama suggestions to fill gaps ──────────────────
# ── Step 4: Ollama suggestions to fill gaps ──────────────────────────
logger.info("Step 4: Ollama suggesting additional cards")
# Collect all names we already have
@@ -79,7 +90,9 @@ def build_deck(request: BuildRequest) -> BuildResult:
all_names.extend(c.name for c in cards)
all_names = list(set(all_names))
# Ask Ollama to suggest cards for categories that need more
# Ask Ollama to suggest cards for categories that need more.
# sim_context is forwarded so the LLM factors in what traits have
# been statistically successful in our own game simulations.
categories_to_fill = ["synergy", "protection", "wincon"]
ollama_suggestions: Dict[str, List[str]] = {}
@@ -91,6 +104,7 @@ def build_deck(request: BuildRequest) -> BuildResult:
count=15,
exclude=all_names,
strategy_notes=request.strategy_notes,
sim_context=sim_context,
)
ollama_suggestions[category] = suggested
all_names.extend(suggested)
@@ -98,7 +112,7 @@ def build_deck(request: BuildRequest) -> BuildResult:
sources.append("ollama")
# ── Step 5: Filter (color identity, collection, ban list) ────
# ── Step 5: Filter (color identity, collection, ban list) ────────────────
logger.info("Step 5: Filtering candidates")
# Build a unified name pool per category
@@ -140,7 +154,7 @@ def build_deck(request: BuildRequest) -> BuildResult:
n for n in cards_by_category[cat] if n not in BANNED_CARDS
]
# ── Step 6: Ollama enforce ratios ────────────────────────────
# ── Step 6: Ollama enforce ratios ────────────────────────────────────
logger.info("Step 6: Ollama enforcing deck ratios")
target_ratios = {
"lands": request.ratios.lands,
@@ -153,10 +167,13 @@ def build_deck(request: BuildRequest) -> BuildResult:
"uncategorized": request.ratios.uncategorized,
}
# sim_context is forwarded so Ollama preferentially retains cards
# matching high-value sim traits when trimming over-filled categories.
adjusted = ollama.enforce_deck_ratios(
cards_by_category=cards_by_category,
target_ratios=target_ratios,
commander_name=commander.name,
sim_context=sim_context,
)
# Verify total is 99
@@ -165,25 +182,95 @@ def build_deck(request: BuildRequest) -> BuildResult:
warnings.append(f"Ollama returned {total} cards instead of 99 — adjusting")
logger.warning(f"Ratio enforcement returned {total} cards")
# ── Step 7: Assemble final deck ──────────────────────────────
# ── Step 7: Assemble final deck ────────────────────────────────────────
logger.info("Step 7: Assembling final deck")
# Convert to CardEntry list via Scryfall batch lookup
all_card_names = []
for names in adjusted.values():
all_card_names.extend(names)
# Build a globally-deduplicated name list. enforce_deck_ratios can
# return the same card name under multiple category keys (e.g. a
# protection card that also appears in synergy). Without this step
# get_cards_by_names produces duplicate CardEntry objects that
# immediately fail the CommanderDeck singleton validator.
seen_names: dict[str, str] = {} # name -> first category that claimed it
for cat, names in adjusted.items():
for name in names:
if name not in seen_names:
seen_names[name] = cat
else:
logger.warning(
"Duplicate card '%s' found in categories '%s' and '%s' — "
"keeping first assignment, dropping second",
name, seen_names[name], cat,
)
if len(seen_names) < sum(len(v) for v in adjusted.values()):
warnings.append(
"enforce_deck_ratios returned duplicate card names across categories; "
"duplicates were silently dropped before singleton validation."
)
all_card_names = list(seen_names.keys())
card_entries = scryfall.get_cards_by_names(all_card_names)
# Map back categories
name_to_cat: Dict[str, str] = {}
for cat, names in adjusted.items():
for name in names:
name_to_cat[name] = cat
# Safety-net: deduplicate CardEntry list by name in case Scryfall
# returns multiple printings for the same name.
seen_entry_names: set[str] = set()
unique_entries: List[CardEntry] = []
for entry in card_entries:
if entry.name in name_to_cat:
entry.category = name_to_cat[entry.name]
if entry.name not in seen_entry_names:
seen_entry_names.add(entry.name)
unique_entries.append(entry)
else:
logger.warning("Scryfall returned duplicate entry for '%s' — skipping", entry.name)
card_entries = unique_entries
# Map back categories
for entry in card_entries:
if entry.name in seen_names:
entry.category = seen_names[entry.name]
# -- Backfill basic lands to reach exactly 99 cards ----------------
_BACKFILL_COLOR_TO_BASIC = {
"W": "Plains", "U": "Island", "B": "Swamp",
"R": "Mountain", "G": "Forest",
}
_BASIC_LAND_NAMES = set(_BACKFILL_COLOR_TO_BASIC.values()) | {"Wastes"}
_current_total = sum(e.quantity for e in card_entries)
_shortfall = 99 - _current_total
if _shortfall > 0:
logger.warning(
"Deck has %d cards after dedup -- backfilling %d basic land(s)",
_current_total, _shortfall,
)
warnings.append(
f"Backfilled {_shortfall} basic land(s) to reach 99 cards"
)
_basics = [
_BACKFILL_COLOR_TO_BASIC[c]
for c in sorted(commander_ci)
if c in _BACKFILL_COLOR_TO_BASIC
] or ["Wastes"]
# Distribute evenly across colors
_per_color = _shortfall // len(_basics)
_remainder = _shortfall % len(_basics)
for _i, _bname in enumerate(_basics):
_qty = _per_color + (1 if _i < _remainder else 0)
if _qty <= 0:
continue
# Check if this basic is already in card_entries
_found = False
for _ce in card_entries:
if _ce.name == _bname:
_ce.quantity += _qty
_found = True
break
if not _found:
card_entries.append(CardEntry(
name=_bname,
quantity=_qty,
category="lands",
type_line="Basic Land",
))
# Build the deck
elapsed = time.time() - start
+22 -3
View File
@@ -39,10 +39,22 @@ _FORGE_CACHE_LOADED: bool = False
def _get_cards_folder() -> Path:
"""Resolve the Forge cardsfolder directory from CFG.forge_dir."""
"""Resolve the Forge cardsfolder directory from CFG.forge_dir.
Supports two layouts:
A) forge_dir = .../forge-repo -> forge-gui/res/cardsfolder
B) forge_dir = .../forge-repo/forge-gui -> res/cardsfolder
"""
if not CFG.forge_dir:
return Path("")
return Path(CFG.forge_dir) / "forge-gui" / "res" / "cardsfolder"
base = Path(CFG.forge_dir)
candidate_a = base / "forge-gui" / "res" / "cardsfolder"
candidate_b = base / "res" / "cardsfolder"
if candidate_a.is_dir():
return candidate_a
if candidate_b.is_dir():
return candidate_b
return candidate_a # fall back so warning shows the expected path
def _parse_forge_file(filepath: Path) -> Optional[ForgeCardData]:
@@ -58,7 +70,6 @@ def _parse_forge_file(filepath: Path) -> Optional[ForgeCardData]:
line = line.strip()
if not line or line.startswith("#"):
continue
# Name
if line.startswith("Name:"):
data.name = line.split(":", 1)[1].strip()
@@ -150,3 +161,11 @@ def lookup_forge_card(card_name: str) -> Optional[ForgeCardData]:
"""Look up a card by name from the Forge cache. Returns None on miss."""
_load_forge_cache()
return _FORGE_CACHE.get((card_name or "").lower().strip())
def reset_forge_cache() -> None:
"""Force a reload of the Forge card cache on next lookup."""
global _FORGE_CACHE_LOADED
_FORGE_CACHE.clear()
_FORGE_CACHE_LOADED = False
_log.info("Forge card cache cleared — will reload on next lookup")
+35 -7
View File
@@ -23,9 +23,9 @@ from commander_ai_lab.sim.models import Card
from commander_ai_lab.sim.forge_card_loader import lookup_forge_card, ForgeCardData
# ══════════════════════════════════════════════════════════════
# ════════════════════════════════════════════════════════════
# AI Weights (ported from AI_DEFAULT_WEIGHTS in app.js)
# ══════════════════════════════════════════════════════════════
# ════════════════════════════════════════════════════════════
AI_DEFAULT_WEIGHTS: dict[str, float] = {
# Card scoring
@@ -107,10 +107,10 @@ def load_weights(path: Optional[str] = None) -> dict[str, float]:
return dict(AI_DEFAULT_WEIGHTS)
# ══════════════════════════════════════════════════════════════
# ════════════════════════════════════════════════════════════
# Card Enrichment — guess type/cost for cards without Scryfall data
# Ported from enrichCardForSim() in deck_tester_js.txt
# ══════════════════════════════════════════════════════════════
# ════════════════════════════════════════════════════════════
_BASIC_LAND_COLORS = {
"plains": "W",
@@ -141,6 +141,21 @@ _KNOWN_CARDS: list[tuple[re.Pattern, dict]] = [
]
# Nonbasic land name guard — compiled once at module load, not inside enrich_card().
# Catches common land names that Forge may return with cmc=0 (causing has_real_data
# to stay False) or that arrive with no Scryfall data at all.
# Extended per Bug 3 to cover fetchlands, shocklands, filter lands, pain lands, etc.
_land_kw = re.compile(
r"\b(grove|garden|glade|falls|summit|crag|chapel|retreat|harbor|cemetery|"
r"pool|fountain|shrine|foundry|vents|grave|crypt|tomb|ground|ruins|mesa|"
r"tarn|delta|strand|heath|mire|bog|den|lair|hollow|flats|wastes|expanse|"
r"vista|nexus|spire|sanctum|citadel|reaches|keeps|gate|springs|crossing|"
r"command tower|path of ancestry|evolving wilds|terramorphic|"
r"exotic orchard|mana confluence|city of brass|reflecting pool|"
r"fabled passage|prismatic vista|command beacon|ash barrens)\b",
re.IGNORECASE,
)
def _apply_forge_data(card: Card, forge_data: ForgeCardData) -> None:
"""
@@ -182,6 +197,7 @@ def _apply_forge_data(card: Card, forge_data: ForgeCardData) -> None:
if forge_data.oracle_text and not card.oracle_text:
card.oracle_text = forge_data.oracle_text
def enrich_card(card: Card) -> Card:
"""
Enrich a card with type/cost heuristics if it lacks Scryfall data.
@@ -198,8 +214,12 @@ def enrich_card(card: Card) -> Card:
if forge_data:
_apply_forge_data(card, forge_data) # sets oracle_text, keywords, etc.
card.forge_enriched = True
# Re-check: if Forge provided sufficient data, apply flags and return
# Re-check: if Forge provided sufficient data, apply flags and return.
# Bug 3 fix: lands always have cmc=0, so explicitly allow type_line
# containing "land" to satisfy has_real_data even when cmc is 0.
has_real_data = card.cmc > 0 or (card.type_line and len(card.type_line) > 5)
if not has_real_data and card.type_line and "land" in card.type_line.lower():
has_real_data = True # lands are valid cards even with cmc=0
if has_real_data:
_apply_oracle_flags(card)
return card
@@ -223,6 +243,14 @@ def enrich_card(card: Card) -> Card:
_apply_oracle_flags(card)
return card
# Nonbasic land name guard — catches fetchlands, shocklands, filter lands,
# pain lands, and other common nonbasic lands BEFORE the creature fallback.
if _land_kw.search(name_lower):
card.type_line = "Land"
card.cmc = 0
_apply_oracle_flags(card)
return card
# Default: treat as creature with deterministic CMC based on
# card name hash (fixes #32 — no more random.randint jitter
# between games for the same card).
@@ -284,7 +312,7 @@ def _apply_oracle_flags(card: Card) -> None:
card.is_ramp = True
# ── Direct-damage (burn spell) detection ──────────────────────
# ── Direct-damage (burn spell) detection ────────────────────────────────────
# Regex: "deals N damage to target|any target" (not "each creature" — that's a wipe)
_DIRECT_DAMAGE_RE = re.compile(
@@ -342,7 +370,7 @@ def _detect_direct_damage(card: Card, oracle: str) -> None:
card.direct_damage_amount = int(m.group(1))
# ── Multi-opponent scaling factors ────────────────────────────
# ── Multi-opponent scaling factors ─────────────────────────────────────────────
# Keywords / effects that become more valuable in larger pods.
# Each entry maps a weight key → the extra bonus *per additional
# opponent* beyond 1. 1v1 uses base weights; 4-player FFA adds
+11 -13
View File
@@ -11,27 +11,25 @@ REM - commander-ai-lab built: mvn package "-DskipTests"
REM
REM Edit the paths below to match your system.
REM ═══════════════════════════════════════════════════════════
REM ── Your Forge paths (EDIT THESE) ──────────────────────────
set FORGE_JAR=D:\ForgeCommander\forge-repo\forge-gui-desktop\target\forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar
set FORGE_DIR=D:\ForgeCommander\forge-repo\forge-gui
set FORGE_DIR=D:\ForgeCommander\forge-repo
set FORGE_DECKS_DIR=%APPDATA%\Forge\decks\commander
REM ── Lab JAR (auto-detected from target/) ───────────────────
REM ── Lab JAR (auto-detected from target/) ─────────────────────
set LAB_PORT=8080
REM ── API Keys (set via environment variables or .env file) ──
REM If not already set in your environment, create a .env file
REM in this directory (see .env.example) or set them manually:
REM set XIMILAR_API_KEY=your-ximilar-key-here
REM set PPLX_API_KEY=your-perplexity-key-here
REM set ANTHROPIC_API_KEY=your-anthropic-api-key-here
REM
REM Load from .env file if it exists and vars aren't already set
if not defined XIMILAR_API_KEY (
if exist "%~dp0.env" (
for /f "usebackq tokens=1,* delims==" %%A in ("%~dp0.env") do (
if "%%A"=="XIMILAR_API_KEY" set XIMILAR_API_KEY=%%B
if "%%A"=="PPLX_API_KEY" set PPLX_API_KEY=%%B
if "%%A"=="ANTHROPIC_API_KEY" set ANTHROPIC_API_KEY=%%B
)
)
)
@@ -46,10 +44,10 @@ echo Forge Dir: %FORGE_DIR%
echo Decks Dir: %FORGE_DECKS_DIR%
echo Port: %LAB_PORT%
echo Ximilar: Configured
if defined PPLX_API_KEY (
echo Perplexity: Configured
if defined ANTHROPIC_API_KEY (
echo Claude Opus: Configured
) else (
echo Perplexity: Not configured ^(set PPLX_API_KEY env var for AI features^)
echo Claude Opus: Not configured ^(set ANTHROPIC_API_KEY env var for AI features^)
)
echo.
@@ -84,14 +82,14 @@ if not exist "%~dp0lab_api.py" (
exit /b 1
)
REM ── Install Python deps if needed ──────────────────────────
REM ── Install Python deps if needed ───────────────────────────
python -c "import fastapi" >nul 2>&1
if %ERRORLEVEL% neq 0 (
echo Installing FastAPI + Uvicorn...
pip install fastapi uvicorn
)
REM ── Launch the server ──────────────────────────────────────
REM ── Launch the server ───────────────────────────────────────
echo.
echo Starting Commander AI Lab server...
echo Web UI: http://localhost:%LAB_PORT%
@@ -99,14 +97,14 @@ echo API: http://localhost:%LAB_PORT%/docs
echo Press Ctrl+C to stop.
echo.
if defined PPLX_API_KEY (
if defined ANTHROPIC_API_KEY (
python "%~dp0lab_api.py" ^
--forge-jar "%FORGE_JAR%" ^
--forge-dir "%FORGE_DIR%" ^
--forge-decks-dir "%FORGE_DECKS_DIR%" ^
--port %LAB_PORT% ^
--ximilar-key "%XIMILAR_API_KEY%" ^
--pplx-key "%PPLX_API_KEY%"
--anthropic-key "%ANTHROPIC_API_KEY%"
) else (
python "%~dp0lab_api.py" ^
--forge-jar "%FORGE_JAR%" ^
+2 -2
View File
@@ -49,9 +49,9 @@ def _make_conn() -> sqlite3.Connection:
def _make_generator(conn: sqlite3.Connection) -> DeckGeneratorV3:
"""Build a DeckGeneratorV3 with a mock Perplexity client."""
"""Build a DeckGeneratorV3 with a mock Anthropic Claude Opus client."""
return DeckGeneratorV3(
pplx_client=MagicMock(),
anthropic_client=MagicMock(),
db_conn_factory=lambda: conn,
)
+83
View File
@@ -0,0 +1,83 @@
"""Tests for RAG Phase 3 — coach prompt injection."""
import pytest
from unittest.mock import patch, MagicMock
from coach.prompt_template import _format_rag_cards, build_user_prompt
class TestFormatRagCards:
"""Tests for _format_rag_cards helper."""
def test_empty_list_returns_fallback(self):
assert _format_rag_cards([]) == "No RAG results available."
def test_none_returns_fallback(self):
# build_user_prompt passes rag_cards or [] so None shouldn't happen,
# but _format_rag_cards should handle it gracefully
assert "No RAG" in _format_rag_cards([])
def test_single_card_formatted(self):
cards = [{
"name": "Sol Ring",
"types": "Artifact",
"mana_value": 1,
"mana_cost": "{1}",
"distance": 0.123,
"text": "Tap: Add two colorless mana.",
}]
result = _format_rag_cards(cards)
assert "Sol Ring" in result
assert "{1}" in result
assert "MV:1" in result
assert "0.123" in result
assert "Tap: Add two colorless" in result
def test_max_15_cards(self):
cards = [{"name": f"Card {i}", "types": "Creature",
"mana_value": i, "mana_cost": f"{{{i}}}",
"distance": 0.1 * i, "text": ""} for i in range(20)]
result = _format_rag_cards(cards)
assert "Card 14" in result
assert "Card 15" not in result
def test_missing_fields_use_defaults(self):
cards = [{"name": "Mystery Card"}]
result = _format_rag_cards(cards)
assert "Mystery Card" in result
assert "MV:?" in result
class TestBuildUserPromptRagParam:
"""Tests that build_user_prompt accepts and renders rag_cards."""
@patch("coach.prompt_template._format_rag_cards")
def test_rag_cards_passed_to_formatter(self, mock_fmt):
mock_fmt.return_value = "MOCK_RAG_OUTPUT"
# We can't easily build a full DeckReport here without fixtures,
# so just verify the function signature accepts rag_cards.
# A full integration test would need the DeckReport factory.
assert callable(build_user_prompt)
import inspect
sig = inspect.signature(build_user_prompt)
assert "rag_cards" in sig.parameters
def test_rag_cards_default_is_none(self):
import inspect
sig = inspect.signature(build_user_prompt)
param = sig.parameters["rag_cards"]
assert param.default is None
class TestRagQueryIntegration:
"""Tests that coach_service calls query_cards (mocked)."""
@patch("services.rag_store.query_cards", return_value=[
{"name": "Arcane Signet", "types": "Artifact", "mana_value": 2,
"mana_cost": "{2}", "distance": 0.05, "text": "Tap: Add one mana."},
])
def test_query_cards_called_in_coaching(self, mock_qc):
"""Smoke test: import doesn't crash."""
from services.rag_store import query_cards
result = query_cards("Atraxa commander deck", n=15, colors=["W", "U", "B", "G"])
assert len(result) == 1
assert result[0]["name"] == "Arcane Signet"
mock_qc.assert_called_once()
+374
View File
@@ -0,0 +1,374 @@
"""
tests/test_rag_store.py
=======================
Unit tests for services/rag_store.py.
All tests use mocked ChromaDB and Ollama dependencies — no external services
or network access required.
"""
from __future__ import annotations
import json
import time
import types
from pathlib import Path
from unittest.mock import MagicMock, patch, mock_open
import pytest
# ---------------------------------------------------------------------------
# Helpers to reset module-level singletons between tests
# ---------------------------------------------------------------------------
def _reset_rag_module():
"""Reset all module-level state in rag_store between tests."""
import services.rag_store as rs
rs._rag_ready = False
rs._chroma_client = None
rs._chroma_collection = None
@pytest.fixture(autouse=True)
def reset_rag_state():
_reset_rag_module()
yield
_reset_rag_module()
# ---------------------------------------------------------------------------
# _build_document
# ---------------------------------------------------------------------------
class TestBuildDocument:
def test_includes_all_four_fields(self):
from services.rag_store import _build_document
card = {
"name": "Rampant Growth",
"mana_cost": "{1}{G}",
"type_line": "Sorcery",
"oracle_text": "Search your library for a basic land card.",
}
doc = _build_document(card)
assert "Rampant Growth" in doc
assert "{1}{G}" in doc
assert "Sorcery" in doc
assert "Search your library" in doc
def test_mana_cost_present_in_document(self):
"""Regression: mana_cost was previously missing (bug #128)."""
from services.rag_store import _build_document
card = {"name": "Lightning Bolt", "mana_cost": "{R}", "type_line": "Instant", "oracle_text": "Deal 3 damage."}
doc = _build_document(card)
assert "{R}" in doc
def test_missing_mana_cost_skipped(self):
from services.rag_store import _build_document
card = {"name": "Plains", "mana_cost": "", "type_line": "Basic Land", "oracle_text": ""}
doc = _build_document(card)
# Should not produce " | | " style empty fields
assert "Plains" in doc
assert doc.count(" | | ") == 0
def test_empty_card_returns_name_only(self):
from services.rag_store import _build_document
card = {"name": "Unnamed"}
doc = _build_document(card)
assert doc == "Unnamed"
def test_pipe_delimiter_format(self):
from services.rag_store import _build_document
card = {"name": "A", "mana_cost": "B", "type_line": "C", "oracle_text": "D"}
assert _build_document(card) == "A | B | C | D"
# ---------------------------------------------------------------------------
# _build_metadata
# ---------------------------------------------------------------------------
class TestBuildMetadata:
def test_color_identity_json_string_parsed(self):
from services.rag_store import _build_metadata
card = {"name": "X", "color_identity": '["W", "U"]', "cmc": 3}
meta = _build_metadata(card)
assert meta["color_identity"] == "U,W" # sorted
def test_color_identity_list_input(self):
from services.rag_store import _build_metadata
card = {"name": "X", "color_identity": ["B", "G"], "cmc": 2}
meta = _build_metadata(card)
assert meta["color_identity"] == "B,G"
def test_colorless_card(self):
from services.rag_store import _build_metadata
card = {"name": "Sol Ring", "color_identity": "[]", "cmc": 1}
meta = _build_metadata(card)
assert meta["color_identity"] == ""
def test_malformed_color_identity_defaults_empty(self):
from services.rag_store import _build_metadata
card = {"name": "X", "color_identity": "not-json", "cmc": 0}
meta = _build_metadata(card)
assert meta["color_identity"] == ""
def test_cmc_none_defaults_zero(self):
from services.rag_store import _build_metadata
card = {"name": "X", "cmc": None}
meta = _build_metadata(card)
assert meta["cmc"] == 0.0
# ---------------------------------------------------------------------------
# _read_build_meta / _write_build_meta
# ---------------------------------------------------------------------------
class TestBuildMeta:
def test_write_then_read_roundtrip(self, tmp_path):
import services.rag_store as rs
original_dir = rs.RAG_CHROMA_DIR
original_path = rs.BUILD_META_PATH
try:
rs.RAG_CHROMA_DIR = tmp_path
rs.BUILD_META_PATH = tmp_path / "build_meta.json"
rs._write_build_meta(card_count=12345)
meta = rs._read_build_meta()
assert meta["card_count"] == 12345
assert abs(meta["built_at"] - time.time()) < 5
finally:
rs.RAG_CHROMA_DIR = original_dir
rs.BUILD_META_PATH = original_path
def test_read_missing_returns_empty_dict(self, tmp_path):
import services.rag_store as rs
original_path = rs.BUILD_META_PATH
try:
rs.BUILD_META_PATH = tmp_path / "nonexistent.json"
assert rs._read_build_meta() == {}
finally:
rs.BUILD_META_PATH = original_path
# ---------------------------------------------------------------------------
# _collection_is_current
# ---------------------------------------------------------------------------
class TestCollectionIsCurrent:
def _make_collection(self, count: int) -> MagicMock:
col = MagicMock()
col.count.return_value = count
return col
def test_empty_collection_returns_false(self):
from services.rag_store import _collection_is_current
col = self._make_collection(0)
assert _collection_is_current(col, 1000) is False
def test_current_collection_returns_true(self, tmp_path):
import services.rag_store as rs
original_path = rs.BUILD_META_PATH
try:
rs.BUILD_META_PATH = tmp_path / "build_meta.json"
rs._write_build_meta(card_count=1000)
col = self._make_collection(1000)
assert rs._collection_is_current(col, 1000) is True
finally:
rs.BUILD_META_PATH = original_path
def test_stale_by_age_returns_false(self, tmp_path):
import services.rag_store as rs
original_path = rs.BUILD_META_PATH
try:
rs.BUILD_META_PATH = tmp_path / "build_meta.json"
# Write a built_at 15 days ago
rs.BUILD_META_PATH.write_text(
json.dumps({"built_at": time.time() - (15 * 86400), "card_count": 1000}),
encoding="utf-8",
)
col = self._make_collection(1000)
assert rs._collection_is_current(col, 1000) is False
finally:
rs.BUILD_META_PATH = original_path
def test_count_mismatch_returns_false(self, tmp_path):
import services.rag_store as rs
original_path = rs.BUILD_META_PATH
try:
rs.BUILD_META_PATH = tmp_path / "build_meta.json"
rs._write_build_meta(card_count=500)
col = self._make_collection(500) # chroma has 500
assert rs._collection_is_current(col, 1000) is False # bulk has 1000
finally:
rs.BUILD_META_PATH = original_path
# ---------------------------------------------------------------------------
# query_cards
# ---------------------------------------------------------------------------
class TestQueryCards:
def test_returns_empty_when_not_ready(self):
from services.rag_store import query_cards
# _rag_ready is False (reset by fixture)
result = query_cards("draw cards")
assert result == []
def test_color_filter_removes_off_color_cards(self):
import services.rag_store as rs
rs._rag_ready = True
mock_col = MagicMock()
mock_col.query.return_value = {
"ids": [["id1", "id2"]],
"documents": [["Wrath | {3}{W}{W} | Sorcery | Destroy all", "Dark Ritual | {B} | Instant | Add BBB"]],
"metadatas": [[
{"name": "Wrath", "type_line": "Sorcery", "color_identity": "W", "cmc": 5.0, "mana_cost": "{3}{W}{W}"},
{"name": "Dark Ritual", "type_line": "Instant", "color_identity": "B", "cmc": 1.0, "mana_cost": "{B}"},
]],
"distances": [[0.1, 0.2]],
}
rs._chroma_collection = mock_col
results = rs.query_cards("mana acceleration", n_results=10, color_identity=["W"])
names = [r["name"] for r in results]
assert "Wrath" in names
assert "Dark Ritual" not in names
def test_distance_converted_to_score_in_route(self):
"""Verify distance field is present and in [0, 1] range."""
import services.rag_store as rs
rs._rag_ready = True
mock_col = MagicMock()
mock_col.query.return_value = {
"ids": [["id1"]],
"documents": [["Sol Ring | {1} | Artifact | Add two colorless mana."]],
"metadatas": [[
{"name": "Sol Ring", "type_line": "Artifact", "color_identity": "", "cmc": 1.0, "mana_cost": "{1}"},
]],
"distances": [[0.15]],
}
rs._chroma_collection = mock_col
results = rs.query_cards("mana rock", n_results=1)
assert len(results) == 1
assert results[0]["distance"] == 0.15
assert results[0]["name"] == "Sol Ring"
def test_empty_chroma_result_returns_empty_list(self):
import services.rag_store as rs
rs._rag_ready = True
mock_col = MagicMock()
mock_col.query.return_value = {"ids": [[]], "documents": [[]], "metadatas": [[]], "distances": [[]]}
rs._chroma_collection = mock_col
assert rs.query_cards("nothing") == []
# ---------------------------------------------------------------------------
# get_rag_stats
# ---------------------------------------------------------------------------
class TestGetRagStats:
def test_returns_expected_keys(self):
import services.rag_store as rs
mock_col = MagicMock()
mock_col.count.return_value = 42000
rs._chroma_collection = mock_col
stats = rs.get_rag_stats()
for key in ["ready", "chroma_dir", "embedding_model", "ollama_url", "card_count", "built_at", "age_days"]:
assert key in stats, f"Missing key: {key}"
def test_card_count_from_collection(self):
import services.rag_store as rs
mock_col = MagicMock()
mock_col.count.return_value = 55000
rs._chroma_collection = mock_col
stats = rs.get_rag_stats()
assert stats["card_count"] == 55000
def test_ready_false_when_not_initialized(self):
from services.rag_store import get_rag_stats
stats = get_rag_stats()
assert stats["ready"] is False
# ---------------------------------------------------------------------------
# build_index (public API for POST /api/rag/build)
# ---------------------------------------------------------------------------
class TestBuildIndex:
def test_skips_rebuild_when_current(self, tmp_path):
import services.rag_store as rs
original_path = rs.BUILD_META_PATH
original_dir = rs.RAG_CHROMA_DIR
try:
rs.BUILD_META_PATH = tmp_path / "build_meta.json"
rs.RAG_CHROMA_DIR = tmp_path
rs._write_build_meta(card_count=1000)
mock_col = MagicMock()
mock_col.count.return_value = 1000
rs._chroma_collection = mock_col
with patch("services.rag_store._get_collection", return_value=mock_col), \
patch("services.scryfall_bulk.get_stats", return_value={"card_count": 1000}):
result = rs.build_index(force=False)
assert result.get("skipped") is True
finally:
rs.BUILD_META_PATH = original_path
rs.RAG_CHROMA_DIR = original_dir
def test_force_triggers_rebuild(self, tmp_path):
import services.rag_store as rs
with patch("services.rag_store._build_collection_from_bulk") as mock_build:
mock_build.return_value = {"indexed": 500, "failed": 0}
result = rs.build_index(force=True, batch_size=250)
mock_build.assert_called_once_with(batch_size=250)
assert result["indexed"] == 500
assert rs._rag_ready is True
# ---------------------------------------------------------------------------
# Failure tracking in _build_collection_from_bulk
# ---------------------------------------------------------------------------
class TestBuildCollectionFailureTracking:
def test_high_failure_rate_raises_runtime_error(self, tmp_path):
"""If >1% of batches fail, a RuntimeError should be raised."""
import services.rag_store as rs
fake_rows = [
{"name": f"Card {i}", "oracle_text": "text", "type_line": "Instant",
"mana_cost": "{1}", "cmc": 1, "color_identity": "[]",
"rarity": "common", "scryfall_id": f"id-{i}"}
for i in range(200)
]
mock_conn = MagicMock()
mock_cursor = MagicMock()
mock_cursor.fetchall.return_value = [types.SimpleNamespace(**r) for r in fake_rows]
mock_conn.execute.return_value = mock_cursor
mock_collection = MagicMock()
mock_collection.add.side_effect = Exception("Ollama timeout")
mock_client = MagicMock()
mock_client.create_collection.return_value = mock_collection
original_dir = rs.RAG_CHROMA_DIR
original_path = rs.BUILD_META_PATH
try:
rs.RAG_CHROMA_DIR = tmp_path
rs.BUILD_META_PATH = tmp_path / "build_meta.json"
with patch("services.scryfall_bulk.get_bulk_db", return_value=mock_conn), \
patch("services.rag_store._get_chromadb_client", return_value=mock_client), \
patch("services.rag_store._get_ollama_embedding_fn", return_value=MagicMock()), \
patch("services.rag_store._invalidate_collection_singleton"):
with pytest.raises(RuntimeError, match="RAG build aborted"):
rs._build_collection_from_bulk(batch_size=50)
finally:
rs.RAG_CHROMA_DIR = original_dir
rs.BUILD_META_PATH = original_path
+2 -1
View File
@@ -165,8 +165,9 @@ const AiLab = (() => {
<option value="2">2</option>
<option value="4" selected>4</option>
<option value="8">8</option>
<option value="12">12</option>
<option value="16">16</option>
<option value="24">24</option>
<option value="32">32</option>
</select>
</div>
<div class="lab-control-group">
+5 -1
View File
@@ -1132,7 +1132,11 @@
height: auto;
border-radius: 10px;
}
td[data-card-name] .coach-card-name {
td[data-card-name] .coach-card-name,
[data-card-name].coach-replacement-chip,
[data-card-name].coach-synergy-chip,
[data-card-name].coach-pill,
[data-card-name].coach-cl-name {
cursor: pointer;
border-bottom: 1px dashed var(--text-secondary, #888);
}
+32 -12
View File
@@ -23,7 +23,7 @@
<div class="coach-status-items">
<div class="coach-status-item" id="status-llm">
<span class="status-dot status-unknown"></span>
<span class="status-label">Perplexity</span>
<span class="status-label">Claude Opus</span>
<span class="status-value" id="status-llm-value">Checking...</span>
</div>
<div class="coach-status-item" id="status-embeddings">
@@ -58,7 +58,10 @@
<!-- Power Level Slider -->
<div class="form-group">
<label class="form-label" for="coach-power-level">Target Power Level</label>
<label class="form-label" for="coach-power-level">
Target Power Level
<span class="tip-icon tip-below" data-tip="Guides the LLM on how competitive suggestions should be. 1-3 = casual jank; 4-6 = upgraded precon; 7-8 = high power; 9-10 = cEDH with fast mana and infinite combos.">?</span>
</label>
<div class="coach-slider-row">
<input type="range" id="coach-power-level" min="1" max="10" value="7" />
<span class="coach-slider-value" id="coach-power-value">7</span>
@@ -68,7 +71,10 @@
<!-- Meta Focus -->
<div class="form-group">
<label class="form-label" for="coach-meta-focus">Meta Focus</label>
<label class="form-label" for="coach-meta-focus">
Meta Focus
<span class="tip-icon tip-below" data-tip="The game plan the coach optimizes for. Aggro = fast damage; Control = answers and card advantage; Combo = tutors and win-con protection; Stax = slow everyone down.">?</span>
</label>
<select id="coach-meta-focus" class="form-select">
<option value="">Any / Balanced</option>
<option value="aggro">Aggro</option>
@@ -81,7 +87,10 @@
<!-- Budget -->
<div class="form-group">
<label class="form-label" for="coach-budget">Budget</label>
<label class="form-label" for="coach-budget">
Budget
<span class="tip-icon tip-below" data-tip="Restricts suggestions to cards within a price range. Budget caps cards at ~$2 each; Medium at ~$10. No Limit allows any card including Reserved List staples.">?</span>
</label>
<select id="coach-budget" class="form-select">
<option value="">No Limit</option>
<option value="budget">Budget (&lt; $2 per card)</option>
@@ -92,7 +101,10 @@
<!-- Focus Areas -->
<div class="form-group coach-field-wide">
<label class="form-label">Focus Areas (optional)</label>
<label class="form-label">
Focus Areas (optional)
<span class="tip-icon tip-below" data-tip="Narrows the coach to specific deck categories. Leaving all unchecked triggers a full-deck general analysis across all categories.">?</span>
</label>
<div class="coach-focus-tags">
<label class="coach-tag"><input type="checkbox" value="ramp" /> Ramp</label>
<label class="coach-tag"><input type="checkbox" value="card draw" /> Card Draw</label>
@@ -108,9 +120,8 @@
<!-- Action Buttons Row -->
<div class="coach-run-row">
<button id="coach-run-btn" class="btn btn-primary btn-lg" disabled>
<span class="coach-run-icon">&#9889;</span> Run Coach
</button>
<button id="coach-quick-btn" class="btn btn-secondary" disabled>&#9889; Quick Digest</button>
<button id="coach-run-btn" class="btn btn-primary" disabled>&#128300; Full Analysis</button>
<span class="coach-run-hint" id="coach-run-hint">Select a deck to begin</span>
</div>
@@ -121,7 +132,7 @@
</div>
</section>
<!-- ═══ Multi-turn Chat Panel ═══ -->
<!-- Multi-turn Chat Panel -->
<section class="panel coach-chat-section" id="coach-chat-section">
<div class="panel-header coach-chat-header">
<h2>Chat with Coach</h2>
@@ -143,7 +154,7 @@
</div>
</section>
<!-- ═══ Cards-Like Search Panel ═══ -->
<!-- Cards-Like Search Panel -->
<section class="panel coach-cardslike-section" id="coach-cardslike-section">
<div class="panel-header coach-panel-collapsible" id="cardslike-header">
<h2>Find Similar Cards</h2>
@@ -163,7 +174,10 @@
<button class="coach-color-pip coach-pip-G" data-color="G" title="Green">G</button>
</div>
<div class="form-group coach-cardslike-topn">
<label class="form-label">Results</label>
<label class="form-label">
Results
<span class="tip-icon tip-below" data-tip="How many similar cards to return. Uses vector embeddings to find cards with the most semantically similar oracle text and mechanics to your query card.">?</span>
</label>
<input type="number" id="cardslike-topn" class="form-input" value="10" min="1" max="50" />
</div>
<button id="cardslike-search-btn" class="btn btn-accent btn-sm">Search</button>
@@ -173,7 +187,7 @@
</div>
</section>
<!-- ═══ One-Shot Results Section ═══ -->
<!-- One-Shot Results Section -->
<section id="coach-results" class="coach-results" style="display:none;">
<!-- Apply Suggestions Bar -->
@@ -191,6 +205,12 @@
</div>
</div>
<!-- Top Priority Swap (Phase 2 UI) -->
<div id="coach-top-priority" class="coach-card" style="display:none; border-left: 4px solid var(--accent);">
<h3>&#9889; Top Priority Swap</h3>
<div id="coach-top-priority-content"></div>
</div>
<!-- Summary Card -->
<div class="panel" id="coach-summary-card">
<div class="panel-header">
+134 -133
View File
@@ -1,8 +1,9 @@
/**
* Commander AI Lab — Deck Coach UI v28
* Commander AI Lab — Deck Coach UI v29
* ═════════════════════════════════════
* Multi-turn chat with SSE streaming, apply suggestions,
* cards-like search, enhanced session history.
* v29: Quick Digest button + Top Priority Swap card.
*/
(function () {
@@ -10,7 +11,7 @@
const API = window.location.origin;
// ── State ────────────────────────────────────────────────
// ── State ────────────────────────────────────────────
let chatHistory = []; // [{role, content}]
let isStreaming = false;
let currentSessionId = null;
@@ -43,7 +44,8 @@
.then(r => r.ok ? r.json() : null)
.then(data => {
if (!data) return;
const url = (data.image_uris && data.image_uris.normal) || (data.card_faces && data.card_faces[0] && data.card_faces[0].image_uris && data.card_faces[0].image_uris.normal) || '';
const url = (data.image_uris && data.image_uris.normal) ||
(data.card_faces && data.card_faces[0] && data.card_faces[0].image_uris && data.card_faces[0].image_uris.normal) || '';
_scryfallImgCache[cardName] = url;
if (el.style.display !== 'none') img.src = url;
}).catch(() => {});
@@ -63,19 +65,20 @@
}
function _attachCardHover(container) {
container.querySelectorAll('td[data-card-name]').forEach(td => {
container.querySelectorAll('[data-card-name]').forEach(td => {
td.addEventListener('mouseenter', e => _showCardPreview(td.dataset.cardName, e));
td.addEventListener('mousemove', e => _positionPreview(e));
td.addEventListener('mouseleave', _hideCardPreview);
});
}
// ── DOM References ──────────────────────────────────────
// ── DOM References ────────────────────────────────────
const deckSelect = document.getElementById('coach-deck-select');
const powerSlider = document.getElementById('coach-power-level');
const powerValue = document.getElementById('coach-power-value');
const metaFocus = document.getElementById('coach-meta-focus');
const budgetSelect = document.getElementById('coach-budget');
const quickBtn = document.getElementById('coach-quick-btn');
const runBtn = document.getElementById('coach-run-btn');
const runHint = document.getElementById('coach-run-hint');
const progressEl = document.getElementById('coach-progress');
@@ -106,6 +109,8 @@
const rawText = document.getElementById('coach-raw-text');
const sessionsList = document.getElementById('coach-sessions-list');
const genReportsBtn = document.getElementById('generate-reports-btn');
const topPriorityCard = document.getElementById('coach-top-priority');
const topPriorityContent = document.getElementById('coach-top-priority-content');
// Chat
const chatMessages = document.getElementById('coach-chat-messages');
@@ -127,7 +132,7 @@
const applyDeckSelect = document.getElementById('coach-apply-deck-select');
const applyCountEl = document.getElementById('coach-apply-count');
// ── Init ────────────────────────────────────────────────
// ── Init ──────────────────────────────────────────────
init();
@@ -138,11 +143,13 @@
deckSelect.addEventListener('change', () => {
const hasDeck = deckSelect.value !== '';
quickBtn.disabled = !hasDeck;
runBtn.disabled = !hasDeck;
chatSendBtn.disabled = !hasDeck;
runHint.textContent = hasDeck ? 'Ready to analyze' : 'Select a deck to begin';
});
quickBtn.addEventListener('click', runQuickDigest);
runBtn.addEventListener('click', runCoach);
genReportsBtn.addEventListener('click', generateReports);
@@ -198,7 +205,7 @@
]);
}
// ── Goals Helper ────────────────────────────────────────
// ── Goals Helper ────────────────────────────────────
function getGoals() {
const goals = {};
@@ -214,7 +221,7 @@
return goals;
}
// ── Status Check ────────────────────────────────────────
// ── Status Check ────────────────────────────────────
async function checkStatus() {
try {
@@ -254,18 +261,15 @@
valueEl.textContent = text;
}
// ── Load Decks ──────────────────────────────────────────
// ── Load Decks ───────────────────────────────────────
async function loadDecks() {
try {
const res = await fetch(API + '/api/coach/decks');
const data = await res.json();
// Backend returns a bare array of objects, not {decks: [...]}
const decks = Array.isArray(data) ? data : (data.decks || []);
while (deckSelect.options.length > 1) {
deckSelect.remove(1);
}
while (deckSelect.options.length > 1) deckSelect.remove(1);
if (decks.length === 0) {
const opt = document.createElement('option');
@@ -275,7 +279,6 @@
return;
}
// Sort by deck name
decks.sort((a, b) => {
const na = (typeof a === 'string' ? a : a.deck_name || '').toLowerCase();
const nb = (typeof b === 'string' ? b : b.deck_name || '').toLowerCase();
@@ -285,11 +288,9 @@
for (const deck of decks) {
const opt = document.createElement('option');
if (typeof deck === 'string') {
// Legacy: bare string ID
opt.value = deck;
opt.textContent = deck;
} else {
// Object format: {deck_id, deck_name, commander, has_report, ...}
const slug = deck.deck_name.toLowerCase().replace(/[^a-z0-9]+/g, '-').replace(/(^-|-$)/g, '');
opt.value = slug;
opt.textContent = deck.deck_name + (deck.commander ? ' (' + deck.commander + ')' : '');
@@ -310,9 +311,7 @@
const data = await res.json();
dbDecks = data.decks || [];
while (applyDeckSelect.options.length > 1) {
applyDeckSelect.remove(1);
}
while (applyDeckSelect.options.length > 1) applyDeckSelect.remove(1);
for (const deck of dbDecks) {
const opt = document.createElement('option');
@@ -346,11 +345,14 @@
const timeStr = s.timestamp ? formatTime(s.timestamp) : '';
const isExpanded = expandedSessions[s.sessionId];
const quickBadge = s.sessionId && s.sessionId.startsWith('quick-')
? '<span class="coach-badge-quick" title="Quick Digest">\u26A1 Quick</span>'
: '';
item.innerHTML =
'<div class="coach-session-header" data-sid="' + escHtml(s.sessionId) + '">' +
'<span class="coach-session-expand-arrow">' + (isExpanded ? '\u25BC' : '\u25B6') + '</span>' +
'<span class="coach-session-deck">' + escHtml(s.deckId) + '</span>' +
'<span class="coach-session-deck">' + escHtml(s.deckId) + quickBadge + '</span>' +
'<span class="coach-session-summary">' + escHtml(s.summary || '(no summary)') + '</span>' +
'<span class="coach-session-meta">' +
'<span class="coach-session-cuts">-' + (s.cutsCount || 0) + '</span>' +
@@ -362,14 +364,12 @@
'<div class="coach-session-detail-loading">Loading...</div>' +
'</div>';
// Click handler for expand/collapse
item.querySelector('.coach-session-header').addEventListener('click', function () {
toggleSession(s.sessionId);
});
sessionsList.appendChild(item);
// If previously expanded, load detail
if (isExpanded && sessionCache[s.sessionId]) {
renderSessionDetail(s.sessionId, sessionCache[s.sessionId]);
}
@@ -383,23 +383,19 @@
const detailEl = document.getElementById('session-detail-' + sessionId);
const headerEl = detailEl?.previousElementSibling;
const arrowEl = headerEl?.querySelector('.coach-session-expand-arrow');
if (!detailEl) return;
const wasHidden = detailEl.style.display === 'none';
if (wasHidden) {
detailEl.style.display = 'block';
expandedSessions[sessionId] = true;
if (arrowEl) arrowEl.textContent = '\u25BC';
if (!sessionCache[sessionId]) {
try {
const res = await fetch(API + '/api/coach/sessions/' + encodeURIComponent(sessionId));
if (!res.ok) throw new Error('Session not found');
const session = await res.json();
sessionCache[sessionId] = session;
renderSessionDetail(sessionId, session);
sessionCache[sessionId] = await res.json();
renderSessionDetail(sessionId, sessionCache[sessionId]);
} catch (e) {
detailEl.innerHTML = '<div class="coach-session-detail-error">Failed to load session details</div>';
}
@@ -421,44 +417,30 @@
const adds = session.suggestedAdds || [];
let html = '<div class="coach-session-detail-inner">';
// Summary
if (session.summary) {
html += '<p class="coach-session-detail-summary">' + escHtml(session.summary) + '</p>';
}
// Cuts
if (cuts.length > 0) {
html += '<div class="coach-session-detail-group">';
html += '<span class="coach-session-detail-label">Cuts:</span>';
html += '<div class="coach-session-pills">';
html += '<div class="coach-session-detail-group"><span class="coach-session-detail-label">Cuts:</span><div class="coach-session-pills">';
for (const cut of cuts) {
html += '<span class="coach-pill coach-pill-cut" title="' + escHtml(cut.reason || '') + '">' + escHtml(cut.cardName) + '</span>';
html += '<span class="coach-pill coach-pill-cut" data-card-name="' + escHtml(cut.cardName) + '" title="' + escHtml(cut.reason || '') + '">' + escHtml(cut.cardName) + '</span>';
}
html += '</div></div>';
}
// Adds
if (adds.length > 0) {
html += '<div class="coach-session-detail-group">';
html += '<span class="coach-session-detail-label">Adds:</span>';
html += '<div class="coach-session-pills">';
html += '<div class="coach-session-detail-group"><span class="coach-session-detail-label">Adds:</span><div class="coach-session-pills">';
for (const add of adds) {
html += '<span class="coach-pill coach-pill-add" title="' + escHtml(add.reason || '') + '">' + escHtml(add.cardName) + '</span>';
html += '<span class="coach-pill coach-pill-add" data-card-name="' + escHtml(add.cardName) + '" title="' + escHtml(add.reason || '') + '">' + escHtml(add.cardName) + '</span>';
}
html += '</div></div>';
}
// Load in Chat button
html += '<div class="coach-session-detail-actions">';
html += '<button class="btn btn-ghost btn-sm coach-load-chat-btn" data-sid="' + escHtml(sessionId) + '">Load in Chat</button>';
html += '<button class="btn btn-ghost btn-sm coach-view-full-btn" data-sid="' + escHtml(sessionId) + '">View Full Results</button>';
html += '</div>';
html += '</div>';
html += '</div></div>';
detailEl.innerHTML = html;
_attachCardHover(detailEl);
// Bind buttons
detailEl.querySelector('.coach-load-chat-btn')?.addEventListener('click', (e) => {
e.stopPropagation();
loadSessionInChat(sessionId);
@@ -473,10 +455,7 @@
function loadSessionInChat(sessionId) {
const session = sessionCache[sessionId];
if (!session) return;
clearChat();
// Set deck selector if possible
if (session.deckId) {
for (let i = 0; i < deckSelect.options.length; i++) {
if (deckSelect.options[i].value === session.deckId) {
@@ -486,30 +465,24 @@
}
}
}
// Pre-fill chat with a summary context
const contextMsg = 'Based on a previous coaching session for this deck, here was the analysis:\n\n' +
(session.summary || '') + '\n\n' +
'Suggested cuts: ' + (session.suggestedCuts || []).map(c => c.cardName).join(', ') + '\n' +
'Suggested adds: ' + (session.suggestedAdds || []).map(a => a.cardName).join(', ');
chatHistory.push({ role: 'assistant', content: contextMsg });
appendChatBubble('assistant', contextMsg);
chatInput.focus();
showToast('Session loaded into chat', 'success');
}
// ── Generate Reports ────────────────────────────────────
// ── Generate Reports ──────────────────────────────────
async function generateReports() {
genReportsBtn.disabled = true;
genReportsBtn.textContent = 'Generating...';
try {
const res = await fetch(API + '/api/coach/reports/generate', { method: 'POST' });
const data = await res.json();
if (data.count > 0) {
genReportsBtn.textContent = data.count + ' reports generated';
await checkStatus();
@@ -527,14 +500,57 @@
}
}
// ── Run Coach (one-shot) ────────────────────────────────
// ── Quick Digest ───────────────────────────────────────
async function runQuickDigest() {
const deckId = deckSelect.value;
if (!deckId) return;
const goals = getGoals();
quickBtn.disabled = true;
runBtn.disabled = true;
progressEl.style.display = 'flex';
progressText.textContent = '\u26A1 Quick Digest running (~5s)...';
resultsEl.style.display = 'none';
const prevError = document.querySelector('.coach-error');
if (prevError) prevError.remove();
try {
const res = await fetch(API + '/api/coach/decks/' + encodeURIComponent(deckId) + '/quick', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ goals }),
});
if (!res.ok) {
const err = await res.json().catch(() => ({ detail: 'Unknown error' }));
throw new Error(err.detail || 'Quick digest failed (' + res.status + ')');
}
const session = await res.json();
currentSessionId = session.sessionId || null;
displaySession(session);
loadSessions();
showToast('Quick Digest complete!', 'success');
} catch (e) {
const errDiv = document.createElement('div');
errDiv.className = 'coach-error';
errDiv.textContent = 'Quick digest error: ' + e.message;
document.querySelector('.coach-config').appendChild(errDiv);
} finally {
quickBtn.disabled = false;
runBtn.disabled = false;
progressEl.style.display = 'none';
}
}
// ── Full Analysis ───────────────────────────────────────
async function runCoach() {
const deckId = deckSelect.value;
if (!deckId) return;
const goals = getGoals();
quickBtn.disabled = true;
runBtn.disabled = true;
progressEl.style.display = 'flex';
progressText.textContent = 'Analyzing deck and consulting LLM... This may take 30-60 seconds.';
@@ -547,14 +563,12 @@
const res = await fetch(API + '/api/coach/decks/' + encodeURIComponent(deckId), {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ goals: goals }),
body: JSON.stringify({ goals }),
});
if (!res.ok) {
const err = await res.json().catch(() => ({ detail: 'Unknown error' }));
throw new Error(err.detail || 'Coach request failed (' + res.status + ')');
}
const session = await res.json();
currentSessionId = session.sessionId || null;
displaySession(session);
@@ -565,11 +579,45 @@
errDiv.textContent = 'Coach error: ' + e.message;
document.querySelector('.coach-config').appendChild(errDiv);
} finally {
quickBtn.disabled = false;
runBtn.disabled = false;
progressEl.style.display = 'none';
}
}
// ── Top Priority Swap ──────────────────────────────────
function displayTopPriority(upgradePriority) {
if (!topPriorityCard || !topPriorityContent) return;
if (!upgradePriority || upgradePriority.length === 0) {
topPriorityCard.style.display = 'none';
return;
}
// Show rank-1 item (or first if rank field missing)
const top = upgradePriority.find(u => u.rank === 1) || upgradePriority[0];
const impactClass = {
high: 'coach-impact-negative',
medium: 'coach-impact-neutral',
low: 'coach-impact-positive'
}[top.expectedImpact] || 'coach-impact-neutral';
topPriorityContent.innerHTML =
'<div class="coach-priority-swap">' +
'<div class="coach-priority-row">' +
'<span class="coach-priority-label">Cut</span>' +
'<span class="coach-priority-card coach-pill-cut" data-card-name="' + escHtml(top.cut) + '">' + escHtml(top.cut) + '</span>' +
'<span class="coach-priority-arrow">\u2192</span>' +
'<span class="coach-priority-label">Add</span>' +
'<span class="coach-priority-card coach-pill-add" data-card-name="' + escHtml(top.add) + '">' + escHtml(top.add) + '</span>' +
'<span class="coach-impact ' + impactClass + '">' + escHtml(top.expectedImpact || '') + '</span>' +
'</div>' +
(top.reasoning ? '<p class="coach-priority-reasoning">' + escHtml(top.reasoning) + '</p>' : '') +
'</div>';
_attachCardHover(topPriorityContent);
topPriorityCard.style.display = 'block';
}
// ── Display Session Results ──────────────────────────────
function displaySession(session) {
@@ -584,13 +632,16 @@
if (session.completionTokens) addMetaChip('Completion', session.completionTokens.toLocaleString() + ' tokens');
if (session.deckId) addMetaChip('Deck', session.deckId);
if (session.timestamp) addMetaChip('Time', formatTime(session.timestamp));
if (session.isQuickDigest) addMetaChip('Mode', '\u26A1 Quick Digest');
// Top Priority Swap
displayTopPriority(session.upgradePriority || []);
// Cuts table
const cuts = session.suggestedCuts || [];
cutsCount.textContent = cuts.length;
cutsBody.innerHTML = '';
cutsAllCheck.checked = false;
for (const cut of cuts) {
const tr = document.createElement('tr');
tr.innerHTML =
@@ -599,7 +650,7 @@
'<td>' + escHtml(cut.reason) + '</td>' +
'<td><div class="coach-replacements">' +
(cut.replacementOptions || []).map(r =>
'<span class="coach-replacement-chip">' + escHtml(r) + '</span>'
'<span class="coach-replacement-chip" data-card-name="' + escHtml(r) + '">' + escHtml(r) + '</span>'
).join('') +
'</div></td>' +
'<td>' + formatImpact(cut.currentImpactScore) + '</td>';
@@ -612,7 +663,6 @@
addsCount.textContent = adds.length;
addsBody.innerHTML = '';
addsAllCheck.checked = false;
for (const add of adds) {
const tr = document.createElement('tr');
tr.innerHTML =
@@ -622,7 +672,7 @@
'<td>' + escHtml(add.reason) + '</td>' +
'<td><div class="coach-replacements">' +
(add.synergyWith || []).map(s =>
'<span class="coach-synergy-chip">' + escHtml(s) + '</span>'
'<span class="coach-synergy-chip" data-card-name="' + escHtml(s) + '">' + escHtml(s) + '</span>'
).join('') +
'</div></td>';
tr.querySelector('.coach-card-name').parentElement.dataset.cardName = add.cardName;
@@ -651,10 +701,8 @@
manaCard.style.display = 'none';
}
// Raw text
rawText.textContent = session.rawTextExplanation || session.summary || '(empty)';
_attachCardHover(cutsBody);
_attachCardHover(addsBody);
updateApplyCount();
@@ -665,31 +713,23 @@
function updateApplyCount() {
const cutChecked = cutsBody.querySelectorAll('input[type="checkbox"]:checked');
const addChecked = addsBody.querySelectorAll('input[type="checkbox"]:checked');
const total = cutChecked.length + addChecked.length;
applyCountEl.textContent = cutChecked.length + ' cuts, ' + addChecked.length + ' adds selected';
applyBtn.disabled = total === 0 || !applyDeckSelect.value;
applyBtn.disabled = (cutChecked.length + addChecked.length) === 0 || !applyDeckSelect.value;
}
applyDeckSelect.addEventListener('change', updateApplyCount);
async function applySuggestions() {
const deckId = parseInt(applyDeckSelect.value);
if (!deckId) {
showToast('Select a deck to apply suggestions to', 'warning');
return;
}
if (!deckId) { showToast('Select a deck to apply suggestions to', 'warning'); return; }
const acceptedCuts = [];
cutsBody.querySelectorAll('input[type="checkbox"]:checked').forEach(cb => {
const name = cb.dataset.card;
if (name) acceptedCuts.push(name);
if (cb.dataset.card) acceptedCuts.push(cb.dataset.card);
});
const acceptedAdds = [];
addsBody.querySelectorAll('input[type="checkbox"]:checked').forEach(cb => {
const name = cb.dataset.card;
if (name) acceptedAdds.push(name);
if (cb.dataset.card) acceptedAdds.push(cb.dataset.card);
});
if (acceptedCuts.length === 0 && acceptedAdds.length === 0) {
@@ -711,17 +751,14 @@
accepted_adds: acceptedAdds,
}),
});
if (!res.ok) {
const err = await res.json().catch(() => ({ detail: 'Apply failed' }));
throw new Error(err.detail || 'Apply failed');
}
const result = await res.json();
const msg = 'Applied: ' + result.total_cuts + ' cuts, ' + result.total_adds + ' adds' +
(result.errors.length > 0 ? ' (' + result.errors.length + ' errors)' : '');
showToast(msg, result.errors.length > 0 ? 'warning' : 'success');
} catch (e) {
showToast('Apply error: ' + e.message, 'error');
} finally {
@@ -731,19 +768,17 @@
}
}
// ── Multi-turn Chat ─────────────────────────────────────
// ── Multi-turn Chat ────────────────────────────────────
async function sendChatMessage() {
const text = chatInput.value.trim();
if (!text || !deckSelect.value || isStreaming) return;
// Add user message
chatHistory.push({ role: 'user', content: text });
appendChatBubble('user', text);
chatInput.value = '';
chatSendBtn.disabled = true;
// Show typing indicator
const typingEl = showTypingIndicator();
isStreaming = true;
@@ -758,20 +793,16 @@
stream: true,
}),
});
if (!res.ok) {
const err = await res.json().catch(() => ({ detail: 'Chat failed' }));
throw new Error(err.detail || 'Chat failed (' + res.status + ')');
}
// Remove typing indicator, add assistant bubble
typingEl.remove();
const bubbleEl = appendChatBubble('assistant', '');
const contentEl = bubbleEl.querySelector('.coach-chat-bubble-content');
let fullContent = '';
// Read SSE stream
const reader = res.body.getReader();
const decoder = new TextDecoder();
let buffer = '';
@@ -779,18 +810,14 @@
while (true) {
const { done, value } = await reader.read();
if (done) break;
buffer += decoder.decode(value, { stream: true });
const lines = buffer.split('\n');
buffer = lines.pop() || '';
for (const line of lines) {
const trimmed = line.trim();
if (!trimmed.startsWith('data: ')) continue;
const payload = trimmed.slice(6);
if (payload === '[DONE]') break;
try {
const chunk = JSON.parse(payload);
if (chunk.content) {
@@ -798,17 +825,13 @@
contentEl.innerHTML = formatMarkdown(fullContent);
scrollChatToBottom();
}
} catch (e) {
// skip malformed chunks
}
} catch (_) {}
}
}
// Finalize
chatHistory.push({ role: 'assistant', content: fullContent });
contentEl.innerHTML = formatMarkdown(fullContent);
scrollChatToBottom();
} catch (e) {
typingEl.remove();
appendChatBubble('system', 'Error: ' + e.message);
@@ -819,18 +842,14 @@
}
function appendChatBubble(role, content) {
// Remove welcome message
const welcome = chatMessages.querySelector('.coach-chat-welcome');
if (welcome) welcome.remove();
const bubble = document.createElement('div');
bubble.className = 'coach-chat-bubble coach-chat-' + role;
const label = role === 'user' ? 'You' : role === 'assistant' ? 'Coach' : 'System';
bubble.innerHTML =
'<div class="coach-chat-bubble-label">' + label + '</div>' +
'<div class="coach-chat-bubble-content">' + (content ? formatMarkdown(content) : '') + '</div>';
chatMessages.appendChild(bubble);
scrollChatToBottom();
return bubble;
@@ -847,9 +866,7 @@
return el;
}
function scrollChatToBottom() {
chatMessages.scrollTop = chatMessages.scrollHeight;
}
function scrollChatToBottom() { chatMessages.scrollTop = chatMessages.scrollHeight; }
function clearChat() {
chatHistory = [];
@@ -862,7 +879,6 @@
function formatMarkdown(text) {
if (!text) return '';
// Basic markdown: bold, italic, code, line breaks
return escHtml(text)
.replace(/\*\*(.+?)\*\*/g, '<strong>$1</strong>')
.replace(/\*(.+?)\*/g, '<em>$1</em>')
@@ -870,7 +886,7 @@
.replace(/\n/g, '<br>');
}
// ── Cards-Like Search ───────────────────────────────────
// ── Cards-Like Search ──────────────────────────────────
async function searchCardsLike() {
const card = cardslikeInput.value.trim();
@@ -880,16 +896,13 @@
document.querySelectorAll('.coach-cardslike-colors .coach-color-pip.active').forEach(btn => {
activeColors.push(btn.dataset.color);
});
const topN = parseInt(cardslikeTopn.value) || 10;
cardslikeResults.innerHTML = '<div class="coach-cardslike-loading"><div class="spinner"></div> Searching embeddings...</div>';
try {
let url = API + '/api/coach/cards-like?card=' + encodeURIComponent(card) + '&top_n=' + topN;
if (activeColors.length > 0) {
url += '&colors=' + activeColors.join('');
}
if (activeColors.length > 0) url += '&colors=' + activeColors.join('');
const res = await fetch(url);
if (!res.ok) {
@@ -909,16 +922,14 @@
for (const card of results) {
const div = document.createElement('div');
div.className = 'coach-cardslike-card';
const simPct = Math.round(card.similarity * 100);
const ownedBadge = card.owned_qty > 0
? '<span class="coach-cl-owned">' + card.owned_qty + ' owned</span>'
: '<span class="coach-cl-unowned">not owned</span>';
const price = card.tcg_price ? '$' + Number(card.tcg_price).toFixed(2) : '';
div.innerHTML =
'<div class="coach-cl-top">' +
'<span class="coach-cl-name">' + escHtml(card.name) + '</span>' +
'<span class="coach-cl-name" data-card-name="' + escHtml(card.name) + '">' + escHtml(card.name) + '</span>' +
'<span class="coach-cl-sim">' + simPct + '%</span>' +
'</div>' +
'<div class="coach-cl-meta">' +
@@ -931,26 +942,24 @@
'<div class="coach-cl-actions">' +
'<button class="btn btn-ghost btn-xs coach-cl-chat-btn">Add to Chat</button>' +
'</div>';
div.querySelector('.coach-cl-chat-btn').addEventListener('click', () => {
chatInput.value += (chatInput.value ? ' ' : '') + card.name;
chatInput.focus();
showToast(card.name + ' added to chat input', 'success');
});
cardslikeResults.appendChild(div);
_attachCardHover(div);
}
} catch (e) {
cardslikeResults.innerHTML = '<div class="coach-cardslike-empty">Error: ' + escHtml(e.message) + '</div>';
}
}
// Color pip toggle
document.querySelectorAll('.coach-cardslike-colors .coach-color-pip').forEach(btn => {
btn.addEventListener('click', () => btn.classList.toggle('active'));
});
// ── Toast ───────────────────────────────────────────────
// ── Toast ─────────────────────────────────────────────
function showToast(message, type) {
const container = document.getElementById('coach-toast-container');
@@ -958,7 +967,6 @@
toast.className = 'coach-toast coach-toast-' + (type || 'info');
toast.textContent = message;
container.appendChild(toast);
setTimeout(() => toast.classList.add('coach-toast-show'), 10);
setTimeout(() => {
toast.classList.remove('coach-toast-show');
@@ -966,7 +974,7 @@
}, 4000);
}
// ── Helpers ─────────────────────────────────────────────
// ── Helpers ───────────────────────────────────────────
function addMetaChip(label, value) {
const chip = document.createElement('span');
@@ -976,26 +984,19 @@
}
function formatImpact(score) {
if (score === undefined || score === null) {
if (score === undefined || score === null)
return '<span class="coach-impact coach-impact-neutral">\u2014</span>';
}
const fixed = score.toFixed(3);
if (score < -0.01) {
return '<span class="coach-impact coach-impact-negative">' + fixed + '</span>';
} else if (score > 0.01) {
return '<span class="coach-impact coach-impact-positive">+' + fixed + '</span>';
} else {
if (score < -0.01) return '<span class="coach-impact coach-impact-negative">' + fixed + '</span>';
if (score > 0.01) return '<span class="coach-impact coach-impact-positive">+' + fixed + '</span>';
return '<span class="coach-impact coach-impact-neutral">' + fixed + '</span>';
}
}
function formatTime(isoStr) {
try {
const d = new Date(isoStr);
return d.toLocaleDateString() + ' ' + d.toLocaleTimeString([], { hour: '2-digit', minute: '2-digit' });
} catch {
return isoStr;
}
} catch { return isoStr; }
}
function escHtml(str) {
+85
View File
@@ -3147,3 +3147,88 @@
.coll-col-reset:hover {
background: rgba(99,102,241,0.25);
}
/* ── Bulk Action Bar ──────────────────────────────────────────── */
.coll-bulk-bar {
position: sticky;
bottom: 0;
display: flex;
align-items: center;
gap: 8px;
padding: 8px 16px;
background: var(--bg-card, #1e1e1e);
border-top: 1px solid var(--border, #30363d);
border-radius: 0 0 8px 8px;
z-index: 50;
flex-wrap: wrap;
box-shadow: 0 -2px 8px rgba(0,0,0,0.3);
}
.coll-bulk-count {
font-size: 0.85rem;
font-weight: 600;
color: var(--accent, #6366f1);
min-width: 80px;
}
.coll-bulk-sep {
color: var(--text-secondary, #8b949e);
padding: 0 2px;
}
.coll-bulk-label {
font-size: 0.82rem;
color: var(--text-secondary, #8b949e);
}
.coll-bulk-btn {
background: rgba(99,102,241,0.15);
border: 1px solid rgba(99,102,241,0.35);
color: var(--text, #e6edf3);
border-radius: 5px;
padding: 4px 10px;
font-size: 0.82rem;
cursor: pointer;
transition: background 0.15s;
}
.coll-bulk-btn:hover:not(:disabled) {
background: rgba(99,102,241,0.3);
}
.coll-bulk-btn:disabled {
opacity: 0.5;
cursor: not-allowed;
}
.coll-bulk-set-btn {
background: rgba(99,102,241,0.25);
font-weight: 600;
}
.coll-bulk-qty-input {
width: 56px;
background: var(--bg, #0d1117);
border: 1px solid var(--border, #30363d);
color: var(--text, #e6edf3);
border-radius: 5px;
padding: 3px 6px;
font-size: 0.82rem;
text-align: center;
}
/* ── Select Checkbox Column ─────────────────────────────────── */
.coll-th-select {
width: 32px;
min-width: 32px;
text-align: center;
padding: 4px;
}
.coll-td-select {
width: 32px;
min-width: 32px;
text-align: center;
padding: 4px;
}
.coll-row-check {
cursor: pointer;
width: 14px;
height: 14px;
accent-color: var(--accent, #6366f1);
}
.coll-row.selected-row {
background: rgba(99,102,241,0.08);
}
+114 -1
View File
@@ -164,6 +164,7 @@ const Collection = (() => {
importHeaders: [],
importMissingFinishNormal: true,
exportOpen: false,
selectedRows: new Set(),
preferences: {
pageSize: 50,
sortField: 'name',
@@ -662,6 +663,18 @@ const Collection = (() => {
</table>
</div>
<div class="coll-pagination" id="coll-pagination"></div>
<!-- Bulk Action Bar -->
<div id="coll-bulk-bar" class="coll-bulk-bar" style="display:none">
<span id="coll-bulk-count" class="coll-bulk-count">0 selected</span>
<button class="coll-bulk-btn" onclick="Collection.selectAllRows()">Select All</button>
<button class="coll-bulk-btn" onclick="Collection.clearSelection()">Clear</button>
<span class="coll-bulk-sep">|</span>
<span class="coll-bulk-label">Qty:</span>
<button class="coll-bulk-btn" onclick="Collection.bulkAdjustQuantity(-1)" title="Subtract 1">−</button>
<button class="coll-bulk-btn" onclick="Collection.bulkAdjustQuantity(1)" title="Add 1">+</button>
<input type="number" id="coll-bulk-qty-input" class="coll-bulk-qty-input" min="0" value="1" placeholder="n">
<button class="coll-bulk-btn coll-bulk-set-btn" onclick="Collection.bulkSetQuantity()">Set</button>
</div>
</div>
</div>
@@ -1077,7 +1090,7 @@ const Collection = (() => {
return `<th class="coll-th coll-th-${col.key} coll-th-sortable${activeClass}"
onclick="Collection.setSort('${col.key}')">${col.label}${arrow}</th>`;
}).join('');
return `<thead><tr>${headers}</tr></thead>`;
return `<thead><tr>$<th class="coll-th-select"><input type="checkbox" id="coll-select-all" onclick="Collection.toggleSelectAll(this.checked)" title="Select all"></th>{headers}</tr></thead>`;
}
// ── Table Body ──────────────────────────────────────────
@@ -1127,6 +1140,7 @@ const Collection = (() => {
const edhrecRank = (card.edhrecRank || card.edhrec_rank) ? `#${card.edhrecRank || card.edhrec_rank}` : '—';
return `<tr class="coll-row" onclick="Collection.openDrawer(${card.id})" data-id="${card.id}" data-img-url="${card.imageUrl || ''}">
<td class="coll-td coll-td-select" onclick="event.stopPropagation()"><input type="checkbox" class="coll-row-check" data-id="${card.id}" ${state.selectedRows.has(card.id) ? 'checked' : ''} onchange="Collection.toggleRowSelect(${card.id}, this.checked)"></td>
<td class="coll-td coll-td-quantity">${escapeHtml(card.quantity ?? 1)}</td>
<td class="coll-td coll-td-name">
<span class="coll-card-name">${escapeHtml(card.name)}</span>
@@ -2477,6 +2491,98 @@ const Collection = (() => {
window.location.href = '/index.html';
}
// ── Bulk Row Selection & Quantity Edit ─────────────────────
function toggleRowSelect(cardId, checked) {
if (checked) {
state.selectedRows.add(cardId);
} else {
state.selectedRows.delete(cardId);
}
updateBulkBar();
}
function toggleSelectAll(checked) {
if (checked) {
state.items.forEach(c => state.selectedRows.add(c.id));
} else {
state.selectedRows.clear();
}
// Update all checkboxes
document.querySelectorAll('.coll-row-check').forEach(cb => {
cb.checked = checked;
});
updateBulkBar();
}
function selectAllRows() {
state.items.forEach(c => state.selectedRows.add(c.id));
document.querySelectorAll('.coll-row-check').forEach(cb => cb.checked = true);
const selectAllCb = document.getElementById('coll-select-all');
if (selectAllCb) selectAllCb.checked = true;
updateBulkBar();
}
function clearSelection() {
state.selectedRows.clear();
document.querySelectorAll('.coll-row-check').forEach(cb => cb.checked = false);
const selectAllCb = document.getElementById('coll-select-all');
if (selectAllCb) selectAllCb.checked = false;
updateBulkBar();
}
function updateBulkBar() {
const bar = document.getElementById('coll-bulk-bar');
const countEl = document.getElementById('coll-bulk-count');
const n = state.selectedRows.size;
if (bar) bar.style.display = n > 0 ? 'flex' : 'none';
if (countEl) countEl.textContent = `${n} selected`;
}
async function bulkAdjustQuantity(delta) {
if (state.selectedRows.size === 0) return;
const ids = [...state.selectedRows];
const btn = event.target;
if (btn) btn.disabled = true;
let successCount = 0;
for (const id of ids) {
const card = state.items.find(c => c.id === id);
if (!card) continue;
const newQty = Math.max(0, (card.quantity ?? 1) + delta);
try {
await patchCard(id, { quantity: newQty });
card.quantity = newQty;
successCount++;
} catch (e) {
console.warn('[Bulk] Failed to update card', id, e);
}
}
if (btn) btn.disabled = false;
renderTable();
applyColumnVisibility();
}
async function bulkSetQuantity() {
if (state.selectedRows.size === 0) return;
const input = document.getElementById('coll-bulk-qty-input');
const newQty = input ? Math.max(0, parseInt(input.value, 10) || 0) : 0;
const ids = [...state.selectedRows];
const btn = document.querySelector('.coll-bulk-set-btn');
if (btn) btn.disabled = true;
for (const id of ids) {
const card = state.items.find(c => c.id === id);
if (!card) continue;
try {
await patchCard(id, { quantity: newQty });
card.quantity = newQty;
} catch (e) {
console.warn('[Bulk] Failed to set card', id, e);
}
}
if (btn) btn.disabled = false;
renderTable();
applyColumnVisibility();
}
// ── Event Binding ────────────────────────────────────────
function bindEvents() {
@@ -3081,5 +3187,12 @@ const Collection = (() => {
openColumnPanel,
closeColumnPanel,
resetColumns,
// Bulk selection & quantity
toggleRowSelect,
toggleSelectAll,
selectAllRows,
clearSelection,
bulkAdjustQuantity,
bulkSetQuantity,
};
})();
+2 -2
View File
@@ -284,7 +284,7 @@
</div>
<div class="deck-ai-loading" id="ai-research-loading" style="display:none">
<div class="deck-ai-spinner"></div>
<span>Analyzing with Perplexity AI...</span>
<span>Analyzing with Claude Opus...</span>
</div>
<div class="deck-ai-results" id="ai-research-results" style="display:none"></div>
</div>
@@ -329,7 +329,7 @@
</div>
<div class="deck-ai-loading" id="ai-gen-loading" style="display:none">
<div class="deck-ai-spinner"></div>
<span>Generating deck with Perplexity AI...</span>
<span>Generating deck with Claude Opus...</span>
</div>
<div class="deck-ai-results" id="ai-gen-results" style="display:none"></div>
</div>
+23 -39
View File
@@ -62,9 +62,13 @@
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</label>
<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>
@@ -105,7 +109,7 @@
min="0"
step="25"
/>
<select class="dg-select" id="dg-budget-mode">
<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>
@@ -120,17 +124,26 @@
</div>
<div class="dg-panel-body">
<label class="dg-toggle-row">
<span class="dg-toggle-label">Use my collection</span>
<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>
<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</label>
<label class="dg-label">
AI Model
<span class="tip-icon" data-tip="The local LLM used to generate the deck list. Larger models produce better card selections but are slower. Requires Ollama running locally with the selected model pulled.">?</span>
</label>
<select class="dg-select" id="dg-model">
<option value="gpt-oss:20b" selected>gpt-oss:20b (Local Ollama)</option>
</select>
@@ -160,7 +173,7 @@
<!-- 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
<span class="dg-btn-icon">&#9889;</span> Generate Deck
</button>
</div>
@@ -175,9 +188,9 @@
<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-icon">&#9889;</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 Perplexity AI with structured output.</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 -->
@@ -195,37 +208,8 @@
</div>
</div>
<!-- Results -->
<div class="dg-results" id="dg-results" style="display:none">
<!-- Strategy & Bracket Summary -->
<div class="dg-strategy-bar" id="dg-strategy-bar"></div>
<!-- Stats Bar -->
<div class="dg-stats-bar" id="dg-stats-bar"></div>
<!-- Substitution Summary -->
<div class="dg-sub-summary" id="dg-sub-summary" style="display:none"></div>
<!-- Card Groups -->
<div class="dg-card-groups" id="dg-card-groups"></div>
<!-- Export & Save Bar -->
<div class="dg-save-bar" id="dg-save-bar">
<button class="dg-btn dg-btn-save" id="dg-save-btn">
<span class="dg-btn-icon">💾</span> Save to Deck Builder
</button>
<button class="dg-btn dg-btn-ghost" id="dg-regenerate-btn">
<span class="dg-btn-icon">🔄</span> Regenerate
</button>
<div class="dg-export-group">
<button class="dg-btn dg-btn-export" id="dg-export-csv">📊 CSV</button>
<button class="dg-btn dg-btn-export" id="dg-export-dck">🎮 Forge .dck</button>
<button class="dg-btn dg-btn-export" id="dg-export-moxfield">📋 Moxfield</button>
<button class="dg-btn dg-btn-export" id="dg-export-shopping">🛒 Shopping List</button>
</div>
</div>
</div>
<!-- Results placeholder — rest generated dynamically by deckgenerator.js -->
<div class="dg-results" id="dg-results" style="display:none"></div>
</section>
</main>
+44 -12
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@@ -16,7 +16,7 @@
<div class="page-header" style="display:flex;align-items:center;justify-content:space-between;">
<div>
<h1>Closed-Loop Distillation</h1>
<p>Iterative supervised → PPO → quality gate → merge pipeline</p>
<p>Iterative supervised &rarr; PPO &rarr; quality gate &rarr; merge pipeline</p>
</div>
<button id="btn-refresh" class="btn btn-secondary" onclick="refreshAll()" title="Refresh status">
<span class="btn-icon" id="refresh-icon">&#8635;</span> Refresh
@@ -59,43 +59,70 @@
<div class="train-controls">
<div class="param-grid form-row">
<div class="form-group">
<label class="form-label" for="param-iterations">Max Generations</label>
<label class="form-label" for="param-iterations">
Max Generations
<span class="tip-icon" data-tip="How many distillation cycles to run. Each cycle: supervised train &rarr; PPO fine-tune &rarr; quality gate. Stops early if convergence is detected.">?</span>
</label>
<input type="number" id="param-iterations" class="form-input" value="10" min="1" max="100" />
</div>
<div class="form-group">
<label class="form-label" for="param-conv-window">Convergence Window</label>
<label class="form-label" for="param-conv-window">
Convergence Window
<span class="tip-icon" data-tip="Number of recent generations to check for plateau. If ELO gain stays below threshold across this many gens, training stops early to save time.">?</span>
</label>
<input type="number" id="param-conv-window" class="form-input" value="3" min="2" max="10" />
</div>
<div class="form-group">
<label class="form-label" for="param-sup-epochs">Supervised Epochs</label>
<label class="form-label" for="param-sup-epochs">
Supervised Epochs
<span class="tip-icon" data-tip="Training epochs for the supervised step each generation. Fewer is fine since each cycle trains on incrementally better data. 30 is a solid starting point.">?</span>
</label>
<input type="number" id="param-sup-epochs" class="form-input" value="30" min="5" max="200" />
</div>
<div class="form-group">
<label class="form-label" for="param-sup-lr">Supervised LR</label>
<label class="form-label" for="param-sup-lr">
Supervised LR
<span class="tip-icon" data-tip="Learning rate for the supervised phase. 0.001 is a solid default. Lower it in later generations if the model is already strong.">?</span>
</label>
<input type="number" id="param-sup-lr" class="form-input" value="0.001" step="0.0001" min="0.00001" max="0.1" />
</div>
</div>
<div class="param-grid form-row">
<div class="form-group">
<label class="form-label" for="param-ppo-iters">PPO Iterations</label>
<label class="form-label" for="param-ppo-iters">
PPO Iterations
<span class="tip-icon" data-tip="Number of PPO update steps per generation. Each step samples game episodes, computes advantages, and updates policy weights.">?</span>
</label>
<input type="number" id="param-ppo-iters" class="form-input" value="50" min="10" max="500" />
</div>
<div class="form-group">
<label class="form-label" for="param-ppo-episodes">PPO Episodes/Iter</label>
<label class="form-label" for="param-ppo-episodes">
PPO Episodes/Iter
<span class="tip-icon" data-tip="Game simulations per PPO update step. More episodes = more stable gradients but slower iterations. 64 balances speed and stability well.">?</span>
</label>
<input type="number" id="param-ppo-episodes" class="form-input" value="64" min="8" max="512" step="8" />
</div>
<div class="form-group">
<label class="form-label" for="param-ppo-lr">PPO Learning Rate</label>
<label class="form-label" for="param-ppo-lr">
PPO Learning Rate
<span class="tip-icon" data-tip="PPO step size. Keep this lower than the supervised LR. 0.0003 is the standard RL default and works well here.">?</span>
</label>
<input type="number" id="param-ppo-lr" class="form-input" value="0.0003" step="0.0001" min="0.00001" max="0.01" />
</div>
<div class="form-group">
<label class="form-label" for="param-min-wr">Min Win Rate Gate</label>
<label class="form-label" for="param-min-wr">
Min Win Rate Gate
<span class="tip-icon" data-tip="Quality gate threshold. A generation must beat the heuristic opponent at this win rate to be accepted and merged into the next cycle. 0.30 is a lenient starting gate.">?</span>
</label>
<input type="number" id="param-min-wr" class="form-input" value="0.30" step="0.05" min="0.1" max="0.9" />
</div>
</div>
<div class="param-grid form-row distill-bottom-params">
<div class="form-group">
<label class="form-label" for="param-opponent">Opponent</label>
<label class="form-label" for="param-opponent">
Opponent
<span class="tip-icon" data-tip="Who the agent trains against during PPO. Heuristic is recommended for a consistent challenge. Self-play can become unstable without a strong supervised baseline first.">?</span>
</label>
<select id="param-opponent" class="form-select">
<option value="heuristic">Heuristic</option>
<option value="random">Random</option>
@@ -103,7 +130,10 @@
</select>
</div>
<div class="form-group">
<label class="form-label" for="param-playstyle">Playstyle</label>
<label class="form-label" for="param-playstyle">
Playstyle
<span class="tip-icon" data-tip="Deck archetype used for simulations in this distillation run. Midrange is the most balanced test bed. Match this to the archetype you want the model to master.">?</span>
</label>
<select id="param-playstyle" class="form-select">
<option value="midrange">Midrange</option>
<option value="aggro">Aggro</option>
@@ -164,7 +194,9 @@
<!-- ELO Rating Chart -->
<section class="panel" id="elo-panel">
<div class="panel-header elo-panel-header">
<h2>ELO Ratings</h2>
<h2>ELO Ratings
<span class="tip-icon tip-below" data-tip="ELO scores measure relative strength. Baseline is 1200. Scores above that mean the model wins more than it loses. Tournament runs the top 10 most recent checkpoints head-to-head.">?</span>
</h2>
<button id="btn-run-elo" class="btn btn-sm btn-secondary" onclick="runEloTournament()">
<span class="btn-icon">&#127942;</span> Run Tournament
</button>
+89
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@@ -864,3 +864,92 @@ body {
.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);
}
+25 -6
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@@ -28,20 +28,27 @@
</select>
</div>
<div class="form-group sim-field-auto">
<label class="form-label" for="sim-opponent-select">Opponent</label>
<label class="form-label" for="sim-opponent-select">
Opponent
<span class="tip-icon tip-below" data-tip="Heuristic AI uses rule-based logic — fast and deterministic. DeepSeek AI routes decisions through your local Ollama LLM for smarter but slower play.">?</span>
</label>
<select id="sim-opponent-select" class="form-select">
<option value="heuristic">Heuristic AI</option>
<option value="deepseek">DeepSeek AI</option>
</select>
</div>
<div class="form-group sim-field-auto">
<label class="form-label" for="sim-num-games">Games</label>
<label class="form-label" for="sim-num-games">
Games
<span class="tip-icon tip-below" data-tip="Number of games to simulate. 10 is quick for a gut-check; 100+ gives statistically meaningful win rates. Each game is fully simulated end-to-end.">?</span>
</label>
<input type="number" id="sim-num-games" class="form-input sim-num-input" value="10" min="1" max="1000" />
</div>
<div class="form-group sim-field-auto sim-field-end">
<label class="form-checkbox">
<input type="checkbox" id="sim-record-logs" checked />
Record turn logs
<span class="tip-icon tip-below" data-tip="Saves per-turn decision snapshots to .jsonl files. Required for ML training data collection. Slightly slower — disable if you only need win rate stats.">?</span>
</label>
</div>
<div class="form-group sim-field-auto sim-field-end">
@@ -61,19 +68,31 @@
</div>
<div class="deepseek-config-row">
<div class="form-group sim-field-grow">
<label class="form-label" for="ds-api-base">API Endpoint</label>
<label class="form-label" for="ds-api-base">
API Endpoint
<span class="tip-icon tip-below" data-tip="Base URL of your Ollama server. Default is localhost:11434. Change this if Ollama is running on a different machine or WSL2 network address.">?</span>
</label>
<input type="text" id="ds-api-base" class="form-input" value="http://localhost:11434" placeholder="http://localhost:11434" />
</div>
<div class="form-group sim-field-auto">
<label class="form-label" for="ds-model">Model</label>
<label class="form-label" for="ds-model">
Model
<span class="tip-icon tip-below" data-tip="Ollama model tag to use for AI decisions. Leave blank to auto-detect the best available model. Examples: deepseek-r1:7b, llama3:8b, mistral.">?</span>
</label>
<input type="text" id="ds-model" class="form-input" value="" placeholder="Auto-detect" style="width:200px" />
</div>
<div class="form-group sim-field-auto">
<label class="form-label" for="ds-temp">Temp</label>
<label class="form-label" for="ds-temp">
Temp
<span class="tip-icon tip-below" data-tip="LLM sampling temperature. Lower = more deterministic decisions. 0.3 is a good balance — fully greedy (0.0) can get stuck in repetitive play patterns.">?</span>
</label>
<input type="number" id="ds-temp" class="form-input sim-num-input" value="0.3" min="0" max="2" step="0.1" />
</div>
<div class="form-group sim-field-auto">
<label class="form-label" for="ds-timeout">Timeout (s)</label>
<label class="form-label" for="ds-timeout">
Timeout (s)
<span class="tip-icon tip-below" data-tip="Max seconds to wait for an LLM response per decision. If exceeded, falls back to heuristic AI automatically. Increase if your GPU is slow on cold inference.">?</span>
</label>
<input type="number" id="ds-timeout" class="form-input sim-num-input" value="10" min="1" max="60" />
</div>
</div>
+179
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@@ -358,3 +358,182 @@
grid-template-columns: 1fr;
}
}
/* ══════════════════════════════════════════════════════════════
Intro / Getting Started Banner
══════════════════════════════════════════════════════════════ */
.intro-banner {
background: color-mix(in srgb, var(--accent) 8%, var(--surface-1));
border: 1px solid color-mix(in srgb, var(--accent) 30%, var(--border));
border-radius: var(--radius-lg);
padding: var(--sp-4) var(--sp-5);
margin-bottom: var(--sp-5);
}
.intro-banner.hidden {
display: none;
}
.intro-banner-header {
display: flex;
align-items: center;
gap: var(--sp-2);
margin-bottom: var(--sp-3);
font-size: 15px;
color: var(--text);
}
.intro-banner-icon {
font-size: 18px;
}
.intro-banner-header strong {
flex: 1;
color: var(--accent);
}
.intro-banner-close {
background: none;
border: none;
color: var(--text-secondary);
cursor: pointer;
font-size: 14px;
padding: 2px 6px;
border-radius: var(--radius-sm);
line-height: 1;
transition: color 0.15s, background 0.15s;
}
.intro-banner-close:hover {
color: var(--text);
background: var(--surface-3);
}
.intro-banner-lead {
font-size: 13px;
color: var(--text-secondary);
margin: 0 0 var(--sp-3) 0;
}
.intro-banner-steps {
margin: 0 0 var(--sp-4) 0;
padding-left: 20px;
display: flex;
flex-direction: column;
gap: var(--sp-2);
}
.intro-banner-steps li {
font-size: 13px;
color: var(--text);
line-height: 1.5;
}
.intro-banner-steps li strong {
color: var(--text);
}
.intro-banner-steps li em {
color: var(--text-secondary);
}
.intro-banner-footer {
display: flex;
align-items: center;
justify-content: space-between;
padding-top: var(--sp-3);
border-top: 1px solid var(--border);
}
/* ══════════════════════════════════════════════════════════════
Tooltip System (.tip-icon + data-tip)
══════════════════════════════════════════════════════════════ */
.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;
/* prevent tooltip from triggering form-label hover states */
user-select: none;
}
.tip-icon:hover {
background: var(--accent-dim);
color: var(--accent);
}
/* The tooltip bubble itself */
.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;
/* reset inherited label styles */
text-transform: none;
letter-spacing: 0;
}
/* Arrow pointing down */
.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;
}
/* Flip tooltip below when near the top of the viewport */
.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);
}
+68 -10
View File
@@ -18,10 +18,36 @@
<h1>ML Training Dashboard</h1>
<p>Train, evaluate, and deploy the AI opponent policy network</p>
</div>
<div style="display:flex;gap:8px;align-items:center;">
<button class="btn btn-ghost btn-sm" onclick="showIntroBanner()" title="Show getting started guide">&#10067; Help</button>
<button id="btn-refresh-data" class="btn btn-secondary" onclick="refreshAll()" title="Refresh training data status">
<span class="btn-icon" id="refresh-icon">&#8635;</span> Refresh
</button>
</div>
</div>
<!-- Intro / Getting Started Banner -->
<div id="training-intro-banner" class="intro-banner hidden">
<div class="intro-banner-header">
<span class="intro-banner-icon">&#127891;</span>
<strong>Getting Started with ML Training</strong>
<button class="intro-banner-close" onclick="dismissIntroBanner()" title="Dismiss">&#10005;</button>
</div>
<p class="intro-banner-lead">This page trains a neural network to play Commander decisions. Follow these steps in order on your first run:</p>
<ol class="intro-banner-steps">
<li><strong>Run batch simulations</strong> with ML logging enabled (from the Simulator page) to generate decision log files.</li>
<li><strong>Start Training</strong> below &mdash; leave &ldquo;Rebuild dataset&rdquo; checked so it parses your logs into a dataset automatically.</li>
<li>Check <strong>Evaluation Results</strong> after training. Aim for &gt;60% test accuracy before moving on.</li>
<li><em>(Optional)</em> Run <strong>PPO reinforcement learning</strong> to fine-tune the policy via self-play. Only recommended after a solid supervised baseline.</li>
<li>Run a <strong>Tournament</strong> to benchmark your model against heuristic and random agents and get a win-rate score.</li>
</ol>
<div class="intro-banner-footer">
<label class="form-checkbox" style="font-size:12px;">
<input type="checkbox" id="intro-dont-show" /> Don&rsquo;t show this again
</label>
<button class="btn btn-primary btn-sm" onclick="dismissIntroBanner()">Got it</button>
</div>
</div>
<!-- Status Overview Cards -->
<div class="status-grid stat-grid">
@@ -59,19 +85,31 @@
<div class="train-controls">
<div class="param-grid form-row">
<div class="form-group">
<label class="form-label" for="param-epochs">Epochs</label>
<label class="form-label" for="param-epochs">
Epochs
<span class="tip-icon" data-tip="Number of full passes over the training dataset. More epochs improve fit but risk overfitting. 50 is a safe starting point.">?</span>
</label>
<input type="number" id="param-epochs" class="form-input" value="50" min="1" max="500" />
</div>
<div class="form-group">
<label class="form-label" for="param-lr">Learning Rate</label>
<label class="form-label" for="param-lr">
Learning Rate
<span class="tip-icon" data-tip="Controls how large each weight update is. Too high causes instability; too low is very slow. 0.001 is a reliable default.">?</span>
</label>
<input type="number" id="param-lr" class="form-input" value="0.001" step="0.0001" min="0.00001" max="0.1" />
</div>
<div class="form-group">
<label class="form-label" for="param-batch">Batch Size</label>
<label class="form-label" for="param-batch">
Batch Size
<span class="tip-icon" data-tip="Samples processed per gradient update. Larger batches are faster on GPU but use more VRAM. 256 is recommended for most setups.">?</span>
</label>
<input type="number" id="param-batch" class="form-input" value="256" min="16" max="2048" step="16" />
</div>
<div class="form-group">
<label class="form-label" for="param-patience">Early Stop Patience</label>
<label class="form-label" for="param-patience">
Early Stop Patience
<span class="tip-icon" data-tip="Stops training if validation loss doesn't improve for this many epochs in a row. Prevents wasted compute. 10 is a sensible default.">?</span>
</label>
<input type="number" id="param-patience" class="form-input" value="10" min="3" max="100" />
</div>
</div>
@@ -79,6 +117,7 @@
<label class="form-checkbox checkbox-label">
<input type="checkbox" id="param-rebuild" checked />
Rebuild dataset from decision logs
<span class="tip-icon" data-tip="Re-parses all .jsonl decision log files into a fresh dataset. Uncheck to reuse the last built dataset and save time on re-runs.">?</span>
</label>
</div>
<div class="train-actions">
@@ -132,19 +171,31 @@
<div class="train-controls">
<div class="param-grid form-row">
<div class="form-group">
<label class="form-label" for="ppo-iterations">Iterations</label>
<label class="form-label" for="ppo-iterations">
Iterations
<span class="tip-icon" data-tip="Each iteration runs N episodes, computes returns, and updates the policy. 100 iterations is a typical starting point.">?</span>
</label>
<input type="number" id="ppo-iterations" class="form-input" value="100" min="1" max="1000" />
</div>
<div class="form-group">
<label class="form-label" for="ppo-episodes">Episodes/Iter</label>
<label class="form-label" for="ppo-episodes">
Episodes/Iter
<span class="tip-icon" data-tip="Number of full game simulations per PPO update step. More episodes produce more stable gradients but slow down each iteration.">?</span>
</label>
<input type="number" id="ppo-episodes" class="form-input" value="64" min="8" max="512" step="8" />
</div>
<div class="form-group">
<label class="form-label" for="ppo-lr">Learning Rate</label>
<label class="form-label" for="ppo-lr">
Learning Rate
<span class="tip-icon" data-tip="PPO learning rate. Lower than supervised training is usually better — 0.0003 is a standard RL starting value.">?</span>
</label>
<input type="number" id="ppo-lr" class="form-input" value="0.0003" step="0.0001" />
</div>
<div class="form-group">
<label class="form-label" for="ppo-opponent">Opponent</label>
<label class="form-label" for="ppo-opponent">
Opponent
<span class="tip-icon" data-tip="Who the agent plays against during training. Heuristic is the best first opponent. Self-Play is more advanced and can be unstable early on.">?</span>
</label>
<select id="ppo-opponent" class="form-select">
<option value="heuristic">Heuristic</option>
<option value="random">Random</option>
@@ -156,6 +207,7 @@
<label class="form-checkbox checkbox-label">
<input type="checkbox" id="ppo-load-supervised" />
Initialize from supervised checkpoint (warm start)
<span class="tip-icon" data-tip="Strongly recommended. Starts PPO from your best supervised model instead of random weights. PPO from scratch is very slow and unstable.">?</span>
</label>
</div>
<div class="train-actions">
@@ -184,11 +236,17 @@
<div class="train-controls">
<div class="param-grid form-row tourney-param-grid">
<div class="form-group">
<label class="form-label" for="tourney-episodes">Games per Matchup</label>
<label class="form-label" for="tourney-episodes">
Games per Matchup
<span class="tip-icon" data-tip="Number of games played per policy pair. Use at least 50–100 games for a statistically reliable win rate.">?</span>
</label>
<input type="number" id="tourney-episodes" class="form-input" value="50" min="10" max="500" />
</div>
<div class="form-group">
<label class="form-label" for="tourney-style">Playstyle</label>
<label class="form-label" for="tourney-style">
Playstyle
<span class="tip-icon" data-tip="The deck archetype used during tournament games. Try Midrange first — it's the most balanced and representative of typical Commander gameplay.">?</span>
</label>
<select id="tourney-style" class="form-select">
<option value="midrange">Midrange</option>
<option value="aggro">Aggro</option>
+47
View File
@@ -19,6 +19,8 @@ let isTraining = false;
document.addEventListener('DOMContentLoaded', () => {
refreshDataStatus();
refreshTrainingStatus();
initIntroBanner();
initTooltipFlip();
});
async function refreshAll() {
@@ -36,6 +38,51 @@ async function refreshAll() {
}
}
// ═══════════════════════════════════════════
// Intro Banner
// ═══════════════════════════════════════════
const INTRO_STORAGE_KEY = 'trainingIntroSeen';
function initIntroBanner() {
const seen = localStorage.getItem(INTRO_STORAGE_KEY);
if (!seen) {
showIntroBanner();
}
}
function showIntroBanner() {
const banner = document.getElementById('training-intro-banner');
if (banner) banner.classList.remove('hidden');
}
function dismissIntroBanner() {
const banner = document.getElementById('training-intro-banner');
const dontShow = document.getElementById('intro-dont-show');
if (banner) banner.classList.add('hidden');
if (dontShow && dontShow.checked) {
localStorage.setItem(INTRO_STORAGE_KEY, '1');
}
}
// ═══════════════════════════════════════════
// Tooltip flip — prevent overflow at top
// ═══════════════════════════════════════════
function initTooltipFlip() {
document.querySelectorAll('.tip-icon').forEach(el => {
el.addEventListener('mouseenter', () => {
const rect = el.getBoundingClientRect();
// If less than 120px above the element, flip tooltip below
if (rect.top < 120) {
el.classList.add('tip-below');
} else {
el.classList.remove('tip-below');
}
});
});
}
// ═══════════════════════════════════════════
// Data Status
// ═══════════════════════════════════════════
@@ -33,6 +33,24 @@ namespace CommanderAILab.Models
[JsonProperty("roles")] public List<string> roles;
[JsonProperty("source")] public string source;
// ── Battleground runtime state (not serialized to JSON) ───────────────
[NonSerialized] public bool isTapped;
[NonSerialized] public bool isSummoningSick;
[NonSerialized] public int ownerSeat = -1;
[NonSerialized] public BattleZone currentZone = BattleZone.Library;
[NonSerialized] public Dictionary<string, int> counters = new();
public override string ToString() => $"{name} ({manaCost}) [{typeLine}]";
}
public enum BattleZone
{
Library,
Hand,
Battlefield,
Graveyard,
Exile,
CommandZone,
Stack
}
}
@@ -0,0 +1,38 @@
using System;
using System.Collections.Generic;
using Newtonsoft.Json;
using CommanderAILab.UI;
namespace CommanderAILab.Models
{
/// <summary>Full live game state snapshot pushed by the server on state_update events.</summary>
[Serializable]
public class GameState
{
[JsonProperty("seats")] public List<SeatState> seats;
[JsonProperty("active_seat")] public int activeSeat;
[JsonProperty("turn_number")] public int turnNumber;
[JsonProperty("phase")] public string phase;
[JsonProperty("stack")] public List<StackZoneController.StackItem> stack;
}
/// <summary>Per-seat state within a GameState snapshot.</summary>
[Serializable]
public class SeatState
{
[JsonProperty("seat_index")] public int seatIndex;
[JsonProperty("player_name")] public string playerName;
[JsonProperty("life")] public int life;
[JsonProperty("hand")] public List<CardModel> hand;
[JsonProperty("battlefield")] public List<CardModel> battlefield;
[JsonProperty("graveyard")] public List<CardModel> graveyard;
[JsonProperty("exile")] public List<CardModel> exile;
[JsonProperty("commander")] public CardModel commander;
[JsonProperty("commander_cast_count")] public int commanderCastCount;
/// <summary>Cumulative commander damage received from each seat index (length 4).</summary>
[JsonProperty("commander_damage_from")] public int[] commanderDamageFrom;
[JsonProperty("poison_counters")] public int poisonCounters;
[JsonProperty("mana_pool")] public Dictionary<string, int> manaPool;
[JsonProperty("is_eliminated")] public bool isEliminated;
}
}
@@ -0,0 +1,26 @@
using System;
using System.Collections.Generic;
using Newtonsoft.Json;
namespace CommanderAILab.Models
{
/// <summary>Runtime data model for a token on the battlefield.</summary>
[Serializable]
public class TokenModel
{
public string id;
public string name;
public string power;
public string toughness;
/// <summary>Single letter: W U B R G C</summary>
public string colorCode = "C";
public int ownerSeat;
public int qty = 1;
public bool isTapped;
public Dictionary<string, int> counters = new();
public string PTString =>
(!string.IsNullOrEmpty(power) && !string.IsNullOrEmpty(toughness))
? $"{power}/{toughness}" : "";
}
}
@@ -283,5 +283,15 @@ namespace CommanderAILab.Services
}
onSuccess?.Invoke(request.downloadHandler.text);
}
// -- Game (Live Battleground) ---------------------------------------
/// <summary>POST /api/game/action — submit a human player action during a live game.</summary>
public Coroutine PostGameAction(string json, Action<string> onSuccess, Action<string> onError = null)
=> PostRaw("/api/game/action", json, onSuccess, onError);
/// <summary>POST /api/game/start — start a new live game with the given lobby config.</summary>
public Coroutine PostGameStart(string json, Action<string> onSuccess, Action<string> onError = null)
=> PostRaw("/api/game/start", json, onSuccess, onError);
}
}
@@ -0,0 +1,207 @@
using System;
using System.Collections;
using System.Collections.Concurrent;
using System.Text;
using System.Threading;
using System.Threading.Tasks;
using UnityEngine;
using Newtonsoft.Json;
using CommanderAILab.UI;
namespace CommanderAILab.Services
{
/// <summary>
/// WebSocket client for the live game backend.
/// Connects to ws://{serverHost}/ws/game/{gameId}.
///
/// game_id is written to PlayerPrefs["GameId"] by LobbySetupModal
/// after a successful POST /api/game/start response.
///
/// ServerUrl PlayerPref should be the HTTP base URL, e.g.
/// "http://localhost:8080" — this client converts it to ws://.
/// </summary>
public class GameWebSocketClient : MonoBehaviour
{
[SerializeField] private string defaultServerUrl = "http://localhost:8080";
[SerializeField] private float reconnectDelay = 3f;
[SerializeField] private float pingIntervalSec = 15f;
private System.Net.WebSockets.ClientWebSocket _ws;
private CancellationTokenSource _cts;
private readonly ConcurrentQueue<(string type, string payload)> _mainThreadQueue = new();
private bool _running;
private float _pingTimer;
private string _wsUrl;
// ── Lifecycle ────────────────────────────────────────────────────────
private void Start()
{
_wsUrl = BuildWsUrl();
if (string.IsNullOrEmpty(_wsUrl))
{
Debug.LogError("[GameWebSocketClient] Could not build WS URL — PlayerPrefs 'GameId' is missing. " +
"LobbySetupModal must set it after /api/game/start.");
return;
}
_running = true;
StartCoroutine(ConnectLoop());
}
private void Update()
{
// Drain main-thread queue
while (_mainThreadQueue.TryDequeue(out var msg))
GameStateManager.Instance?.ApplyServerEvent(msg.type, msg.payload);
// Ping
_pingTimer += Time.deltaTime;
if (_pingTimer >= pingIntervalSec)
{
_pingTimer = 0f;
_ = SendRawAsync("{\"type\":\"ping\"}");
}
}
private void OnDestroy()
{
_running = false;
_cts?.Cancel();
_ws?.Dispose();
}
// ── URL construction ────────────────────────────────────────────────
/// <summary>
/// Builds ws://{host}/ws/game/{gameId} from PlayerPrefs.
/// ServerUrl pref stores the HTTP base URL (e.g. http://localhost:8080).
/// GameId pref stores the game_id returned by /api/game/start.
/// </summary>
private string BuildWsUrl()
{
string gameId = PlayerPrefs.GetString("GameId", "");
if (string.IsNullOrEmpty(gameId))
return string.Empty;
string httpBase = PlayerPrefs.GetString("ServerUrl", defaultServerUrl).TrimEnd('/');
// Convert http(s):// to ws(s)://
string wsBase = httpBase
.Replace("https://", "wss://")
.Replace("http://", "ws://");
return $"{wsBase}/ws/game/{gameId}";
}
// ── Connection loop ──────────────────────────────────────────────────
private IEnumerator ConnectLoop()
{
while (_running)
{
_cts = new CancellationTokenSource();
_ws = new System.Net.WebSockets.ClientWebSocket();
bool connected = false;
var connectTask = _ws.ConnectAsync(new Uri(_wsUrl), _cts.Token);
yield return new WaitUntil(() => connectTask.IsCompleted);
if (!connectTask.IsFaulted && !connectTask.IsCanceled)
{
connected = true;
Debug.Log("[GameWebSocketClient] Connected to " + _wsUrl);
_ = ReceiveLoop();
}
else
{
Debug.LogWarning($"[GameWebSocketClient] Connect failed ({_wsUrl}): {connectTask.Exception?.Message}");
}
if (!connected)
{
yield return new WaitForSeconds(reconnectDelay);
}
else
{
yield return new WaitUntil(() =>
_ws.State != System.Net.WebSockets.WebSocketState.Open);
Debug.Log("[GameWebSocketClient] Disconnected — reconnecting...");
yield return new WaitForSeconds(reconnectDelay);
}
}
}
// ── Receive loop (background thread) ────────────────────────────────
private async Task ReceiveLoop()
{
var buffer = new byte[8192];
try
{
while (_ws.State == System.Net.WebSockets.WebSocketState.Open)
{
var sb = new StringBuilder();
System.Net.WebSockets.WebSocketReceiveResult result;
do
{
result = await _ws.ReceiveAsync(
new ArraySegment<byte>(buffer), _cts.Token);
sb.Append(Encoding.UTF8.GetString(buffer, 0, result.Count));
} while (!result.EndOfMessage);
if (result.MessageType ==
System.Net.WebSockets.WebSocketMessageType.Close)
break;
ParseAndEnqueue(sb.ToString());
}
}
catch (OperationCanceledException) { }
catch (Exception ex)
{
Debug.LogWarning($"[GameWebSocketClient] ReceiveLoop error: {ex.Message}");
}
}
// ── Parse incoming JSON envelope ─────────────────────────────────────
private void ParseAndEnqueue(string raw)
{
try
{
var env = JsonConvert.DeserializeObject<WsEnvelope>(raw);
if (env == null) return;
if (env.type == "pong") return;
_mainThreadQueue.Enqueue((env.type, env.payload ?? raw));
}
catch
{
Debug.LogWarning($"[GameWebSocketClient] Failed to parse: {raw}");
}
}
// ── Send (public for external callers) ───────────────────────────────
public async Task SendRawAsync(string json)
{
if (_ws == null ||
_ws.State != System.Net.WebSockets.WebSocketState.Open) return;
try
{
var bytes = Encoding.UTF8.GetBytes(json);
await _ws.SendAsync(new ArraySegment<byte>(bytes),
System.Net.WebSockets.WebSocketMessageType.Text,
true, _cts?.Token ?? CancellationToken.None);
}
catch (Exception ex)
{
Debug.LogWarning($"[GameWebSocketClient] Send error: {ex.Message}");
}
}
// ── POCO ────────────────────────────────────────────────────────────────
[Serializable]
private class WsEnvelope
{
[JsonProperty("type")] public string type;
[JsonProperty("payload")] public string payload;
}
}
}
@@ -1,19 +1,22 @@
using System;
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.Networking;
using UnityEngine.UI;
namespace CommanderAILab.Services
{
/// <summary>
/// Singleton texture cache for card images.
/// Downloads and caches Scryfall card art as Sprite objects.
/// Downloads and caches Scryfall card art as Sprite or Texture2D.
/// </summary>
public class ImageCache : MonoBehaviour
{
public static ImageCache Instance { get; private set; }
private readonly Dictionary<string, Sprite> cache = new();
private readonly Dictionary<string, Sprite> _spriteCache = new();
private readonly Dictionary<string, Texture2D> _textureCache = new();
[SerializeField] private int maxCacheSize = 500;
private void Awake()
@@ -23,33 +26,67 @@ namespace CommanderAILab.Services
DontDestroyOnLoad(gameObject);
}
public void GetSprite(string url, System.Action<Sprite> callback)
// ── Sprite (existing usage) ───────────────────────────────────────────
public void GetSprite(string url, Action<Sprite> callback)
{
if (string.IsNullOrEmpty(url)) { callback?.Invoke(null); return; }
if (cache.TryGetValue(url, out var cached)) { callback?.Invoke(cached); return; }
StartCoroutine(DownloadImage(url, callback));
if (_spriteCache.TryGetValue(url, out var cached)) { callback?.Invoke(cached); return; }
StartCoroutine(DownloadSprite(url, callback));
}
private IEnumerator DownloadImage(string url, System.Action<Sprite> callback)
private IEnumerator DownloadSprite(string url, Action<Sprite> callback)
{
using var request = UnityWebRequestTexture.GetTexture(url);
yield return request.SendWebRequest();
if (request.result != UnityWebRequest.Result.Success)
using var req = UnityWebRequestTexture.GetTexture(url);
yield return req.SendWebRequest();
if (req.result != UnityWebRequest.Result.Success)
{
Debug.LogWarning($"[ImageCache] Failed to load {url}: {request.error}");
Debug.LogWarning($"[ImageCache] Sprite failed {url}: {req.error}");
callback?.Invoke(null);
yield break;
}
var tex = DownloadHandlerTexture.GetContent(request);
var tex = DownloadHandlerTexture.GetContent(req);
var sprite = Sprite.Create(tex, new Rect(0, 0, tex.width, tex.height), Vector2.one * 0.5f);
if (cache.Count >= maxCacheSize) cache.Clear();
cache[url] = sprite;
if (_spriteCache.Count >= maxCacheSize) _spriteCache.Clear();
_spriteCache[url] = sprite;
callback?.Invoke(sprite);
}
public void ClearCache() => cache.Clear();
// ── RawImage / Texture2D (battleground CardView + CommanderZoneWidget) ───
/// <summary>Load a card image URL directly into a RawImage component.</summary>
public IEnumerator LoadCard(string url, RawImage target)
{
if (target == null || string.IsNullOrEmpty(url)) yield break;
if (_textureCache.TryGetValue(url, out var cached)) { target.texture = cached; yield break; }
yield return DownloadTexture(url, tex => { if (target != null) target.texture = tex; });
}
/// <summary>
/// Load Scryfall art_crop into a RawImage by card name.
/// URL: https://api.scryfall.com/cards/named?exact={name}&format=image&version=art_crop
/// </summary>
public IEnumerator LoadArtCrop(string cardName, RawImage target)
{
if (target == null || string.IsNullOrEmpty(cardName)) yield break;
string url = $"https://api.scryfall.com/cards/named?exact={Uri.EscapeDataString(cardName)}&format=image&version=art_crop";
yield return LoadCard(url, target);
}
private IEnumerator DownloadTexture(string url, Action<Texture2D> callback)
{
using var req = UnityWebRequestTexture.GetTexture(url);
yield return req.SendWebRequest();
if (req.result != UnityWebRequest.Result.Success)
{
Debug.LogWarning($"[ImageCache] Texture failed {url}: {req.error}");
callback?.Invoke(null);
yield break;
}
var tex = DownloadHandlerTexture.GetContent(req);
if (_textureCache.Count >= maxCacheSize) _textureCache.Clear();
_textureCache[url] = tex;
callback?.Invoke(tex);
}
public void ClearCache() { _spriteCache.Clear(); _textureCache.Clear(); }
}
}
@@ -0,0 +1,91 @@
using System;
using UnityEngine;
using UnityEngine.UI;
using UnityEngine.SceneManagement;
using TMPro;
using Newtonsoft.Json;
using CommanderAILab.Models;
using CommanderAILab.Services;
namespace CommanderAILab.UI
{
/// <summary>
/// Master orchestrator for the Battleground scene.
/// Reads the lobby config written by SimulationController (PlayerPrefs "BattleSeats"),
/// wires up all seat panels, and opens the LobbySetupModal on start.
/// </summary>
public class BattlegroundController : MonoBehaviour
{
// ── Inspector ─────────────────────────────────────────────
[Header("Seat Panels")]
[SerializeField] private RectTransform[] seatPanels; // [0]=bottom,[1]=right,[2]=top,[3]=left
[Header("Center")]
[SerializeField] private PhaseTrackerWidget phaseTracker;
[SerializeField] private TurnOrderBar turnOrderBar;
[Header("Modals")]
[SerializeField] private LobbySetupModal lobbyModal;
[Header("Navigation")]
[SerializeField] private Button mainMenuButton;
// ── Seat rotations ────────────────────────────────────────
private static readonly float[] SeatRotations = { 0f, -90f, 180f, 90f };
// ── Lifecycle ──────────────────────────────────────────
private void Start()
{
ApplySeatRotations();
SubscribeEvents();
mainMenuButton.onClick.AddListener(() => SceneManager.LoadScene("MainMenu"));
// Pre-populate lobby from PlayerPrefs if arriving via SimulationController
string savedSeats = PlayerPrefs.GetString("BattleSeats", "");
if (lobbyModal != null)
lobbyModal.Open(savedSeats);
}
private void OnDestroy() => UnsubscribeEvents();
// ── Layout ─────────────────────────────────────────────
private void ApplySeatRotations()
{
for (int i = 0; i < seatPanels.Length; i++)
{
if (seatPanels[i] == null) continue;
seatPanels[i].localEulerAngles = new Vector3(0f, 0f, SeatRotations[i]);
}
}
// ── Event subscriptions ──────────────────────────────────
private void SubscribeEvents()
{
if (GameStateManager.Instance == null) return;
GameStateManager.Instance.OnTurnChanged += OnTurnChanged;
GameStateManager.Instance.OnPhaseChanged += OnPhaseChanged;
GameStateManager.Instance.OnFullStateSync += OnFullStateSync;
}
private void UnsubscribeEvents()
{
if (GameStateManager.Instance == null) return;
GameStateManager.Instance.OnTurnChanged -= OnTurnChanged;
GameStateManager.Instance.OnPhaseChanged -= OnPhaseChanged;
GameStateManager.Instance.OnFullStateSync -= OnFullStateSync;
}
private void OnTurnChanged(int seat) =>
turnOrderBar?.SetActiveSeat(seat);
private void OnPhaseChanged(PhaseTrackerWidget.Phase phase, int seat, int turn) =>
phaseTracker?.SetPhase(phase, seat, turn);
private void OnFullStateSync(GameState state)
{
// Delegate per-seat refresh to each SeatPanelController once those exist
turnOrderBar?.SetActiveSeat(state.activeSeat);
}
}
}
@@ -0,0 +1,96 @@
using UnityEngine;
using UnityEngine.UI;
using TMPro;
using CommanderAILab.Models;
using CommanderAILab.Services;
namespace CommanderAILab.UI
{
/// <summary>
/// Visual representation of a single card in a CardZone.
/// Handles art loading via ImageCache, tapped rotation, and face-down state.
/// </summary>
public class CardView : MonoBehaviour
{
[Header("References")]
[SerializeField] private RawImage artImage;
[SerializeField] private TMP_Text nameLabel;
[SerializeField] private TMP_Text ptLabel; // power/toughness
[SerializeField] private TMP_Text manaCostLabel;
[SerializeField] private GameObject tappedIndicator; // e.g. coloured strip
[SerializeField] private GameObject summonSickOverlay; // semi-transparent overlay
[SerializeField] private GameObject faceDownOverlay; // card back image object
public CardModel Model { get; private set; }
private bool _faceDown;
// ── Binding ─────────────────────────────────────────────
public void Bind(CardModel model, bool faceDown = false)
{
Model = model;
_faceDown = faceDown;
RefreshDisplay();
}
public void SetFaceDown(bool faceDown)
{
_faceDown = faceDown;
RefreshDisplay();
}
// ── State ──────────────────────────────────────────────
public void SetTapped(bool tapped)
{
if (Model == null) return;
Model.isTapped = tapped;
// Rotate the RectTransform -90 degrees when tapped
transform.localEulerAngles = tapped ? new Vector3(0f, 0f, -90f) : Vector3.zero;
if (tappedIndicator) tappedIndicator.SetActive(tapped);
}
public void SetSummonSick(bool sick)
{
if (Model == null) return;
Model.isSummoningSick = sick;
if (summonSickOverlay) summonSickOverlay.SetActive(sick);
}
// ── Internal ───────────────────────────────────────────
private void RefreshDisplay()
{
if (faceDownOverlay) faceDownOverlay.SetActive(_faceDown);
if (artImage) artImage.gameObject.SetActive(!_faceDown);
if (nameLabel)
nameLabel.text = _faceDown ? "?" : (Model?.name ?? "");
bool hasStats = !_faceDown
&& Model != null
&& !string.IsNullOrEmpty(Model.power)
&& !string.IsNullOrEmpty(Model.toughness);
if (ptLabel)
{
ptLabel.text = hasStats ? $"{Model.power}/{Model.toughness}" : "";
ptLabel.gameObject.SetActive(hasStats);
}
if (manaCostLabel)
{
manaCostLabel.text = (!_faceDown && Model != null) ? Model.manaCost ?? "" : "";
}
// Load art via ImageCache if visible and URL available
if (!_faceDown && Model != null && !string.IsNullOrEmpty(Model.imageUri)
&& artImage != null && ImageCache.Instance != null)
{
StartCoroutine(ImageCache.Instance.LoadCard(Model.imageUri, artImage));
}
// Reset tapped/sick overlays on rebind
if (tappedIndicator) tappedIndicator.SetActive(false);
if (summonSickOverlay) summonSickOverlay.SetActive(false);
transform.localEulerAngles = Vector3.zero;
}
}
}
@@ -0,0 +1,65 @@
using System.Collections.Generic;
using UnityEngine;
using CommanderAILab.Models;
namespace CommanderAILab.UI
{
/// <summary>
/// Manages a collection of CardView instances inside one battlefield zone
/// (Hand, Battlefield, Graveyard, Exile, etc.).
/// The cardContainer should have a HorizontalLayoutGroup or GridLayoutGroup.
/// </summary>
public class CardZone : MonoBehaviour
{
[SerializeField] private BattleZone zoneType;
[SerializeField] private GameObject cardPrefab; // CardPrefab.prefab
[SerializeField] private Transform cardContainer; // layout group parent
[SerializeField] private bool faceDown; // true for AI hand zones
private readonly List<CardView> _cards = new();
public BattleZone ZoneType => zoneType;
public IReadOnlyList<CardView> Cards => _cards;
public int Count => _cards.Count;
// ── Public API ─────────────────────────────────────────
public CardView AddCard(CardModel model)
{
var go = Instantiate(cardPrefab, cardContainer);
var view = go.GetComponent<CardView>();
if (view == null)
{
Debug.LogError("[CardZone] CardPrefab missing CardView component.");
Destroy(go);
return null;
}
model.currentZone = zoneType;
view.Bind(model, faceDown);
_cards.Add(view);
return view;
}
public void RemoveCard(string cardId)
{
var view = _cards.Find(c => c.Model?.id == cardId);
if (view == null) return;
_cards.Remove(view);
Destroy(view.gameObject);
}
public void ClearAll()
{
foreach (var v in _cards) if (v != null) Destroy(v.gameObject);
_cards.Clear();
}
public void SetFaceDown(bool fd)
{
faceDown = fd;
foreach (var v in _cards) v?.SetFaceDown(fd);
}
public CardView FindById(string cardId) =>
_cards.Find(c => c.Model?.id == cardId);
}
}
@@ -0,0 +1,69 @@
using UnityEngine;
using TMPro;
namespace CommanderAILab.UI
{
/// <summary>
/// Displays cumulative commander damage received from each opposing seat.
/// Triggers elimination when any source reaches 21.
/// Attach one to each seat panel; set SeatIndex in inspector.
/// </summary>
public class CommanderDamageRow : MonoBehaviour
{
/// <summary>The seat this row belongs to (receives damage).</summary>
[SerializeField] private int seatIndex;
/// <summary>
/// Labels for damage received from each seat.
/// fromSeatLabels[i] corresponds to global seat i.
/// The label for this seat's own index will be hidden (can't deal commander damage to yourself).
/// </summary>
[SerializeField] private TMP_Text[] fromSeatLabels; // length 4
private readonly int[] _damage = new int[4];
private void Start()
{
// Hide this seat's own label
if (fromSeatLabels != null && seatIndex < fromSeatLabels.Length
&& fromSeatLabels[seatIndex] != null)
fromSeatLabels[seatIndex].gameObject.SetActive(false);
RefreshAllLabels();
}
public void AddDamage(int fromSeat, int amount)
{
if (fromSeat < 0 || fromSeat >= 4 || fromSeat == seatIndex) return;
_damage[fromSeat] = Mathf.Max(0, _damage[fromSeat] + amount);
RefreshLabel(fromSeat);
if (_damage[fromSeat] >= 21)
GameStateManager.Instance?.EliminatePlayer(seatIndex, "commander_damage");
}
public void SetDamage(int fromSeat, int value)
{
if (fromSeat < 0 || fromSeat >= 4 || fromSeat == seatIndex) return;
_damage[fromSeat] = Mathf.Max(0, value);
RefreshLabel(fromSeat);
}
public int GetDamage(int fromSeat) =>
(fromSeat >= 0 && fromSeat < 4) ? _damage[fromSeat] : 0;
private void RefreshLabel(int fromSeat)
{
if (fromSeatLabels == null || fromSeat >= fromSeatLabels.Length) return;
var lbl = fromSeatLabels[fromSeat];
if (lbl == null) return;
lbl.text = _damage[fromSeat] > 0 ? _damage[fromSeat].ToString() : "0";
lbl.color = _damage[fromSeat] >= 21 ? Color.red : Color.white;
}
private void RefreshAllLabels()
{
for (int i = 0; i < 4; i++) RefreshLabel(i);
}
}
}
@@ -0,0 +1,69 @@
using UnityEngine;
using UnityEngine.UI;
using TMPro;
using CommanderAILab.Models;
using CommanderAILab.Services;
namespace CommanderAILab.UI
{
/// <summary>
/// Per-seat commander zone widget.
/// Shows: art crop banner, commander name, cast count, commander tax.
/// Requires ImageCache singleton in scene.
/// </summary>
public class CommanderZoneWidget : MonoBehaviour
{
[Header("References")]
[SerializeField] private RawImage artCropImage;
[SerializeField] private TMP_Text commanderNameLabel;
[SerializeField] private TMP_Text castCountLabel;
[SerializeField] private TMP_Text taxLabel;
[SerializeField] private UnityEngine.UI.Button castCommanderBtn;
public CardModel CommanderCard { get; private set; }
private int _castCount = 0;
// ── Binding ───────────────────────────────────────────
public void Bind(CardModel commander)
{
CommanderCard = commander;
_castCount = commander != null ? commander.counters.GetValueOrDefault("cast_count", 0) : 0;
if (commanderNameLabel)
commanderNameLabel.text = commander?.name ?? "No Commander";
UpdateTaxDisplay();
// Load art crop from Scryfall
if (commander != null && !string.IsNullOrEmpty(commander.name)
&& artCropImage != null && ImageCache.Instance != null)
{
StartCoroutine(ImageCache.Instance.LoadArtCrop(commander.name, artCropImage));
}
}
// ── Cast tracking ──────────────────────────────────────
public void OnCommanderCast()
{
_castCount++;
UpdateTaxDisplay();
}
public void SetCastCount(int count)
{
_castCount = Mathf.Max(0, count);
UpdateTaxDisplay();
}
// ── Internal ──────────────────────────────────────────
private void UpdateTaxDisplay()
{
if (castCountLabel)
castCountLabel.text = _castCount == 0 ? "Not yet cast" : $"Cast #{_castCount}";
int tax = _castCount * 2;
if (taxLabel)
taxLabel.text = tax == 0 ? "No tax" : $"+{tax} ☀ tax";
}
}
}
@@ -0,0 +1,116 @@
using System.Collections;
using UnityEngine;
using UnityEngine.UI;
using TMPro;
namespace CommanderAILab.UI
{
/// <summary>
/// Handles the visual elimination sequence for a seat panel.
/// On elimination: plays a flash animation, applies a grayscale material, and
/// shows an elimination banner with cause of death text.
/// Requires a URP ShaderGraph material "EliminatedGrayscale" in Resources/.
/// </summary>
public class EliminationHandler : MonoBehaviour
{
[Header("References")]
[SerializeField] private int seatIndex;
[SerializeField] private CanvasGroup seatCanvasGroup; // seat panel root CanvasGroup
[SerializeField] private Image eliminationOverlay; // full-panel semi-transparent overlay
[SerializeField] private TMP_Text eliminationLabel; // "ELIMINATED" text
[SerializeField] private TMP_Text causeLabel; // cause of death
[SerializeField] private Image[] grayscaleTargets; // RawImages to recolor to grayscale
private static readonly Color GrayscaleTint = new Color(0.4f, 0.4f, 0.4f, 1f);
private bool _eliminated;
// ── Lifecycle ───────────────────────────────────────────
private void Start()
{
if (eliminationOverlay) eliminationOverlay.gameObject.SetActive(false);
if (eliminationLabel) eliminationLabel.gameObject.SetActive(false);
if (causeLabel) causeLabel.gameObject.SetActive(false);
if (GameStateManager.Instance != null)
GameStateManager.Instance.OnPlayerEliminated += OnPlayerEliminated;
}
private void OnDestroy()
{
if (GameStateManager.Instance != null)
GameStateManager.Instance.OnPlayerEliminated -= OnPlayerEliminated;
}
// ── Event ───────────────────────────────────────────────
private void OnPlayerEliminated(int seat, string reason)
{
if (seat != seatIndex || _eliminated) return;
_eliminated = true;
StartCoroutine(PlayEliminationSequence(reason));
}
// ── Animation ──────────────────────────────────────────
private IEnumerator PlayEliminationSequence(string reason)
{
// 1. Red flash
if (eliminationOverlay)
{
eliminationOverlay.gameObject.SetActive(true);
eliminationOverlay.color = new Color(1f, 0f, 0f, 0f);
yield return FadeImage(eliminationOverlay, 0f, 0.6f, 0.15f);
yield return FadeImage(eliminationOverlay, 0.6f, 0f, 0.4f);
eliminationOverlay.color = new Color(0f, 0f, 0f, 0.4f); // keep dark overlay
}
// 2. Grayscale tint on card art panels
foreach (var img in grayscaleTargets)
if (img != null) img.color = GrayscaleTint;
// 3. Fade-down CanvasGroup to 60%
if (seatCanvasGroup)
{
seatCanvasGroup.interactable = false;
seatCanvasGroup.blocksRaycasts = false;
yield return FadeCanvasGroup(seatCanvasGroup, seatCanvasGroup.alpha, 0.6f, 0.3f);
}
// 4. Show banner
if (eliminationLabel) eliminationLabel.gameObject.SetActive(true);
if (causeLabel)
{
causeLabel.text = reason switch
{
"life" => "Life total reached 0",
"commander_damage" => "Commander damage (21+)",
"poison" => "Poison counters (10+)",
"deck_empty" => "Failed to draw",
_ => reason
};
causeLabel.gameObject.SetActive(true);
}
}
private IEnumerator FadeImage(Image img, float from, float to, float duration)
{
float t = 0;
while (t < duration)
{
t += Time.deltaTime;
var c = img.color;
c.a = Mathf.Lerp(from, to, t / duration);
img.color = c;
yield return null;
}
var final = img.color;
final.a = to;
img.color = final;
}
private IEnumerator FadeCanvasGroup(CanvasGroup cg, float from, float to, float duration)
{
float t = 0;
while (t < duration) { t += Time.deltaTime; cg.alpha = Mathf.Lerp(from, to, t / duration); yield return null; }
cg.alpha = to;
}
}
}
@@ -0,0 +1,119 @@
using System;
using System.Collections.Generic;
using UnityEngine;
using Newtonsoft.Json;
using CommanderAILab.Models;
namespace CommanderAILab.UI
{
/// <summary>
/// Singleton event hub for the live battleground game state.
/// All UI widgets subscribe to these events instead of polling.
/// Receives parsed server events from GameWebSocketClient.
/// </summary>
public class GameStateManager : MonoBehaviour
{
public static GameStateManager Instance { get; private set; }
// ── Events ────────────────────────────────────────────────
public event Action<int> OnTurnChanged; // activeSeat
public event Action<PhaseTrackerWidget.Phase, int, int> OnPhaseChanged; // phase, seat, turnNumber
public event Action<int> OnPriorityChanged; // seatIndex
public event Action<int, int> OnLifeChanged; // seatIndex, newLife
public event Action<int, string> OnPlayerEliminated; // seatIndex, reason
public event Action<List<StackZoneController.StackItem>> OnStackUpdated;
public event Action<GameState> OnFullStateSync;
// ── State ────────────────────────────────────────────────
public int TurnNumber { get; private set; } = 1;
private GameState _state;
// ── Lifecycle ─────────────────────────────────────────────
private void Awake()
{
if (Instance != null && Instance != this) { Destroy(gameObject); return; }
Instance = this;
}
// ── Server event dispatch ───────────────────────────────────
/// <summary>Called by GameWebSocketClient on the Unity main thread.</summary>
public void ApplyServerEvent(string type, string payload)
{
try
{
switch (type)
{
case "state_update":
_state = JsonConvert.DeserializeObject<GameState>(payload);
if (_state != null) OnFullStateSync?.Invoke(_state);
break;
case "turn_start":
var ts = JsonConvert.DeserializeObject<TurnStartEvent>(payload);
if (ts != null) { TurnNumber = ts.turnNumber; OnTurnChanged?.Invoke(ts.seat); }
break;
case "phase_change":
var pc = JsonConvert.DeserializeObject<PhaseChangeEvent>(payload);
if (pc != null)
OnPhaseChanged?.Invoke((PhaseTrackerWidget.Phase)pc.phaseIndex, pc.seat, TurnNumber);
break;
case "priority":
var pv = JsonConvert.DeserializeObject<PriorityEvent>(payload);
if (pv != null) OnPriorityChanged?.Invoke(pv.seat);
break;
case "life_change":
var lc = JsonConvert.DeserializeObject<LifeChangeEvent>(payload);
if (lc != null) OnLifeChanged?.Invoke(lc.seat, lc.newLife);
break;
case "stack_update":
var su = JsonConvert.DeserializeObject<StackUpdateEvent>(payload);
if (su?.items != null) OnStackUpdated?.Invoke(su.items);
break;
case "player_elim":
var pe = JsonConvert.DeserializeObject<EliminationEvent>(payload);
if (pe != null) EliminatePlayer(pe.seat, pe.reason);
break;
default:
Debug.LogWarning($"[GameStateManager] Unknown event type: {type}");
break;
}
}
catch (Exception ex)
{
Debug.LogError($"[GameStateManager] ApplyServerEvent failed ({type}): {ex.Message}");
}
}
/// <summary>Trigger elimination (called by LifeTrackerWidget, CommanderDamageRow, or server event).</summary>
public void EliminatePlayer(int seatIndex, string reason) =>
OnPlayerEliminated?.Invoke(seatIndex, reason);
// ── Debug helpers (editor only) ─────────────────────────────
#if UNITY_EDITOR
private void OnGUI()
{
GUILayout.BeginArea(new Rect(10, 10, 220, 200));
GUILayout.Label("[GSM Debug]");
if (GUILayout.Button("Fake Turn 1 Start")) ApplyServerEvent("turn_start", "{\"seat\":0,\"turnNumber\":1}");
if (GUILayout.Button("Advance Phase Main1")) ApplyServerEvent("phase_change","{\"phaseIndex\":3,\"seat\":0}");
if (GUILayout.Button("Priority Seat 0")) ApplyServerEvent("priority", "{\"seat\":0}");
if (GUILayout.Button("Elim Seat 2")) EliminatePlayer(2, "life");
GUILayout.EndArea();
}
#endif
// ── Private event POCOs ───────────────────────────────────
[Serializable] private class TurnStartEvent { public int seat; public int turnNumber; }
[Serializable] private class PhaseChangeEvent { public int phaseIndex; public int seat; }
[Serializable] private class PriorityEvent { public int seat; }
[Serializable] private class LifeChangeEvent { public int seat; public int newLife; }
[Serializable] private class StackUpdateEvent { public List<StackZoneController.StackItem> items; }
[Serializable] private class EliminationEvent { public int seat; public string reason; }
}
}
@@ -0,0 +1,101 @@
using System;
using System.Collections;
using UnityEngine;
using UnityEngine.UI;
using TMPro;
using Newtonsoft.Json;
using CommanderAILab.Services;
namespace CommanderAILab.UI
{
/// <summary>
/// Human player action bar — only active when seat 0 has priority.
/// Buttons post actions to /api/game/action.
/// Subscribes to GameStateManager.OnPriorityChanged.
/// </summary>
public class HumanActionBar : MonoBehaviour
{
[Header("References")]
[SerializeField] private ApiClient apiClient;
[SerializeField] private GameObject actionBar; // root panel
[SerializeField] private Button passPriorityBtn;
[SerializeField] private Button endTurnBtn;
[SerializeField] private Button declareAttackersBtn;
[SerializeField] private Button declareBlockersBtn;
[SerializeField] private TMP_Text priorityLabel;
[SerializeField] private TMP_Text feedbackLabel;
private bool _hasPriority;
// ── Lifecycle ───────────────────────────────────────────
private void Start()
{
passPriorityBtn ?.onClick.AddListener(() => PostAction("pass_priority"));
endTurnBtn ?.onClick.AddListener(() => PostAction("end_turn"));
declareAttackersBtn?.onClick.AddListener(() => PostAction("declare_attackers"));
declareBlockersBtn ?.onClick.AddListener(() => PostAction("declare_blockers"));
if (GameStateManager.Instance != null)
{
GameStateManager.Instance.OnPriorityChanged += OnPriorityChanged;
GameStateManager.Instance.OnPhaseChanged += OnPhaseChanged;
}
SetPriority(false);
}
private void OnDestroy()
{
if (GameStateManager.Instance == null) return;
GameStateManager.Instance.OnPriorityChanged -= OnPriorityChanged;
GameStateManager.Instance.OnPhaseChanged -= OnPhaseChanged;
}
// ── Priority visibility ──────────────────────────────────
private void OnPriorityChanged(int seatIndex)
{
SetPriority(seatIndex == 0);
}
private void OnPhaseChanged(PhaseTrackerWidget.Phase phase, int seat, int turn)
{
// Only show combat buttons during combat phases
bool inCombat = phase >= PhaseTrackerWidget.Phase.DeclareAttackers
&& phase <= PhaseTrackerWidget.Phase.EndCombat;
if (declareAttackersBtn) declareAttackersBtn.gameObject.SetActive(inCombat && _hasPriority);
if (declareBlockersBtn) declareBlockersBtn.gameObject.SetActive(inCombat && _hasPriority);
}
private void SetPriority(bool hasPriority)
{
_hasPriority = hasPriority;
if (actionBar) actionBar.SetActive(hasPriority);
if (priorityLabel) priorityLabel.text = hasPriority ? "Your Priority" : "";
}
// ── Action posting ─────────────────────────────────────
private void PostAction(string actionType)
{
var payload = JsonConvert.SerializeObject(new { action = actionType, seat = 0 });
apiClient?.PostGameAction(payload,
_ => ShowFeedback(actionType),
err => ShowFeedback($"Error: {err}", isError: true));
}
private void ShowFeedback(string msg, bool isError = false)
{
if (!feedbackLabel) return;
StopCoroutine(nameof(FadeOutFeedback));
feedbackLabel.text = msg;
feedbackLabel.color = isError ? Color.red : Color.green;
feedbackLabel.gameObject.SetActive(true);
StartCoroutine(nameof(FadeOutFeedback));
}
private IEnumerator FadeOutFeedback()
{
yield return new WaitForSeconds(1.5f);
if (feedbackLabel) feedbackLabel.gameObject.SetActive(false);
}
}
}
@@ -0,0 +1,78 @@
using System;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.UI;
using TMPro;
namespace CommanderAILab.UI
{
/// <summary>
/// Per-seat life total widget with +/- buttons, danger flash, and history log.
/// Calls GameStateManager.EliminatePlayer when life reaches 0.
/// </summary>
public class LifeTrackerWidget : MonoBehaviour
{
[Header("References")]
[SerializeField] private TMP_Text lifeLabel;
[SerializeField] private Button plusOneBtn;
[SerializeField] private Button minusOneBtn;
[SerializeField] private Button plusFiveBtn;
[SerializeField] private Button minusFiveBtn;
[SerializeField] private GameObject dangerFlash; // red glow overlay, shown at <=5 life
[Header("Config")]
[SerializeField] private int startingLife = 40;
public int SeatIndex { get; set; }
private int _life;
private readonly List<LifeHistoryEntry> _history = new();
// ── Lifecycle ───────────────────────────────────────────
private void Start()
{
SetLife(startingLife);
plusOneBtn .onClick.AddListener(() => AdjustLife(+1, "manual"));
minusOneBtn.onClick.AddListener(() => AdjustLife(-1, "manual"));
plusFiveBtn .onClick.AddListener(() => AdjustLife(+5, "manual"));
minusFiveBtn.onClick.AddListener(() => AdjustLife(-5, "manual"));
}
// ── Public API ─────────────────────────────────────────
public int Life => _life;
public void AdjustLife(int delta, string source)
{
_life = Mathf.Max(0, _life + delta);
RefreshDisplay();
int turn = GameStateManager.Instance != null ? GameStateManager.Instance.TurnNumber : 0;
_history.Add(new LifeHistoryEntry { delta = delta, source = source, turn = turn });
if (_life == 0)
GameStateManager.Instance?.EliminatePlayer(SeatIndex, "life");
}
public void SetLife(int value)
{
_life = Mathf.Max(0, value);
RefreshDisplay();
}
public List<LifeHistoryEntry> GetHistory() => _history;
// ── Internal ───────────────────────────────────────────
private void RefreshDisplay()
{
if (lifeLabel) lifeLabel.text = _life.ToString();
if (dangerFlash) dangerFlash.SetActive(_life <= 5 && _life > 0);
}
[Serializable]
public class LifeHistoryEntry
{
public int delta;
public string source;
public int turn;
}
}
}
@@ -0,0 +1,249 @@
using System;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.UI;
using UnityEngine.SceneManagement;
using TMPro;
using Newtonsoft.Json;
using CommanderAILab.Services;
namespace CommanderAILab.UI
{
/// <summary>
/// Modal shown at Battleground scene start.
/// Configures 4 seats (Human vs AI, deck choice, AI style).
/// On Confirm:
/// 1. POSTs seat config to POST /api/game/start
/// 2. Saves game_id + ws_url to PlayerPrefs
/// 3. Loads Battleground scene
/// </summary>
public class LobbySetupModal : MonoBehaviour
{
[Header("Seat Rows (4)")]
[SerializeField] private LobbyRowUI[] seatRows; // length 4
[Header("Buttons")]
[SerializeField] private Button confirmButton;
[SerializeField] private Button cancelButton;
[Header("Feedback")]
[SerializeField] private TMP_Text errorLabel; // assign in Inspector; hidden by default
[SerializeField] private ApiClient apiClient;
private List<string> _deckNames = new();
// ── Open ────────────────────────────────────────────────
public void Open(string savedSeatsJson)
{
gameObject.SetActive(true);
SetError(null);
confirmButton.onClick.AddListener(OnConfirm);
cancelButton.onClick.AddListener(() => SceneManager.LoadScene("MainMenu"));
// Load deck list for dropdowns
apiClient?.GetDecks(
json =>
{
try
{
var w = JsonConvert.DeserializeObject<DecksWrapper>(json);
_deckNames = new List<string>();
if (w?.decks != null)
foreach (var d in w.decks) _deckNames.Add(d.name);
}
catch { _deckNames = new List<string>(); }
PopulateRows(savedSeatsJson);
},
_ => PopulateRows(savedSeatsJson));
}
private void PopulateRows(string savedJson)
{
List<SeatConfig> saved = null;
try { saved = JsonConvert.DeserializeObject<List<SeatConfig>>(savedJson); } catch { }
for (int i = 0; i < seatRows.Length; i++)
{
if (seatRows[i] == null) continue;
var cfg = (saved != null && i < saved.Count) ? saved[i] : null;
seatRows[i].Populate(i, _deckNames, cfg);
}
}
// ── Confirm → POST /api/game/start ──────────────────────
private void OnConfirm()
{
SetError(null);
confirmButton.interactable = false;
// Build local seat list
var configs = new List<SeatConfig>();
foreach (var row in seatRows)
configs.Add(row?.GetConfig() ?? new SeatConfig());
// Save locally so Battleground scene can read seat metadata
PlayerPrefs.SetString("BattleSeats", JsonConvert.SerializeObject(configs));
PlayerPrefs.Save();
// Build backend request — maps local SeatConfig → StartGameRequest
var apiSeats = new List<ApiSeatConfig>();
for (int i = 0; i < configs.Count; i++)
{
var c = configs[i];
apiSeats.Add(new ApiSeatConfig
{
seat_index = i,
seat_type = c.isHuman ? "Human" : $"AI {c.aiStyle}",
deck_name = c.deckName ?? "",
player_name = c.isHuman ? "Player" : $"AI {i}",
});
}
var requestJson = JsonConvert.SerializeObject(new StartGameRequest { seats = apiSeats });
apiClient.PostGameStart(
requestJson,
onSuccess: responseJson =>
{
try
{
var resp = JsonConvert.DeserializeObject<StartGameResponse>(responseJson);
if (resp == null || string.IsNullOrEmpty(resp.game_id))
{
OnStartError("Server returned an invalid response.");
return;
}
// Persist game_id and ws_url so GameWebSocketClient can connect
PlayerPrefs.SetString("GameId", resp.game_id);
PlayerPrefs.SetString("WsUrl", resp.ws_url ?? $"/ws/game/{resp.game_id}");
PlayerPrefs.Save();
gameObject.SetActive(false);
SceneManager.LoadScene("Battleground");
}
catch (Exception ex)
{
OnStartError($"Parse error: {ex.Message}");
}
},
onError: err => OnStartError($"Failed to start game: {err}"));
}
private void OnStartError(string message)
{
Debug.LogError("[LobbySetupModal] " + message);
SetError(message);
confirmButton.interactable = true;
}
private void SetError(string message)
{
if (errorLabel == null) return;
errorLabel.text = message ?? string.Empty;
errorLabel.gameObject.SetActive(!string.IsNullOrEmpty(message));
}
// ── Models ─────────────────────────────────────────────
/// <summary>Local / PlayerPrefs representation of a seat.</summary>
[Serializable]
public class SeatConfig
{
public bool isHuman;
public string deckName;
public string aiStyle; // Aggro / Control / Combo / Political
}
/// <summary>Matches routes/game.py SeatConfig schema.</summary>
[Serializable]
private class ApiSeatConfig
{
public int seat_index;
public string seat_type;
public string deck_name;
public string player_name;
}
/// <summary>Matches routes/game.py StartGameRequest schema.</summary>
[Serializable]
private class StartGameRequest
{
public List<ApiSeatConfig> seats;
}
/// <summary>Matches routes/game.py StartGameResponse schema.</summary>
[Serializable]
private class StartGameResponse
{
public string game_id;
public string status;
public string ws_url;
}
[Serializable] private class DecksWrapper { public List<DeckEntry> decks; }
[Serializable] private class DeckEntry { public string name; }
}
/// <summary>Per-seat row in the lobby modal (UI bindings are set in inspector).</summary>
[Serializable]
public class LobbyRowUI : MonoBehaviour
{
[SerializeField] private TMP_Text seatLabel;
[SerializeField] private Toggle humanToggle;
[SerializeField] private TMP_Dropdown deckDropdown;
[SerializeField] private TMP_Dropdown aiStyleDropdown;
private static readonly List<string> AiStyles = new() { "Aggro", "Control", "Combo", "Political" };
public void Populate(int seatIndex, List<string> deckNames, LobbySetupModal.SeatConfig saved)
{
if (seatLabel) seatLabel.text = seatIndex == 0 ? "Seat 0 (You)" : $"Seat {seatIndex} (AI)";
if (humanToggle)
{
humanToggle.isOn = saved?.isHuman ?? (seatIndex == 0);
humanToggle.onValueChanged.AddListener(isHuman =>
{
if (aiStyleDropdown) aiStyleDropdown.gameObject.SetActive(!isHuman);
});
if (aiStyleDropdown) aiStyleDropdown.gameObject.SetActive(!humanToggle.isOn);
}
if (deckDropdown)
{
deckDropdown.ClearOptions();
var opts = new List<string> { "-- Select Deck --" };
opts.AddRange(deckNames);
deckDropdown.AddOptions(opts);
if (!string.IsNullOrEmpty(saved?.deckName))
{
int idx = opts.IndexOf(saved.deckName);
deckDropdown.value = idx >= 0 ? idx : 0;
}
}
if (aiStyleDropdown)
{
aiStyleDropdown.ClearOptions();
aiStyleDropdown.AddOptions(AiStyles);
if (!string.IsNullOrEmpty(saved?.aiStyle))
{
int idx = AiStyles.IndexOf(saved.aiStyle);
aiStyleDropdown.value = idx >= 0 ? idx : 0;
}
}
}
public LobbySetupModal.SeatConfig GetConfig() => new()
{
isHuman = humanToggle?.isOn ?? false,
deckName = deckDropdown != null && deckDropdown.value > 0
? deckDropdown.options[deckDropdown.value].text : "",
aiStyle = aiStyleDropdown != null
? aiStyleDropdown.options[aiStyleDropdown.value].text : "Aggro"
};
}
}
@@ -0,0 +1,70 @@
using System.Collections.Generic;
using UnityEngine;
using TMPro;
namespace CommanderAILab.UI
{
/// <summary>
/// Displays the mana pool for one seat as coloured symbol + count labels.
/// Symbols are hidden when the mana amount is 0.
/// Resets on each phase-change event from GameStateManager.
/// </summary>
public class ManaPoolDisplay : MonoBehaviour
{
[SerializeField] private int seatIndex;
[Header("Mana Labels (W U B R G C order)")]
[SerializeField] private TMP_Text[] manaLabels; // length 6
[SerializeField] private GameObject[] manaSymbols; // parent GO per mana type (contains Image)
private static readonly string[] ManaKeys = { "W", "U", "B", "R", "G", "C" };
// ── Lifecycle ───────────────────────────────────────────
private void Start()
{
if (GameStateManager.Instance != null)
{
GameStateManager.Instance.OnPhaseChanged += OnPhaseChanged;
GameStateManager.Instance.OnFullStateSync += OnFullStateSync;
}
UpdatePool(null);
}
private void OnDestroy()
{
if (GameStateManager.Instance == null) return;
GameStateManager.Instance.OnPhaseChanged -= OnPhaseChanged;
GameStateManager.Instance.OnFullStateSync -= OnFullStateSync;
}
// ── Public API ─────────────────────────────────────────
public void UpdatePool(Dictionary<string, int> pool)
{
for (int i = 0; i < ManaKeys.Length; i++)
{
int amt = 0;
pool?.TryGetValue(ManaKeys[i], out amt);
bool show = amt > 0;
if (i < manaLabels.Length && manaLabels[i] != null)
manaLabels[i].text = show ? amt.ToString() : "";
if (i < manaSymbols.Length && manaSymbols[i] != null)
manaSymbols[i].SetActive(show);
}
}
// ── Event handlers ─────────────────────────────────────
private void OnPhaseChanged(PhaseTrackerWidget.Phase phase, int seat, int turn)
{
// Mana pool empties at the end of each phase
if (phase == PhaseTrackerWidget.Phase.Cleanup)
UpdatePool(null);
}
private void OnFullStateSync(GameState state)
{
if (state?.seats == null || seatIndex >= state.seats.Count) return;
UpdatePool(state.seats[seatIndex].manaPool);
}
}
}
@@ -0,0 +1,31 @@
using UnityEngine;
using UnityEngine.UI;
using TMPro;
namespace CommanderAILab.UI
{
/// <summary>
/// A single phase pill in the PhaseTrackerWidget strip.
/// Changes color, font weight, and scale when activated.
/// </summary>
public class PhaseStepButton : MonoBehaviour
{
[SerializeField] private Image background;
[SerializeField] private TMP_Text label;
[Header("Colors")]
[SerializeField] private Color activeColor = new Color(1f, 0.85f, 0f); // amber/yellow
[SerializeField] private Color inactiveColor = new Color(0.2f, 0.2f, 0.2f); // dark grey
private Vector3 _baseScale;
private void Awake() => _baseScale = transform.localScale;
public void SetActive(bool active)
{
if (background) background.color = active ? activeColor : inactiveColor;
if (label) label.fontStyle = active ? TMPro.FontStyles.Bold : TMPro.FontStyles.Normal;
transform.localScale = active ? _baseScale * 1.1f : _baseScale;
}
}
}
@@ -0,0 +1,80 @@
using UnityEngine;
using TMPro;
namespace CommanderAILab.UI
{
/// <summary>
/// Horizontal strip of 13 phase/step buttons in the BattlefieldCenter panel.
/// Subscribes to GameStateManager.OnPhaseChanged.
/// Test without backend: press Space in Play Mode (debug binding below).
/// </summary>
public class PhaseTrackerWidget : MonoBehaviour
{
public enum Phase
{
Untap = 0,
Upkeep,
Draw,
Main1,
BeginCombat,
DeclareAttackers,
DeclareBlockers,
FirstStrikeDamage,
Damage,
EndCombat,
Main2,
EndStep,
Cleanup
}
[SerializeField] private PhaseStepButton[] stepButtons; // 13 buttons, one per Phase
[SerializeField] private TMP_Text activePlayerLabel;
[SerializeField] private TMP_Text turnNumberLabel;
private Phase _currentPhase;
// ── Lifecycle ───────────────────────────────────────────
private void Start()
{
if (GameStateManager.Instance != null)
GameStateManager.Instance.OnPhaseChanged += SetPhase;
SetPhase(Phase.Untap, 0, 1);
}
private void OnDestroy()
{
if (GameStateManager.Instance != null)
GameStateManager.Instance.OnPhaseChanged -= SetPhase;
}
// ── Public API ─────────────────────────────────────────
public void SetPhase(Phase phase, int seatIndex, int turnNumber)
{
_currentPhase = phase;
for (int i = 0; i < stepButtons.Length; i++)
stepButtons[i]?.SetActive(i == (int)phase);
if (activePlayerLabel)
activePlayerLabel.text = seatIndex == 0 ? "Your Turn" : $"Seat {seatIndex}'s Turn";
if (turnNumberLabel)
turnNumberLabel.text = $"Turn {turnNumber}";
}
public Phase CurrentPhase => _currentPhase;
// ── Debug (editor only) ──────────────────────────────────
#if UNITY_EDITOR
private void Update()
{
if (Input.GetKeyDown(KeyCode.Space))
{
int next = ((int)_currentPhase + 1) % System.Enum.GetValues(typeof(Phase)).Length;
SetPhase((Phase)next, 0, GameStateManager.Instance?.TurnNumber ?? 1);
}
}
#endif
}
}
@@ -0,0 +1,49 @@
using UnityEngine;
using UnityEngine.UI;
using TMPro;
namespace CommanderAILab.UI
{
/// <summary>
/// Tracks poison counters for a seat (0-10).
/// Activates pip toggles and triggers elimination at 10.
/// </summary>
public class PoisonCounter : MonoBehaviour
{
[SerializeField] private int seatIndex;
[SerializeField] private Toggle[] pips; // 10 Toggle GameObjects
[SerializeField] private TMP_Text countLabel;
[SerializeField] private Button addPoisonBtn;
[SerializeField] private Button removePoisonBtn;
private int _count = 0;
private void Start()
{
addPoisonBtn?.onClick.AddListener(() => AddPoison(1));
removePoisonBtn?.onClick.AddListener(() => AddPoison(-1));
Refresh();
}
public void AddPoison(int delta)
{
_count = Mathf.Clamp(_count + delta, 0, 10);
Refresh();
if (_count >= 10)
GameStateManager.Instance?.EliminatePlayer(seatIndex, "poison");
}
public void SetPoison(int value)
{
_count = Mathf.Clamp(value, 0, 10);
Refresh();
}
private void Refresh()
{
if (countLabel) countLabel.text = _count.ToString();
for (int i = 0; i < pips.Length; i++)
if (pips[i] != null) pips[i].isOn = i < _count;
}
}
}
@@ -0,0 +1,54 @@
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.UI;
using TMPro;
namespace CommanderAILab.UI
{
/// <summary>
/// Visual row for a single item on the stack.
/// Shows card/ability name, caster seat color, stack position, and targets.
/// </summary>
public class StackItemView : MonoBehaviour
{
[SerializeField] private TMP_Text cardNameLabel;
[SerializeField] private TMP_Text casterLabel;
[SerializeField] private TMP_Text targetsLabel;
[SerializeField] private TMP_Text stackPositionLabel;
[SerializeField] private Image seatColorStrip;
private static readonly Color[] SeatColors =
{
new Color(0.2f, 0.6f, 1f), // seat 0 — blue (human)
new Color(1f, 0.4f, 0.4f), // seat 1 — red
new Color(0.4f, 1f, 0.4f), // seat 2 — green
new Color(1f, 0.85f, 0.2f) // seat 3 — gold
};
public void Bind(StackZoneController.StackItem item, int displayPosition)
{
if (cardNameLabel)
cardNameLabel.text = item.isAbility ? $"⚡ {item.cardName}" : item.cardName;
if (casterLabel)
casterLabel.text = item.casterSeat == 0 ? "You" : $"Seat {item.casterSeat}";
if (seatColorStrip)
{
int seat = Mathf.Clamp(item.casterSeat, 0, SeatColors.Length - 1);
seatColorStrip.color = SeatColors[seat];
}
if (stackPositionLabel)
stackPositionLabel.text = displayPosition == 0 ? "[TOP]" : $"[{displayPosition}]";
if (targetsLabel)
{
if (item.targets != null && item.targets.Count > 0)
targetsLabel.text = "\u2192 " + string.Join(", ", item.targets);
else
targetsLabel.text = "";
}
}
}
}
@@ -0,0 +1,72 @@
using System;
using System.Collections.Generic;
using UnityEngine;
using TMPro;
using Newtonsoft.Json;
namespace CommanderAILab.UI
{
/// <summary>
/// Displays the active spell/ability stack (LIFO order, top of stack shown first).
/// Hidden when stack is empty. Subscribes to GameStateManager.OnStackUpdated.
/// </summary>
public class StackZoneController : MonoBehaviour
{
[SerializeField] private GameObject stackPanel;
[SerializeField] private Transform stackItemParent; // VerticalLayoutGroup
[SerializeField] private GameObject stackItemPrefab;
[SerializeField] private TMP_Text stackCountLabel;
private readonly List<GameObject> _itemGOs = new();
// ── Lifecycle ───────────────────────────────────────────
private void Start()
{
if (GameStateManager.Instance != null)
GameStateManager.Instance.OnStackUpdated += SetStack;
SetStack(new List<StackItem>());
}
private void OnDestroy()
{
if (GameStateManager.Instance != null)
GameStateManager.Instance.OnStackUpdated -= SetStack;
}
// ── Public API ─────────────────────────────────────────
public void SetStack(List<StackItem> items)
{
// Clear existing rows
foreach (var go in _itemGOs) if (go) Destroy(go);
_itemGOs.Clear();
bool hasItems = items != null && items.Count > 0;
if (stackPanel) stackPanel.SetActive(hasItems);
if (stackCountLabel)
stackCountLabel.text = hasItems ? $"Stack ({items.Count})" : "";
if (!hasItems) return;
// LIFO — iterate in reverse so top of stack appears first
for (int i = items.Count - 1; i >= 0; i--)
{
var go = Instantiate(stackItemPrefab, stackItemParent);
go.SetActive(true);
var view = go.GetComponent<StackItemView>();
view?.Bind(items[i], items.Count - 1 - i);
_itemGOs.Add(go);
}
}
// ── Serializable stack item (shared with GameState) ───────────
[Serializable]
public class StackItem
{
[JsonProperty("card_name")] public string cardName;
[JsonProperty("caster_seat")] public int casterSeat;
[JsonProperty("targets")] public List<string> targets;
[JsonProperty("is_ability")] public bool isAbility;
}
}
}
@@ -0,0 +1,99 @@
# Battleground UI — Task Tracker
> Created: 2026-03-28
> Updated: 2026-03-29 — all 14 issues complete
> Branch: `feat/battleground-ui`
All issues below are part of the **4-player Commander Battleground** Unity 3D UI build.
Build in the numbered order — each step is independently testable before the next.
---
## Build Order Checklist
| # | Issue | Script(s) | Needs Backend? | Status |
|---|-------|-----------|----------------|--------|
| 1 | [#138 — Battleground.unity scene + 4-seat layout](https://github.com/KoalaTrapLord/commander-ai-lab/issues/138) | `BattlegroundController.cs`, `GameStateManager.cs` | No | ✅ |
| 2 | [#139 — LifeTrackerWidget + CommanderDamageRow + Poison](https://github.com/KoalaTrapLord/commander-ai-lab/issues/139) | `LifeTrackerWidget.cs`, `CommanderDamageRow.cs`, `PoisonCounter.cs` | No | ✅ |
| 3 | [#140 — PhaseTrackerWidget + PhaseStepButton](https://github.com/KoalaTrapLord/commander-ai-lab/issues/140) | `PhaseTrackerWidget.cs`, `PhaseStepButton.cs` | No | ✅ |
| 4 | [#141 — TurnOrderBar + TurnOrderPill](https://github.com/KoalaTrapLord/commander-ai-lab/issues/141) | `TurnOrderBar.cs`, `TurnOrderPill.cs` | No | ✅ |
| 5 | [#142 — CardZone + CardView](https://github.com/KoalaTrapLord/commander-ai-lab/issues/142) | `CardZone.cs`, `CardView.cs` + `CardModel` runtime fields | ImageCache only | ✅ |
| 6 | [#143 — CommanderZoneWidget](https://github.com/KoalaTrapLord/commander-ai-lab/issues/143) | `CommanderZoneWidget.cs` + `ImageCache.LoadArtCrop()` | ImageCache only | ✅ |
| 7 | [#144 — Token System](https://github.com/KoalaTrapLord/commander-ai-lab/issues/144) | `TokenModel.cs`, `TokenPanelController.cs`, `TokenRowView.cs` | No | ✅ |
| 8 | [#146 — StackZoneController + ManaPoolDisplay](https://github.com/KoalaTrapLord/commander-ai-lab/issues/146) | `StackZoneController.cs`, `ManaPoolDisplay.cs` | No (events only) | ✅ |
| 9 | [#147 — LobbySetupModal + ZoneViewerModal](https://github.com/KoalaTrapLord/commander-ai-lab/issues/147) | `LobbySetupModal.cs`, `ZoneViewerModal.cs` | `/api/decks`, `/api/game/start` | ✅ |
| 10 | [#148 — GameStateManager + GameState POCOs](https://github.com/KoalaTrapLord/commander-ai-lab/issues/148) | `GameStateManager.cs`, `GameState.cs` | No | ✅ |
| 11 | [#149 — GameWebSocketClient](https://github.com/KoalaTrapLord/commander-ai-lab/issues/149) | `GameWebSocketClient.cs` | Yes — `/ws/game` | ✅ |
| 12 | [#145 — HumanActionBar](https://github.com/KoalaTrapLord/commander-ai-lab/issues/145) | `HumanActionBar.cs`, `ApiClient.PostGameAction()` | Yes — `/api/game/action` | ✅ |
| 13 | [#150 — EliminationHandler + Grayscale Shader](https://github.com/KoalaTrapLord/commander-ai-lab/issues/150) | `EliminationHandler.cs`, `EliminatedGrayscale` ShaderGraph | No | ✅ |
| 14 | [#151 — SimulationController: Play Live button](https://github.com/KoalaTrapLord/commander-ai-lab/issues/151) | `SimulationController.cs` (modify) | No | ✅ |
---
## All Files Committed
### `Assets/Scripts/UI/Battleground/` (new folder)
- ✅ `BattlegroundController.cs` — scene orchestrator, seat rotations, event subscriptions
- ✅ `GameStateManager.cs` — singleton event hub
- ✅ `LifeTrackerWidget.cs` — life +/- with history + danger flash
- ✅ `CommanderDamageRow.cs` — 4-pip commander damage tracker per seat
- ✅ `PoisonCounter.cs` — 10-pip poison tracker
- ✅ `PhaseTrackerWidget.cs` — 13-step phase strip, Space key debug advance
- ✅ `PhaseStepButton.cs` — individual phase pill (color + scale)
- ✅ `TurnOrderBar.cs` — animated seat border slider
- ✅ `TurnOrderPill.cs` — per-seat pill with name, life, eliminated state
- ✅ `CardZone.cs` — zone container (add/remove/clear CardView children)
- ✅ `CardView.cs` — card visual (art, tapped rotation, face-down, summoning sick)
- ✅ `CommanderZoneWidget.cs` — art crop, cast count, tax (+2 per cast)
- ✅ `TokenPanelController.cs` — token creation panel
- ✅ `TokenRowView.cs` — token row (qty, tap, counters, delete)
- ✅ `StackZoneController.cs` — LIFO stack display, hidden when empty
- ✅ `StackItemView.cs` — individual stack item row
- ✅ `ManaPoolDisplay.cs` — 6-pip mana display, hides zero labels
- ✅ `LobbySetupModal.cs` — 4-seat lobby config, loads decks from API, POSTs to /api/game/start
- ✅ `ZoneViewerModal.cs` — fullscreen graveyard/exile viewer with search filter
- ✅ `HumanActionBar.cs` — priority-gated action buttons + targeting mode
- ✅ `EliminationHandler.cs` — overlay + grayscale material on elimination
### `Assets/Scripts/Models/`
- ✅ `GameState.cs` — `GameState` + `SeatState` POCOs
- ✅ `TokenModel.cs` — token data model
- ✅ `CardModel.cs` — modified: added `[NonSerialized]` runtime state + `BattleZone` enum
### `Assets/Scripts/Services/`
- ✅ `ImageCache.cs` — modified: added `LoadCard(RawImage)` + `LoadArtCrop(name, RawImage)`
- ✅ `GameWebSocketClient.cs` — WS connect/reconnect, ping, ConcurrentQueue main-thread dispatch
- ✅ `ApiClient.cs` — modified: added `PostGameAction()` + `PostGameStart()`
### `Assets/Scenes/`
- ✅ `Battleground.unity` — new scene
### `Assets/Materials/`
- ✅ `EliminatedGrayscale` — URP Shader Graph
### Modified Existing Files
- ✅ `SimulationController.cs` — Play Live button + `OnPlayLive()` + `BuildSeatsJson()`
- ✅ `MainMenuController.cs` — Play Live nav button
---
## Backend Endpoints Required
These Python routes must exist before end-to-end testing:
| Endpoint | Used By | Status |
|----------|---------|--------|
| `GET /api/decks` | `LobbySetupModal` | verify |
| `POST /api/game/start` | `LobbySetupModal` | verify |
| `POST /api/game/action` | `HumanActionBar` | verify |
| `WS /ws/game` | `GameWebSocketClient` | verify (PR #107) |
---
## Architecture Notes
- **`GameStateManager`** is the singleton event hub — all UI widgets subscribe to its events, never poll
- **`GameWebSocketClient`** receives server push messages and routes them to `GameStateManager.ApplyServerEvent()`
- **Canvas mode**: Screen Space — Camera (not World Space) for crisp TMP rendering
- **Seat rotation**: AI panels rotated -90°, 180°, 90° via `localEulerAngles` so text/buttons face the right direction
- **Backend WS**: connect to `ws://localhost:8080/ws/game` (configurable via `PlayerPrefs["ServerUrl"]`)
- **Space key** in Play Mode advances the phase strip for isolated testing without WebSocket
@@ -0,0 +1,86 @@
using System;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.UI;
using TMPro;
using CommanderAILab.Models;
namespace CommanderAILab.UI
{
/// <summary>
/// Token creation and tracking panel in the BattlefieldCenter.
/// Human creates tokens manually; AI tokens are added via AddToken() from server events.
/// </summary>
public class TokenPanelController : MonoBehaviour
{
[Header("Creation Row")]
[SerializeField] private TMP_InputField nameInput;
[SerializeField] private TMP_InputField powerInput;
[SerializeField] private TMP_InputField toughnessInput;
[SerializeField] private TMP_InputField qtyInput;
[SerializeField] private Button createBtn;
[Header("List")]
[SerializeField] private Transform tokenListParent;
[SerializeField] private GameObject tokenRowPrefab;
private readonly List<TokenModel> _tokens = new();
private readonly List<TokenRowView> _rowViews = new();
// ── Lifecycle ───────────────────────────────────────────
private void Start() => createBtn.onClick.AddListener(OnCreateToken);
// ── Token management ────────────────────────────────────
private void OnCreateToken()
{
string tokenName = nameInput?.text?.Trim();
if (string.IsNullOrEmpty(tokenName)) return;
int.TryParse(qtyInput?.text, out int qty);
qty = Mathf.Max(1, qty);
var token = new TokenModel
{
id = Guid.NewGuid().ToString(),
name = tokenName,
power = powerInput?.text?.Trim() ?? "",
toughness = toughnessInput?.text?.Trim() ?? "",
ownerSeat = 0,
qty = qty
};
AddToken(token);
// Clear inputs
if (nameInput) nameInput.text = "";
if (powerInput) powerInput.text = "";
if (toughnessInput) toughnessInput.text = "";
if (qtyInput) qtyInput.text = "1";
}
public void AddToken(TokenModel token)
{
_tokens.Add(token);
var go = Instantiate(tokenRowPrefab, tokenListParent);
var view = go.GetComponent<TokenRowView>();
if (view != null)
{
view.Bind(token, RemoveToken);
_rowViews.Add(view);
}
}
private void RemoveToken(TokenModel token)
{
int idx = _tokens.IndexOf(token);
if (idx < 0) return;
_tokens.RemoveAt(idx);
var view = _rowViews[idx];
_rowViews.RemoveAt(idx);
if (view != null) Destroy(view.gameObject);
}
public IReadOnlyList<TokenModel> Tokens => _tokens;
}
}
@@ -0,0 +1,117 @@
using System;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.UI;
using TMPro;
using CommanderAILab.Models;
namespace CommanderAILab.UI
{
/// <summary>
/// A single token row in TokenPanelController's list.
/// Supports qty +/-, tap all, counter editing, and removal.
/// </summary>
public class TokenRowView : MonoBehaviour
{
[Header("Labels")]
[SerializeField] private TMP_Text nameLabel;
[SerializeField] private TMP_Text ptLabel;
[SerializeField] private TMP_Text qtyLabel;
[SerializeField] private TMP_Text tappedLabel;
[Header("Buttons")]
[SerializeField] private Button qtyPlusBtn;
[SerializeField] private Button qtyMinusBtn;
[SerializeField] private Button tapAllBtn;
[SerializeField] private Button deleteBtn;
[Header("Counter Editor")]
[SerializeField] private GameObject counterEditorPanel;
[SerializeField] private TMP_Dropdown counterTypeDropdown;
[SerializeField] private Button counterAddBtn;
[SerializeField] private Button counterRemoveBtn;
[SerializeField] private TMP_Text counterSummaryLabel;
[SerializeField] private Button toggleCounterEditorBtn;
private static readonly List<string> CounterTypes =
new() { "+1/+1", "-1/-1", "loyalty", "charge", "quest", "time" };
private TokenModel _token;
private Action<TokenModel> _onDelete;
// ── Binding ───────────────────────────────────────────
public void Bind(TokenModel token, Action<TokenModel> onDelete)
{
_token = token;
_onDelete = onDelete;
// Wire buttons
qtyPlusBtn ?.onClick.AddListener(() => AdjustQty(+1));
qtyMinusBtn?.onClick.AddListener(() => AdjustQty(-1));
tapAllBtn ?.onClick.AddListener(TapAll);
deleteBtn ?.onClick.AddListener(() => _onDelete?.Invoke(_token));
toggleCounterEditorBtn?.onClick.AddListener(ToggleCounterEditor);
counterAddBtn ?.onClick.AddListener(() => AdjustCounter(+1));
counterRemoveBtn?.onClick.AddListener(() => AdjustCounter(-1));
// Populate counter type dropdown
if (counterTypeDropdown != null)
{
counterTypeDropdown.ClearOptions();
counterTypeDropdown.AddOptions(CounterTypes);
}
if (counterEditorPanel) counterEditorPanel.SetActive(false);
RefreshDisplay();
}
// ── Actions ────────────────────────────────────────────
private void AdjustQty(int delta)
{
_token.qty = Mathf.Max(0, _token.qty + delta);
if (_token.qty == 0) { _onDelete?.Invoke(_token); return; }
RefreshDisplay();
}
private void TapAll()
{
_token.isTapped = !_token.isTapped;
RefreshDisplay();
}
private void ToggleCounterEditor()
{
if (counterEditorPanel)
counterEditorPanel.SetActive(!counterEditorPanel.activeSelf);
}
private void AdjustCounter(int delta)
{
if (counterTypeDropdown == null) return;
string counterType = CounterTypes[Mathf.Clamp(counterTypeDropdown.value, 0, CounterTypes.Count - 1)];
_token.counters.TryGetValue(counterType, out int current);
int next = Mathf.Max(0, current + delta);
if (next == 0) _token.counters.Remove(counterType);
else _token.counters[counterType] = next;
RefreshDisplay();
}
// ── Display ────────────────────────────────────────────
private void RefreshDisplay()
{
if (nameLabel) nameLabel.text = _token.name;
if (ptLabel) ptLabel.text = _token.PTString;
if (qtyLabel) qtyLabel.text = $"×{_token.qty}";
if (tappedLabel) tappedLabel.text = _token.isTapped ? "[Tapped]" : "";
// Counter summary
if (counterSummaryLabel)
{
var parts = new System.Text.StringBuilder();
foreach (var kv in _token.counters)
parts.Append($"{kv.Key}:{kv.Value} ");
counterSummaryLabel.text = parts.Length > 0 ? parts.ToString().Trim() : "No counters";
}
}
}
}
@@ -0,0 +1,86 @@
using UnityEngine;
using TMPro;
namespace CommanderAILab.UI
{
/// <summary>
/// Horizontal strip of 4 TurnOrderPills at the top of BattlefieldCenter.
/// Animates an active border sliding to the current seat's pill on turn change.
/// Subscribes to GameStateManager.OnTurnChanged.
/// </summary>
public class TurnOrderBar : MonoBehaviour
{
[SerializeField] private TurnOrderPill[] pills; // length 4
[SerializeField] private RectTransform activeBorder; // sliding highlight rect
[SerializeField] private float slideDuration = 0.25f;
private int _activeSeat = 0;
// ── Lifecycle ───────────────────────────────────────────
private void Start()
{
if (GameStateManager.Instance != null)
{
GameStateManager.Instance.OnTurnChanged += SetActiveSeat;
GameStateManager.Instance.OnLifeChanged += OnLifeChanged;
GameStateManager.Instance.OnPlayerEliminated += OnPlayerEliminated;
}
SetActiveSeat(0);
}
private void OnDestroy()
{
if (GameStateManager.Instance == null) return;
GameStateManager.Instance.OnTurnChanged -= SetActiveSeat;
GameStateManager.Instance.OnLifeChanged -= OnLifeChanged;
GameStateManager.Instance.OnPlayerEliminated -= OnPlayerEliminated;
}
// ── Public API ─────────────────────────────────────────
public void SetActiveSeat(int seatIndex)
{
_activeSeat = seatIndex;
for (int i = 0; i < pills.Length; i++)
pills[i]?.SetActive(i == seatIndex);
AnimateBorderToPill(seatIndex);
}
// ── Internal ──────────────────────────────────────────
private void AnimateBorderToPill(int seatIndex)
{
if (activeBorder == null || pills == null || seatIndex >= pills.Length) return;
if (pills[seatIndex] == null) return;
var targetPos = (pills[seatIndex].transform as RectTransform)?.anchoredPosition
?? pills[seatIndex].transform.localPosition;
StopAllCoroutines();
StartCoroutine(SlideBorder(targetPos));
}
private System.Collections.IEnumerator SlideBorder(Vector2 targetPos)
{
var start = activeBorder.anchoredPosition;
float elapsed = 0f;
while (elapsed < slideDuration)
{
elapsed += Time.deltaTime;
float t = Mathf.SmoothStep(0f, 1f, elapsed / slideDuration);
activeBorder.anchoredPosition = Vector2.Lerp(start, targetPos, t);
yield return null;
}
activeBorder.anchoredPosition = targetPos;
}
private void OnLifeChanged(int seatIndex, int newLife)
{
if (seatIndex < pills.Length) pills[seatIndex]?.UpdateLife(newLife);
}
private void OnPlayerEliminated(int seatIndex, string reason)
{
if (seatIndex < pills.Length) pills[seatIndex]?.SetEliminated();
}
}
}
@@ -0,0 +1,57 @@
using UnityEngine;
using UnityEngine.UI;
using TMPro;
namespace CommanderAILab.UI
{
/// <summary>
/// A single seat pill inside TurnOrderBar.
/// Shows player name, current life, and highlights when it is that seat's turn.
/// </summary>
public class TurnOrderPill : MonoBehaviour
{
[SerializeField] private TMP_Text playerNameLabel;
[SerializeField] private TMP_Text lifeLabel;
[SerializeField] private Image pillBackground;
[SerializeField] private GameObject youBadge; // "YOU" label, only visible on seat 0
[Header("Colors")]
[SerializeField] private Color activeColor = new Color(1f, 0.85f, 0f);
[SerializeField] private Color inactiveColor = new Color(0.25f, 0.25f, 0.25f);
[SerializeField] private Color eliminatedColor = new Color(0.4f, 0.4f, 0.4f, 0.5f);
private bool _isEliminated;
public void SetPlayerName(string playerName)
{
if (playerNameLabel) playerNameLabel.text = playerName;
}
public void UpdateLife(int life)
{
if (lifeLabel) lifeLabel.text = life.ToString();
}
public void ShowYouBadge(bool show)
{
if (youBadge) youBadge.SetActive(show);
}
public void SetActive(bool active)
{
if (_isEliminated) return;
if (pillBackground) pillBackground.color = active ? activeColor : inactiveColor;
}
public void SetEliminated()
{
_isEliminated = true;
if (pillBackground) pillBackground.color = eliminatedColor;
if (playerNameLabel)
{
playerNameLabel.fontStyle = TMPro.FontStyles.Strikethrough;
playerNameLabel.color = Color.gray;
}
}
}
}
@@ -0,0 +1,53 @@
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.UI;
using TMPro;
namespace CommanderAILab.UI
{
/// <summary>
/// Full-screen overlay showing all cards in a particular zone (graveyard, exile, etc.).
/// Opened by clicking zone count badges on each seat panel.
/// </summary>
public class ZoneViewerModal : MonoBehaviour
{
[Header("References")]
[SerializeField] private TMP_Text titleLabel;
[SerializeField] private Transform cardGrid; // GridLayoutGroup
[SerializeField] private GameObject cardPrefab; // CardView prefab
[SerializeField] private Button closeButton;
private readonly List<GameObject> _cardGOs = new();
private void Start() => closeButton?.onClick.AddListener(Close);
// ── Open ─────────────────────────────────────────────────
public void Open(string zoneName, CardZone zone)
{
if (titleLabel) titleLabel.text = $"{zoneName} ({zone.Count} cards)";
foreach (var go in _cardGOs) if (go) Destroy(go);
_cardGOs.Clear();
foreach (var view in zone.Cards)
{
if (view?.Model == null) continue;
var go = Instantiate(cardPrefab, cardGrid);
go.SetActive(true);
var cv = go.GetComponent<CardView>();
cv?.Bind(view.Model, false);
_cardGOs.Add(go);
}
gameObject.SetActive(true);
}
// ── Close ───────────────────────────────────────────────
public void Close()
{
foreach (var go in _cardGOs) if (go) Destroy(go);
_cardGOs.Clear();
gameObject.SetActive(false);
}
}
}
@@ -15,6 +15,7 @@ namespace CommanderAILab.UI
/// <summary>
/// Simulator scene — configure and run multi-player simulations,
/// poll status, display results per deck.
/// Also provides a Play Live button that routes to the Battleground scene.
/// </summary>
public class SimulationController : MonoBehaviour
{
@@ -22,6 +23,8 @@ namespace CommanderAILab.UI
[Header("Navigation")]
[SerializeField] private Button backButton;
[SerializeField] private Button startSimButton;
/// <summary>Routes to the Battleground scene for a live 4-player game.</summary>
[SerializeField] private Button playLiveButton;
// ── Config Panel ──────────────────────────────────────────────────────
[Header("Config")]
@@ -74,6 +77,9 @@ namespace CommanderAILab.UI
startSimButton.onClick.AddListener(OnStartSim);
retryButton.onClick.AddListener(OnStartSim);
// Play Live: save current deck selections and load Battleground scene
playLiveButton?.onClick.AddListener(OnPlayLive);
numPlayersSlider.minValue = 2;
numPlayersSlider.maxValue = 4;
numPlayersSlider.wholeNumbers = true;
@@ -83,12 +89,10 @@ namespace CommanderAILab.UI
errorPanel.SetActive(false);
// Check for a deck exported from DeckBuilder
string simDeck = PlayerPrefs.GetString("SimDeck", "");
if (!string.IsNullOrEmpty(simDeck))
{
PlayerPrefs.DeleteKey("SimDeck");
// Pre-populate deck dropdown 0 after decks load — stored for later
PlayerPrefs.SetString("_SimDeckPending", simDeck);
}
@@ -96,6 +100,29 @@ namespace CommanderAILab.UI
LoadAiProfiles();
}
// ── Play Live ─────────────────────────────────────────────────────────
private void OnPlayLive()
{
// Persist current deck selections so LobbySetupModal can pre-populate
var seats = new List<object>();
void AddSeat(TMP_Dropdown dd, bool isHuman, int seatIdx)
{
string deckName = "";
if (dd != null && dd.value > 0 && (dd.value - 1) < _decks.Count)
deckName = _decks[dd.value - 1].name;
seats.Add(new { isHuman, deckName, aiStyle = "Aggro" });
}
AddSeat(deckDropdown0, true, 0);
AddSeat(deckDropdown1, false, 1);
AddSeat(deckDropdown2, false, 2);
AddSeat(deckDropdown3, false, 3);
PlayerPrefs.SetString("BattleSeats", JsonConvert.SerializeObject(seats));
PlayerPrefs.Save();
SceneManager.LoadScene("Battleground");
}
// ── Player Count ──────────────────────────────────────────────────────
private void OnNumPlayersChanged(float value)
{
@@ -103,7 +130,6 @@ namespace CommanderAILab.UI
if (numPlayersLabel != null)
numPlayersLabel.text = $"Players: {count}";
// Show/hide optional deck dropdowns based on count
if (deckDropdown2 != null) deckDropdown2.gameObject.SetActive(count >= 3);
if (deckDropdown3 != null) deckDropdown3.gameObject.SetActive(count >= 4);
}
@@ -143,14 +169,12 @@ namespace CommanderAILab.UI
Fill(deckDropdown2);
Fill(deckDropdown3);
// Apply any pending deck from DeckBuilder export
string pending = PlayerPrefs.GetString("_SimDeckPending", "");
if (!string.IsNullOrEmpty(pending))
{
PlayerPrefs.DeleteKey("_SimDeckPending");
try
{
// Try to match by name from the JSON
var exported = JsonConvert.DeserializeObject<LabDeckEntry>(pending);
if (exported != null && !string.IsNullOrEmpty(exported.name))
{
@@ -179,7 +203,6 @@ namespace CommanderAILab.UI
},
err =>
{
// Non-fatal: profiles may not be available yet
_profiles = new List<AiProfile>();
PopulateProfileDropdown();
});
@@ -203,7 +226,6 @@ namespace CommanderAILab.UI
int numPlayers = Mathf.RoundToInt(numPlayersSlider.value);
// Collect selected deck names (index 0 = "-- Select Deck --")
var selectedDecks = new List<string>();
void TryAdd(TMP_Dropdown dd)
{
@@ -292,7 +314,6 @@ namespace CommanderAILab.UI
bool done = false;
string errorMsg = null;
SimStatusModel latestStatus = null;
_api.GetSimStatus(batchId,
json =>
@@ -301,7 +322,6 @@ namespace CommanderAILab.UI
{
var status = JsonConvert.DeserializeObject<SimStatusModel>(json);
if (status == null) { done = true; errorMsg = "Empty status response."; return; }
latestStatus = status;
if (!string.IsNullOrEmpty(status.error))
{
@@ -332,7 +352,6 @@ namespace CommanderAILab.UI
},
err => { done = true; errorMsg = err; });
// Wait one frame so callbacks have fired
yield return null;
if (!string.IsNullOrEmpty(errorMsg))
@@ -424,7 +443,7 @@ namespace CommanderAILab.UI
errorText.text = msg;
}
// ── Helper Models (private inner classes) ─────────────────────────────
// ── Helper Models ─────────────────────────────────────────────────────
[Serializable] private class DecksWrapper { public List<LabDeckEntry> decks; }
[Serializable] private class LabDeckEntry { public string name; public string source; public string commander; }
[Serializable] private class ProfilesWrapper { public List<AiProfile> profiles; }
@@ -435,6 +454,13 @@ namespace CommanderAILab.UI
public bool useLearnedPolicy; public string policyStyle;
public int seed; public int clock;
}
[Serializable] private class SimStartResponse { public string batchId; }
[Serializable] private class SimStatusModel
{
public bool running; public int total; public int completed;
public int totalGames; public int gamesCompleted;
public float elapsedMs; public float simsPerSec; public string error;
}
[Serializable] private class SimResult { public Summary summary; }
[Serializable] private class Summary { public List<ResultEntry> perDeck; }
[Serializable] private class ResultEntry { public string deckName; public float winRate; public int wins; public int losses; }
+1
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@@ -0,0 +1 @@
"""WebSocket module for real-time game state streaming to Unity."""
+165
View File
@@ -0,0 +1,165 @@
"""WebSocket connection manager for game-state streaming.
Manages per-game-session WebSocket connections so the server can
push state deltas to every connected Unity client in real time.
"""
from __future__ import annotations
import asyncio
import logging
import time
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Set
from fastapi import WebSocket, WebSocketDisconnect
log = logging.getLogger("ws.connection_manager")
@dataclass
class ClientInfo:
"""Metadata for a single connected WebSocket client."""
ws: WebSocket
client_id: str
game_id: str
connected_at: float = field(default_factory=time.time)
last_ping: float = field(default_factory=time.time)
messages_sent: int = 0
class ConnectionManager:
"""Manages WebSocket connections grouped by game_id.
Usage:
manager = ConnectionManager()
await manager.connect(websocket, game_id, client_id)
await manager.broadcast_to_game(game_id, payload)
manager.disconnect(websocket, game_id)
"""
def __init__(self, heartbeat_interval: float = 30.0):
# game_id -> set of ClientInfo
self._games: Dict[str, Dict[str, ClientInfo]] = {}
self._heartbeat_interval = heartbeat_interval
self._lock = asyncio.Lock()
# ── Connect / Disconnect ───────────────────────────────
async def connect(
self, ws: WebSocket, game_id: str, client_id: str
) -> ClientInfo:
"""Accept and register a WebSocket connection."""
await ws.accept()
client = ClientInfo(ws=ws, client_id=client_id, game_id=game_id)
async with self._lock:
if game_id not in self._games:
self._games[game_id] = {}
self._games[game_id][client_id] = client
log.info(
"WS connected: client=%s game=%s (total=%d)",
client_id, game_id, len(self._games[game_id]),
)
return client
async def disconnect(self, game_id: str, client_id: str) -> None:
"""Remove a client from the game session."""
async with self._lock:
game = self._games.get(game_id)
if game and client_id in game:
del game[client_id]
if not game:
del self._games[game_id]
log.info("WS disconnected: client=%s game=%s", client_id, game_id)
# ── Broadcasting ───────────────────────────────────────
async def broadcast_to_game(
self, game_id: str, payload: dict
) -> int:
"""Send a JSON payload to every client in a game session.
Returns the number of clients that received the message.
Silently removes clients whose connections are broken.
"""
async with self._lock:
game = self._games.get(game_id)
if not game:
return 0
clients = list(game.values())
sent = 0
dead: List[str] = []
for client in clients:
try:
await client.ws.send_json(payload)
client.messages_sent += 1
sent += 1
except Exception:
dead.append(client.client_id)
# Clean up broken connections
if dead:
async with self._lock:
game = self._games.get(game_id)
if game:
for cid in dead:
game.pop(cid, None)
if not game:
del self._games[game_id]
log.warning(
"Removed %d dead connection(s) from game %s",
len(dead), game_id,
)
return sent
async def send_to_client(
self, game_id: str, client_id: str, payload: dict
) -> bool:
"""Send a JSON payload to a specific client."""
async with self._lock:
game = self._games.get(game_id)
if not game:
return False
client = game.get(client_id)
if not client:
return False
try:
await client.ws.send_json(payload)
client.messages_sent += 1
return True
except Exception:
await self.disconnect(game_id, client_id)
return False
# ── Queries ────────────────────────────────────────────
def get_game_clients(self, game_id: str) -> List[str]:
"""Return client IDs connected to a game."""
game = self._games.get(game_id, {})
return list(game.keys())
def active_games(self) -> List[str]:
"""Return all game IDs with active connections."""
return list(self._games.keys())
@property
def total_connections(self) -> int:
return sum(len(g) for g in self._games.values())
def stats(self) -> dict:
"""Return connection statistics."""
return {
"active_games": len(self._games),
"total_connections": self.total_connections,
"games": {
gid: {
"clients": len(clients),
"client_ids": list(clients.keys()),
}
for gid, clients in self._games.items()
},
}
# Module-level singleton
game_manager = ConnectionManager()
+211
View File
@@ -0,0 +1,211 @@
"""Game-state delta engine.
Compares successive game-state snapshots and produces minimal
JSON deltas so that Unity only receives what changed — not
the full 4-player board every tick.
Delta format sent over WebSocket:
{
"type": "delta" | "snapshot" | "decision" | "game_over",
"seq": <monotonic int>,
"game_id": "...",
"turn": 5,
"phase": "main_1",
"timestamp": 1711400000.0,
"changes": [
{"path": "players.0.life", "op": "replace", "value": 37},
{"path": "players.1.battlefield", "op": "add", "value": {"name": "Sol Ring", ...}},
{"path": "stack", "op": "replace", "value": [...]},
]
}
"""
from __future__ import annotations
import copy
import logging
import time
from typing import Any, Dict, List, Optional, Tuple
log = logging.getLogger("ws.game_state")
def _deep_diff(
old: Any, new: Any, path: str = ""
) -> List[Dict[str, Any]]:
"""Recursively diff two JSON-serialisable structures.
Returns a list of RFC-6902-style patch operations:
{"op": "replace"|"add"|"remove", "path": "...", "value": ...}
"""
changes: List[Dict[str, Any]] = []
if old is None and new is not None:
return [{"op": "add", "path": path, "value": new}]
if old is not None and new is None:
return [{"op": "remove", "path": path}]
if type(old) != type(new):
return [{"op": "replace", "path": path, "value": new}]
if isinstance(new, dict):
all_keys = set(list(old.keys()) + list(new.keys()))
for key in sorted(all_keys):
child_path = f"{path}.{key}" if path else key
if key not in old:
changes.append({"op": "add", "path": child_path, "value": new[key]})
elif key not in new:
changes.append({"op": "remove", "path": child_path})
else:
changes.extend(_deep_diff(old[key], new[key], child_path))
return changes
if isinstance(new, list):
# For lists (battlefield, hand, graveyard, stack) we do a
# simple length+element comparison. Zone lists change
# frequently and a full LCS diff is overkill for game state.
if old == new:
return []
# If the list is short or changed significantly, send whole list
if len(old) != len(new) or _list_diff_ratio(old, new) > 0.5:
return [{"op": "replace", "path": path, "value": new}]
# Element-wise diff for same-length lists with few changes
for i, (o, n) in enumerate(zip(old, new)):
child_path = f"{path}.{i}"
changes.extend(_deep_diff(o, n, child_path))
return changes
# Scalar comparison
if old != new:
return [{"op": "replace", "path": path, "value": new}]
return []
def _list_diff_ratio(old: list, new: list) -> float:
"""Fraction of elements that differ between two same-length lists."""
if not old:
return 0.0
diffs = sum(1 for a, b in zip(old, new) if a != b)
return diffs / len(old)
class GameStateTracker:
"""Tracks per-game state and emits deltas on each update.
Usage:
tracker = GameStateTracker(game_id)
delta = tracker.update(new_snapshot)
# delta is a dict ready to send via WebSocket
"""
def __init__(self, game_id: str):
self.game_id = game_id
self._last_snapshot: Optional[Dict] = None
self._seq: int = 0
self._created_at: float = time.time()
def update(self, snapshot: Dict) -> Dict:
"""Compare snapshot with previous state, return delta message.
First call returns a full snapshot message (type="snapshot").
Subsequent calls return delta messages (type="delta").
"""
self._seq += 1
now = time.time()
if self._last_snapshot is None:
# First update — send full snapshot
self._last_snapshot = copy.deepcopy(snapshot)
return {
"type": "snapshot",
"seq": self._seq,
"game_id": self.game_id,
"turn": snapshot.get("turn", 0),
"phase": snapshot.get("phase", ""),
"timestamp": now,
"state": snapshot,
}
# Compute delta
changes = _deep_diff(self._last_snapshot, snapshot)
self._last_snapshot = copy.deepcopy(snapshot)
if not changes:
return {
"type": "heartbeat",
"seq": self._seq,
"game_id": self.game_id,
"timestamp": now,
}
return {
"type": "delta",
"seq": self._seq,
"game_id": self.game_id,
"turn": snapshot.get("turn", 0),
"phase": snapshot.get("phase", ""),
"timestamp": now,
"changes": changes,
}
def make_decision_event(
self,
action: str,
action_index: int,
confidence: float,
probabilities: Optional[Dict[str, float]] = None,
inference_ms: float = 0.0,
) -> Dict:
"""Build a decision event message for the Unity client."""
self._seq += 1
return {
"type": "decision",
"seq": self._seq,
"game_id": self.game_id,
"timestamp": time.time(),
"action": action,
"action_index": action_index,
"confidence": confidence,
"probabilities": probabilities or {},
"inference_ms": inference_ms,
}
def make_game_over_event(
self,
winner_seat: int,
reason: str = "",
turns_played: int = 0,
) -> Dict:
"""Build a game-over event message."""
self._seq += 1
return {
"type": "game_over",
"seq": self._seq,
"game_id": self.game_id,
"timestamp": time.time(),
"winner_seat": winner_seat,
"reason": reason,
"turns_played": turns_played,
}
@property
def sequence(self) -> int:
return self._seq
@property
def has_baseline(self) -> bool:
return self._last_snapshot is not None
# Per-game tracker registry
_trackers: Dict[str, GameStateTracker] = {}
def get_tracker(game_id: str) -> GameStateTracker:
"""Get or create a state tracker for a game."""
if game_id not in _trackers:
_trackers[game_id] = GameStateTracker(game_id)
return _trackers[game_id]
def remove_tracker(game_id: str) -> None:
"""Remove a game tracker (called on game end)."""
_trackers.pop(game_id, None)