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Author SHA1 Message Date
fez5587andClaude Sonnet 5.5 7d92387bb8 coach: accept rules_context in build_user_prompt (every Coach request failed)
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016Er49soaMyG7Yburq57PeB
2026-10-26 23:01:48 -05:00
fez5587andClaude Sonnet 5.5 a09eb5b21a farmhouse: sync Forge fixes (display, one deck folder, absolute result path) and phase model-seat safety (pre-validation, circuit-breaker, cost counters)
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016Er49soaMyG7Yburq57PeB
2026-10-26 22:32:14 -05:00
fez5587andClaude Sonnet 5.5 0efd4847a3 FarmHouse: Forge in the image, rules-accurate simulation with phase-rs, model seats
Forge restored as the default batch engine (and documented: farmhouse/forge). New: routes/farmhouse_phase.py and ui/phase-sim.html (four saved decks play real Commander games on the phase-rs engine; wins by seat, 95% interval, warnings), reference copies of the phase-sim service, Dockerfiles, deployment and the llm-seat crate (model-driven seats that choose among the engine's own legal options, with fallback to phase's AI and a call cap) under farmhouse/phase. Patches registered in lab_api.py, linked from the home page; Forge batches clamped to one thread.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016Er49soaMyG7Yburq57PeB
2026-10-26 20:20:10 -05:00
fez5587andClaude Sonnet 5.5 946a3706da docs: assessment of phase-rs and commander-playtester for the Magic component
Researched from public sources only; nothing run or vendored. phase: rules engine, headless ai-duel, phase-llm model seats speaking the System One protocol (Laya/Kev), 96% of the saved decks' cards supported. playtester: browser app on phase, no licence file.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016Er49soaMyG7Yburq57PeB
2026-10-26 19:50:51 -05:00
fez5587andClaude Sonnet 5.5 6569bb6f0d FarmHouse: Laya connections (Forge concrete path, extra questions, capture, fine-tuning data, card roles)
farmhouse_laya.py: Laya as a second opinion over Forge's concrete candidate ids (shadow by default; assist never overrides a pick Forge tagged stops_immediate_win/wins_now), two extra typed questions in the same forward pass, a laya-shadow-v1 capture log, batched cached card-role classification. scripts/farmhouse_laya_dataset.py turns the capture log into train/valid files with the baselines to beat; farmhouse_laya_roles_eval.py measures role accuracy. Hooks into farmhouse_decide.py. Reference copies of the Laya image, manifests and CI in farmhouse/laya/. docs/LAYA-CONNECTIONS.md. 39 offline tests. All off by default.

The matching hook in routes/policy.py is in the working tree only: that route belongs to the pending strategic-evaluator work.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016Er49soaMyG7Yburq57PeB
2026-10-26 19:39:01 -05:00
fez5587andClaude Sonnet 5.5 32ab268f88 FarmHouse: card search returns each card once (index has one entry per printing)
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016Er49soaMyG7Yburq57PeB
2026-10-26 18:48:20 -05:00
fez5587andClaude Sonnet 5.5 abb26a679c FARMHOUSE-CHANGES: decision model section, settings, ollama package
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016Er49soaMyG7Yburq57PeB
2026-10-26 18:23:07 -05:00
fez5587andClaude Sonnet 5.5 635890deb6 Fix 34e0fc6: keep deepseek_brain.py to the decision-model wiring only
That commit also picked up uncommitted work from another editor of the same working tree (strategic_context.py and candidate_actions), importing a module that is not in the repository. This restores the file to the decision wiring alone; the other work is untouched in the working tree.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016Er49soaMyG7Yburq57PeB
2026-10-26 18:05:47 -05:00
fez5587andClaude Sonnet 5.5 34e0fc6b2f FarmHouse: decision-model client (Laya/Kev) with shadow and assist modes
farmhouse_decide.py asks a /v1/systemone server which action to take; DECISION_MODE=shadow records agreement with the LLM, assist uses it when DECISION_MIN_CONF sure; off by default. scripts/farmhouse_decision_eval.py replays recorded decisions through it. 17 offline tests.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016Er49soaMyG7Yburq57PeB
2026-10-26 18:05:07 -05:00
fez5587andClaude Sonnet 5.5 b779987802 FARMHOUSE-CHANGES: notes on making LLM-opponent turns faster
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016Er49soaMyG7Yburq57PeB
2026-10-26 16:53:23 -05:00
fez5587andClaude Sonnet 5.5 fee8ded52d FarmHouse: run Python-only on a local LLM; simulator plays real games
Starts from upstream 54cc5e9. Cards reach the simulator with type and text (filled from the Scryfall bulk DB) and the right quantities; the LLM engine asks several questions per turn and with reasoning off; Moxfield and Archidekt deck links work in every import and card names resolve locally; deck builds are fitted to the commander's colour identity; Scryfall format/User-Agent/429 handling fixed; coach init fixed. See FARMHOUSE-CHANGES.md.

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_016Er49soaMyG7Yburq57PeB
2026-10-26 16:42:58 -05:00
rgriffithandClaude Opus 4.6 54cc5e9b2e fix(lan-client): swap P3/P4 grid positions for clockwise turn order
Swapped CSS grid-area, zone-row anchoring, commander-row anchoring,
label corners, and life-panel positions for .p3/.p4 so the turn
sequence P1→P2→P3→P4 reads clockwise around the battlefield.
Also added PLAYER_INDEX_TO_QUADRANT lookup in player-panels.js.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 22:22:32 -04:00
rgriffithandClaude Opus 4.6 9b79d18201 feat(lan-client): triggers panel + CMD DMG score badges + fix encoding project-wide
Left action panel revised to show triggers list + Create Token/Auto-Arrange.
CMD DMG badges added to center life-panel score areas (click opens matrix).
Removed renderEnhancedPanels calls that split the battlefield on damage adjust.
Added encoding='utf-8' to all bare open() calls across 17 Python files to
prevent cp1252 crashes on MTG card text with smart quotes/em-dashes.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 22:14:56 -04:00
rgriffithandClaude Opus 4.6 45ca961064 fix(lan-client): wire exit button to location.reload()
executeReset() references #game-board which doesn't exist in this HTML,
so the function threw a TypeError and silently did nothing. Swap the
exit button's onclick to location.reload() — the browser-refresh path
the user confirmed works to return to the setup screen.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 18:36:25 -04:00
rgriffithandClaude Opus 4.6 69bdd94ef2 feat(lan-client): hide AI hands + persist Scryfall image cache
AI hand privacy (rendering layer only):
- renderHandZone(): when player.isAI, render face-down violet card backs
  (dark purple diagonal hatch + ornate ring) instead of face-up thumbnails.
  Count stays public (standard MTG). No image, name, preview, drag, or
  click-to-open-modal on face-down thumbs.
- renderZoneModal(): filter the "Hand" tab out of the zone modal when
  inspecting an AI opponent; reset activeZoneTab to 'graveyard' if it was
  'hand' when an AI modal opens.
- New CSS: .hand-thumb.face-down (+ .overflow variant) with violet hatch
  pattern, radial glow, and centered purple ring pseudo-element.

Scryfall image cache persistence (eliminates per-game refetching):
- _scryfallCache now saves to localStorage under commanderLab.scryfallCache.v1
- 30-day TTL, 20,000-entry cap, graceful quota-exceeded handling
- Debounced auto-save (750ms) after each batch enrichment
- Cache-hit logging: logs hit ratio when refetching, or "from cache" when
  a whole game's card pool is already in localStorage
- clearScryfallCache() helper exposed on window for manual purges

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 18:32:43 -04:00
rgriffithandClaude Opus 4.6 8fbebc21f3 feat(lan-client): charcoal felt battlefield + top-bar polish + return-to-start
Battlefield (charcoal felt):
- Darken --felt / --felt-light / --felt-dark to #181818 / #202020 / #0d0d0d
- Tighter --felt-line opacities (0.06 / 0.12) on the darker surface
- Finer-grain noise texture (baseFrequency 1.2, tile 180px) at 0.14 alpha
  with mix-blend-mode: overlay so the grain rides on the felt
- Deeper vignette (35% center, rgba(0,0,0,0.6) at edges)

Top bar:
- .top-bar-logo: 14px → 28px, align-self: stretch, flex-centered so
  "COMMANDER" fills the full 44px height
- .phase-step: 10px → 11px, roomier padding (2px 8px), gap 2→4px,
  letter-spacing 0.02em
- .btn-util: 22×22 → 32×32, font 10px → 15px (readable icons again)
- New .btn-util.btn-util-exit ✕ button (red-tinted, wired to
  confirmReset() → return to setup screen)
- Remove deck generator (⚡) and collection (📚) util buttons from the
  top bar (those panels are still reachable from other entry points)

Setup screen:
- Swap .setup-title from MTG gold to the same neon violet sign effect
  as the top-bar logo (4-layer text-shadow, uppercase, ls 0.08em)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 18:05:47 -04:00
rgriffithandClaude Opus 4.6 7cfdecb2c3 feat(lan-client): polish setup screen — charcoal bg, no rainbow bar, fit-to-screen
- .setup-card background: rgba(26,26,46,0.97) violet-tint → rgba(22,22,22,0.97)
  true charcoal that matches --color-surface-3
- Remove the 3px rainbow p1→p4 gradient strip from .setup-card::before
  (display: none)
- Compress spacing so the full lobby fits without scrolling:
  - padding 2.5rem → 1.25rem 1.5rem
  - title text-3xl → text-2xl, subtitle 2rem margin → 0.85rem
  - section margin 1.5rem → 0.75rem
  - label text-sm → text-xs with tighter margin
  - player-count-btn padding 0.75rem → 0.4rem
  - deck-precon-list max-height 140px → 90px
  - btn-start padding 1rem → 0.6rem

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 17:53:27 -04:00
rgriffithandClaude Opus 4.6 38f3837f5a feat(lan-client): animated smoky purple summoning-sick overlay
Replace the nearly-invisible black diagonal stripes with a layered
animated purple hatch so sick creatures read instantly as "can't attack":

- Double-layer repeating-linear-gradient (two purple stripe opacities)
- sick-drift animation (3s linear) — stripes crawl diagonally
- sick-pulse animation (2.4s ease-in-out) — soft opacity breathing
- Inset 14px purple box-shadow for hazy edge glow

Matches the --accent / violet palette from the AI Lab redesign.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 17:42:27 -04:00
rgriffithandClaude Opus 4.6 681888adcd feat(lan-client): port pure-black + violet + neon palette from AI Lab redesign
Apply the same 5-step visual redesign to the LAN client's MTG battlefield
theme while preserving functional game colors (mana, felt table, player
quadrants p1-p4).

- Swap Google Fonts import from Cinzel+Inter to Barlow Condensed + Barlow
- Pure black surfaces (#080808 / 0e / 11 / 16 / 1a)
- Swap primary accent from blue #5b8dd9 to electric violet #8b5cf6
- Square industrial radii (2/4/6/8px)
- Replace all hardcoded 'Cinzel' / 'Inter' with var(--font-display) /
  var(--font-body)
- Pure black top bar with violet underglow shadow
- Convert .top-bar-logo "Commander" title from gold to 4-layer violet
  neon sign with hover brightening
- Sweep 42 hardcoded rgba(91,141,217,...) blue values downstream to
  rgba(139,92,246,...) violet across buttons, zone tabs, stack items,
  combat UI, etc.

MTG functional colors (mana W/U/B/R/G, felt table, player quadrants)
remain intact.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 17:39:30 -04:00
rgriffithandClaude Opus 4.6 88c0d94d39 feat(ui): swap Inter for Barlow / Barlow Condensed display font
Load Barlow + Barlow Condensed from Google Fonts across all 8 HTML
entry points. In global.css introduce --font-display (Barlow Condensed)
alongside --font (Barlow), then wire the display font into every
chrome/heading selector: h1-h6, .page-header, .panel-header, .btn,
.stat-label/.stat-value, .form-label, .badge-status, .data-table
thead, .tag, .nav-brand, .nav-links. Body copy stays in Barlow for
readability.

Also normalize a few hardcoded font-families to design-system vars:
collection.css Cascadia Code stacks and deckgenerator.css Courier New
now use var(--font-mono); canvas rendering in distillation.js and
ai-lab.js swapped from Inter to Barlow so charts match the new type.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 17:26:06 -04:00
KoalaTrapLord 4fdddf332e fix: add logging.basicConfig to trainer.py so training output is visible
The trainer used logger.info() for all output but never configured a
handler, so training ran silently with no console output.
2026-04-05 18:10:09 +00:00
KoalaTrapLord 213b9dc32d fix: total_mem → total_memory in trainer.py (PyTorch API) 2026-04-05 18:06:50 +00:00
KoalaTrapLord 2af1d58104 fix: write ML JSONL to commander-ai-lab/results/ (not Forge CWD)
LabCli passed relative 'results' to enableMlLogging(), which resolved
to Forge-Repo/forge-gui/results/ because the Java subprocess CWD is
forgeWorkDir. Now uses outputPath's parent directory (absolute) so
JSONL training data goes to commander-ai-lab/results/ where the
Python ML pipeline and Training page expect it.
2026-04-05 17:29:21 +00:00
KoalaTrapLord 547a84d8c2 fix: DecisionExtractor regex patterns to match actual Forge verbose output
Forge verbose log uses different verb forms and prefixes than the
patterns expected:
  - Turn: 'Turn: Turn 5 (Ai(1)' not 'Turn 5 (Ai(1)'
  - Cast: 'cast' (no s) not 'casts', 'Add To Stack:' prefix, no period
  - Land: 'played' not 'plays', 'Land:' prefix, card ID suffix '(76)'
  - Draw: 'drew' variant, card ID suffix

Updated all four patterns to match both old and new Forge output
formats. This is the final fix for the training data pipeline —
DecisionExtractor will now find cast/land/draw/attack events in
the Forge game log and emit JSONL training snapshots.
2026-04-05 17:24:34 +00:00
KoalaTrapLord 71b86b74eb fix: compile error in BatchRunner deck name log line 2026-04-05 16:55:38 +00:00
KoalaTrapLord d00093897a fix: read Name= from .dck header for Forge deck lookup
Forge's sim mode looks up decks by the Name= field inside the .dck
file metadata header, NOT by filename. For example:
  Filename: Counter_Intelligence.dck
  Header:   Name=Counter Intelligence
  Forge expects: 'Counter Intelligence' (with space)

BatchRunner now reads the Name= field from the .dck file and passes
that to Forge. Falls back to filename with underscores→spaces if
the Name= field can't be read.
2026-04-05 16:54:38 +00:00
KoalaTrapLord 0b01f1c467 fix: pass bare deck names to Forge sim (not full paths)
Forge's sim mode looks up decks by NAME in its own
%APPDATA%/Forge/decks/commander/ directory. Passing a full path like
D:\...\precon-decks\Counterpunch.dck causes 'Could not load deck'.

Now strips the path and .dck extension so Forge sees just the bare
name (e.g. 'Counterpunch') and finds it in its deck folder.

Precon .dck files must be copied to %APPDATA%/Forge/decks/commander/
for Forge to find them.
2026-04-05 16:47:54 +00:00
KoalaTrapLord 65873555bc fix: show all batch log lines in Live Output (was truncated to last 30)
Frontend: removed .slice(-30) — now shows all lines from backend.
Backend: bumped log line limit from 200 to 2000 so full Forge game
output is captured for debugging.
2026-04-05 16:35:21 +00:00
KoalaTrapLord 8995d743af fix: remove !quiet guard from ML decision logging in BatchRunner
The condition 'mlLoggingEnabled && !quiet && mlDecisionLogger != null'
silently skipped ML training data extraction whenever quiet mode was
active. DecisionExtractor works with the same log lines regardless of
quiet mode, so the guard was unnecessary and prevented JSONL generation.

Now: mlLoggingEnabled && mlDecisionLogger != null — ML logging runs in
all modes as long as --ml-log is passed.
2026-04-05 16:19:56 +00:00
KoalaTrapLord 14ff543135 fix: DecisionLogger JSONL filename prefix — ml-decisions-sim-* for dataset_builder discovery
The Java DecisionLogger was writing files as ml-decisions-{batchId}.jsonl
but the Python dataset_builder.py scans for ml-decisions-sim-*.jsonl to
categorize them as 'forge' source training data.

This one-line fix adds the 'sim-' prefix so the Forge batch heuristic
path now feeds directly into the ML training pipeline:

  BatchRunner → DecisionExtractor → DecisionLogger
    → results/ml-decisions-sim-{batchId}.jsonl
    → dataset_builder.py discovers as 'forge' source
    → encode → train

The full chain was already wired (Issue #83 Phase 1 infrastructure),
this naming mismatch was the only thing preventing it from working.

Refs: Issue #83 Phase 1
2026-04-05 16:04:12 +00:00
rgriffithandClaude Opus 4.6 ea23f97b9c chore: ignore runtime artifacts, add patch scripts & training manifest
Ignore static/, logs/, data/rag_chroma/, api/, image_cache/, .claude/,
precon-decks/, and dependency-reduced-pom.xml — all runtime/build
artifacts that were polluting git status. Also stage the one-off
patch scripts (fix_paths.py, patch_eval.py, patch_phase1.py,
forge_evaluator_FIXED.py) and training_manifest.json so they're
tracked alongside the code they support.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 10:48:35 -04:00
KoalaTrapLord a23fdc55a4 feat: PolicyControllerAi integration — Forge AI decision interception via policy server
## New File
- PolicyControllerAi.java (840 lines): Extends Forge's PlayerControllerAi
  to intercept AI decisions at chooseSpellAbilityToPlay() and route them
  through the Python policy server. Falls back to Forge built-in AI on
  any failure. Inner PolicyAiController overrides AiController with full
  legal action gathering via ComputerUtilAbility.

## Wiring
- GameSession.java: Import + Javadoc for PolicyControllerAi decision flow.
  Logs decision pipeline when policy server connects. End-of-game reward
  submission via PolicyControllerAi lifecycle.
- BatchRunner.java: New setPolicyServer(url) method. Appends
  --policy midrange --policyServer <url> to Forge subprocess command when
  policy server URL is configured.

## Path Updates (Forge-Repo rename)
- README.md: Forge link → KoalaTrapLord/Forge-Repo, build path updated
- .env.example: FORGE_JAR_PATH and FORGE_WORKING_DIR → Forge-Repo
- lab_api.py: Auto-detect paths try Forge-Repo first, forge-repo as legacy

## MacroActionExecutor
- Added public isRemovalCard(), isDrawCard(), isRampCard() wrappers so
  PolicyControllerAi can consult the same card-role databases.

Refs: Issue #83 Phase 2.4
2026-04-05 14:47:15 +00:00
rgriffithandClaude Opus 4.6 4cae97ab44 fix(lan-client): compact phase pills, wrap NEXT PHASE/NEXT TURN labels
The phase track (BEGIN · MAIN 1 · COMBAT · END) was losing labels to
ellipsis on narrow top bars, and the primary action buttons were
clipping "NEXT PHASE"/"NEXT TURN" because they had white-space: nowrap
and a fixed height.

.phase-step:
  - tighter padding (1px 5px) and gap (2px)
  - no letter-spacing, smaller border-radius, flex-shrink: 1 so pills
    compress uniformly when the top bar is crowded
  - merged duplicate .phase-step.active rule and made it bolder so the
    active step still reads well after the shrink

.btn-phase (NEXT PHASE / NEXT TURN):
  - white-space: normal + word-break: break-word enable two-line wrap
  - height: auto + min-height: 34px lets the label breathe inside
    the 44px top bar
  - min-width: 64px / max-width: 80px keeps the buttons square-ish
  - display: flex centers the wrapped text vertically & horizontally
  - align-items: stretch on .top-bar-actions so both buttons match
    the tallest sibling's height

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 10:39:18 -04:00
rgriffithandClaude Opus 4.6 d308e8f413 fix: AI land-only bug — enforce 1-land/turn, populate legal moves, tune policy
Addresses the three root causes in commander-ai-lab-land-only-bug-analysis.md
plus several collateral fixes discovered along the way.

P0 — Land-per-turn enforcement (engine.py, deepseek_engine.py, turn_manager.py)
  * Guard _play_land() against double plays by tracking
    _lands_played_this_turn vs _land_plays_this_turn_limit on each player
  * Reset per-turn counters at the start of each player's turn
  * Ramp cards that grant additional plays can bump the limit

P0 — Legal move pipeline (new legal_move_generator.py, game_state.py)
  * New generate_legal_moves() emits the full macro-action set
    (play_land, cast_creature, cast_removal, cast_ramp, cast_draw,
    cast_commander, attack, block, pass_priority) based on phase,
    active player, land-drop state, mana, and commander tax
  * get_legal_moves() delegates to the new generator instead of the
    pass-only stub that blocked the turn_manager/policy-server path
  * apply_move() gains handlers for play_land, cast_spell,
    cast_commander, and declare_attacker — spells resolve to
    battlefield or graveyard, stats update correctly

P1 — Policy exploration (LearnedPolicy.java, scope.py)
  * Default greedy=false so the neural policy samples its
    distribution instead of always taking argmax
  * Raise ppo_entropy_coeff 0.01 → 0.04 to keep the action
    distribution flat during PPO self-play and prevent the
    pass/land collapse

Collateral fixes pulled in during the same session:
  * lan-client/precon-loader.js: await enrichAllZonesWithScryfall()
    so opening-hand cards have typeLine/cmc before the AI's first turn
  * lan-client/ui.js: closeZoneModal auto-shuffles the library
    whenever the zone modal closes
  * routes/game.py: BatchState() was missing total_games/threads,
    crashing POST /api/game/start
  * routes/deckgen.py: dead code after raise left headers undefined,
    breaking the Anthropic API path

All sim/phase/integration tests pass (650+).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 10:34:37 -04:00
rgriffithandClaude Opus 4.6 44123f7622 fix: AI only plays lands — sync card enrichment, mana pool check, field alignment
- Add _enrichCardFromCache() in precon-loader.js for synchronous card
  enrichment at draw time (typeLine, cmc, oracle roles available before
  async Scryfall batch completes)
- Call _enrichCardFromCache in drawCard() so AI has complete card data
  on its first turn
- Improve _canAfford in ai-bridge.js: check actual mana pool before
  falling back to untapped land count; free spells (cmc 0) always castable
- Fix totalCards field name mismatch in ui.js and engine.js (lab.py
  returns totalCards, not card_count/total_cards)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 01:43:29 -04:00
rgriffithandClaude Opus 4.6 9eaa41a4ae feat: deck dropdown, user-decks pipeline, deckgen fixes, gpt-oss removal
- Add user-decks/ directory and USER_DECKS_DIR config for exported decks
- Update export-to-sim to write to user-decks/ with static mount in lab_api
- Replace import/paste UI with API-backed "My Decks" dropdown in LAN client
  and AI Lab, fetching from GET /api/lab/decks with source grouping
- Fix GET /api/lab/decks to JOIN deck_cards for totalCards count
- Fix deckgenerator: add missing DOM elements (dg-strategy-bar, dg-stats-bar,
  dg-card-groups) inside #dg-results, null guards on render functions,
  fix setLoading always hiding results
- Replace gpt-oss:20b with claude-opus-4-20250514 across all files
- Add user-decks/ to _BUILTIN_DECK_DIRS in deck_service.py
- Enrich missing type_line from Forge card cache in DB deck path
- Collapse import bar behind <details> toggle in AI Lab

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 01:17:30 -04:00
rgriffithandClaude Opus 4.6 409aba26f4 feat: AI bug fixes, deck/collection migration, and enrichment pipeline
- Fix AI turn pipeline: add cmc to Scryfall cache, tag isRamp/isRemoval/
  isBoardWipe from oracle text during enrichment, enable post-combat spell
  casting for all difficulty levels
- Migrate deck builder, deck generator, and collection from ui/ to
  lan-client/ as iframe-embedded panels with LAB_API_BASE patching
- Fix ai-bridge.js: turnNumber, deck_name, battlefield serialization,
  5-playstyle mapping, window exports for cross-script access
- Fix engine.js: battlefieldCards flat array (not player.battlefield),
  aiDeclareAttacks extraction, combat overlay fixes
- Fix collection.js: template literal, column visibility alignment,
  set_code key, oracle text column, sortable category
- Add exile/graveyard last-card image rendering in ui.js
- Make state_encoder.py resilient to mixed input types
- Enrich type_line from Scryfall at card insert in deckbuilder.py

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-05 00:05:23 -04:00
rgriffithandClaude Opus 4.6 30c3ab5a89 fix: pass gameState to _canAfford in MacroActionMapper
Static method was referencing bare gameState which is always null.
Now accepts gameState as parameter, and all 5 callers pass it through.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-04 17:02:05 -04:00
KoalaTrapLord b7f9c119cb feat(overnight): add --ds-weight arg to pass DeepSeek upsampling weight to /api/ml/train 2026-04-04 17:01:10 -04:00
rgriffithandClaude Opus 4.6 bbb7f7a966 fix: _getBattlefieldCards reads battlefieldCards instead of battlefield
Was reading gameState.battlefield (doesn't exist) causing AI to
receive empty board state. Now reads gameState.battlefieldCards
and matches by ownerIndex.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-04 17:00:39 -04:00
rgriffithandClaude Opus 4.6 ef739e1f49 feat: UI improvements — silver commander border, life panel border, hand drag-and-drop, card placement
- Add bright silver 3D smoky border around commander card zones
- Wrap all 4 life panels in a single bordered container
- Add drag-and-drop from hand zone to battlefield with ghost preview
- Update card placement to center of battlefield, avoiding overlaid zones
- Fix card dimensions in placement calc (110x150 -> 63x84)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-04 16:50:56 -04:00
rgriffithandClaude Opus 4.6 920d7d9787 fix: remove player color border and owner bar from battlefield cards
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-04 15:55:56 -04:00
rgriffithandClaude Opus 4.6 52d51ebd5e feat: battlefield UI overhaul — life panels, card sizing, layout
- Move life/cmd-dmg/poison into 3-box life panels as a top-level
  overlay (z-index 50) centered at quadrant intersections
- Remove center emblem
- Match battlefield card size to hand cards (63x84px)
- Overlay zone-row and commander-row on battlefield edges to
  eliminate dead space and maximize battlefield area
- Restructure poison badges from per-player absolute positioning
  into life-panel counters box

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-04 15:50:35 -04:00
rgriffithandClaude Opus 4.6 db3675aa6a feat: enlarge hand card thumbnails to fill zone height
Scale .hand-thumb from 32x45px to 63x84px so cards fill the
hand zone vertically while maintaining MTG card proportions.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-04 14:36:50 -04:00
rgriffith 99044342c2 fix: remove stray } after showManaPool stub (line 1856) 2026-04-04 13:33:37 -04:00
rgriffith d5fd648139 fix: update tracked paths — files moved from root to lan-client/ 2026-04-03 23:31:36 -04:00
rgriffith 022f89478d fix: restore ui.js from placeholder corruption + Phase 1 patches 2026-04-03 23:25:01 -04:00
KoalaTrapLord 336f913d4b feat(phase-1): no-op mana pool display functions in ui.js
- renderManaPool() -> stub (floating #mana-pool-display removed from UI)
- showManaPool() -> stub
- Remove 'Mana Auto-Clear' row from renderSettingsPanel()

Internal mana tracking state (manaPool object, canPayManaCost,
clearManaPool, clearAllManaPools, etc.) fully preserved in engine.js.
createInitialState() already had energy/experience/rad/emblems.
setupManaPoolDrag() in engine.js already a no-op stub.
index.html and style.css require no changes.
2026-04-03 18:54:22 -04:00
KoalaTrapLord 661847a4d8 feat(phase-1): no-op mana pool display functions in ui.js
- renderManaPool() → stub comment (floating #mana-pool-display removed)
- showManaPool() → stub comment
- Remove 'Mana Auto-Clear' row from renderSettingsPanel()

Internal mana tracking state (manaPool object, canPayManaCost,
clearManaPool, etc.) fully preserved in engine.js.
createInitialState() already had energy/experience/rad/emblems.
setupManaPoolDrag() in engine.js already stubbed.
index.html and style.css require no changes.

Closes #190 Phase 1
2026-04-03 18:51:15 -04:00
KoalaTrapLord e710fd5e03 placeholder 2026-04-03 18:50:51 -04:00
KoalaTrapLord 85af163123 Wire Forge IPC into POST /api/game/start via run_batch_subprocess + --policy flag 2026-04-03 17:57:35 -04:00
KoalaTrapLord f2e87feb4e Fix default port: 8000 -> 8080 2026-04-03 17:54:15 -04:00
KoalaTrapLord 948f8e0f8f Fix PS1 parse errors: escape apostrophes, fix string terminators, fix scriptblock braces 2026-04-03 17:52:08 -04:00
KoalaTrapLord 84d56bf901 Add Test-PolicyServer.ps1 — smoke-test health + decide endpoint from PowerShell 2026-04-03 17:50:04 -04:00
KoalaTrapLord 74d24d606c fix: wire PolicyClient into real Forge IPC path via Priority interception
Forge's sim mode does not honour --policy/--policyServer CLI flags, so
the Python brain was never called during live games. AI only played lands
because Forge ran its built-in AI for every decision.

Fix: when parseForgeLine() detects a Priority event for an AI seat, the
session now immediately calls policyClient.decide() with the current
state snapshot and POSTs the result to /api/policy/override before Forge
reads the next line. This mirrors what handleHumanTurn() already does
for seat 0. The synthetic fallback path is unchanged.
2026-04-03 17:42:01 -04:00
KoalaTrapLord 98e1092ebc feat: wire DecisionExporter into Forge pipeline + fix reward call signatures
- Add _export_forge_episode() adapter: translates ForgeEpisode transitions
  back into DecisionExporter.record_step() calls so Forge-sourced wins flow
  into the distillation JSONL pipeline (closes Phase 1 for real games)
- ppo_consumer() now accepts an optional `exporter: DecisionExporter` arg;
  begin_episode / record_step / end_episode are called around each episode
  as it is dequeued
- run_forge_pipeline() forwards the exporter kwarg down to ppo_consumer and
  calls exporter.flush() after the pipeline completes, returning the output
  path in the stats dict under "exporter_file"
- Fix snapshots_to_episode(): compute_terminal_reward() was called with
  non-existent kwargs `turns=` and `life_remaining=`; compute_intermediate_reward()
  was called with non-existent kwargs `life_delta=`, `cards_drawn=`,
  `creatures_played=`.  Both now use the correct signatures from reward.py
  (prev_state/curr_state/action_taken for intermediate; won/draw/timeout for
  terminal).  Forge snapshots carry `players` arrays so we reconstruct minimal
  prev/curr dicts for the intermediate path.

Ref: docs/CLOSED-LOOP-DISTILLATION.md Phase 1
2026-04-03 17:09:05 -04:00
KoalaTrapLord 9338ac881b fix(labeler): infer cast sub-type from hand contents when action.card absent
The Forge DecisionExporter writes action={"type":"cast"} without a card
field — action.get("card") and action.get("raw") always returned "",
causing every cast to fall through to the CAST_CREATURE default.

Fix: when card_name is empty, scan the active player's hand against
the removal / draw / ramp / commander name databases and pick the
highest-priority match found.  Priority order mirrors cast importance:
  removal > ramp > draw > commander > creature (default)

A hand-level match is a reasonable proxy for the decision made:
Forge's AI picks the highest-priority action from available options,
so if the hand contains a removal spell during main phase, the cast
action almost certainly represents that removal spell being played.

Also fixes print_label_distribution: was sorting by dict key (string)
instead of by index, causing the display order to be alphabetical
rather than matching the MacroAction enum order — making cast_creature
appear at position 0 in the sorted output even when it had counts.
Now prints in descending count order with correct enum-keyed lookup.
2026-04-03 16:55:59 -04:00
KoalaTrapLord c3ff9f8888 feat(data): add DeepSeek ds- source with 4x default upweight
_discover_jsonl_files now recognises ml-decisions-ds-*.jsonl as its own
"ds" source key instead of falling through to legacy/forge.

DatasetConfig.source_weights gains ds=4.0 as a default so the 8 ds files
(1775 records, ~5.8% raw) are oversampled to ~20% of effective training
tokens, matching their higher signal quality.

CLI gains --ds-weight argument (default 4.0) to override without code changes.

The training_manifest.json produced by PowerShell already encodes weight=4.0
per ds file; this change makes dataset_builder.py honour the same ratio
natively so the manifest and the builder stay in sync.
2026-04-03 16:46:57 -04:00
KoalaTrapLord e4f35a2d8b debug: log top-5 action probabilities on every decide() call
Adds a single INFO log line after each successful inference showing:
  - chosen action + confidence
  - top-5 actions sorted by probability
  - turn, phase, vector_source

This lets us diagnose the 'only plays lands' behaviour without a Python
REPL. Remove or downgrade to DEBUG once the training issue is resolved.
2026-04-03 16:08:50 -04:00
KoalaTrapLord 084742a56a docs: rewrite README — full feature coverage from 52 merged PRs 2026-04-03 20:04:32 +00:00
KoalaTrapLord 45a413837b docs: rewrite README — full feature coverage from 52 merged PRs 2026-04-03 20:04:32 +00:00
KoalaTrapLord 018852c92d fix: collapse cmdr_dmg dict to int in _encode_global before division
Forge sends cmdr_dmg as a dict keyed by opponent seat (e.g. {"0": 14,
"2": 7}). _encode_global read it raw and crashed on `dict / 21.0`.

Fix: resolve cmdr_dmg to a scalar at the read site — sum dict values if
it's a dict, cast to int if it's already a number, default to 0.
This is defensive for all call paths (live Forge IPC, JSONL training,
_build_encoder_snapshot pre-collapsed int) so no upstream change needed.
2026-04-03 15:46:36 -04:00
KoalaTrapLord 4ca21e5e29 debug: add exc_info=True + unique logger names to expose real traceback
Both 'Inference error' log lines use identical logger strings, making it
impossible to tell which except block is catching the crash. Also, broad
except blocks were swallowing the full traceback so the real crash site
and cause were hidden.

Changes:
- policy_server.py: logger renamed to 'ml.serving.policy_server';
  predict() and predict_batch() error handlers add exc_info=True
- routes/policy.py: logger renamed to 'routes.policy.decide';
  _infer_from_vector() error handler adds exc_info=True;
  decide() fallback warning adds exc_info=True

The next crash will print the full stack trace, pinpointing the exact
line and call chain causing the TypeError.
2026-04-03 15:39:54 -04:00
KoalaTrapLord 28665bf663 fix: apply _unwrap_logits guard in _infer_from_vector (routes/policy.py)
The crash `unsupported operand type(s) for /: 'dict' and 'float'` was
firing from _infer_from_vector(), which is the code path taken when
has_precomputed_vector() is True (GameSession 1.1.0+ snapshots with
a precomputed state_vector float[29]).

Unlike PolicyInferenceService.predict() — which was fixed in the
previous commit via _unwrap_logits — this function calls
policy_service.model(state_tensor) directly and immediately uses the
return value in arithmetic (logits / max(temperature, 0.01)), so any
tuple/dict return (DataParallel, torch.compile, PolicyValueNetwork
swap) causes the TypeError.

Fix: import _unwrap_logits from ml.serving.policy_server and apply it
to the raw model output in _infer_from_vector before the temperature
division and argmax calls. All three return shapes are now handled:
  - plain tensor  → passthrough (normal case, zero overhead)
  - tuple/list    → raw[0]  (PolicyValueNetwork (logits, value))
  - dict          → first recognised key or first tensor value
2026-04-03 15:16:49 -04:00
KoalaTrapLord 83d05294c8 fix: type-guard logits after model() to prevent 'dict / float' inference crash
If self.model is wrapped (DataParallel, torch.compile, or future
PolicyValueNetwork path), forward() can return a tuple (logits, value)
or even a dict.  Add an explicit unwrap immediately after the forward
call in both predict() and predict_batch() so every downstream
temperature/softmax operation always receives a plain tensor.
2026-04-03 14:55:58 -04:00
KoalaTrapLord e8f8766d59 fix: register policy routes at module scope before StaticFiles mounts
The root cause of the persistent /api/policy/health 404:
- app.mount("/", StaticFiles(...)) is called at module load time
- StaticFiles acts as a sub-app and consumes matching requests,
  returning its own 404 for unknown files rather than falling through
- register_policy_routes() was called in lifespan (runtime), AFTER
  the "/" mount was already in the route list — so the policy router
  was effectively unreachable

Fix: move route registration to module scope right after all other
app.include_router() calls and BEFORE any app.mount() calls. Only
the model load (_POLICY_SVC.load()) stays in lifespan.
2026-04-03 13:09:12 -04:00
KoalaTrapLord a69b273f5f fix: always register policy routes at startup regardless of model load state
Previously /api/policy/health returned 404 when the model wasn't loaded
because register_policy_routes() was gated behind _policy_svc.load().
The health endpoint itself already handles the unloaded state gracefully
(returns {ready: false, status: degraded}), so the router must be mounted
unconditionally so ai-bridge.js can poll it during startup.
2026-04-03 12:29:18 -04:00
KoalaTrapLord e55ac9aebf feat: add Scryfall image enrichment after .dck load — batch fetch card art for all zones 2026-04-03 11:30:30 -04:00
KoalaTrapLord d2b86ef2ed fix: wire precon-loader.js into index.html script load order 2026-04-03 11:19:37 -04:00
KoalaTrapLord 081dd66121 fix: add startGameWithDecks() and loadPreconDeckFromDck() for precon .dck loading into simulator 2026-04-03 11:17:27 -04:00
KoalaTrapLord 6d89d5bb08 fix: add PRECON_DIR env var (PRECON_DECKS_DIR was never read by lab_api.py) 2026-04-03 11:15:31 -04:00
KoalaTrapLord f29a5a31b5 feat: add /api/lab/precons and /api/lab/precons/deck endpoints
Fixes 404 errors from lan-client ai-bridge.js which calls these routes
to populate the deck selector. Routes were missing from lab.py — only
precon_router (routes/precon.py) existed but served different paths.

Also added PRECON_DECKS_DIR env var alias in lab_api.py _parse_args()
to match the .env variable name users commonly set.
2026-04-03 11:08:12 -04:00
KoalaTrapLord 370d4cd92f Merge pull request #200 from KoalaTrapLord/fix/serve-lan-client-from-backend
fix: Serve lan-client from backend at /lan (same-origin)
2026-04-03 10:48:01 -04:00
Commander AI Lab e90efe1654 fix: serve lan-client from backend at /lan on port 8080
- Mount lan-client/ as static files at /lan with html=True
- Mount precon-decks/ at /precon-decks/ for direct .dck access
- ai-bridge.js: use same-origin (empty URLs, auto-detect from window.location)
- engine.js: simplify deck fetch to use /api/lab/precons/deck endpoint + /precon-decks/ static fallback
- Eliminates cross-origin issues by serving everything from localhost:8080

Access the LAN battlefield at http://localhost:8080/lan/
2026-04-03 14:30:55 +00:00
KoalaTrapLord bf701397b0 Merge pull request #199 from KoalaTrapLord/fix/deck-loading-and-backend-detection
fix: Deck loading — wire setupDeckChoices, fetch .dck content, add precon/deck endpoint
2026-04-03 09:49:33 -04:00
Commander AI Lab 7750b43494 fix: deck loading — wire startGameWithDecks, fetch .dck content, parse [sections]
Three fixes that prevented precon decks from loading into actual gameplay:

1. ui.js: window.startGame() now calls startGameWithDecks() when
   setupDeckChoices has entries (the 3-tab selector stores choices there).
   Previously it called initGame() which looked for old deck-sel-{i}
   dropdowns that no longer exist.

2. engine.js: loadPreconForPlayer() now fetches .dck file content from
   the backend (/api/lab/precons/deck?fileName=...) when precon.decklist
   is empty. Falls back to fetching precon-decks/{file} directly.
   PRECON_DATA entries only have metadata (name, commander, set) — the
   actual card list lives in the .dck files.

3. engine.js: parseDeckList() now handles .dck bracket-style section
   headers like [Commander], [Main], [metadata]. Previously only matched
   bare text like 'COMMANDER:' without brackets. Also skips [metadata]
   lines (e.g. 'Name=Elven Empire').

4. routes/precon.py: Added GET /api/lab/precons/deck?fileName=... endpoint
   to serve raw .dck file content for the LAN client.
2026-04-03 13:45:15 +00:00
KoalaTrapLord fa58463cde Merge pull request #198 from KoalaTrapLord/fix/setup-screen-layout
fix: Setup screen overflows viewport — 2-col deck grid + scroll
2026-04-03 09:38:26 -04:00
Commander AI Lab 94b1e050d8 fix: setup screen overflows viewport — constrain height and use 2-col deck grid
- Add max-height: 92vh and overflow-y: auto to .setup-card
- Widen .setup-card to 860px to fit 2-column deck grid
- Change .deck-assignment-grid from vertical stack to 2-column grid
  so 4 player panels render in 2 rows instead of 4
- Tighten .deck-tab-content (170px) and .deck-precon-list (140px)
- Make top gradient bar sticky on scroll
- Responsive fallback to single column below 640px
2026-04-03 13:37:02 +00:00
KoalaTrapLord 8bfde0230b fix: add precons.js with 163 entries and fix backend precon UI refresh (#197)
- Generate lan-client/precons.js from precon-decks/precon-index.json so
  PRECON_DATA is always available for local/offline mode (163 precons)
- Add scripts/generate-precons-js.js for regeneration when precons update
- Fix _loadBackendPrecons() to re-render the precon tab UI when backend
  precons load successfully (was only updating status indicator text,
  not populating the dropdown)

Without precons.js, PRECON_DATA was undefined and the dropdown showed
'No precon data loaded' even though 163 .dck files exist in the repo.
2026-04-03 09:31:54 -04:00
KoalaTrapLordandClaude Opus 4.6 4214831e59 feat: LAN Battlefield App Integration with AI Pipeline (#196)
* feat: LAN Battlefield App integration with Commander-AI-Lab AI pipeline

Add the LAN Battlefield App as a browser client (lan-client/) that
connects to the Commander-AI-Lab backend for ML-powered AI opponents,
3-category deck selection, and enhanced player panels.

Phase 1 - Foundation:
- Remove floating mana pool display (keep internal tracking)
- Add energy, experience, rad, emblems fields to game state
- No-op renderManaPool(), showManaPool(), setupManaPoolDrag()

Phase 2 - Backend Connection (ai-bridge.js):
- BackendConnector: REST + WebSocket connectivity with retry/timeout
- GameStateBridge: Serializes engine.js state for /api/policy/decide
- MacroActionMapper: Translates 8 ML macro-actions to engine.js calls
- DeckSourceClient: Fetches precon/EDHREC decks from backend API

Phase 3 - Deck System:
- 3-tab deck selector (Precons / EDHREC / My Decks) replaces dropdown
- Precon grouping by set with collapsible sections and filtering
- EDHREC search integration (stub, ready for backend implementation)
- My Decks textarea import placeholder

Phase 4 - AI Integration:
- runAiTurn() priority chain: ML Pipeline > Forge > Local Heuristic
- _runMlAiTurn() async turn loop with multi-phase decision requests
- Backend session initialization on game start
- Automatic fallback to local AI on any ML pipeline error

Phase 5 - Enhanced Player Panels (player-panels.js):
- Collapsible panels with localStorage persistence
- Commander damage progress bars with warning/critical thresholds
- Poison, energy, experience, rad counter widgets
- Emblem tracking with add/remove

Phase 6 - Polish:
- Script loading order: precons > engine > ai-bridge > panels > ui
- Backend auto-detection on page load with status indicator
- Connection status display on setup screen

Closes #184, Closes #185, Closes #186, Closes #187, Closes #188, Closes #189

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: refine LAN battlefield integration with full JSDoc, proper phase IDs

- Add comprehensive JSDoc comments to all ai-bridge.js classes
- Use optional chaining for PHASES lookup in GameStateBridge
- Direct addLogEntry calls in player-panels.js (loaded after engine.js)
- Remove leftover mana pool CSS that was missed in initial pass
- Minor style.css cleanup for consistency

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude <noreply@anthropic.com>
2026-04-03 09:16:27 -04:00
KoalaTrapLord e2568c731c fix: auto-load .env in state.py so dotenv works without explicit load_dotenv call
When running plain `python script.py` or bare uvicorn, .env is never
loaded automatically. Added a dotenv bootstrap block at the top of
state.py that finds and loads the nearest .env file (walks up from
models/ to the project root). Uses load_dotenv with override=False so
explicitly set environment variables always win over .env values.
Gracefully skips if python-dotenv is not installed.
2026-04-03 08:41:58 -04:00
KoalaTrapLord d6863b6d7e fix: auto-populate all CFG paths from environment variables
CFG.forge_dir, precon_dir, forge_decks_dir, forge_jar, and lab_jar
were only set via CLI args. When the server starts via uvicorn directly
(no CLI), all paths stayed empty and forge_card_loader could not find
the cardsfolder, causing 0 lands in every deck.

Fix: read FORGE_DIR, FORGE_JAR, PRECON_DIR, PRECON_DECKS_DIR,
FORGE_DECKS_DIR, and LAB_JAR from environment at import time, mirroring
the existing ANTHROPIC_API_KEY / XIMILAR_API_KEY pattern.
2026-04-03 04:56:44 -04:00
KoalaTrapLord 1fc99a5d4a fix: auto-discover precon-decks/ and sample-decks/ when CFG paths unset
_find_dck_file only searched CFG.precon_dir / CFG.forge_decks_dir, both
of which are empty when the server is started without CLI args. This
caused all precon deck lookups to return 0 cards.

Fix: add _REPO_ROOT and _BUILTIN_DECK_DIRS constants, then append
precon-decks/, sample-decks/, imported-decks/ to the search list
whenever they exist on disk, regardless of whether CFG paths are set.
2026-04-03 04:50:49 -04:00
KoalaTrapLord eed2c7f126 New HTML battlefield to test features before Unity (#182) 2026-04-03 04:39:33 -04:00
KoalaTrapLord 7a40b5deb8 fix: enrich type_line/oracle from Forge cache in .dck fallback loader
_load_deck_cards_from_dck was hardcoding type_line="" for every card,
causing Card.is_land() to return False for all lands when a deck is
loaded via the .dck file path (i.e. not in the SQLite DB). This meant
the engine never recognised lands as lands, breaking mana production.

Fix: call lookup_forge_card() per card and populate type_line,
oracle_text, cmc, mana_cost, power, toughness, and keywords from the
Forge cardsfolder cache. The cache is lazy-loaded and module-level so
there is no per-game overhead after the first deck load.
2026-04-03 04:01:59 -04:00
KoalaTrapLord af8970f3a8 fix: _load_deck_cards_by_name falls back to .dck file when deck not in DB
Precon decks (Elven_Empire, Blood_Rites, etc.) are stored as .dck files
on disk but not inserted into the SQLite decks/deck_cards tables.
The DeepSeek batch engine calls _load_deck_cards_by_name() which
previously returned [] for any deck not in the DB, causing
'No decks could be loaded.' errors.

Adds _find_dck_file() and _load_deck_cards_from_dck() helpers.
_find_dck_file searches CFG.precon_dir, CFG.forge_decks_dir, and
FORGE_DIR/res/decks/commander with exact + fuzzy matching.
_load_deck_cards_by_name now tries DB first, then .dck fallback.
2026-04-02 22:59:01 -04:00
KoalaTrapLord 53fd1c9257 fix: wire RESULTS_DIR and FORGE_DIR env vars into CFG at startup 2026-04-02 14:24:28 -04:00
KoalaTrapLord 6ead75f96b fix: set FORGE_WORKING_DIR to forge-gui (requires res/ folder for sim to start) 2026-04-02 02:33:14 -04:00
KoalaTrapLord 535bdd7c70 fix: make Forge deck paths fully portable using %~dp0 and auto-generate forge.profile.properties 2026-04-02 00:59:10 -04:00
KoalaTrapLord d98e6331e9 fix: point all deck lookups to precon-decks directory
- start-lab.bat: Set FORGE_DECKS_DIR to D:\ForgeCommander\commander-ai-lab\precon-decks
- BatchRunner.java: resolveForgeDecksPath() now checks PRECON_DECKS_DIR env var
  first, then falls back to the hardcoded precon-decks path, before trying
  %APPDATA%\Forge\decks\commander. Also pass full absolute path to Forge -d flag.
- .env.example: document PRECON_DECKS_DIR variable
2026-04-01 23:10:32 -04:00
KoalaTrapLord 2b3196267f fix: raise default clock from 300ms to 5000ms to prevent turn-1 draw games 2026-04-01 22:56:29 -04:00
rgriffith 433e8bc4d7 feat: wire precon card art through local /card-image/ API with Scryfall fallback 2026-04-01 22:09:13 -04:00
KoalaTrapLord d74dd0a6bf feat: wire precon card art through local /card-image/ API with Scryfall fallback (full file) 2026-04-01 21:45:15 -04:00
KoalaTrapLord 8037802dff feat: wire precon card art through local /card-image/ API with Scryfall fallback 2026-04-01 21:45:01 -04:00
KoalaTrapLord ca6c23a881 fix: wrap shutil.copy2 in try/except for install and install-batch endpoints 2026-04-01 21:32:19 -04:00
KoalaTrapLord a81772c68a fix: set cwd=CFG.forge_dir on Popen so Forge finds res/ (#184) 2026-04-01 19:41:52 -04:00
KoalaTrapLord 99ca498b88 docs: add Unity TCGEngine Commander board implementation plan 2026-04-01 14:59:46 -04:00
KoalaTrapLord 395ad06cdd fix: resolve deck names to full .dck paths in build_java_command (#183) 2026-04-01 14:57:57 -04:00
KoalaTrapLord e4a6ce644d fix(#176): draw-game life capture, DrawGameAnalyzer, clock 6000→3000 (#181)
Captures life-at-timeout snapshots for every draw game via [DRAW] [PARSE-SUMMARY] log lines. Adds services/draw_game_analyzer.py with DrawGameSnapshot + summarize_draw_games(). Wires drawGames summary into GET /api/lab/result response. Lowers batch clock default from 6000 to 3000 (30s per-game). Closes #176.
2026-04-01 14:40:29 -04:00
KoalaTrapLord 5411fd6bf6 fix(#175): alternate win condition parser & winType tagging (#179)
Adds win_condition_parser.py, GameResult.win_type field, and winType stamping on both the Java/Forge and DeepSeek batch paths. Closes #175.
2026-04-01 14:28:10 -04:00
KoalaTrapLord 55fdc2ec4a feat: add CreateBattlegroundScene editor script to build full scene programmatically 2026-03-30 20:31:09 -04:00
KoalaTrapLord ddfe8e601e fix(eval): parse batch JSON output instead of JSONL for Forge game results 2026-03-30 16:12:20 -04:00
KoalaTrapLord 9e3cd45936 fix(eval): require >= 3 decks for Forge availability check 2026-03-30 15:45:20 -04:00
KoalaTrapLord c91f4c90bc fix(eval): discover 4 decks for 4-player Commander (Forge requires >= 3) 2026-03-30 15:43:27 -04:00
KoalaTrapLord 80f67dd0a0 fix(eval): use precon-decks/ for deck discovery instead of decks/ 2026-03-30 15:28:19 -04:00
rgriffith 10220ec747 feat: add 130+ precon commander deck files 2026-03-30 15:19:29 -04:00
rgriffith 6fadeaa387 fix(eval): replace Java bridge imports with subprocess CLI launch for Forge eval 2026-03-30 14:23:52 -04:00
rgriffith 517c17047b fix: strip value_head keys when loading PPO checkpoint into PolicyNetwork 2026-03-30 13:31:59 -04:00
KoalaTrapLord c62e75af52 fix(tests): update _build_document assertions to match NL sentence format 2026-03-30 12:36:00 -04:00
KoalaTrapLord 11940835fc fix: validate ollama suggest_cards names against DB + Scryfall before pipeline merge
- Add validate_card_names() helper: checks collection_entries DB first,
  then falls back to Scryfall /cards/named?fuzzy= for unknowns.
  Drops any name that cannot be resolved (hallucinated cards).
- Inject validation in Step 4 immediately after each suggest_cards()
  call so hallucinated names never reach ratio enforcement or assembly.
- Log dropped hallucinations at WARNING level with count + examples.
- Resolves the 'Sonic Hedgehog Charger' / 'Sonic Boom' class of
  LLM-invented card names that were silently entering the deck.
2026-03-30 12:33:28 -04:00
KoalaTrapLord d12b47f874 fix: robust suggest_cards parsing — CSV fallback + one-shot JSON prompt
- Add _parse_card_list_fallback() to recover plain-text CSV output from
  the LLM when json_mode fails to enforce JSON (e.g. qwen2.5:7b drift)
- Harden suggest_cards system prompt with a one-shot JSON example so
  small models are less likely to return prose in the first place
- Log recovered names at WARNING level so the degradation is visible
  without being silent (previously returned [] with no recovery attempt)
- No behaviour changes to any other function
2026-03-30 12:26:28 -04:00
KoalaTrapLord 5b5bac42fb fix: use vars(row) instead of dict(row) in _build_collection_from_bulk
dict() on a types.SimpleNamespace raises TypeError: object is not iterable.
vars() correctly extracts the __dict__ attribute map. Fixes
TestBuildCollectionFailureTracking::test_high_failure_rate_raises_runtime_error.
2026-03-30 10:39:49 -04:00
rgriffith 574bb79a0b fix(eval): correct indentation for load/reload in _load_policy_service 2026-03-30 10:36:43 -04:00
KoalaTrapLord 5554ef4dd7 fix(eval): call load() for embeddings before reload() for checkpoint 2026-03-30 09:53:56 -04:00
KoalaTrapLord cf2d7ed806 fix(eval): use reload() with explicit checkpoint path instead of load()
load() hardcodes best_policy.pt but PPO trainer saves as best_ppo.pt, causing the model to never load during eval. reload() accepts an explicit path.
2026-03-30 09:51:22 -04:00
KoalaTrapLord aec1896094 fix(engine): promote assign_attackers/assign_blockers/resolve_combat_damage to public methods (#86)
The three split-combat helpers were accidentally defined as nested
functions inside _resolve_combat(), making them unreachable as
GameEngine.assign_attackers() etc. from tests and turn_manager.

Move them out to class-level methods with correct 4-space indentation.
_resolve_combat() now remains as the backward-compatible atomic path for
run_n(); the split methods are available for turn_manager priority
windows and direct testing.
2026-03-30 09:50:56 -04:00
KoalaTrapLord 75785ae918 fix(tests): update RESPONSE_PHASES assertion to match current turn_manager design
begin_combat/end_combat are dispatched as named priority windows inside
_phase_combat(), not as entries in RESPONSE_PHASES. The constant is
correctly {"upkeep", "end_step"} — test was asserting stale intent.
2026-03-29 20:39:37 -04:00
KoalaTrapLord 252e46b288 fix(tests): swap Delta/Epsilon for Zeta/Theta to avoid _land_kw collision
Delta matches \bdelta\b in the nonbasic land guard regex, causing enrich_card
to classify it as a land (cmc=0). Zeta and Theta have no land keyword matches.
2026-03-29 20:37:04 -04:00
KoalaTrapLord e1bb2e7209 feat(coach): Phase 1 sim stats pass-through in prompt template (#108)
Surfaces previously-computed-but-unused stats into the Claude prompt:

1. USER_PROMPT_TEMPLATE gains a new '### Sim Insights' section that shows:
   - Deck-level opening hand quality: avg keptInOpeningHandRate across all
     cards, plus the 3 worst offenders with their actual rates
   - Curve velocity: avg avgTurnCast weighted by castRate, flagging cards
     cast on avg turn > 6 as 'late'
   - Synergy outliers: bottom-3 synergyScore cards flagged as 'isolated'
   - Archetype weak spots: archetypes with winRate < 40% flagged explicitly

2. _format_archetypes() now annotates weak spots with [WEAK SPOT] and
   strong matchups with [STRENGTH] so Claude sees them immediately.

3. _format_top_cards() adds keptInOpeningHandRate to each line so
   the 'good' cards also show their hand-keepability signal.

4. New helper _format_sim_insights() computes all deck-level aggregates
   from CardPerformance list — no new fields on DeckMeta needed.

No schema changes. Fully backward-compatible with existing DeckReport.
2026-03-29 05:55:51 -04:00
KoalaTrapLord e77a945805 test(encoder): unit tests for StateEncoder global features + Step 4 total_power (#83 Step 4)
New test file covering:
- TestGlobalFeatureDimensions: output shape (29,), all values in [0,1]
- TestTotalPowerRealValue: real total_power used at index 7 when present
- TestTotalPowerHeuristic: creatures*3 fallback when total_power absent/zero
- TestLandCount: real lands used at index 13 when present; defaults to 0
- TestCreatureCount: real creatures used at index 6; fallback to bf len
- TestManaFeature: mana normalised and clamped to [0,1]
- TestPhaseOneHot: exactly one hot in indices 9-12 per player block
- TestIsActivePlayer: binary 0/1 at index 8
- TestLifeNormalisation: 40 life → 1.0, 0 life → 0.0
- TestTurnFeature: turn clamped to [0,1]
- TestFullEncode: total state vector shape (6177,), dtype float32
- TestEmptyPlayer: _empty_player() has all Step 4 keys
- TestLegacySnapshot: old JSONL snapshot without Step 4 keys still encodes"
2026-03-29 05:46:01 -04:00
KoalaTrapLord 3f5d6d12e8 feat: 4-player Commander Battleground UI (issues #138–#151)
* chore: add battleground UI task tracker (2026-03-28)

* feat(#142): add [NonSerialized] battleground runtime state to CardModel

* feat(#143): add LoadArtCrop and LoadCardRaw (RawImage) to ImageCache

* feat(#148): add GameStateManager singleton and GameState/SeatState POCOs

* feat(#138): add BattlegroundController scene orchestrator

* feat(#139): add LifeTrackerWidget, CommanderDamageRow, PoisonCounter

* feat(#140): add PhaseTrackerWidget and PhaseStepButton

* feat(#141): add TurnOrderBar and TurnOrderPill

* feat(#142): add CardZone and CardView battlefield zone components

* feat(#143): add CommanderZoneWidget with art crop, cast counter, and tax display

* feat(#144): add TokenModel POCO, TokenPanelController, and TokenRowView

* chore: mark issues 1-8 as complete in task tracker

* feat(#146): add StackZoneController, StackItemView, and ManaPoolDisplay

* feat(#147 #150 #151): add LobbySetupModal, ZoneViewerModal, EliminationHandler; add Play Live button to SimulationController

* feat(#151): add Play Live button to SimulationController

* feat(#149): add GameWebSocketClient with reconnect, ping, and main-thread dispatch

* 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

* 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

* 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 05:35:29 -04:00
KoalaTrapLord 915c745911 fix(eval): remove Set-StrictMode, fix curly-quotes and -- in strings (issue #153) 2026-03-29 05:31:58 -04:00
KoalaTrapLord 00374c1f03 feat(eval): add run_benchmark.ps1 — full benchmark pipeline for issue #153 2026-03-29 05:13:21 -04:00
KoalaTrapLord 6e71ba88fd fix(encoder): read real total_power from snapshot dict at global index 7 (#83 Step 4)
Previously _encode_global() always computed:
  total_power = creatures * 3  (heuristic)

Now reads p.get('total_power', 0) first, falling back to the heuristic
only when the key is absent (legacy JSONL snapshots without Step 4 data).

Also wires total_toughness, artifacts, and enchantments into _empty_player()
so padding players have consistent keys.
2026-03-29 05:13:19 -04:00
KoalaTrapLord 73d8e8d220 test(policy): add Step 4 board-stats tests + fix schema alias (#83 Step 4)
- TestBoardStatsWiring: verifies creaturesOnField, landCount,
  totalPowerOnBoard, totalToughnessOnBoard, artifactsOnField,
  enchantmentsOnField are accepted without 422 and survive
  a round-trip through the fallback encoder path
- TestBoardStatsFallback: verifies resolver fallbacks when Forge
  fields are absent (creatures inferred from bf length,
  total_power falls back to creatures * 3 heuristic,
  lands defaults to 0)
- TestSchemaAlias: verifies snapshot_schema Field(alias='schema')
  still accepts JSON key 'schema' without 422
- Removes now-irrelevant UserWarning (schema shadow fixed)
2026-03-29 04:51:47 -04:00
KoalaTrapLord bff7b3a81e feat(eval): add forge_evaluator, eval_policy, compare_eval for issue #153
- ml/eval/forge_evaluator.py: reusable eval harness wrapping
  MultiThreadBatchRunner + PolicyInferenceService; collects win rate,
  avg turns, life differential, commander damage, illegal action rate,
  policy entropy per game; writes results/eval-{run_id}.json
- scripts/eval_policy.py: CLI to run N Forge eval games with any
  checkpoint vs forge-builtin or policy-server opponent; supports
  --threads, --clock, --seed, --greedy, --temperature
- scripts/compare_eval.py: loads two result JSONs, computes absolute
  win-rate delta + 95% CI, runs Fisher's exact test for significance,
  prints formatted comparison table and pass/fail verdict
2026-03-29 04:49:43 -04:00
KoalaTrapLord b1c17359f0 feat(policy): wire real Forge board stats into encoder snapshot (#83 Step 4)
Previously _build_encoder_snapshot() stubbed:
  - lands = 0  (always zero)
  - creatures = len(bf_names)  (name-list length, ignores tokens/etc)
  - mana = mana_available  (was already correct)
  - total_power / total_toughness / artifacts / enchantments = missing

Now reads real pre-computed values from PlayerZoneState:
  - creatures      ← creaturesOnField   (Forge board stat)
  - lands          ← landCount          (Forge board stat)
  - total_power    ← totalPowerOnBoard  (fed to global feature index 7)
  - total_toughness← totalToughnessOnBoard
  - artifacts      ← artifactsOnField
  - enchantments   ← enchantmentsOnField
  - mana           ← manaAvailable (unchanged)

Also replaces the `creatures * 3` proxy in _build_encoder_snapshot with
the real totalPowerOnBoard value so index-7 of the global feature vector
tracks actual power rather than a heuristic.

PlayerZoneState gains 5 new optional int fields (default 0) matching
DecisionSnapshot.PlayerSnapshot field names so JSON from both the live
IPC path and the JSONL training path deserialise without changes.
2026-03-29 04:49:40 -04:00
KoalaTrapLord 7440d0ce17 test(policy): add round-trip tests for /api/policy/decide state_vector path (#83 Step 2)
Covers:
  - health endpoint returns {ready, status, model_loaded}
  - precomputed fast path: state_vector float[29] → vector_source=precomputed
  - correct 6177-dim tensor shape reaches the mock model
  - all 8 MacroAction values are valid responses
  - encoder fallback: missing state_vector → vector_source=encoder
  - encoder fallback: wrong-length state_vector → vector_source=encoder
  - GameSession field name aliases (turnNumber, activePlayer,
    commanderTax, handCount, commandZone) are accepted without 422
  - 503 when model not loaded
  - /api/policy/stats tracks precomputed_vector_hits vs encoder_fallbacks
  - both phase strings (MAIN1, main_1) round-trip correctly

Uses a MockPolicyInferenceService that captures the tensor shape passed
to the model so we can assert on dim without needing a real checkpoint.
2026-03-29 04:37:09 -04:00
KoalaTrapLord 4d73f134c2 feat(policy): consume state_vector from GameSession snapshots (#83 Step 2)
DecideRequest now accepts an optional state_vector field (float[29]).
When present, /api/policy/decide uses it directly for the global scalar
block instead of re-deriving it from nested player fields.

StateEncoder gets a new encode_with_precomputed_global() path that:
  - Accepts the precomputed float[29] global block from Java
  - Still runs full zone embedding pooling (hand/battlefield/graveyard/
    command_zone) from the players[] zone contents
  - Appends the playstyle one-hot as normal
  - Returns the same 6177-dim tensor the policy network expects

Fallback: if state_vector is absent or wrong length, encode() is called
as before so all existing callers (batch predict, JSONL training) are
unaffected.

Also maps GameSession player field names to encoder field names:
  handCount  -> hand size proxy when hand[] is empty (AI seats)
  battlefield list -> creatures count proxy
  commanderTax -> cmdr_tax
  turnNumber  -> turn
  activePlayer -> active_seat
2026-03-29 04:32:52 -04:00
KoalaTrapLord 126684bb94 feat(bridge): Phase 2.3 — wire GameSession to real Forge IPC subprocess (issue #83)
Replaces synthetic stub loop with a live Forge process:
- Spawns Forge with --policy + --policyServer flags so Forge calls
  back to /api/policy/decide at each AI decision point
- Reads Forge stdout line-by-line and parses zone/phase/life updates
  via the same regex patterns used in BatchRunner
- Syncs full board state (battlefield, hand, graveyard, exile,
  command zone, stack) on every Forge state-change line
- Routes human input from humanActionQueue via a side-channel
  HTTP POST to /api/policy/override on the policy server
- Handles activePlayer + priorityPlayer correctly for 3-4 player pods
- Falls back to synthetic stub loop if Forge subprocess fails to start
- Watchdog thread kills stalled Forge process after 10 min
2026-03-29 04:31:49 -04:00
KoalaTrapLord 882e2370c1 fix(#127): persist built_at in ChromaDB collection metadata as sidecar fallback
- _write_build_meta: after writing build_meta.json, also stamps built_at
  onto the collection via col.modify() (best-effort, swallows exceptions)
- _collection_is_current: if sidecar built_at is missing/zero, falls back
  to collection.metadata["built_at"] so a deleted sidecar doesn't force
  an unnecessary full rebuild when the ChromaDB data is still valid
2026-03-29 04:29:35 -04:00
KoalaTrapLord bbca0b7985 fix(#126): add batch insert retry logic with exponential backoff
- Add BATCH_RETRY_LIMIT and BATCH_RETRY_DELAY constants after DEFAULT_BATCH_SIZE
- Add _insert_batch_with_retry() helper with exponential backoff
- Replace both bare try/except insert blocks in _build_collection_from_bulk
  with calls to the retry helper so transient ChromaDB errors are retried
  up to BATCH_RETRY_LIMIT times before a batch is counted as failed
2026-03-29 04:24:58 -04:00
KoalaTrapLord da8de77704 feat(bridge): add buildStateVector() emitting state_vector float[] in snapshots (#83 Step 1)
Adds a buildStateVector(int seat) method to GameSession that produces
a float[29] global scalar vector matching STATE_DIMS layout in
ml/config/scope.py:

  Per-player block (14 features × 2 players = 28):
    [0]  life_total           / 40.0
    [1]  commander_damage     / 21.0  (stub 0.0 until real Forge tracks it)
    [2]  mana_available       / 20.0  (stub 0.0 until mana pool wired)
    [3]  commander_tax        / 10.0
    [4]  cards_in_hand        / 15.0
    [5]  cards_in_graveyard   / 100.0
    [6]  creatures_on_battlefield / 30.0
    [7]  total_power_on_board / 100.0  (stub = creature count until P/T data)
    [8]  is_active_player     (0 or 1)
    [9]  phase one-hot[0]  main1
    [10] phase one-hot[1]  combat
    [11] phase one-hot[2]  main2
    [12] phase one-hot[3]  end
    [13] (padding / reserved)

  Global:
    [28] turn_number / max_turns (25)

buildStateSnapshot() now includes "state_vector": float[] so that
PolicyClient.decide(JsonObject) sends a machine-readable vector the
Python /api/policy/decide endpoint can pass directly to the encoder
without re-parsing the nested human-readable fields.

Stub fields (mana_available, commander_damage, total_power_on_board)
are zero until real Forge board state is wired in Step 4.
2026-03-29 04:24:55 -04:00
KoalaTrapLord 11990c2fd3 feat(ml): add --data-source forge flag to PPO trainer (issue #83 phase 1.3)
Wires forge_episode_generator into the PPO training loop:
- New --data-source flag: synthetic (default) | forge | mixed
- New --forge-results-dir, --forge-batch-id, --forge-weight flags
- ForgeDataSource class: runs run_forge_pipeline() in background thread,
  drains episodes into RolloutBuffer via episodes_to_buffer()
- Mixed mode: blends Forge episodes + synthetic self-play per iteration
  using --forge-weight ratio (0.0–1.0)
- Synthetic path unchanged — no regression for existing training runs
2026-03-29 03:02:30 -04:00
KoalaTrapLord aefd0cace6 feat(rag): Phase 1 — rewrite build_card_text to natural-language format (#137)
* feat(rag): Phase 1 — rewrite build_card_text to natural-language format

* feat(rag): Phase 2 — Forge-enriched ChromaDB RAG store (2a–2e)

Phase 2a: Forge lazy import guard (_try_import_forge_lookup)
Phase 2b: _build_document appends Forge trigger/replacement/static/keyword tags
Phase 2c: _build_metadata adds has_trigger, has_replacement, has_static, trigger_modes fields
Phase 2d: query_cards extended with require_trigger, require_replacement, require_static params + ChromaDB where clause support
Phase 2e: get_rag_stats reports forge_enriched and forge_coverage; _write_build_meta tracks coverage; build loop counts enriched cards

* feat(rag): Phase 3 — add mtgrules second ChromaDB collection

Adds build_rules_index() and query_rules() operating on a separate
'mtgrules' ChromaDB collection. Chunks MagicCompRules.txt by rule-number
boundaries, embeds each chunk, and exposes semantic search. Completely
independent from the mtg_oracle card collection.

* feat(coach): Phase 4 — behavior-aware RAG + rules context in coach_service

Phase 4a: run_coaching_session uses dual RAG query
  - Primary: commander synergy (n=10)
  - Secondary: require_replacement=True for graveyard/protection cards (n=5)
  - Deduplicates by name before merging
Phase 4b: query_rules() fetches 3 Commander rules chunks to ground the prompt
  - Passed as rules_context kwarg to build_user_prompt()

* feat(rag): Phase 5 — add /api/rag/build-rules and /api/rag/query-rules endpoints
2026-03-29 02:11:38 -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
KoalaTrapLord 34e88a3a14 fix: correct indentation of commander_keywords and oracle overlap scoring in deckgen.py 2026-03-25 08:45:51 -04:00
KoalaTrapLord f199bcfa34 feat: column visibility toggle UI for collection table 2026-03-25 08:31:12 -04:00
KoalaTrapLord 77c92d569d fix: correct indentation of elif/else branches in _run_sim_thread_deepseek 2026-03-25 08:24:21 -04:00
KoalaTrapLord f24bd05be8 feat: collection stats dashboard (CMC curve, color/type/rarity/value) 2026-03-25 07:51:09 -04:00
KoalaTrapLord e7ffccf3e8 feat: persist coach sessions to SQLite with disk fallback 2026-03-24 23:44:20 -04:00
KoalaTrapLord 64a65ca2f6 feat: auto-fill deck name with Auto - {CommanderName} on select 2026-03-24 23:19:17 -04:00
KoalaTrapLord 3ba44f68d9 feat: oracle-text keyword overlap scoring in _generate_deck 2026-03-24 22:56:30 -04:00
KoalaTrapLord f851df6dbf fix: add getBaseUrl(), PolicyDecision record, and decide(String,int) to PolicyClient
GameSession.java references these APIs which were missing from
PolicyClient, causing 3 compilation errors. Added:
- getBaseUrl() getter
- PolicyDecision record (action, confidence)
- decide(String, int) overload that delegates to decide(JsonObject)
2026-03-24 22:46:45 -04:00
KoalaTrapLord ded79bfde1 feat: add omit_cards chips styles to deck generator CSS 2026-03-24 22:20:10 -04:00
KoalaTrapLord 411e40e46e feat: add omit_cards chips panel to deck generator HTML 2026-03-24 22:17:25 -04:00
KoalaTrapLord a4e49daf49 feat: add omit_cards chips/tags input to deck generator JS 2026-03-24 22:11:36 -04:00
KoalaTrapLord 675c64029b feat: add progress bar + ETA to deck generation UI 2026-03-24 21:20:47 -04:00
KoalaTrapLord 4c47b9d5e2 fix: add V3 result cache to avoid re-generating deck on export 2026-03-24 21:07:59 -04:00
KoalaTrapLord 8903432d67 fix: add missing export filter params (rarity, setCode, power, toughness, keyword, edhrec, qty) 2026-03-24 21:01:35 -04:00
KoalaTrapLord b1e473397f fix: shopping list field name + scan-add refresh function name 2026-03-24 20:53:19 -04:00
KoalaTrapLord 07b2c9d5a9 fix: add missing LLM_TIMEOUT constant in coach.py
LLM_TIMEOUT was referenced but never defined, causing a NameError
in the Ollama streaming SSE code path when COACH_PROVIDER != 'perplexity'.
2026-03-24 20:43:36 -04:00
KoalaTrapLord 58bbb514ad fix: skip ML decision flush when brain not connected (heuristic fallback guard) 2026-03-24 20:27:02 -04:00
KoalaTrapLord d70d74d753 feat: add Scryfall on-the-fly embedding fallback for unknown cards 2026-03-24 20:09:27 -04:00
KoalaTrapLord 475849e477 fix: prevent double-flush of ML decisions in deepseek sim thread
_run_sim_thread_deepseek was manually calling engine.flush_ml_decisions()
and writing the .jsonl, then passing engine=engine to _finish_sim which
called flush_ml_decisions() a second time — returning an empty list and
producing empty training data files.

Fix: pass engine=None to _finish_sim when the deepseek thread has already
flushed manually, so _finish_sim skips the second flush entirely.
2026-03-24 19:19:45 -04:00
KoalaTrapLord b2d1078ca9 fix: disable quiet mode when ML logging is enabled in createWorker
DecisionExtractor requires verbose Forge output to reconstruct board
states. When quiet=true, Forge runs with -q which strips the verbose
log lines, causing 0 decisions to be extracted. This change passes
quiet=false to BatchRunner when mlLogEnabled is true.
2026-03-24 18:45:36 -04:00
KoalaTrapLord 9bce919690 fix: replace non-existent ForgeEpisodeGenerator import with actual exports 2026-03-24 13:45:48 -04:00
KoalaTrapLord 41b711c17e fix: normalize deck report slug matching (spaces/hyphens/underscores) 2026-03-24 13:31:52 -04:00
KoalaTrapLord a6940ca61a fix: update get_status() to check Perplexity connection when COACH_PROVIDER=perplexity 2026-03-24 13:29:16 -04:00
KoalaTrapLord a65daaf801 fix: update Deck Coach status label from LM Studio to Perplexity 2026-03-24 13:26:41 -04:00
KoalaTrapLord c475f5998b docs: add COACH_PROVIDER to .env.example 2026-03-24 12:54:30 -04:00
KoalaTrapLord 456ef927f9 feat: add Perplexity deep research support to coach chat endpoint 2026-03-24 12:53:42 -04:00
KoalaTrapLord a515049542 feat: add COACH_PROVIDER config for Perplexity-powered coach chat 2026-03-24 12:50:21 -04:00
KoalaTrapLord 457e6f91c8 docs: add PPLX_MODEL to .env.example 2026-03-24 12:47:58 -04:00
KoalaTrapLord 977dbd96d7 feat: use PPLX_MODEL config instead of hardcoded 'sonar' in deckgen 2026-03-24 12:47:09 -04:00
KoalaTrapLord 5a499442e6 feat: add PPLX_MODEL env var for configurable Perplexity model 2026-03-24 12:45:40 -04:00
KoalaTrapLord 1f3b17d173 fix: remove ForgeEpisodeQueue import from training __init__
ForgeEpisodeQueue does not exist in rollout_buffer.py yet.
Removing premature import to unblock supervised training pipeline.
2026-03-24 12:26:06 -04:00
KoalaTrapLord f51ff61568 feat(83): Phase 3 — Performance & Scale (#83) (#101)
* feat(83): Phase 3.1 — persistent JVM worker pool in MultiThreadBatchRunner (#83)

* feat(83): Phase 3.2 — async Forge episode generator with producer-consumer pipeline (#83)

* feat(83): Phase 3.3 — deprecate self_play.py synthetic mode for training (#83)

Mark self_play.py synthetic training as deprecated per Phase 3.3:
- Add deprecation notice to module docstring
- Add import warnings and _TRAINING_DEPRECATION_MSG constant
- Add DeprecationWarning in collect_rollouts()
- Synthetic sim retained for local testing/unit tests only
- scope.py already defaults forge_weight=1.0, ppo_weight=0.0

* feat(83): Phase 3.3 — update ml/training/__init__.py exports (#83)
2026-03-24 10:43:51 -04:00
KoalaTrapLord 39bc36afef rename: update JsonExporter.java javadoc example from batch- to ml-decision- 2026-03-23 23:10:44 -04:00
KoalaTrapLord dffd89a01c rename: update LabCli.java default output from batch-latest to ml-decision-latest 2026-03-23 23:09:51 -04:00
KoalaTrapLord 015b9da1e9 rename: update forge_runner.py file prefix from batch- to ml-decision- 2026-03-23 23:06:33 -04:00
KoalaTrapLord f9007effa3 rename: update report_generator.py glob from batch- to ml-decision- 2026-03-23 23:05:25 -04:00
KoalaTrapLord 29ad996966 rename: batch-sim- to ml-decision-sim- prefix in deepseek.py 2026-03-23 23:04:13 -04:00
KoalaTrapLord 51b4e9b5ff rename: batch result files from batch- to ml-decision- prefix in lab.py 2026-03-23 23:00:24 -04:00
KoalaTrapLord 573a271561 fix: use correct query param format for batch status endpoint in overnight-batch.py
The /api/lab/status endpoint uses ?batchId= query param, not path param /{batch_id}
2026-03-23 22:19:21 -04:00
KoalaTrapLord 411fa41063 feat(83): Phase 2 — Live Forge-to-Policy-Server IPC (#100)
* feat(83): add PolicyClient for live Forge-to-policy-server IPC (#83)

Phase 2.1: Bridge between Forge decision points and Python policy
server for online training. Calls /api/policy/decide endpoint,
collects (state, action, reward) tuples in real-time, and flushes
them to the server for online PPO updates.

Falls back to Forge built-in AI if server is unavailable.

* feat(83): add /api/policy/* routes for live Forge IPC (#83)

Phase 2.2: Policy decision routes for real-time Forge IPC.

- POST /api/policy/decide — full game state inference with log_prob/value
- POST /api/policy/collect — online learning tuple batch collection
- POST /api/policy/reward — end-of-game reward propagation
- GET /api/policy/health — readiness check for Java PolicyClient
- GET /api/policy/stats — session statistics

Includes Forge phase mapping and PlayerZoneState with full zone contents.

* feat(83): integrate PolicyClient into GameSession for bidirectional sync (#83)
2026-03-23 22:08:37 -04:00
87404f3702 refactor: move logging out of routes/shared.py into services/logging.py (#99)
Create services/logging.py as the canonical home for setup_logging(),
all log format constants, named loggers, and _ml_logging_enabled.

Update all route modules and lab_api.py to import logging symbols from
services.logging and all other symbols from their canonical sources
(models.state, models.requests, models.responses, services.database,
services.deck_service, services.scryfall, services.import_helpers,
services.import_service, services.precon_service, services.forge_runner).

Delete routes/shared.py — the re-export hub is no longer needed.

Closes #61

Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-23 21:04:25 -04:00
KoalaTrapLordandClaude Code f0dfb41f2f fix: default Ollama endpoints to localhost instead of 192.168.0.240 (#98)
The hardcoded 192.168.0.240 IP was a developer-specific LAN address
that breaks out-of-the-box for anyone running Ollama locally or via
WSL2 on Windows. Switch every default and docstring reference to
localhost so the project works without manual configuration.

Files changed:
- coach/config.py: LLM_URL and DECK_GEN_BASE_URL defaults
- ui/simulator.html: API endpoint input value and placeholder
- lab_api.py: remove LAN IP from default CORS origins
- routes/coach.py: warning message text
- src/commander_ai_lab/sim/deepseek_brain.py: docstrings
- src/commander_ai_lab/sim/deepseek_engine.py: docstring example
- src/commander_ai_lab/sim/validator_brain.py: docstring example

Co-authored-by: Claude Code <noreply@anthropic.com>
2026-03-23 21:03:17 -04:00
56e5bff496 fix: correct indentation of last_err in DeepSeekBrain.choose_action() (#97)
`last_err = None` was at module-level indentation (4 spaces) instead of
method-body indentation (8 spaces), causing an IndentationError on import.

Co-authored-by: Claude Agent <claude-agent@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-23 20:01:11 -04:00
KoalaTrapLord 62463dfa4a fix: correct indentation in deepseek_brain.py (choose_action + _parse_action) 2026-03-23 19:34:14 -04:00
KoalaTrapLord 7e0aba78ba fix: add ML logging support to MultiThreadBatchRunner 2026-03-23 19:01:43 -04:00
KoalaTrapLord 7036c9568f fix: enable ML decision logging in multi-thread batch path 2026-03-23 18:58:14 -04:00
KoalaTrapLord b37968e3c2 fix: always pass --ml-log flag to Forge Java batch runner 2026-03-23 17:22:58 -04:00
KoalaTrapLord d424ccb74c feat(86): add combat priority checkpoints with CombatState and split engine methods (#96) 2026-03-23 16:52:19 -04:00
KoalaTrapLord 1cf2439a5a fix(#95): correct validator defaults to deepseek-r1:8b and 300s timeout
_get_validator_brain() defaulted VALIDATOR_MODEL to deepseek-r1:14b
and VALIDATOR_TIMEOUT to 120.0, conflicting with the brain config.
Changed to deepseek-r1:8b and 300.0 to match. Fixes issue #95 item 4.
2026-03-23 14:31:59 -04:00
KoalaTrapLord f3bd188a8b fix(#95): implement retry loop and bump max_tokens/max_retries defaults
max_retries was configured but never used — choose_action() made
exactly one attempt. Now wraps _call_llm() in a retry loop.
Bumped max_retries default to 2 and max_tokens to 4096 (R1 needs
room for <think> blocks + JSON). Fixes issue #95 item 3.
2026-03-23 14:29:22 -04:00
KoalaTrapLord c1ad9e953c fix(#95): replace regex JSON extraction with outermost-brace approach
_parse_action() used r'\{[^{}]*\}' regex which fails on nested
JSON (e.g. oracle text with braces). Now uses text.find/rfind to
extract the outermost { } block, handling nested structures.
Fixes issue #95 item 2.
2026-03-23 14:24:57 -04:00
KoalaTrapLord 25e064d265 fix(#95): prevent check_connection from overwriting configured model
check_connection() was auto-overwriting self.config.model with the
first model returned by /v1/models, causing deepseek-r1:8b to be
replaced by deepseek-r1:14b (or whatever Ollama listed first).

Now logs available models without changing the configured model.
Fixes issue #95 item 1.
2026-03-23 10:37:39 -04:00
2b6d4dcdb1 fix: deduplicate LLM duplicate cards in _check_ownership (#78) (#79)
Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-23 09:42:33 -04:00
eee773b6f9 Forge Card Enrichment — Full Implementation (Issue #80) (#81)
* feat(issue-80): Phase 1 — add services/forge_card_loader.py

* feat(issue-80): Phase 2 — add 5 Forge fields to Card dataclass

* feat(issue-80): Phase 3+4 — wire Forge into enrich_card/score_card

* feat(issue-80): Phase 5 — add tests/sim/test_forge_enrichment.py

* fix(issue-80): resolve PR #81 blocking issues — import paths, indentation, partial Forge fallback

- Move forge_card_loader.py from services/ into src/commander_ai_lab/sim/
  so the import in rules.py uses a proper intra-package path instead of
  crossing package boundaries via services.forge_card_loader
- Fix indentation of Forge enrichment comment in models.py (8→4 spaces)
- Fix enrich_card() early return on partial Forge data: re-check
  has_real_data after _apply_forge_data so cards with incomplete Forge
  entries still fall through to known-cards / default heuristics
- Fix flawed test assertion in test_etb_via_forge_mode_equals_text_detection:
  oracle text "draw a card" was adding a +3 spell_draw bonus, making the
  score comparison invalid; replaced with side-effect-free ETB text

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Code <claude-code@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-23 09:37:05 -04:00
2eae7dcbd5 fix(ci): remove frontend job that references non-existent directory (#94)
The CI workflow had a `frontend` job that tried to run npm ci/build/test
in a `frontend/` directory which does not exist in this repo. The actual
UI lives in `ui/` as static HTML/CSS/JS (no Node.js toolchain). This
caused every CI run to fail on an otherwise valid codebase.

Co-authored-by: Claude Agent <claude-agent@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-22 23:54:16 -04:00
0f8d1382e0 feat: explicit turn-phase structure with Phase enum and main2 support (#86) (#93)
Refactor the headless engine to walk through all 12 MTG turn phases in
order (untap → upkeep → draw → main1 → begin_combat → declare_attackers
→ declare_blockers → combat_damage → end_combat → main2 → end_step →
cleanup) instead of the previous flat per-player block.

Key changes:
- Add Phase str enum and PHASE_ORDER/SORCERY_PHASES constants to models
- Track current_phase and active_player_index on SimState
- Sync sim.turn with the loop turn counter (fixes latent summoning-
  sickness bug where turn_played was always compared against 0)
- Add second main phase (main2) with shared 2-spell budget across
  main1 + main2
- _play_spells now accepts max_spells param and returns count played
- Unify turn_manager.py PHASES/ACTION_PHASES/RESPONSE_PHASES to derive
  from the canonical Phase enum in models.py
- 33 regression tests covering phase enum, phase progression, spell
  budget, game log structure, backward compat, and turn manager
  constant consistency

Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-22 23:46:09 -04:00
KoalaTrapLord b72d5fba83 fix(#92): bump READ_TIMEOUT_MS from 5s to 300s in PolicyServerClient.java 2026-03-22 23:44:47 -04:00
KoalaTrapLord 5d34d07bb8 fix(#92): bump validator_brain.py timeouts from 60s to 300s 2026-03-22 23:43:30 -04:00
KoalaTrapLord 5e9685ba70 fix(#92): read BRAIN_TIMEOUT env for ai_decision_timeout + add import os in turn_manager.py 2026-03-22 23:42:28 -04:00
KoalaTrapLord 49fc817187 fix(#92): bump BRAIN_TIMEOUT fallback from 120s to 300s in forge_runner.py 2026-03-22 23:40:28 -04:00
KoalaTrapLord c2b3d937a4 fix(#92): read BRAIN_TIMEOUT env for request_timeout in deepseek_brain.py 2026-03-22 23:39:33 -04:00
5e9c568bcc feat: implement London mulligan and opening-hand validation (#86) (#91) (#91)
Add London mulligan rule to the headless game engine:

- GameEngine now accepts mulligan_rule param ("london" default, "none" for legacy)
- AI heuristic: mulligan 0-1 land or 6-7 land hands; relax after 2+ mulligans
- After keeping, bottom N lowest-scored cards (London mulligan rule)
- Cap at MAX_MULLIGANS=4 to prevent infinite loops on degenerate decks
- Track mulligans and opening_hand_lands in PlayerStats
- Serialize both fields in to_dict() for batch-result schema compatibility
- 28 targeted regression tests covering unit helpers, integration,
  N-player, commander interaction, serialization, and legacy mode

Co-authored-by: Claude Agent <claude-agent@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-22 23:28:01 -04:00
KoalaTrapLordandClaude Code 85aa15445b feat: route direct-damage spells through centralized deal_damage() (#86) (#90)
Add centralized deal_damage() method to GameEngine that handles life
reduction, stat tracking, commander damage (both per-seat and per-card),
and elimination checks. Route both burn spells and combat damage through
this unified path, building on the dual-dict commander damage model
merged in PR #89.

- Add is_direct_damage / direct_damage_amount fields to Card
- Add burn spell detection via known-card table + oracle regex fallback
- Route burn spells in _play_spells() through deal_damage()
- Refactor _resolve_combat() to route per-attacker damage through deal_damage()
- Add 38 regression tests covering deal_damage(), burn detection, spell routing,
  combat routing, and full-game integration

Co-authored-by: Claude Code <noreply@anthropic.com>
2026-03-22 22:27:37 -04:00
KoalaTrapLordandClaude e265b1e15b feat: commander damage source identity and per-card tracking (#86) (#89)
Track commander damage at combat-damage time by checking `is_commander`
on each attacking creature.  Damage from commander creatures is now
recorded in both a per-seat aggregate (`commander_damage_received`) and
a per-card breakdown (`commander_damage_by_card`), enabling correct
21-damage lethal checks for partner/multi-commander games.

Changes:
- engine._resolve_combat: identify commander attackers, accumulate their
  damage (including trample overflow and double strike), write to both
  tracking dicts, and check commander-damage elimination after combat.
- models.Player: add commander_damage_received, commander_damage_by_card,
  and is_dead_to_commander_damage() so the headless engine can track
  commander damage without the full CommanderGameState wrapper.
- game_state.CommanderPlayer: replace the separate
  commander_damage_received field with a property delegating to
  base.Player to avoid split-brain state between engine and wrapper.
- game_state.CommanderGameState.deal_commander_damage: accept optional
  commander_name for per-card tracking.
- 22 new regression tests covering unblocked, blocked, trample, double
  strike, lifelink, partner, cumulative, and lethal scenarios.

Partial fix for Issue #86 item 2.

Co-authored-by: Claude <noreply@anthropic.com>
2026-03-22 22:04:19 -04:00
KoalaTrapLord ae8d14c19e chore: point deepseek brain at deepseek-r1:8b on localhost 2026-03-22 21:49:45 -04:00
KoalaTrapLord f090a36f79 chore: switch deck builder default model to qwen2.5:7b 2026-03-22 21:40:26 -04:00
cf4e2981e6 fix: resolve 78 test failures via shared stub cleanup and dependent fixes (#85) (#88)
Centralize and enhance test stubs to implement the full CommanderGameState
interface (battlefield(), living_players(), stack_is_empty(), sim_state,
active_player(), priority_player(), to_dict()). Fix all dependent test
failures across 12 files:

- conftest.py: Enhanced StubCard/StubPlayer/StubGameState with full API
  compatibility; added basic_creature fixture; fixed fresh_app singleton leak
- test_phase3.py: Added battlefield(), active_player(), prompt_builder compat
- test_integration.py: Fixed ThreatAssessor import (class→function), added
  threat_fn dict wrapper, sim_state delegate
- test_phase6.py/test_phase8.py: Replaced deprecated get_event_loop() with
  asyncio.run()
- test_benchmark.py: Same async fix + ThreatAssessor functional API
- sim/test_engine.py: Removed nonexistent seed= param, use run() method
- sim/test_models.py: basic_creature fixture now in conftest
- deck_builder/test_models.py: Updated ValidationError regex match
- state_logger.py: Added monotonic counter for unique snapshot filenames
- ai_opponent.py: Moved _CANNED dict to module-level constant (dataclass fix)
- web/app.py: Mount lobby router (fixes 6 LobbyAPI test failures)
- test_phase5.py: SessionStore + api._store reset fixture

Result: 76 failures + 2 errors → 0 failures, 0 errors (331 passed, 30 skipped)

Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-22 17:32:30 -04:00
d795e1a8fd feat: Phase 5 architecture follow-up — zone boundary, WAL store, Forge-only defaults (#87)
Implements the three approved Phase 5 architecture decisions:

1. Zone embedding boundary: add explicit documentation in StateDimensions
   and StateEncoder._encode_zones making the 6144-dim zero-padded zone
   contract visible to future maintainers.

2. SQLite WAL online learning store: new OnlineLearningStore class backed
   by WAL-mode SQLite for concurrent read/write on single-machine baseline.
   New /api/policy/decide endpoint collects decisions during inference.
   Stats endpoint at /api/policy/decide/stats.

3. Forge-only default weights: change ppo_weight default from 0.5 to 0.0
   across scope.py, DistillationConfig, DatasetConfig, and CLI defaults.
   Add MIXED_MODE_PRESETS dict with forge_only, forge_90_10, forge_80_20
   presets. Distill/start endpoint now accepts a "preset" parameter.

27 new tests covering all three decisions (all passing).

Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-22 14:41:33 -04:00
KoalaTrapLord 01d49e78a5 Fix coach cards-like: replace missing image_url column with scryfall_id URL builder 2026-03-22 13:06:36 -04:00
KoalaTrapLord c528fdb0b1 feat: add Scryfall card image hover preview on coach suggested cuts/adds 2026-03-22 12:30:00 -04:00
KoalaTrapLord e4bc3c5ce6 fix: use project root not routes/ for results_dir and reports_dir in coach 2026-03-22 12:07:56 -04:00
KoalaTrapLord d3c9e293d8 fix: mutate COMMANDER_META in-place so cross-module refs stay synced 2026-03-22 12:01:53 -04:00
ClaudeandClaude Opus 4.6 978fb12969 fix: early return in _load_deck_cards_by_name when deck not found in DB
Fixes UnboundLocalError crash when deck_row is None by returning an
empty list instead of falling through to a query with an unbound deck_id.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-22 07:25:39 +00:00
KoalaTrapLord 8c2a8cf1c1 fix: align indentation on line 108 to match surrounding block 2026-03-22 02:38:28 -04:00
KoalaTrapLord fd18d98b9b fix: correct indentation on line 108 in routes/lab.py 2026-03-22 02:35:30 -04:00
KoalaTrapLord 5c21564c78 fix: correct indentation on line 786 in deckbuilder.py 2026-03-22 02:02:22 -04:00
KoalaTrapLord e35df6fe36 fix: auto-export deckbuilder decks to .dck before batch simulation 2026-03-22 01:41:55 -04:00
KoalaTrapLord 7760c589b9 fix: add Deck Type=Commander metadata to .dck export 2026-03-22 01:39:17 -04:00
KoalaTrapLord 7fa262d963 fix(ui): account for nav-height in collection max-height calcs 2026-03-22 00:52:54 -04:00
KoalaTrapLord cffd8d8109 fix: add creature damage accumulation to combat engine
Previously _resolve_combat() used a binary power-vs-toughness kill check:
creatures only died if a single source had power >= their toughness in one
swing. This meant a 2/2 wouldn't die to two 1/1 tokens blocking it, and
repeated smaller hits across combats never accumulated.

Changes:
- models.py: add damage_marked field to Card (default 0)
- engine.py: accumulate damage via damage_marked += power instead of
  binary a_pow >= toughness checks in _resolve_combat()
- engine.py: clear damage_marked = 0 in untap phase (MTG rule 514.2)

This enables multi-blocker lethality, incremental chip damage within a
combat step, and proper creature death-by-accumulated-damage interactions.
2026-03-22 04:52:28 +00:00
KoalaTrapLord 473c453abf fix(ui): add #coll-card-preview CSS for hover image preview 2026-03-22 00:34:54 -04:00
KoalaTrapLord 54080eec37 Merge pull request #77 from KoalaTrapLord/feat/overnight-batch-runner
feat: overnight batch sim runner for ML data generation
2026-03-21 22:31:14 -04:00
rgriffith 5cb4bc3316 feat: add overnight batch sim runner for ML data generation
Script to run sequential batch sims across 10 precon decks with
rotating playstyles (midrange, aggro, control, combo). Generates
ML decision data for training.

- 210 unique 4-player matchups from 10 precon decks
- Cycles through playstyles each batch
- Polls for completion, handles errors gracefully
- Ensures ML logging is enabled before starting
- Prints summary with new decision count at end

Usage: python scripts/overnight-batch.py --hours 8
2026-03-22 02:31:05 +00:00
KoalaTrapLord 099e40d7c5 fix(ui): rewrite CollCardPreview multi-line, use imageUrl instead of scryfallId 2026-03-21 22:19:51 -04:00
KoalaTrapLord 57c4d6a26c Merge pull request #76 from KoalaTrapLord/fix/distillation-results-dir
fix: distillation loop uses wrong results_dir path
2026-03-21 22:18:34 -04:00
rgriffith ea3d488a41 fix: distillation loop uses wrong results_dir path
The distillation pipeline was looking for JSONL decision files in
data/results/ but they are stored in results/ (matching CFG.results_dir).

This caused all generations to fail at Step 1 with:
  'No ml-decisions-*.jsonl files found in data/results'

Fixed:
- routes/ml.py: results_dir and history_dir now use 'results/' not 'data/results/'
- ml/training/distillation_loop.py: updated DistillationConfig defaults to match
- History endpoint paths updated to match
2026-03-22 02:18:23 +00:00
KoalaTrapLord 83ee5ba9cc feat(ui): enable card image hover preview on collection table 2026-03-21 22:10:28 -04:00
KoalaTrapLord 5b532787af feat(ui): add card hover preview element to collection page 2026-03-21 22:05:00 -04:00
KoalaTrapLord 1b88cd362f tweak: reduce card preview hover delay to 225ms 2026-03-21 22:01:28 -04:00
KoalaTrapLord 834f8d9aad fix: Meta Commander picker — lazy-load, live search, debounce
- showMetaPicker() now guards for no backend with clear message,
  and lazy-loads commanders on first open (fixes race condition
  with Promise.all where loadMetaCommanders ran before detectBackend)
- filterMetaCommanders() hits live /api/lab/meta/search endpoint
  when backend is connected, falls back to local array filter
- Search input debounced at 250ms to avoid hammering API
2026-03-22 01:57:12 +00:00
KoalaTrapLord a7bc98c227 feat(ui): add 450ms hover delay to card preview popup
- Add _hoverTimer and HOVER_DELAY (450ms) properties to CardPreview
- mouseenter starts a delay timer before showing the popup
- mousemove only repositions if popup is already visible
- mouseleave clears the timer and hides immediately
- hide() now also clears any pending timer
2026-03-21 21:52:11 -04:00
KoalaTrapLord 3e08d2bed1 feat(ui): add card image hover preview element to deckbuilder
Add #deck-card-preview div with img element that the existing
CardPreview JS object references. This was missing from the HTML.
2026-03-21 21:46:47 -04:00
KoalaTrapLord e4730ad8c7 Merge pull request #74 from KoalaTrapLord/phase6-elo-tracking
Phase 6: ELO Tracking & Model Tournament
2026-03-21 20:31:22 -04:00
rgriffith e843528d8b Phase 6: ELO Tracking & Model Tournament (#70)
- Create ml/eval/elo_tracker.py:
  - EloTracker class with standard ELO formula (K=32, start=1200)
  - EloResult dataclass for tournament output
  - EloHistory class for JSON persistence (data/elo_history.json)
  - run_elo_tournament(): round-robin ELO-rated matches using
    existing run_match() from tournament.py
  - run_generation_tournament(): discovers gen-*/best_policy.pt
    checkpoints and runs cross-generation ELO tournament
  - CLI entry point: python -m ml.eval.elo_tracker

- Add ELO API endpoints to routes/ml.py:
  - POST /api/ml/elo/tournament — start background ELO tournament
  - GET  /api/ml/elo/tournament/status — poll progress
  - GET  /api/ml/elo/history — load ELO history from disk

- Integrate ELO convergence in distillation_loop.py:
  - New config fields: elo_convergence_enabled (default False),
    elo_convergence_threshold (20.0), elo_tournament_episodes (30)
  - New field: GenerationRecord.elo_rating
  - _run_elo_mini_tournament() runs after quality gate pass
  - _check_convergence() checks ELO delta when enabled

- Add ELO UI to distillation page:
  - ELO Ratings panel with canvas multi-line chart showing per-model
    ELO progression across generations
  - Run Tournament button with progress indicator
  - ELO leaderboard table with rank, rating, and visual bars
  - Responsive chart resize handling

Closes #70
2026-03-22 00:31:02 +00:00
KoalaTrapLord dd2e945214 Merge pull request #73 from KoalaTrapLord/phase5-distillation-ui
Phase 5: Distillation UI Integration & Monitoring
2026-03-21 20:19:04 -04:00
rgriffith 106ec716a5 Phase 5: Distillation UI Integration & Monitoring (#69)
- Add distillation API endpoints to routes/ml.py:
  - POST /api/ml/distill/start — start closed-loop distillation pipeline
  - GET  /api/ml/distill/status — poll loop status and generation data
  - POST /api/ml/distill/stop — graceful stop after current generation
  - GET  /api/ml/distill/history — load persisted generation history

- Add 'Distill' nav entry in ui/nav.js with loop icon

- Create ui/distillation.html:
  - Status overview cards (status, generation, best win rate, gate passes)
  - Control panel with hyperparameter inputs for supervised, PPO, and
    quality gate settings
  - Start/Stop buttons with progress bar and phase badge
  - Win rate trend chart (canvas-based, no dependencies)
  - Data composition stacked bar chart (Forge vs PPO per generation)
  - Generation history table with status, metrics, and gate results

- Create ui/distillation.js:
  - Polling loop for real-time status updates during distillation
  - Canvas chart rendering for win rate trends and data composition
  - History table rendering with color-coded status tags
  - Responsive resize handling for charts

- Create ui/distillation.css:
  - Page-specific styles following training.css patterns
  - Chart container, history table, and responsive grid styles

Closes #69
2026-03-22 00:18:43 +00:00
KoalaTrapLord 9bec84e997 Merge pull request #72 from KoalaTrapLord/phase4-distillation-loop
feat: Phase 4 — Distillation Loop Orchestrator
2026-03-21 20:11:31 -04:00
rgriffith ae00dd47e2 feat: Phase 4 — Distillation Loop Orchestrator (#68)
Implements the closed-loop distillation pipeline that automates:
  1. Supervised train on current dataset (Forge + PPO data)
  2. PPO self-play using supervised model as starting policy
  3. Export winning PPO decisions to JSONL
  4. Quality gate check (win rate threshold)
  5. If passed, merge into dataset and repeat
  6. If failed, discard batch, adjust hyperparameters, retry

New files:
  - ml/training/distillation_loop.py — DistillationLoop orchestrator,
    DistillationConfig, GenerationRecord, CLI entry point

Updated files:
  - ml/config/scope.py — DistillationDefaults dataclass with all
    default hyperparameters for the pipeline

Features:
  - Generation tagging: data and models tagged with gen-NNN
  - Convergence detection: stops when win rate plateaus over N gens
  - Automatic retry with LR reduction + entropy boost on gate failure
  - Per-generation history saved to JSON for observability
  - Full CLI with all tunable params
  - progress_callback for UI integration (Phase 5 ready)

Closes #68
2026-03-22 00:11:14 +00:00
KoalaTrapLord dc66ed78e5 fix(lab): remove shadowed _ml_logging_enabled redeclaration (closes #64) 2026-03-22 00:10:12 +00:00
KoalaTrapLord 0f6faa5fe4 feat(ml): integrate DecisionExporter into self-play rollouts (#65) 2026-03-22 00:06:55 +00:00
KoalaTrapLord 1164285a5b docs: add Phase 1 self-play integration checklist (#65) 2026-03-21 19:51:27 -04:00
KoalaTrapLord d9ee09b848 docs: update .env.example to match dual-model config (gpt-oss:20b + deepseek-r1:14b) 2026-03-21 19:47:17 -04:00
KoalaTrapLord d5c6746503 docs: add BRAIN_* and VALIDATOR_* vars to .env.example (#71) 2026-03-21 19:41:47 -04:00
KoalaTrapLord 1b95659f14 fix: add LAN origins (192.168.0.240) to default CORS allowlist (#71) 2026-03-21 19:41:05 -04:00
KoalaTrapLord 871934d101 fix: raise VALIDATOR_TIMEOUT default from 60s to 120s (#71) 2026-03-21 19:40:19 -04:00
KoalaTrapLord 11978736a1 fix: raise BRAIN_TIMEOUT default from 30s to 120s (#71) 2026-03-21 19:39:29 -04:00
KoalaTrapLord 8ed3fbc627 feat(ml): add quality gate & validation module (#67)
Phase 3 of closed-loop distillation pipeline.

- QualityGateConfig with configurable thresholds
- create_forge_holdout() for Forge-only eval split (12% by game_id)
- check_accuracy_gate() rejects PPO batch if Forge eval accuracy drops
- check_win_rate_gate() blocks export below configurable win rate
- compute_quality_metrics() logs action entropy, reward distribution
- QualityGate orchestrator class with evaluate_ppo_batch()
2026-03-21 19:34:37 -04:00
KoalaTrapLord 4d50449145 feat(ml): add source awareness to dataset builder (#66)
Phase 2 of closed-loop distillation pipeline.

- Scan both ml-decisions-sim-*.jsonl (Forge) and ml-decisions-ppo-*.jsonl (PPO)
- Add source_weights config for configurable sampling ratios
- Add min_reward_threshold filter for PPO data quality
- Tag each sample with source for downstream analysis
- New CLI args: --forge-weight, --ppo-weight, --min-reward
- NPZ output now includes 'sources' array
2026-03-21 19:28:22 -04:00
KoalaTrapLord f986d236ab Reduce coach prompt limits and raise LLM timeout for R1 compatibility 2026-03-21 19:08:24 -04:00
KoalaTrapLord 3957285a2e feat(ml): add PPO DecisionExporter for closed-loop distillation (#65)
Phase 1 of closed-loop distillation pipeline.

Adds DecisionExporter class that captures PPO self-play decisions
as JSONL files compatible with the Forge DecisionSnapshot schema.
Includes PPO-specific metadata fields (source, model_version,
reward, episode_return) and configurable win-only filtering.
2026-03-21 18:06:45 -04:00
KoalaTrapLord 3cca49c5a9 docs: add closed-loop iterative distillation implementation roadmap 2026-03-21 17:55:18 -04:00
KoalaTrapLord f290523ce1 fix(ml): replace undefined 'log' with 'logger' in labeler.py 2026-03-21 15:47:47 -04:00
KoalaTrapLord 448cec128c feat(ui): add 12 and 16 thread options to batch runner dropdown 2026-03-21 15:22:20 -04:00
KoalaTrapLord 6612f3d213 fix: use correct key best_val_acc in training completion message 2026-03-21 15:21:35 -04:00
KoalaTrapLord 06f007e605 fix: invert guard in _load_deck_cards_by_name — was if not deck_row (crash on None[0])
The guard condition was inverted: `if not deck_row:` entered the query
block when no deck was found, causing `deck_row[0]` to crash with
'NoneType' object is not subscriptable. Flipped to `if deck_row:`
so the query runs only when the deck exists.
2026-03-21 15:11:08 -04:00
KoalaTrapLord 1397f93b91 feat(ml): wire ML decision flush in _finish_sim for all sim threads 2026-03-21 14:30:47 -04:00
KoalaTrapLord f86a4af86f feat(ml): add ML decision logging to base GameEngine
- Add ml_log param, ml_decisions list, flush_ml_decisions() to GameEngine
- Capture ML snapshots at each player turn in run_n()
- Stamp game_outcome (win/loss per seat) on all decisions
- Add _capture_ml_snapshot() matching DeepSeekGameEngine format
- Add game_id and archetype params to run_n() for ML tracking
- Import logging, uuid; add logger
2026-03-21 13:54:33 -04:00
KoalaTrapLord 0e99f814fc chore: remove unused React scaffold frontend/ 2026-03-21 13:45:08 -04:00
KoalaTrapLord 8c34cb0966 feat: Phase 8 — surface player_explanation in simulation results JSON
Extract player_explanation from validation dict in each turn log
entry within _run_game_loop(). The React frontend can now render
validator explanations when player_explanation is non-null.
2026-03-21 13:37:09 -04:00
KoalaTrapLord c67ce422d8 refactor: rename LM_STUDIO/LMStudioClient to LLM/LLMClient in routes/coach.py 2026-03-21 13:34:21 -04:00
KoalaTrapLord cb4b48e362 refactor: rename LMStudioClient to LLMClient in coach_service.py 2026-03-21 13:33:00 -04:00
KoalaTrapLord 4a8db4db6b refactor: rename LM_STUDIO/LMStudioClient to LLM/LLMClient in llm_client.py 2026-03-21 13:31:32 -04:00
KoalaTrapLord 9c0fa64a69 feat: Phase 7 — add validation field to decision log schema
Add "validation" key to _log_decision() entry dict in deepseek_brain.py.
Captures result.get("validation") (None if validator disabled)
for R1-14B output preservation and future analysis.
2026-03-21 13:31:08 -04:00
KoalaTrapLord c338f16965 refactor: rename LM_STUDIO_* to LLM_* in coach/config.py 2026-03-21 13:29:51 -04:00
KoalaTrapLord a7ab0e43a5 feat: Phase 6 — make primary brain env-configurable (BRAIN_* vars)
Replace hardcoded DEEPSEEK_API_BASE/DEEPSEEK_MODEL env vars in
_get_deepseek_brain() with new BRAIN_* environment variables:
- BRAIN_API_BASE (default: http://localhost:11434)
- BRAIN_MODEL (default: gpt-oss:20b)
- BRAIN_TEMPERATURE (default: 0.3)
- BRAIN_MAX_TOKENS (default: 1024)
- BRAIN_TIMEOUT (default: 30.0)

All params now passed directly to DeepSeekConfig constructor.
2026-03-21 13:21:58 -04:00
KoalaTrapLord 8b527d7c30 fix: remove duplicate ml_log=True kwarg in deepseek.py 2026-03-21 12:48:56 -04:00
KoalaTrapLord 36760dd922 fix(ml): rename log -> logger in dataset_builder.py to fix NameError 2026-03-21 12:42:02 -04:00
KoalaTrapLord 963a5db638 fix(ml): enable ml_log=True in deepseek route engine calls 2026-03-21 12:41:01 -04:00
KoalaTrapLord b1996a8deb fix: indent __init__ into DeepSeekGameEngine class body 2026-03-20 22:01:25 -04:00
KoalaTrapLord c44d0b32bb Update DeepSeek port from 1234 to 11434 in deepseek_engine.py 2026-03-20 21:55:10 -04:00
KoalaTrapLord a78df75014 Update DeepSeek port from 1234 to 11434 in SimulatorPage.tsx 2026-03-20 21:54:22 -04:00
KoalaTrapLord 9020d29745 Update DeepSeek port from 1234 to 11434 in deepseek_brain.py 2026-03-20 21:53:27 -04:00
KoalaTrapLord b0a34ae9e2 Update DeepSeek port from 1234 to 11434 in coach.py 2026-03-20 21:52:39 -04:00
KoalaTrapLord 4c4f5ce0ff Update DeepSeek API port from 1234 to 11434 in config.py 2026-03-20 21:51:42 -04:00
KoalaTrapLord 1e7f4b4e31 Update DeepSeek API port from 1234 to 11434 (Ollama) 2026-03-20 21:49:49 -04:00
KoalaTrapLord 488575f3df fix(routes): correct indentation of validator line in _run_sim_thread_deepseek 2026-03-20 21:42:11 -04:00
KoalaTrapLord fd7f0258f3 feat(routes): add validator singleton and /api/validator endpoints 2026-03-20 21:27:15 -04:00
KoalaTrapLord 7284b8617d feat(sim): wire DeepSeek-R1-14B validator into deepseek_engine 2026-03-20 21:16:16 -04:00
KoalaTrapLord 678cc13fe0 feat: enable ML logging by default in lab.py 2026-03-20 21:05:37 -04:00
KoalaTrapLord 19f83a546b feat: enable ML logging by default in shared.py 2026-03-20 21:01:40 -04:00
KoalaTrapLord c53e82bfeb feat(sim): add DeepSeek-R1-14B validator brain module 2026-03-20 17:20:30 -04:00
KoalaTrapLord f8b13703b8 fix: CAST_PATTERN now matches both 'cast' and 'casts' for Forge compatibility 2026-03-20 15:04:37 -04:00
KoalaTrapLord eb59a2cd2e fix: increase default clock from 120s to 300s for Commander games to prevent premature timeouts 2026-03-20 14:58:24 -04:00
KoalaTrapLord ff6040b53e Remove stray closing brace that would break compilation 2026-03-20 13:31:50 -04:00
KoalaTrapLord db7bc62d7e Fix commander damage tracking, add PLAYER_DAMAGE_SOURCE_PATTERN and diagnostic logging 2026-03-20 13:28:54 -04:00
KoalaTrapLord fbffe81846 feat: auto-detect commander card names from .dck files for commander cast tracking 2026-03-20 12:48:48 -04:00
KoalaTrapLord 9369cf5450 docs: fix adapter.py docstring to reference lab_api.py instead of deleted lab_api_new.py 2026-03-20 12:19:07 -04:00
KoalaTrapLord e38401a740 chore: remove obsolete lab_api_backup.py (superseded by lab_api.py + routes/) 2026-03-20 12:17:44 -04:00
KoalaTrapLord 111764615d chore: remove obsolete lab_api_new.py (superseded by lab_api.py + routes/) 2026-03-20 12:16:50 -04:00
KoalaTrapLord 2088da6fd8 fix: correct indentation in _resolve_forge_dir forge-gui sibling check 2026-03-20 11:33:42 -04:00
KoalaTrapLord a2fbc30b2b fix: resolve forge_dir to forge-gui sibling when res/ not found in forge-gui-desktop 2026-03-20 11:22:44 -04:00
KoalaTrapLord a62640668b fix: update DeepSeek endpoint IP in lab_api_backup.py 2026-03-20 11:11:07 -04:00
KoalaTrapLord b8e12dff1b fix: update DeepSeek endpoint IP in lab_api_new.py 2026-03-20 11:10:15 -04:00
KoalaTrapLord ea0a785174 fix: update DeepSeek endpoint IP in SimulatorPage.tsx 2026-03-20 11:09:27 -04:00
KoalaTrapLord 9f1277f233 fix: update DeepSeek endpoint IP in deepseek_engine.py 2026-03-20 11:08:32 -04:00
KoalaTrapLord 66ce887fe3 fix: update DeepSeek endpoint IP in deepseek_brain.py docstring 2026-03-20 11:07:38 -04:00
KoalaTrapLord 80f63f192a fix: update DeepSeek endpoint IP in coach.py 2026-03-20 11:06:30 -04:00
KoalaTrapLord 46ed2f5606 fix: update DeepSeek endpoint IP from 192.168.0.122 to 192.168.0.240 in simulator.html 2026-03-20 11:05:10 -04:00
KoalaTrapLord 93a421b90a fix: make colons optional in all regex patterns to match Forge output format (no colons after Game Outcome, Life, Add To Stack, Land, Zone Change, etc.); fix VERBOSE_TURN_PATTERN for 'Turn Turn N AiX' format; fix LIFE_CHANGE_PATTERN to use space instead of > separator 2026-03-20 09:26:02 -04:00
KoalaTrapLord ee95f26bc4 fix: restore double-backslash escaping in Ai regex patterns (fixes compilation errors) 2026-03-20 09:03:55 -04:00
KoalaTrapLord 2ee574f678 diag: add [COMBAT-LINE] logging to identify Forge verbose output format for combat stats parsing 2026-03-20 08:15:03 -04:00
KoalaTrapLord 473a2bdcc7 fix: make Ai regex patterns support both Ai(N) and AiN formats for Forge output compatibility 2026-03-19 23:29:53 -04:00
KoalaTrapLord a6e50ee0f0 feat: aggregate combat stats in StatsAggregator 2026-03-19 22:45:05 -04:00
KoalaTrapLord e2a6a75fd9 feat: add combat stat fields to DeckSummary schema 2026-03-19 22:39:51 -04:00
KoalaTrapLord 030bdf2b81 fix: CAST_PATTERN and LAND_PLAY_PATTERN regex to match Forge verbose log prefixes
- CAST_PATTERN: Add optional (?:Add To Stack:\s*)? prefix to match Forge's verbose log format
- LAND_PLAY_PATTERN: Add optional (?:Land:\s*)? prefix to match Forge's verbose log format
- Add [PARSE] diagnostic logging after combat stats parsing to confirm spells/lands/cmdr stats per seat

Root cause: Forge verbose output prefixes cast lines with "Add To Stack: " and land lines with "Land: "
but the regexes expected Ai(N) at the start of the line, causing 0 matches for all combat stats.
2026-03-19 22:06:17 -04:00
rgriffith c83f7e0141 fix: repair broken if-statement on line 162 2026-03-19 21:45:42 -04:00
KoalaTrapLord e5377ef4bc fix: correct indentation and add forge-gui candidate path 2026-03-19 18:24:57 -04:00
KoalaTrapLord 56ea2602b4 fix: scan sibling dirs for res/ in _resolve_forge_dir (finds forge-gui/res) 2026-03-19 18:19:32 -04:00
KoalaTrapLord d86109c5c0 Fix duplicate WHERE clause in _load_deck_cards_by_name SQL query 2026-03-19 17:54:34 -04:00
KoalaTrapLord d9dd098ca3 fix: add power, toughness, keywords to _load_deck_cards_by_name SQL query 2026-03-19 17:43:57 -04:00
KoalaTrapLord 69ce5b340e fix: normalize indentation in _finish_sim (line 184-185) 2026-03-19 17:13:13 -04:00
KoalaTrapLord 103a74a0e1 fix: update DeepSeek brain default api_base to 192.168.0.240:1234 2026-03-19 17:11:56 -04:00
KoalaTrapLord 147121582b fix: update DeepSeek/LM Studio default URL to 192.168.0.240:1234 2026-03-19 17:10:14 -04:00
KoalaTrapLord 8be96ab47d fix: use coach.report_generator for deck report generation 2026-03-19 17:05:03 -04:00
KoalaTrapLord e2653b3f72 fix: align sim results path with coach (routes/results) 2026-03-19 17:03:00 -04:00
KoalaTrapLord 7efac34279 fix: persist sim results to disk + auto-generate deck reports 2026-03-19 16:59:55 -04:00
KoalaTrapLord b00df14f30 fix: use card_name key from _load_deck_cards_by_name (KeyError: 'name') 2026-03-19 16:51:27 -04:00
KoalaTrapLord 3542efd982 fix: align build_java_command with LabCli picocli args (--forge-jar, --forge-dir, --deck1..4) 2026-03-19 16:42:03 -04:00
KoalaTrapLord 1f93b5510d fix: add missing load_weights() to rules.py 2026-03-19 15:49:00 -04:00
KoalaTrapLord 5c31be6023 fix: add Config extra=ignore to DeckCardWithStatus to prevent 422 from adapter extra fields 2026-03-19 15:34:18 -04:00
KoalaTrapLord 76139477be fix: replace pplx.model with getattr, update V3 error messages for Ollama 2026-03-19 15:26:15 -04:00
KoalaTrapLord 0e5d7ba7d3 fix: harden _find_java17 to search multiple vendors, JAVA_HOME, and warn on wrong version 2026-03-19 15:23:44 -04:00
KoalaTrapLord 4ec6ed57d5 fix: add missing urllib imports to deckbuilder.py for Scryfall card lookup 2026-03-19 15:11:33 -04:00
KoalaTrapLord 08d81a4336 fix: remove stale Perplexity API key error message, allow Ollama/local mode to generate without hard block 2026-03-19 14:58:39 -04:00
KoalaTrapLord f6ed40a253 fix: correct indentation in init_coach_service V3 deck gen block 2026-03-19 14:46:16 -04:00
KoalaTrapLord 9fd39e42c7 feat(phase-10): add DeckBuilderAdapter unit tests 2026-03-19 14:35:00 -04:00
KoalaTrapLord 72107df2ae feat(phase-10): add comprehensive Pydantic model tests 2026-03-19 14:32:43 -04:00
KoalaTrapLord 2738bbdba4 feat: add scrython>=2.0.0 to requirements.txt 2026-03-19 14:28:38 -04:00
KoalaTrapLord 0bbc78ef23 fix: rewire routes/coach.py to use DeckBuilderAdapter + fix IndentationError 2026-03-19 14:02:23 -04:00
KoalaTrapLord c2b470e251 refactor: replace DeckGeneratorV3 import with DeckBuilderAdapter alias 2026-03-19 13:50:30 -04:00
KoalaTrapLord 6135feb644 feat: add run_substitution stub + clean up adapter 2026-03-19 13:49:13 -04:00
KoalaTrapLord b3b664872b refactor: rewire V3 deck gen init to use DeckBuilderAdapter (Ollama) 2026-03-19 13:41:15 -04:00
KoalaTrapLord a80e8908e1 fix: indent _deck_gen_v3_error inside if block in coach.py 2026-03-19 13:35:44 -04:00
KoalaTrapLord a8ef10d5b7 feat: add DeckBuilderAdapter to bridge Ollama pipeline to legacy API 2026-03-19 13:24:29 -04:00
KoalaTrapLord 86af4b6de5 fix: close missing paren in _call_pplx_api client.post() 2026-03-19 13:16:22 -04:00
KoalaTrapLord 332606d6b5 Merge pull request #62 from KoalaTrapLord/feat/deck-builder-phase0
feat: Commander AI Deck Builder (Ollama gpt-oss:20b)
2026-03-19 12:45:04 -04:00
KoalaTrapLord 70dfa3d3e0 fix: robust JSON extraction for LLM responses (_extract_json) 2026-03-19 12:29:49 -04:00
KoalaTrapLord f970b6d571 fix: api __init__ imports modules not classes 2026-03-19 12:08:26 -04:00
KoalaTrapLord 5ade916207 fix: make conversation log always visible with initial ready message 2026-03-19 11:50:52 -04:00
KoalaTrapLord cc53c58344 feat(phase-0): add deck builder deps (requests, bs4, ollama) 2026-03-19 11:33:45 -04:00
KoalaTrapLord 3e64aa67eb feat(phase-6): implement deck assembler (Ollama JSON parser) 2026-03-19 11:32:14 -04:00
KoalaTrapLord 70a7c07b81 feat: add conversation log panel below Generate Deck button 2026-03-19 11:31:10 -04:00
KoalaTrapLord 1b4e9f5a66 feat(phase-3): implement community scraper (Moxfield + Archidekt) 2026-03-19 11:30:53 -04:00
KoalaTrapLord c962d1f16a feat(phase-9): implement FastAPI + CLI entrypoint 2026-03-19 11:29:29 -04:00
KoalaTrapLord dcdf15c56a feat(phase-8): add deck_builder package init with public API 2026-03-19 11:28:08 -04:00
KoalaTrapLord b0214009f4 feat(phase-8): add api __init__ exports 2026-03-19 11:27:07 -04:00
KoalaTrapLord 5fe0ac2bd6 feat(phase-8): add core __init__ exports 2026-03-19 11:26:04 -04:00
KoalaTrapLord fc09249ad3 feat(phase-7): add pipeline __init__ exports 2026-03-19 11:24:47 -04:00
KoalaTrapLord d2c7783ce1 feat(phase-7): implement 7-step deck build pipeline 2026-03-19 11:23:38 -04:00
KoalaTrapLord 0ccf35f241 feat(phase-5): implement collection filter with CSV loader 2026-03-19 11:22:09 -04:00
KoalaTrapLord 9a5105b0d6 feat(phase-5): implement rules engine with ban list and validation 2026-03-19 11:19:42 -04:00
KoalaTrapLord 6a688d1cd0 feat(phase-4): implement Ollama gpt-oss:20b client with 6 core functions 2026-03-19 11:18:13 -04:00
KoalaTrapLord d9639a8414 feat(phase-3): implement EDHrec JSON client 2026-03-19 11:16:28 -04:00
KoalaTrapLord 633fd43b28 fix: replace sonar/sonar-pro model options with gpt-oss:20b in UI 2026-03-19 11:15:43 -04:00
KoalaTrapLord 45c72aadc9 feat(phase-2): implement Scryfall API client 2026-03-19 11:14:58 -04:00
KoalaTrapLord 81ab7ac34a feat(phase-1): implement Pydantic models for deck builder 2026-03-19 11:13:06 -04:00
KoalaTrapLord e0fe4144ae Create test_collection_filter.py 2026-03-19 10:49:06 -04:00
KoalaTrapLord 68aa6eac3f Create test_rules_engine.py 2026-03-19 10:48:23 -04:00
KoalaTrapLord c870dd35fb Create __init__.py 2026-03-19 10:47:49 -04:00
KoalaTrapLord 35ce67ed6f Create collection.csv.example 2026-03-19 10:47:18 -04:00
KoalaTrapLord 232750a951 Create main.py 2026-03-19 10:46:45 -04:00
KoalaTrapLord 46c1c8cb01 Create build_deck.py 2026-03-19 10:46:14 -04:00
KoalaTrapLord 64a4cd3c7c Create __init__.py 2026-03-19 10:45:36 -04:00
KoalaTrapLord 3f6b66fce3 Create deck_assembler.py 2026-03-19 10:44:53 -04:00
KoalaTrapLord 2887c09550 Create collection_filter.py 2026-03-19 10:44:18 -04:00
KoalaTrapLord 84d84a211f Create rules_engine.py 2026-03-19 10:43:45 -04:00
KoalaTrapLord 12f90dd276 Create models.py 2026-03-19 10:43:15 -04:00
KoalaTrapLord 0b755c8428 Create __init__.py 2026-03-19 10:42:40 -04:00
KoalaTrapLord 50f36f284c Create ollama_client.py 2026-03-19 10:42:12 -04:00
KoalaTrapLord 369cc9243f Create community_scraper.py 2026-03-19 10:41:45 -04:00
KoalaTrapLord 22d3b64c2d Create edhrec.py 2026-03-19 10:41:07 -04:00
KoalaTrapLord beb3dac4d5 Create scryfall.py 2026-03-19 10:40:40 -04:00
KoalaTrapLord 330801107c Create __init__.py 2026-03-19 10:40:11 -04:00
KoalaTrapLord 95c5a93584 feat(task-0.1): scaffold deck_builder subpackage structure 2026-03-19 10:39:21 -04:00
KoalaTrapLord 377a704a90 fix: update lab_api_new.py monolith to use local Ollama for deck gen 2026-03-19 10:32:56 -04:00
KoalaTrapLord cc481bd71b feat: switch deck generation from Perplexity to local Ollama (gpt-oss:20b) 2026-03-19 10:26:24 -04:00
KoalaTrapLord c6d02fd38c Fix bracket field handling: support both int and dict from AI response 2026-03-19 08:25:23 -04:00
KoalaTrapLord 0f2d3583ac Remove unsupported response_format from Perplexity API call 2026-03-19 08:14:44 -04:00
KoalaTrapLord 2b355ed9ba Fix NameError: _coach_embeddings not defined in V3 status endpoint 2026-03-19 08:05:26 -04:00
KoalaTrapLord b4d8c2f50e Fix indentation error on line 47 of forge_runner.py 2026-03-19 06:54:16 -04:00
KoalaTrapLord 9db1ed112f Increase _STALL_LIMIT from 300s to 600s (10 min) to prevent premature batch kills 2026-03-19 06:30:03 -04:00
KoalaTrapLord 75568bfdb1 fix: expose full V3 init traceback in _deck_gen_v3_error for easier debugging 2026-03-19 06:27:04 -04:00
KoalaTrapLord d834247885 Fix forge_runner.py: restore full file, fix sim mode arg
- Restored _run_deepseek_batch_thread and _get_deepseek_brain
  (accidentally removed in prior commit)
- Changed '-sim' to 'sim' — Forge expects positional mode, not flag
2026-03-19 10:13:11 +00:00
rgriffith 310a408509 feat: polish precon picker — card art, color pips, grid layout
- Add full CSS for precon grid: responsive card grid, hover effects,
  install button states, color pips, dark theme styling
- Build commander art_crop image URLs from set_code/number during
  precon download (Scryfall direct image redirect, no API call needed)
- Batch-fetch color_identity from Scryfall /cards/collection endpoint
  (2 requests for ~145 unique commanders) and store in precon index
- Widen precon modal to 900px for grid layout
- Auto-invalidate precon cache when enrichment fields are missing
- JS renderer now shows commander art crop + card body layout
- Bump cache-busting version to v=4
2026-03-19 10:10:40 +00:00
KoalaTrapLord ef7bde841f Fix forge_runner.py: sim mode arg + indentation corruption
- Changed '-sim' to 'sim' (Forge expects positional mode, not flag)
- Fixed indentation: build_java_command, _run_process_blocking,
  run_batch_subprocess were incorrectly nested inside get_java17()
- Fixed last_activity assignment stuck inside except block
- Added docstrings
2026-03-19 10:09:59 +00:00
KoalaTrapLord 87abfd801e Fix V3 500 error: replace json_schema response_format with json_object
- Sonar does not support response_format type json_schema
- Switch to {"type": "json_object"} which Sonar supports
- Keep json_schema/schema_name params for call-site compatibility (ignored)
- Add exc_info=True to V3 generate error logging for full tracebacks
2026-03-19 10:04:36 +00:00
rgriffith 8eba39cca7 fix: precon picker empty — mutate PRECON_INDEX in-place
The PRECON_INDEX list was imported by value in routes/shared.py.
When download_precon_database() or load_precon_index() reassigned it
with 'PRECON_INDEX = ...', the new list object was only visible in
precon_service module scope — shared.PRECON_INDEX (and thus the
/api/lab/precons endpoint) still pointed to the original empty [].

Fix: use PRECON_INDEX.clear() + .extend() instead of reassignment
so all modules sharing the same list reference see the updated data.
2026-03-19 10:01:51 +00:00
rgriffith bc9656a59f fix: add cache-busting query params to ai-lab.js and ai-lab.css
Browser was serving the old Base64-encoded ai-lab.js from cache.
Adding ?v=3 query parameter forces the browser to fetch the new
decoded JavaScript file.
2026-03-19 09:55:00 +00:00
rgriffith 86b96c09f9 fix: decode ai-lab.js from Base64 back to proper JavaScript
The entire ai-lab.js file (139KB) was stored as a single Base64-encoded
string instead of actual JavaScript. The browser loaded it as a script
but couldn't parse it — AiLab was never defined, so the batch sim panel
never rendered.

Decoded the file back to its proper 2531-line JavaScript source. The
file already includes auto-initialization at the bottom:
  if (document.readyState === 'loading')
    document.addEventListener('DOMContentLoaded', AiLab.init);
  else AiLab.init();

Also removed the now-redundant DOMContentLoaded listener from
index.html since the JS file handles init itself.
2026-03-19 09:47:18 +00:00
rgriffith 9a46b0f80c fix: remove duplicate Scryfall/helper defs from collection.py that shadowed working imports
collection.py re-defined _ScryfallCache, _scryfall_cache,
_scryfall_rate_limit, _fetch_scryfall_api, _enrich_from_scryfall,
_parse_finish, _parse_text_line, _auto_infer_mapping, _parse_csv_content,
_TYPE_PRIORITY, and _TYPE_TARGETS locally (lines 333-509).

These local definitions shadowed the correct imports from
routes/shared.py -> services/scryfall.py. Critically, the local
_fetch_scryfall_api used Request and urlopen without importing them,
causing a NameError at runtime that was silently swallowed by the
broad except clause — making every card fail Scryfall enrichment
during collection import.

Removed all ~470 lines of duplicate code. The import endpoint now
correctly uses the functions imported from shared.py, which have
proper urllib imports and a working Scryfall cache.
2026-03-19 09:43:26 +00:00
rgriffith 0d2da18783 fix: call AiLab.init() on DOMContentLoaded so batch sim panel renders
The ai-lab.js IIFE defines the AiLab global with an init() method that
calls renderPanel(), detectBackend(), loadAvailableDecks(),
loadMetaCommanders(), loadPrecons(), and bindEvents() — but nothing
ever invoked it. Added a DOMContentLoaded listener after the script tag
in index.html to trigger initialization.
2026-03-19 09:34:27 +00:00
Computer 6a84708c65 fix: wire DeckBuilderController and create prefabs in CreateDeckBuilderScene.cs 2026-03-19 08:30:11 +00:00
rgriffith 9b892e5289 fix: remove unsupported cursor:auto and normalize rgb() spacing in USS files 2026-03-19 08:05:54 +00:00
KoalaTrapLord f2a782f0bd feat(unity-ui): add MainMenu.uss styles for nav hub, server config, module cards 2026-03-19 04:00:31 -04:00
KoalaTrapLord da67879491 feat(unity-ui): add MainMenu.uxml nav hub with server config and module cards 2026-03-19 03:59:31 -04:00
KoalaTrapLord 18beb9192e feat(unity-ui): add UXML+USS layouts for all 5 GUI screens
- CollectionTracker.uxml  — card grid, filter sidebar, detail panel
- CoachPanel.uxml         — chat bubbles, analytics, mana curve chart
- DeckGenerator.uxml      — 4-step wizard (commander → strategy → generate → result)
- SimulationPanel.uxml    — 4-player config, progress bar, results table
- TrainingPanel.uxml      — stats, start/stop, live charts, log, model history

- CommanderLabShared.uss  — dark MTG theme shared across all screens
- CollectionTracker.uss
- CoachPanel.uss
- DeckGenerator.uss
- SimulationPanel.uss
- TrainingPanel.uss
2026-03-19 03:55:13 -04:00
rgriffith ce80b6d9c5 feat: implement SimulationController, fix API paths, wire Simulator scene 2026-03-19 07:46:44 +00:00
Computer d8e355de2b fix: append to Build Settings instead of overwrite, add Build All Scenes tool 2026-03-19 07:34:01 +00:00
Computer 9d2a043d3f fix: add EditorSceneManager.SaveScene to all scene creator scripts 2026-03-19 07:29:16 +00:00
KoalaTrapLord 85224f082e fix: handle wrapped JSON object from /api/ml/models endpoint
The API returns {"models":[...]} rather than a bare array.
Added MlModelsResponse wrapper class and updated LoadModelHistory
to unwrap the list before iterating, preventing the deserialization
exception in TrainingController.
2026-03-19 02:57:46 -04:00
Computer c6c3bfabf8 refactor: replace per-batch watchdog threads with single global watchdog 2026-03-19 06:56:56 +00:00
Computer c6329d377a fix: register missing /api/ml and /api/health routes 2026-03-19 06:48:41 +00:00
KoalaTrapLord 3f7f40d626 Make _call_pplx_api async with httpx.AsyncClient
- Replace blocking urlopen with async httpx.AsyncClient
- Add await at both call sites (deck_research, deck_generate_ai)
- Uvicorn event loop no longer stalled during Perplexity API calls
2026-03-19 06:32:18 +00:00
KoalaTrapLord bc023ab6f6 Fix indentation in coach.py _build_deck_report_from_db (2-space -> 4-space) 2026-03-19 06:26:27 +00:00
KoalaTrapLord 83a8f288b7 Fix scrambled deck_service.py: close SQL query in _load_deck_cards_by_name, remove orphaned lines 2026-03-19 06:24:13 +00:00
KoalaTrapLord e3f0787c73 Optimize deckgen: push colour-identity filtering into SQL with json_each
- Replace SELECT * + Python-side color loop in _get_collection_for_colors()
  with projected SELECT + NOT EXISTS json_each() sub-query
- Apply same pattern to _build_collection_summary(), removing card_fits_identity()
- Add partial index idx_ce_quantity on collection_entries(quantity) WHERE qty > 0
- Handle NULL and empty-string color_identity via CASE guard
- Fix colorless commander bug: old code skipped color check entirely for []
- Add scripts/bench_deckgen.py benchmark (3-5x speedup on filtered queries)
- Add tests/test_deckgen_color_filter.py with 7 regression tests
2026-03-19 06:18:32 +00:00
Computer 3802bc3d78 fix: resolve CS0103 errors in CreateDeckBuilderScene.cs and CreateCollectionScene.cs 2026-03-19 06:08:11 +00:00
KoalaTrapLord 2936fd37eb Refactor routes/ml.py: replace state dicts with dataclasses + threading locks
- Add @dataclass classes _TrainingState, _PPOState, _TournamentState with snapshot() method
- Wrap all state mutations in threading.Lock() for thread safety
- Replace dict bracket access with attribute access
- Remove unnecessary global declarations from pipeline functions
2026-03-19 06:06:11 +00:00
KoalaTrapLord cad2f00ec7 Fix all Unity C# errors: 4 truncated Editor scripts + 11 controller/model compilation fixes 2026-03-19 05:53:04 +00:00
KoalaTrapLord 6b50c6ee7d Add PreconListWrapper and CollectionResponse classes to DeckGeneratorController 2026-03-19 01:40:50 -04:00
KoalaTrapLord 40a166188a fix: unify DB connection management — init_collection_db() uses _get_db_conn()
Previously, init_collection_db() opened a separate sqlite3.connect() call,
ran migrations, then closed that connection — entirely outside the
thread-local pool that route handlers use via _get_db_conn().

This meant:
- The migration connection and the request-serving connection were
  independent objects with no ordering guarantee under concurrent startup.
- The warm-up connection was thrown away immediately, so every handler
  thread still had to open its own connection on first use.

Fix:
- init_collection_db() now calls _get_db_conn() directly, so schema
  creation and migrations run on the same thread-local connection that
  will serve subsequent requests.
- Added _close_db_conn() helper for explicit teardown on thread exit
  (e.g. FastAPI lifespan shutdown or worker thread cleanup).
- Removed the redundant standalone conn.close() that discarded the
  init connection without ever making it available to route handlers.
2026-03-19 01:24:24 -04:00
KoalaTrapLord a501a9755d fix(unity): resolve all 32 C# compilation errors
- CardModel: add missing `id` field
- CardModel: `imageUrl` → `imageUri` (match JSON property name)
- DeckModel: `commanderName` → `commander` (match JSON property name)
- PreconDeck: `imageUrl` → `imageUri`
- Add missing `PreconListWrapper` model class
- Add `using UnityEngine.Networking` to CollectionController,
  DeckGeneratorController, PreconController
- Remove double StartCoroutine() wrapping on ApiClient calls
  (GetRaw/PatchRaw/Delete/PostRaw already return Coroutine)
  Affected: CollectionController, DeckBuilderController,
  DeckGeneratorController, ScannerController, CoachController
2026-03-19 05:13:00 +00:00
Computer 2d8a55fdc6 refactor: use _write_dck_file and _build_dck_lines in deckgen.py 2026-03-19 05:11:38 +00:00
KoalaTrapLord b0d0b75dbb refactor: add _build_dck_lines() and _write_dck_file() to services/deck_service.py 2026-03-19 01:06:29 -04:00
KoalaTrapLord 94b20f1347 Add GetRaw, PatchRaw, Delete methods to ApiClient.cs 2026-03-19 01:01:18 -04:00
KoalaTrapLord c980e1b6d5 refactor: remove inline Pydantic models from deckgen.py, import from models.requests 2026-03-19 00:55:17 -04:00
KoalaTrapLord 7c7be013c2 refactor: remove inline Pydantic models from coach.py, import from models.requests 2026-03-19 00:53:33 -04:00
KoalaTrapLord 14eb72aaff Update requests.pyrefactor: move inline Pydantic models from coach.py and deckgen.py to models/requests.py 2026-03-19 00:50:01 -04:00
KoalaTrapLord 81a1bc9022 Fix DeckBuilderController.cs: fix truncated for loop, remove stray line at EOF 2026-03-19 00:48:43 -04:00
KoalaTrapLord 6534380f67 fix: replace full-table scan with parameterized WHERE in _build_deck_report_from_db (closes #60) 2026-03-19 00:40:28 -04:00
KoalaTrapLord 1ee55d4428 chore: update LM Studio default IP to 192.168.0.241 2026-03-19 00:30:45 -04:00
KoalaTrapLord 432a16e87d Add missing using UnityEngine.Networking for UnityWebRequest.EscapeURL 2026-03-19 00:11:51 -04:00
KoalaTrapLord c5a1f65e13 Fix DeckBuilderController.cs: add truncated methods (BuildManaCurve, BuildColorPie, ShowError, PromptSave, OnExportToSim) 2026-03-19 00:11:03 -04:00
KoalaTrapLord a95ae45e18 fix: remove API key prefix exposure from /api/pplx/status endpoint 2026-03-19 00:05:33 -04:00
KoalaTrapLord ed255c1a21 Fix TagManager.asset: remove quoted empty strings, use bare null entries 2026-03-19 00:01:57 -04:00
KoalaTrapLord 9899da9b98 Fix TagManager.asset: use quoted empty strings for empty layers 2026-03-19 00:00:04 -04:00
KoalaTrapLord 86ae69112f docs: add ALLOWED_ORIGINS to .env.example 2026-03-18 23:47:23 -04:00
KoalaTrapLord 1cc0b696d3 feat: add dotenv support for .env file loading 2026-03-18 23:46:27 -04:00
KoalaTrapLord b248be867b Unity: add EditorSettings.asset 2026-03-18 23:40:16 -04:00
KoalaTrapLord 2a9b043a24 Unity: add DynamicsManager.asset 2026-03-18 23:39:26 -04:00
KoalaTrapLord 9863f7590b Update lab_api.py 2026-03-18 23:38:55 -04:00
KoalaTrapLord 9436276b69 Unity: add AudioManager.asset 2026-03-18 23:38:32 -04:00
KoalaTrapLord d06618031a Unity: add TimeManager.asset 2026-03-18 23:37:47 -04:00
KoalaTrapLord 58c6908b85 Unity: add InputManager.asset 2026-03-18 23:37:06 -04:00
KoalaTrapLord 682c4191f9 Unity: add EditorBuildSettings.asset 2026-03-18 23:36:10 -04:00
KoalaTrapLord 2add53d3c2 Unity: add TagManager.asset 2026-03-18 23:35:29 -04:00
KoalaTrapLord 918d760dcb Unity: add GraphicsSettings.asset 2026-03-18 23:34:42 -04:00
KoalaTrapLord 18ddd3027b Unity: add QualitySettings.asset 2026-03-18 23:33:46 -04:00
KoalaTrapLord a82a7d4dc6 Unity: add ProjectSettings.asset with URP, .NET Standard 2.1 2026-03-18 23:32:32 -04:00
KoalaTrapLord d5fdc150f7 Unity: add Packages/manifest.json with URP and core dependencies 2026-03-18 23:28:45 -04:00
KoalaTrapLord 173a7f4f3b Add WebGL FileUpload.jslib plugin for Scanner file upload fallback 2026-03-18 22:50:52 -04:00
KoalaTrapLord a988779bb4 ScannerController: add WebGL file upload fallback (#54) 2026-03-18 22:46:59 -04:00
KoalaTrapLord 7901fef495 CoachController: add streaming, precon refresh tab, mobile layout (U9 #54) 2026-03-18 22:39:47 -04:00
KoalaTrapLord d21928da53 Add PreconController.cs - precon browser scene (U12) 2026-03-18 16:57:01 -04:00
KoalaTrapLord 9b1654a81e Add CoachController.cs - AI coach chat + analytics dashboard (U9) 2026-03-18 16:29:50 -04:00
KoalaTrapLord 2435e67a6e Add Unity CI/CD workflow for Windows + WebGL builds (closes #59) 2026-03-18 15:19:13 -04:00
KoalaTrapLord 69fe6cbffd Add MobileInputHandler for touch controls, safe area, responsive UI (closes #58) 2026-03-18 15:18:09 -04:00
KoalaTrapLord 3fe3ba0e62 Add WebGLInput.jslib for WebGL JS interop plugin 2026-03-18 15:16:37 -04:00
KoalaTrapLord 643529e874 Add WebGLBridge for WebGL build support, CORS, file input fallback (closes #57) 2026-03-18 15:15:36 -04:00
KoalaTrapLord 9746a41178 Add CardController with flip, hover, glow, drag, mobile gestures (closes #56) 2026-03-18 15:12:41 -04:00
KoalaTrapLord b0f48cde62 Add CreateTrainingScene editor script for U11 Training scene 2026-03-18 15:09:19 -04:00
KoalaTrapLord ec9719659a Add TrainingController for ML training dashboard (closes #55) 2026-03-18 14:37:18 -04:00
KoalaTrapLord 729283c3bd Create CreateScannerScene.cs 2026-03-18 14:17:41 -04:00
KoalaTrapLord 52dcf95c59 Add ScannerController for scanning and card handling
Implement ScannerController for camera scanning and card management.
2026-03-18 14:16:34 -04:00
KoalaTrapLord 4f5135e85b Add CreateSimulatorScene script for scene setup 2026-03-18 14:06:43 -04:00
KoalaTrapLord ff3b134cb9 Implement DeckGeneratorController for deck generation
This script handles the UI and logic for the DeckGenerator scene, allowing users to select a commander, set strategies and budgets, generate a deck, and view results.
2026-03-18 13:55:47 -04:00
KoalaTrapLord 1c85733743 Add CreateDeckBuilderScene script for UI setup
This script creates a DeckBuilder scene in Unity, setting up the UI elements such as panels, buttons, and input fields for managing decks and cards.
2026-03-18 13:48:06 -04:00
KoalaTrapLord a71abfa7d0 Implement DeckBuilderController for deck management
This file implements the DeckBuilderController class, which manages the deck building UI, including deck list, card search, and commander selection functionalities.
2026-03-18 13:36:47 -04:00
KoalaTrapLord abb1e08249 Add CreateCollectionScene to manage collections
This script creates a new Unity scene for managing collections, including UI elements such as buttons, panels, and input fields for searching and filtering cards.
2026-03-18 13:18:54 -04:00
KoalaTrapLord 57b0598717 Unity U5: Add CollectionController.cs — card grid, search, filter, sort, detail panel (#50)
Implement CollectionController for managing card collection UI, including search, filter, sort, paginImplements all tasks in #50:
- Infinite scroll card grid using CardPrefab instances with object pooling
- Async Scryfall card image loading via ImageCache.GetSprite()
- Search bar wired to GET /api/collection?search=...
- Filter sidebar: color, type, set, rarity, owned/missing dropdowns
- Sort dropdown: name, CMC, color, type, price
- Card detail panel with flip animation trigger and full card view
- "Add to Deck" button routes to DeckBuilder with card stored in PlayerPrefs
- Pagination / load-more button with page info label
- Mobile: filter drawer auto-hides on narrow screens, collapsible via toggle buttonation, and card detail display.
2026-03-18 12:50:59 -04:00
KoalaTrapLord 292801d445 Add CreateMainMenuScene script for Unity editor
This script creates a Main Menu scene in Unity with various UI elements including panels, buttons, and input fields. It also sets up services and saves the scene to the specified path.
2026-03-18 11:31:53 -04:00
ClaudeandClaude Opus 4.6 71c90a588f fix: resolve /api/lab/result 500 and /api/lab/precons/install-batch 400
- GET /api/lab/result: Starlette's JSONResponse uses allow_nan=False,
  so NaN/Infinity floats from simulation output cause a ValueError crash.
  Add _sanitize_floats() to replace non-finite floats with null before
  returning. Also wrap json.load in try/except for better error reporting.

- POST /api/lab/precons/install-batch: Frontend sends {"names": [...]}
  but backend expected {"fileNames": [...]}. Accept both field names.

- POST /api/lab/precons/install: Same mismatch — frontend sends {"name"}
  but backend expected {"fileName"}. Accept both field names.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-18 06:11:14 +00:00
KoalaTrapLord 5550c27744 Unity: Add ImageCache.cs singleton for card art Sprite caching (#50) 2026-03-18 02:06:08 -04:00
KoalaTrapLord 986c501410 Unity U4: Add MainMenuController.cs - server URL, health check, nav (#49)
Implements MainMenuController for managing server connection and scene navigation.
2026-03-18 02:05:15 -04:00
KoalaTrapLord d3431ff246 Unity U3: Add CommanderMeta.cs (closes #48) 2026-03-18 02:03:38 -04:00
KoalaTrapLord 36dba9e90a Unity U3: Add PreconModel.cs (#48) 2026-03-18 02:02:59 -04:00
KoalaTrapLord b9369af52f Unity U3: Add CollectionResponse.cs (#48) 2026-03-18 02:02:20 -04:00
KoalaTrapLord 6dd1de894c Unity U3: Add DeckGenResponse.cs + GeneratedDeckCard (#48)
This file defines the DeckGenResponse class and its associated GeneratedDeckCard class, which map to the response structure from the /api/deckgen/v3 endpoint.
2026-03-18 02:01:35 -04:00
KoalaTrapLord 3f1bcb4d12 Unity U3: Add CoachResponse.cs (#48)
This class maps to the response of the POST /api/coach/chat endpoint, including properties for reply, deck report, and suggestions.
2026-03-18 02:00:43 -04:00
KoalaTrapLord 52762ee0aa Unity U3: Add SimStatusModel.cs + SimStartResponse (#48)
This file defines the SimStatusModel and SimStartResponse classes for mapping JSON responses from the API.
2026-03-18 01:59:58 -04:00
KoalaTrapLord af8e59ed9a Unity U3: Add DeckModel.cs (#48) 2026-03-18 01:59:09 -04:00
KoalaTrapLord 0923b75d79 Unity U3: Add CardModel.cs with Newtonsoft.Json snake_case mapping (#48)
This class represents a Magic: The Gathering card with various properties such as name, oracle ID, mana cost, and more.
2026-03-18 01:58:24 -04:00
KoalaTrapLord 20172c90b4 Unity U2: Implement all FastAPI endpoint methods in ApiClient.cs (closes #47) 2026-03-18 01:56:33 -04:00
KoalaTrapLord 089c1abc8d Unity: Add ProjectSettings/ProjectVersion.txt for 2022.3 LTS (#46) 2026-03-18 01:53:21 -04:00
KoalaTrapLord 646da59e2a Unity: Add .gitignore for Unity project (Library, Temp, Builds) (#46) 2026-03-18 01:52:29 -04:00
KoalaTrapLord 10a7662d51 Unity: Add Fonts/.gitkeep (#46) 2026-03-18 01:51:38 -04:00
KoalaTrapLord a6171b5180 Unity: Add Textures/.gitkeep (#46) 2026-03-18 01:51:10 -04:00
KoalaTrapLord b09fe4b48b Unity: Add Materials/.gitkeep (#46) 2026-03-18 01:50:46 -04:00
KoalaTrapLord cb8867a808 Unity: Add Prefabs/.gitkeep (#46) 2026-03-18 01:50:23 -04:00
KoalaTrapLord 3d6365cfb4 Unity: Add Scripts/Animation/.gitkeep (#46) 2026-03-18 01:50:00 -04:00
KoalaTrapLord 397d02240e Unity: Add Scripts/UI/.gitkeep (#46) 2026-03-18 01:49:37 -04:00
KoalaTrapLord 0c842b97fd Unity: Add Scripts/Models/.gitkeep (#46) 2026-03-18 01:49:16 -04:00
KoalaTrapLord 7e94b29c6d Unity: Add Assets/Scenes/.gitkeep for folder scaffold (#46)
This file keeps the folder in git
2026-03-18 01:48:49 -04:00
Commander AI Lab Agent fbc3bdb6b5 fix: add _resolve_precon_dir() auto-detection so CFG.precon_dir is not empty
The precon deck list was missing after the monolith refactor because
PRECON_DIR was hardcoded in services/precon_service.py with no CLI arg,
env var, or auto-resolve fallback — unlike forge_jar, forge_dir, etc.

Changes:
- Add precon_dir field to Config class (models/state.py)
- Add --precon-dir CLI arg and PRECON_DIR env var support
- Add _resolve_precon_dir() with project-root, parent-dir, and
  forge_dir-relative detection; creates dir as fallback for downloads
- Refactor precon_service.py to use _get_precon_dir() helper that reads
  CFG.precon_dir at runtime instead of a hardcoded module-level Path
- Update routes/precon.py to use _get_precon_dir() for install endpoints
- Wire _on_startup to resolve precon_dir for uvicorn-direct launches
- Remove unused PRECON_DIR/PRECON_INDEX imports from routes/lab.py
- Audit confirms all other CFG path fields already have fallbacks
2026-03-18 04:56:03 +00:00
KoalaTrapLord a5b9d70f04 Unity U2: Add ApiClient.cs singleton HTTP client for FastAPI backend (Epic #45) 2026-03-18 00:55:40 -04:00
KoalaTrapLord 8103a06992 Unity U1: scaffold unity-client/ folder + README (Epic #45) 2026-03-18 00:53:39 -04:00
ClaudeandClaude Opus 4.6 85df46364a fix: add _resolve_forge_dir() auto-detection so CFG.forge_dir is not empty
Same pattern as the previous _resolve_forge_jar() fix — when --forge-dir
is not passed via CLI or FORGE_DIR env var, auto-detect the Forge
installation directory by:
1. Deriving from the already-resolved forge_jar path (parent of target/)
2. Checking common sibling directories (forge/, forge-repo/)
3. Checking platform user data dirs (~/.forge, ~/Forge, %APPDATA%/Forge)

Also audited all other CFG fields — forge_jar, forge_decks_dir, lab_jar,
port, results_dir all already have proper defaults or auto-resolve
fallbacks. Added Forge Dir, Lab JAR, and Results Dir to startup log
output for better diagnostics.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-18 04:50:23 +00:00
Claude 32f3c9dfa3 fix: add _resolve_forge_jar() auto-detection so CFG.forge_jar is not empty
During the refactor from the monolith into services/, the forge JAR path
had no auto-resolve fallback — unlike lab_jar and forge_decks_dir which
both had resolver functions. When --forge-jar CLI arg and FORGE_JAR env
var are both unset, CFG.forge_jar stayed "" causing POST /api/lab/start
to fail with "Forge JAR not found: ".

Add _resolve_forge_jar() that scans common sibling directories for
forge-gui-desktop-*-jar-with-dependencies.jar (matching the pattern
from start-lab.bat), and wire it as the fallback in main().

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-18 04:44:20 +00:00
ClaudeandClaude Opus 4.6 7bc4aac0aa fix: repair run_batch_subprocess signature and _run_process_blocking state attrs in forge_runner.py
The port of run_batch_subprocess from lab_api_backup.py to
services/forge_runner.py truncated the function signature to 8 params +
**kwargs, but the route handler in routes/lab.py passes 13 positional
arguments, causing TypeError at runtime when POST /api/lab/start fires.

Restore all 13 explicit parameters (policy_style, policy_greedy,
ai_simplified, ai_think_time_ms, max_queue_depth) to match the caller
and pass them as keyword args to build_java_command. Also compute
policy_server from CFG.port and add error handling, matching the
original backup implementation.

Additionally, _run_process_blocking was writing to wrong BatchState
attribute names (state.status, state.games_done, state.games_total,
state.throughput, state.log, state.started_at, state.ended_at) instead
of the correct ones (state.running, state.completed_games,
state.total_games, state.sims_per_sec, state.log_lines, state.elapsed_ms).
This caused the /api/lab/status endpoint to never see progress updates.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-18 04:38:40 +00:00
ClaudeandClaude Opus 4.6 70ad16fc79 fix: add missing _get_deepseek_brain and _run_deepseek_batch_thread to forge_runner.py
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-18 04:31:14 +00:00
ClaudeandClaude Opus 4.6 6298f83abd fix: correct indentation errors in services/*.py
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-18 04:22:17 +00:00
Claude d1422d6f64 fix: correct indentation errors in services/database.py 2026-03-18 04:15:06 +00:00
KoalaTrapLord 4e8a999ebd feat: add Unity/Godot WebSocket server (Phase 1 stub)
- pom.xml: add Java-WebSocket 1.5.6 dependency
- bridge/GameWsServer.java: WS server on ws://localhost:7654
- bridge/GameSession.java: Phase 1 stub game loop with human-input queue
- cli/LabCli.java: add ws-server subcommand via picocli
2026-03-18 00:02:18 -04:00
KoalaTrapLord aa5e381c59 feat: add 4-player batch simulator React page with N-player API endpoint
Adds SimulatorPage at /simulator with 4 deck slot selection, config panel, progress polling, per-seat win rate charts, and stats table. Backend: POST /api/sim/run-n-player endpoint using engine.run_n() for 2-4 player batch sims.
2026-03-17 23:59:48 -04:00
Commander AI LabandClaude Opus 4.6 3f66a8ed15 feat: add 4-player batch simulator React page with N-player API endpoint
- Add POST /api/sim/run-n-player endpoint accepting 2-4 deck IDs for
  N-player batch simulations using the existing engine.run_n() method
- Add _run_sim_thread_n_player tracking per-seat wins and stats
- Create SimulatorPage.tsx with 4 deck selection slots, config row,
  progress bar, results table, CSS bar chart, and per-game logs
- Add simulator types in frontend/src/types/simulator.ts
- Add /simulator route and nav bar to App.tsx

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-18 03:39:03 +00:00
KoalaTrapLord f4da7674b6 feat: React + TypeScript frontend migration (all 12 phases)
Complete migration of the Commander AI Lab GUI from vanilla JS to React + TypeScript + Vite + Tailwind.

Phases implemented:
- Phase 1: Vite + React-TS scaffold with proxy config
- Phase 2: TypeScript type definitions (game.ts, constants.ts)
- Phase 3: useGameSocket hook + gameReducer
- Phase 4: Core components (PhaseBar, PlayerZone, CardChip, Board)
- Phase 5: NarrationPanel with auto-scroll
- Phase 6: MoveBar + HandCard
- Phase 7: GameOverOverlay
- Phase 8: App routing + GamePage assembly
- Phase 9: Lobby UI (LobbyPage, RoomPage, useLobbySock, spectator mode)
- Phase 10: Tailwind colour tokens
- Phase 11: Build integration + CI + README
- Phase 12: Test suite (26 tests, all passing)
2026-03-17 22:58:54 -04:00
Claude 0b623ec39e feat: add lobby UI, build integration, and test suite (Phases 9, 11, 12)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-18 02:56:16 +00:00
ClaudeandClaude Opus 4.6 d489f84825 feat: implement game UI components, WebSocket hook, routing (Phases 3-8)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-18 02:43:43 +00:00
ClaudeandClaude Opus 4.6 d1bed9ac20 feat: scaffold React+TS frontend with types and Tailwind tokens (Phases 1, 2, 10)
Phase 1 — Project Scaffold:
- Vite + React-TS project in frontend/ with Tailwind v3, PostCSS, Autoprefixer
- clsx and react-router-dom installed
- Vite proxy configured for /api and /ws to localhost:8000
- Tailwind directives in src/index.css
- Makefile targets: frontend-dev, frontend-build
- app.py updated to serve frontend/dist/ with SPA catch-all fallback

Phase 2 — Type Definitions:
- TypeScript types in frontend/src/types/game.ts (Phase, Card, Player,
  GameState, WsIncoming, NarrationLine)
- Constants in frontend/src/constants.ts (SEAT_COLOURS, PHASES)

Phase 10 — Tailwind Colour Tokens:
- Custom color tokens for bg, accent, life, seat, card color groups
- Additional tokens from index.html audit: border, phase, btn, text, highlight
- Custom pulse animation keyframe

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-18 02:35:50 +00:00
KoalaTrapLord 3f38a9885f Slim install script from 3.2MB to git clone approach (~8KB) - closes #31
This script installs the Commander AI Lab environment by checking prerequisites, cloning the repository, and installing dependencies.
2026-03-17 16:20:19 -04:00
KoalaTrapLord 940836db7e Add GitHub Actions CI workflow (lint + test on push) - closes #33 2026-03-17 16:18:50 -04:00
KoalaTrapLord fd74f157e8 Phase 4: Slim lab_api.py to thin main() - closes #25
Refactor lab_api.py to streamline imports, improve logging, and enhance argument parsing.
2026-03-17 16:17:53 -04:00
KoalaTrapLord 179a53c0af Implement Moxfield deck source adapter (closes #35) 2026-03-17 15:59:10 -04:00
KoalaTrapLord 554b166787 Phase 2d/2e/2f: Extract forge_runner, precon_service, import_service/helpers into services/ 2026-03-17 15:22:19 -04:00
KoalaTrapLord 874561f423 Phase 2c: Extract card analysis to services/card_analysis.py (#12) 2026-03-17 05:14:35 -04:00
KoalaTrapLord c7309d1ace Phase 2b: Extract deck service to services/deck_service.py (#11) 2026-03-17 05:05:52 -04:00
KoalaTrapLord 5aee93dfae Phase 1c: Extract datPhase 2a: Extract Scryfall client to services/scryfall.py (#10)abase service to services/database.py (#9) 2026-03-17 04:54:36 -04:00
KoalaTrapLord e14a6f9500 Phase 1c: Extract database service to services/database.py (#9) 2026-03-17 04:40:44 -04:00
KoalaTrapLord 7baae153ae Phase 1b: Extract Config, BatchState, AI_PROFILES into models/state.py (#8) 2026-03-17 01:57:22 -04:00
KoalaTrapLord eadf54c670 Phase 1a: Extract Pydantic models into models/requests.py + models/responses.py (#7) 2026-03-17 01:45:57 -04:00
KoalaTrapLord 312d13949d feat: extract ML endpoints into routes/ml.py (#36)
Moved all 15 /api/ml/* endpoints, 4 state dicts, and 3 background
pipeline functions from lab_api.py into routes/ml.py. Wired via
app.include_router(ml_router) in lab_api.py.
2026-03-17 00:41:31 -04:00
KoalaTrapLord 192c307ca7 refactor(#42): deduplicate sim thread functions in deepseek.py 2026-03-17 00:20:51 -04:00
KoalaTrapLord 5cb6e6ce81 refactor(#41): remove duplicate _get_deepseek_brain from deepseek.py, import from shared 2026-03-17 00:08:04 -04:00
KoalaTrapLord 01af4d7bcf cleanup(#38): delete stale ROUTES_WIRING.md
This doc referenced the old routes._wire autoload pattern and only listed 5 routers. The project now uses 9 direct include_router() calls. Closes #38.
2026-03-17 00:05:19 -04:00
KoalaTrapLord 301b9d38c8 fix(#40): use Path().parent.parent for correct src/ resolution in routes/deepseek.py 2026-03-17 00:02:22 -04:00
KoalaTrapLord 09b0708c4d fix(models): commander_tax int→dict to match engine per-commander tax tracking 2026-03-16 23:58:07 -04:00
KoalaTrapLord 1121e1cb26 feat: add commander_bonus weight to score_card()
Commanders get +5.0 priority in AI spell selection, making the
AI prioritize casting its commander when affordable.
2026-03-17 03:38:14 +00:00
KoalaTrapLord 4bade21220 feat: route commander deaths to command zone instead of graveyard
Added _send_to_graveyard() helper that checks card.is_commander
and routes to command_zone (resetting tapped/turn_played) or
graveyard. Applied to all 4 death paths: removal, board wipe,
blocker death, and attacker death in combat.
2026-03-17 03:36:36 +00:00
KoalaTrapLord 46421fe8c4 feat: add commander casting from command zone with tax tracking
Second pass in _play_spells() checks command_zone for castable
commanders, applies commander_tax, taps lands, places on
battlefield, and increments tax by 2 for subsequent casts.
2026-03-17 03:34:25 +00:00
KoalaTrapLord da5e9bd74f feat: set is_commander during deck loading, place in command_zone
- Add commander_names param to run(), run_n(), _create_state()
- Commander card flagged is_commander=True and placed in
  player.command_zone instead of shuffled into library/hand
- Only first matching card is extracted (handles singleton rule)
- Backward compatible: commander_names defaults to None
2026-03-17 03:31:35 +00:00
KoalaTrapLord 019a710acd feat: add command_zone and commander_tax to Player dataclass
Adds command_zone (list[Card]) and commander_tax (int, +2 per cast)
to the base Player model so the headless engine can track commander
zone and tax natively without requiring CommanderPlayer wrappers.
2026-03-17 03:18:06 +00:00
KoalaTrapLord 08509b753f fix: add httpx and websockets to runtime dependencies (fixes #37)
Both were missing from [project.dependencies] in pyproject.toml and
requirements.txt. httpx is used in web/routers/art.py, and websockets
is needed for the FastAPI WebSocket endpoints in ws.py and lobby.py.

Closes #37
2026-03-17 02:04:09 +00:00
Commander AI Lab 872f6c38ca security: remove hardcoded API keys, add .env support (fixes #26)
- Remove plaintext Ximilar and Perplexity API keys from start-lab.bat
- Remove hardcoded key defaults from lab_api.py, lab_api_backup.py, lab_api_new.py
- start-lab.bat now reads from environment variables or .env file
- Add .env.example with placeholder values
- .env already in .gitignore (confirmed)
- Git history purged of all key values via git-filter-repo

Closes #26
2026-03-17 02:00:15 +00:00
KoalaTrapLord 9f7fbef7f8 fix: implement all 19 simulator audit bug fixes
Bug fixes across 7 Python files from the deep-dive audit:

engine.py (bugs 1-6):
- Fix summoning sickness (was disabled after turn 0)
- Move untap step before draw (correct MTG ordering)
- Rebuild playable list each iteration (fix mana overspend)
- Menace now allows 2+ blockers instead of preventing all
- Add double strike damage handling (blocked + unblocked)
- (Finish position sort already correct, no change needed)

game_state.py (bugs 7,9,18,19):
- Add .players property returning commander_players
- Add get_legal_moves()/apply_move() stubs
- Initialize commander zone in from_sim_state()
- Sync land_drop_used from engine stats

turn_manager.py (bugs 8,10,11):
- Use sim_state.get_battlefield() + card.tapped attr access
- Replace asyncio.get_event_loop() with get_running_loop()
- Fix APNAP iterations counter (always increment)
- Fix drawn.name and discarded.name (was dict .get())

threat_assessor.py (bugs 12,13,14):
- Use game_state.battlefield(seat) for card iteration
- Use Card dataclass attrs (c.type_line, c.oracle_text, etc)
- Move import re to module top level

models.py (bug 15):
- Word-boundary regex in has_keyword() (fixes reach/ward FPs)

prompt_builder.py (bug 16):
- Remove @staticmethod from module-level _category_label()

rules.py (bug 17):
- Evaluate ramp detection independently of is_removal flag
2026-03-16 20:50:09 +00:00
KoalaTrapLord 2f117eedcb fix: Java 17 compat - inline ternary in this() call instead of local var 2026-03-16 20:34:05 +00:00
KoalaTrapLord 8f8abb79c6 fix: match actual Forge verbose log format - cast/played/Life:/Zone Change: patterns 2026-03-16 20:25:35 +00:00
KoalaTrapLord 78898f6da0 fix: regex patterns match Forge verbose output, single-winner resolution, 300s clock for 4p 2026-03-16 17:34:12 +00:00
KoalaTrapLord 69242a8f6e fix: remove duplicate numPlayers declaration in parseForgeOutput 2026-03-16 17:13:48 +00:00
KoalaTrapLord 2b6ccac3ea fix: hardcoded 3-element arrays in BatchRunner causing empty stats for 4-player games 2026-03-16 17:09:57 +00:00
KoalaTrapLord 94bb369866 feat: add --deck4/--commander4 support for 4-player games in LabCli + BatchRunner 2026-03-16 02:28:06 +00:00
KoalaTrapLord b57fc9dc2f fix: add missing stdlib imports (threading, os, re, csv, io, datetime) across route modules 2026-03-16 01:55:17 +00:00
KoalaTrapLord e1f2b5d89f fix: add missing os import to collection.py 2026-03-16 01:53:17 +00:00
KoalaTrapLord 9b354e019b fix: add missing Path import to collection.py 2026-03-16 01:42:37 +00:00
KoalaTrapLord ac3b76793b fix: add missing Path, os, threading imports to routes/collection.py 2026-03-15 21:05:35 -04:00
KoalaTrapLord c60719a0a9 refactor: extract deckgen (14) + coach (12) routes
- routes/deckgen.py: classic generator, Perplexity V1, V3 generator + exports
  Helpers: _resolve_commander, _generate_deck, _build_collection_summary,
  _call_pplx_api, _fill_basic_lands, etc.
  Uses _coach._deck_gen_v3 via module reference for V3 state
- routes/coach.py: all coaching endpoints + init_coach_service(),
  _build_deck_report_from_db, globals (_coach_service, _deck_gen_v3, etc.)
- lab_api.py: 3265 -> 1063 lines (16 remaining: ML + SPA catch-all)
- Total: 90 extracted + 16 remaining = 106 endpoints verified
2026-03-15 07:26:09 +00:00
KoalaTrapLord 87a2090c68 refactor: extract scanner (6) + deepseek/sim (9) routes
- routes/scanner.py: scan, scan/add, re-enrich, auto-classify, auto-classify-all, edhrec
- routes/deepseek.py: sim/run, sim/status, sim/result, sim/run-from-deck,
  sim/run-deepseek, deepseek/connect, deepseek/status, deepseek/configure, deepseek/logs
- lab_api.py: 4464 -> 3265 lines (42 remaining endpoints)
- Total: 64 extracted + 42 remaining = 106 endpoints verified
2026-03-15 06:56:02 +00:00
KoalaTrapLord 463b7c1483 Phase 8: Full multiplayer — lobby, private hands, LAN discovery, spectator mode, chat 2026-03-14 23:04:32 -04:00
KoalaTrapLord a093ab4949 Phase 7: Integration tests, AI benchmarking suite, conftest, pytest config, coverage setup 2026-03-14 21:13:26 -04:00
KoalaTrapLord b6b1616dad Phase 6: AI politics engine (deal-making, threat comms, spite logic) + targeting memory 2026-03-14 20:42:48 -04:00
KoalaTrapLord 92bb9b9445 Phase 5: FastAPI server, WebSocket game channel, Scryfall art proxy, REST API, static web client, tests 2026-03-14 20:36:06 -04:00
KoalaTrapLord 3c3dceb737 Phase 4: Add Pygame prototype GUI (gui/ package + tests/test_phase4.py) 2026-03-14 20:10:43 -04:00
KoalaTrapLord 605ff774ed Phase 3: Add turn_manager.py, threat_assessor.py, tests/test_phase3.py 2026-03-14 19:35:50 -04:00
KoalaTrapLord 441ba7dd74 Phase 2: Add ai_opponent.py, personality_prompts.py, tests/test_phase2.py 2026-03-14 16:53:43 -04:00
KoalaTrapLord d0e2824038 Phase 1: Add game_state.py, prompt_builder.py, state_logger.py 2026-03-14 16:43:16 -04:00
KoalaTrapLord 8a6fbfaa8a fix: 3 post-refactor bugs (COMMANDER_META, PRECON_INDEX, startup)
Task 1: Move BUILTIN_COMMANDERS + load_commander_meta() to shared.py
        Fix import_routes.py to use _shared.COMMANDER_META (module ref)
Task 2: Rewrite precon.py to use _shared.PRECON_INDEX (module ref)
Task 3: Add @app.on_event('startup') safety net for uvicorn --reload
2026-03-14 19:08:21 +00:00
KoalaTrapLord 36077c94ab refactor: extract 49 endpoints into 5 route modules
- Extract collection (10), deckbuilder (18), precon (4), import (5), lab (12) routes
- All share helpers/models/state from routes/shared.py
- Gut lab_api.py from 8872 to 4487 lines (57 remaining endpoints)
- Delete routes/_wire.py and routes/_autoload.py (replaced by include_router)
- Add _scryfall_fuzzy_lookup to shared.py
- 106 total endpoints verified: 49 extracted + 57 remaining
2026-03-14 18:55:32 +00:00
KoalaTrapLord 2c6748a736 feat(brain): swap default model to GPT-OSS 20B via Ollama (localhost:11434) 2026-03-14 14:34:14 -04:00
KoalaTrapLord 585cc1d4a3 feat(engine): load learned_weights.json automatically via load_weights() 2026-03-14 14:30:28 -04:00
KoalaTrapLord c52eb1c3ef refactor: extract all shared state, helpers, models into routes/shared.py 2026-03-14 03:15:12 -04:00
KoalaTrapLord d68b2368b4 fix: remove export_deck_to_sim and _edhrec_cache* from _wire.py (not yet in lab_api.py) 2026-03-14 03:02:45 -04:00
KoalaTrapLord 8d1a916f04 feat(ml): add update_weights.py — outcome-weighted RL learning from decision logs 2026-03-14 02:58:32 -04:00
KoalaTrapLord 2b2383f5c8 wire routes._autoload.register_startup(app) after CORSMiddleware 2026-03-14 02:02:53 -04:00
KoalaTrapLord 911fde9f5f wire routes._autoload.register_startup(app) after CORSMiddleware 2026-03-14 02:02:26 -04:00
KoalaTrapLord 6799f0c402 wire routes._autoload.register_startup(app) after CORSMiddleware 2026-03-14 01:57:18 -04:00
KoalaTrapLord da4e225db0 wire routes._autoload.register_startup(app) after CORSMiddleware 2026-03-14 01:55:50 -04:00
KoalaTrapLord 71428c89db docs: add ROUTES_WIRING.md — final integration instructions for lab_api.py
Explains exactly what 2 lines to add to lab_api.py to activate
all 5 routers. Part of Batch 6/6.
2026-03-14 01:50:47 -04:00
KoalaTrapLord c14402494b feat(routes): add _autoload.py — FastAPI startup event that wires routers
Adds an @app.on_event('startup') hook that imports routes._wire.
This means lab_api.py needs zero changes — the routers self-register
when the app boots. Drop this file into the repo root and it works.

Alternatively, add to lab_api.py after app creation:
    from routes import _autoload  # noqa
2026-03-14 01:49:52 -04:00
KoalaTrapLord 5ea512bfef feat(routes): add _wire.py — registers shared symbols and includes all routers
Called via `import routes._wire` at the bottom of lab_api.py (after all
symbols are defined). Registers every shared object into routes.shared
and mounts all five APIRouters onto the FastAPI app.

Batch 6/6 — final wiring step.
2026-03-14 01:49:18 -04:00
KoalaTrapLord d92345927f refactor: Batch 5 — routes/deckbuilder.py (deck CRUD, cards, analysis, recs, bulk-add, export) 2026-03-14 01:47:04 -04:00
KoalaTrapLord e1a46202b2 refactor: Batch 4 — routes/collection.py (collection CRUD, import, export, scryfall cache) 2026-03-14 01:46:33 -04:00
KoalaTrapLord 553e07980f refactor: Batch 3 — routes/import_routes.py and routes/precon.py 2026-03-14 01:45:58 -04:00
KoalaTrapLord b0666bb3fd refactor: Batch 2 — routes/lab.py (simulator, status, decks-list, profiles, analytics, deepseek) 2026-03-14 01:45:17 -04:00
KoalaTrapLord 4b1d2f71f5 refactor: Batch 1 — add routes/ package with shared state, config, and models 2026-03-14 01:44:37 -04:00
KoalaTrapLord 2d02fcc770 Add files via upload 2026-03-14 01:00:25 -04:00
KoalaTrapLord 3d00b68566 Delete lab_api_patched.py 2026-03-14 00:59:15 -04:00
KoalaTrapLord dda82ad1e6 Add files via upload 2026-03-14 00:55:54 -04:00
KoalaTrapLord 6c547664fb Delete lab_api.py 2026-03-14 00:55:32 -04:00
KoalaTrapLord d95f0b57c0 fix: commander card correctly flagged in deck tracker
Two fixes:
1. _parse_text_decklist(): replace fragile startswith() section-header
   detection with case-insensitive regex so [Commander], [Main], [Deck]
   etc. are always recognised regardless of capitalisation.

2. add_deck_card endpoint: when a card is added with is_commander=1,
   also update the deck-level commander_name + commander_scryfall_id
   so the portrait and top-bar label stay in sync.
2026-03-14 00:48:19 -04:00
KoalaTrapLord 264fb33957 fix: commander card correctly flagged in deck tracker
Two fixes:
1. _parse_text_decklist(): replace fragile startswith() section header
   detection with proper case-insensitive regex so [Commander], [Main],
   [Deck] etc. are always recognised regardless of capitalisation.

2. add_deck_card endpoint: when a card is added with is_commander=1,
   also update the deck-level commander_name and commander_scryfall_id
   fields so the portrait and top-bar label stay in sync without
   requiring a full reload.
2026-03-14 00:47:59 -04:00
KoalaTrapLord e4ebd84c04 test 2026-03-14 00:47:37 -04:00
KoalaTrapLord 5b072174f8 Add Phase 8 — Multiplayer (Human vs Human + AI) 2026-03-14 00:33:24 -04:00
KoalaTrapLord 661ca9618b Add GUI framework decision and two-phase frontend plan 2026-03-14 00:30:36 -04:00
KoalaTrapLord 7b5c9bdcc6 Update LLM model to GPT-OSS 20B (Q4_K_M) 2026-03-14 00:23:12 -04:00
KoalaTrapLord 6c5c55b637 Add Commander GUI + LLM Opponents roadmap 2026-03-14 00:20:41 -04:00
KoalaTrapLord f05067bbfe feat(brain): pipe GameResult winner_seat + per-player life totals into flush_log() for RL training 2026-03-14 00:13:50 -04:00
KoalaTrapLord dfc1f46097 fix(ui): merge duplicate .stat-card declarations in premium layer 2026-03-14 00:10:14 -04:00
KoalaTrapLord 6cafa36ce4 feat(rules): add load_weights() / save_weights() for simulation-driven weight learning 2026-03-13 23:58:59 -04:00
KoalaTrapLord 7df6b95b4c feat(ui): add premium upgrade layer to global.css 2026-03-13 23:51:47 -04:00
KoalaTrapLord 812e5b34ca feat(tests): add pytest infrastructure and test coverage for models, rules, and sim engine (closes #28) 2026-03-13 22:30:40 -04:00
KoalaTrapLord 8c89119ba1 chore: add requirements.txt for pip install compatibility (closes #29) 2026-03-13 20:53:31 -04:00
KoalaTrapLord b1891c58f8 fix(deps): declare all runtime dependencies in pyproject.toml (closes #29) 2026-03-13 20:53:18 -04:00
KoalaTrapLord b5e7705100 feat(ui): 4-player deck slots, seat colors, chart support (Task 2C) 2026-03-13 19:08:53 -04:00
KoalaTrapLord 4c7c3495fb feat(api): accept 2-4 decks in batch start, dynamic Java CLI args (Task 2C) 2026-03-13 18:59:25 -04:00
KoalaTrapLord c1371849e5 feat(api): accept 2-4 decks in batch start, dynamic Java CLI args (Task 2C) 2026-03-13 18:58:33 -04:00
KoalaTrapLord 3190fa3702 feat(api): accept 2-4 decks in batch start, dynamic Java CLI args (Task 2C) 2026-03-13 18:56:58 -04:00
KoalaTrapLord 9f48e056ff feat(ui): 4-player deck grid, seat colors, responsive breakpoints (Task 2C) 2026-03-13 18:55:17 -04:00
KoalaTrapLord 841e861bea feat: N-player deepseek engine (Task 2A — Issue #27)
- run_n() accepts 2-4 decks; run() wraps for backward compat
- _select_attack_target() heuristic for multiplayer combat targeting
- N-player elimination order tracking with finish positions
- Removal scans all opponents for scariest creature
- ML snapshot captures all N players
- Log entries use per-seat boards dict with legacy boardA/boardB for 2-player
2026-03-13 16:58:23 -04:00
KoalaTrapLord 0be8841cec feat: N-player engine turn loop (Task 2A — Issue #27)
- engine.py: new run_n() method accepts 2-4 decks; run() wraps for backward compat
- _select_attack_target() heuristic: attack weakest non-eliminated opponent
- N-player elimination order tracking with correct finish positions
- Removal spells scan all opponents for best target
- Game logs use per-seat boards dict with boardA/boardB legacy keys for 2-player
- Tested: 2/3/4-player, elimination order, attack targeting
2026-03-13 16:54:55 -04:00
KoalaTrapLord ddc0e4fc05 fix: preserve is_commander flag when loading .dck files
Two places lost the commander flag:

1. _load_deck_cards_by_name() .dck fallback — used a flat in_cards
   boolean for [Main], [Deck], and [Commander] sections so it couldn't
   tell which section a card came from. Now tracks current_section and
   sets is_commander=1 for cards in [Commander].

2. parse_dck_file() — stored section='Commander' in the card dict but
   the frontend filters on c.is_commander, not c.section. Now includes
   is_commander: 1/0 alongside the existing section field.
2026-03-13 20:20:18 +00:00
KoalaTrapLord a1fd290d5f refactor: adopt Python logging module with named loggers
Replace all print() calls with structured logging using the Python
logging module. This provides:

- Named loggers per subsystem: commander_ai_lab.api, .batch, .sim,
  .coach, .deckgen, .collection, .scanner, .ml, .cache, .pplx
- Dual output: console (StreamHandler) + rotating file (RotatingFileHandler)
  at logs/commander-ai-lab.log (25 MB x 5 backups = 125 MB max)
- Configurable via env vars: CAL_LOG_DIR, CAL_LOG_MAX_MB, CAL_LOG_BACKUP_COUNT
- --verbose / -v flag for DEBUG-level output
- overnight-run.py: separate rotating log at logs/overnight-run.log (10 MB x 5)
- Sub-module loggers in ml/, scanner/, deck_sources/, src/ all propagate
  to the root commander_ai_lab logger
- Stripped redundant [TAG] prefixes from messages (logger name carries context)
- Format: '2026-03-13 14:00:00 INFO  [commander_ai_lab.batch] message'

Files changed: lab_api.py, overnight-run.py, and 11 sub-module .py files
176 total print() calls converted (101 in lab_api.py, 76 in sub-modules)
2026-03-13 18:09:52 +00:00
KoalaTrapLord 8d2e6199db feat: Scryfall response caching with SQLite
- _ScryfallCache class: thread-safe SQLite cache for raw Scryfall JSON
  responses, keyed by name|set_code|collector_number, 7-day TTL
- _fetch_scryfall_api() wrapper: checks cache first, live HTTP on miss,
  caches successful responses + resolves spelling normalization
- Refactored _enrich_from_scryfall() to use cache — all 3 callers
  (collection import, deck card add, re-enrich) benefit automatically
- Rate limiting only on actual HTTP calls, not cache hits
- REST endpoints: GET/DELETE /api/cache/scryfall, POST .../evict
- Fixed VACUUM-inside-transaction bug in clear()
- Configurable TTL via SCRYFALL_CACHE_TTL env var
2026-03-13 15:25:30 +00:00
KoalaTrapLord 3d8e81f88e Phase 2: score_card() num_opponents scaling + enrich_card() determinism fix (#32)
- score_card() now accepts num_opponents: int parameter (default 1)
- Keywords/effects scale with pod size via _MULTI_OPP_SCALING dict
- Multiplayer-specific oracle patterns: 'each opponent', pillowfort, 'each player'
- Board wipe and counter spell bonuses scale with opponent count
- Expanded keyword coverage (double strike, first strike, hexproof, indestructible,
  menace, vigilance, ward, infect, annihilator, cascade, defender)
- Triggered ability bonuses: ETB, attack triggers, dies triggers
- enrich_card() replaced random.randint() with hashlib.md5 name-based hash
  for deterministic CMC estimation across games (#32)
- Backward compatible: existing score_card(card, weights) calls unaffected
2026-03-12 20:22:44 +00:00
KoalaTrapLord 648e1b8d38 Fix simulator button stuck on Running — to_dict() backward compat
GameResult.to_dict() now returns both legacy keys (winner, playerA, playerB)
that the UI expects AND new N-player keys (winningSeat, playerResults) for
the Java BatchResult schema.
2026-03-12 18:25:22 +00:00
KoalaTrapLord de4f119d40 Phase 1: N-player data model refactoring
- New PlayerResult dataclass with seat_index, name, life, eliminated, finish_position, stats
- Refactored GameResult to use players: list[PlayerResult] + winner_seat (no hardcoded 2-player fields)
- Backward-compat @property accessors (player_a_stats, player_b_stats, winner, etc.) for existing consumers
- SimState.battlefields is now per-player (list-of-lists) with helper methods:
  get_battlefield(), add_to_battlefield(), remove_from_battlefield(), all_battlefield_cards()
- Updated engine.py, deepseek_engine.py, deepseek_brain.py to use per-player battlefields
- GameResult.to_dict() matches Java BatchResult schema (playerResults array)
- lab_api.py and experiments.py unchanged — backward-compat properties handle all existing access patterns
2026-03-12 18:10:57 +00:00
KoalaTrapLord 731b40b15f Fix duplicate deck cards from collection JOIN fan-out
When collection_entries has multiple rows with the same scryfall_id
(e.g. Counterspell imported from two different sets), the LEFT JOIN
on deck_cards produced duplicate rows — making it appear like 2 copies
were added or removed.

Fixed all 6 deck_cards-to-collection_entries JOINs to use a
GROUP BY scryfall_id subquery, guaranteeing 1:1 cardinality.
2026-03-12 17:51:20 +00:00
KoalaTrapLord f09098d017 Deck generator: deterministic basic land fill + frontend count display
- Added _fill_basic_lands() post-processing for V1 and V3 generators
- Strips LLM-provided basics, then distributes evenly across commander colors
- Always hits exactly 100 cards regardless of LLM output
- Frontend group counts now sum card.count instead of entry count
- Cards with count > 1 show '5x Forest' style prefix
- Colorless commanders get Wastes
2026-03-12 17:44:30 +00:00
KoalaTrapLord 7f7ad62087 Collection: sort bar UI, fix set_code sort field, column header hover styles 2026-03-12 17:08:42 +00:00
KoalaTrapLord 2beab5cc91 Coach: build DeckReport from DB when no simulation report exists
Previously the coach required running simulations first to generate a
deck-reports/*.json file. Now if no simulation report is found, the
endpoint builds a lightweight DeckReport on the fly from the deck_cards
table — pulling card names, types, CMC, and color identity directly from
the DB. The LLM can still analyze composition and give advice, just
without simulation-derived performance metrics (impactScore, etc.).

Also removes the '[no report]' tag from the dropdown since all decks
can now be coached.
2026-03-12 16:36:19 +00:00
KoalaTrapLord 26e554b359 Fix coach deck dropdown: wrong column name d.commander → d.commander_name
The decks table has 'commander_name' not 'commander'. The SQL query
was throwing OperationalError which the frontend silently caught,
resulting in an empty dropdown.
2026-03-12 14:55:46 +00:00
KoalaTrapLord 44bc86b665 Fix deck gen truncation: increase max_tokens to 16384, add JSON repair
Root cause: Perplexity deck generation hit the 8192 max_tokens limit,
truncating the JSON response mid-string at card ~70-80. json.loads()
fails with 'Unterminated string' and the fallback also couldn't recover.

Fixes:
- Bump DECK_GEN_MAX_TOKENS from 8192 to 16384 (primary fix)
- Add _repair_truncated_json() to PerplexityClient — if token limit is
  still hit, salvages all complete card objects from the truncated JSON
  instead of returning a 422 error
- Add OUTPUT EFFICIENCY instructions to system prompt to reduce per-card
  verbosity (shorter reasons, fewer synergy_with entries)
- Add 'truncated' flag to response so UI can warn if deck is incomplete
- Log warning when recovered deck has <90 cards
2026-03-12 14:47:41 +00:00
KoalaTrapLord e571f06894 Fix coach deck dropdown: handle bare array response from /api/coach/decks
Backend returns [{deck_id, deck_name, commander, has_report, ...}] directly,
not {decks: [...]}.  Frontend was reading data.decks which was always undefined.
Now properly unwraps the array, shows deck name + commander, slugifies name
for coach session deck_id, and marks decks without reports.
2026-03-12 14:45:13 +00:00
KoalaTrapLord f1e0b6454a v28: Coach chat UI, analytics/trends tabs, export-to-sim, precon refresh
Features:
- Complete coach page rewrite: chat-based UI with SSE streaming, multi-turn conversations
- Coach cards-like suggestions and apply-recommendation integration
- Coach session management (save/load/delete)
- Export-to-sim button in deckbuilder
- Analytics tab with game stats visualization
- Trends tab with meta trend charts
- Precon refresh button in AI Lab
- Updated installer (v28)
2026-03-12 06:40:41 +00:00
KoalaTrapLord 9c6918f27e v27.5: Revert to legacy HTML/JS/CSS UI as primary interface
- lab_api.py: Flip serving priority — ui/ folder (legacy) served first, React dist/ only as fallback
- Legacy UI has full features: card images, mana costs, type grouping, collection filters, EDHREC recs, ratio dashboard, commander portraits, 3-column deck builder
- React SPA was causing blank pages and missing functionality
- Installer no longer builds or bundles React dist/
- All 7 pages work: Batch Sim, Collection, Deck Builder, Auto Gen, Simulator, Coach, Training
2026-03-12 04:32:33 +00:00
KoalaTrapLord 00bde0221f v27.4: Fix blank Collection/Decks pages — API response unwrapping
- collection.ts: unwrap {items} -> cards, map camelCase params (pageSize, sortField, etc)
- decks.ts: unwrap {decks} from listDecks, {cards} from getDeckCards, normalize field names (commander_name->commander, total_cards->card_count, role_tag->category)
- decks.ts: transform getDeckAnalysis from backend shape (counts_by_type, color_pips) to frontend shape (card_count, land_count, type_distribution, etc)
- types: SetInfo.card_count now optional (backend doesn't provide it)
- Installer now bundles pre-built React dist/ as fallback
2026-03-12 03:47:20 +00:00
KoalaTrapLord 1e05f0fb5e v27.3: Fix blank page — response shape mismatches
1. /api/lab/status: StatusResponse now includes run_id, games_completed,
   total_games, current_decks aliases so the React SPA gets the field
   names it expects.

2. /api/lab/decks returns {decks: [...]}, /api/lab/history returns
   {results: [...]}, /api/lab/precons returns {precons: [...]} — but
   the frontend expected bare arrays. Updated lab.ts API client to
   unwrap these wrapper objects automatically.
2026-03-12 03:36:36 +00:00
KoalaTrapLord e00e7a4dfe v27.2: Fix /api/lab/status 422 crash — make batchId optional
GET /api/lab/status and /api/lab/result required batchId as a
mandatory query param, but the React SPA calls them with no param
on page load. FastAPI returned 422 Unprocessable Content, which
caused the BatchSimPage to crash and the UI to flash then vanish
in a retry loop.

Fix: batchId is now Optional[str] = None. When omitted, returns
the most recent active batch (or idle defaults for status).
2026-03-12 03:31:36 +00:00
KoalaTrapLord eb87626690 v27.1: Fix unused import errors breaking tsc -b strict build
Removed unused imports in DecksPage, TrainingPage, and decks.ts
that caused noUnusedLocals/noUnusedParameters errors during
the React build step in the installer.
2026-03-12 03:26:38 +00:00
KoalaTrapLord e99bbdb33e v27: Coach mega-upgrade — all 9 improvements
Backend:
- POST /api/coach/chat: Multi-turn coaching chat with SSE streaming
- POST /api/coach/apply: Apply suggestions to deck (cut/add cards)
- GET /api/coach/cards-like: Embedding similarity search with ownership/price
- GET /api/coach/decks: Enhanced to show all decks with report status
- Goals config (power level, strategy, budget, focus areas) wired to prompts
- Session history with full diff tracking (cuts/adds/hints)

Frontend (complete CoachPage rewrite):
- Live multi-turn chat with streaming LLM responses
- Apply suggestions UI: checkbox cuts/adds, one-click apply to deck
- Goals config panel (power level slider, strategy chips, budget, focus areas)
- Session results view with summary, tips, mana advice, cuts, adds
- Session history browser with session drill-down
- Cards Like This search (embedding-powered card discovery)
- Price-aware card suggestions with collection ownership badges
- Responsive dark theme, tab navigation
2026-03-12 03:15:30 +00:00
KoalaTrapLord 384c3adfff v26: Wire RL training pipeline UI (phases 1-5)
- Fixed all ML types to match actual backend response shapes:
  MLStatus (logging state + decision files), MLDataStatus (datasets,
  checkpoints, eval results), MLTrainStatus (phases, metrics, result),
  PPOTrainStatus (metrics with win_rate/reward/losses), TournamentStatus
- Updated training API client with full parameter support for all endpoints
- Rewrote TrainingPage with 5 clear pipeline phases:
  Phase 1: Data Collection (toggle logging, view decision files)
  Phase 2: Dataset & Model Artifacts (splits, checkpoints, eval, model info)
  Phase 3: Supervised Training (epochs/lr/batch/patience, live progress,
           phase badges, real-time metrics, final eval results)
  Phase 4: PPO Reinforcement Learning (full config: iterations, episodes,
           clip epsilon, entropy coeff, opponent selection, live win rate)
  Phase 5: Tournament Evaluation (episodes, playstyle, leaderboard table)
- All sections are collapsible with live phase badges and progress bars
- Advanced config toggles for supervised and PPO training
- Proper mutex: can't start PPO while supervised is running, etc.
- Auto-refresh model info and data status when training completes
2026-03-12 02:54:30 +00:00
KoalaTrapLord e36580203a v25: EDHREC recommendations tab in deck builder
- Redesigned Recommendations tab with EDHREC / From Collection sub-tabs
- EDHREC sub-tab: fetches average deck data, groups by role, shows owned/missing status
- Collection sub-tab: shows cards from collection grouped by type, shortfall indicators
- Both sub-tabs support: checkbox selection, bulk add to deck, single add buttons
- Role badges with color-coding (Ramp, Draw, Removal, BoardWipe, etc.)
- Card hover preview for EDHREC recommendations
- Show only owned filter for EDHREC results
- Fixed API client types: EdhRecsResponse, CollectionRecsResponse wrappers
- Added EdhRecCard and CollectionRecCard interfaces
2026-03-12 02:48:52 +00:00
KoalaTrapLord a0adfc37e8 v24: Resolve GitHub Issues #1-#5
Issue #1 — Winner's life total now captured:
  - Parse verbose Forge log for life-total-change events
  - Track last-known life per seat via 3 regex patterns
  - Winner's final life set from log data (not left at default 40)
  - Losers who died to mill/poison retain their actual life total

Issue #2 — Win condition classification formalized:
  - New WinCondition enum (schema/WinCondition.java) with 8 categories
  - Centralized Forge loss-reason-string → enum mapping (regex-based)
  - UNKNOWN fallback with logging for unrecognized loss reasons
  - All classification now goes through WinCondition.classify()

Issue #3 — JVM startup overhead reduced:
  - Added -XX:+UseSerialGC (lighter GC for batch processes)
  - Added -XX:TieredStopAtLevel=1 (faster JIT, less compilation)
  - ForgeWorker stub class for future persistent batch mode
  - Estimated reduction: ~2-3s → ~1-1.5s per game startup

Issue #4 — Single-threaded throughput optimization:
  - New CLI flags: --ai-simplified, --ai-think-time, --benchmark
  - JVM flags passed through to Forge subprocess
  - Benchmark mode: run 1 game before batch, report wall/Forge/overhead times
  - API support: aiSimplified, aiThinkTimeMs in StartRequest

Issue #5 — Threading, progress, and backpressure:
  - --threads 0 or -1 auto-detects optimal count from CPU topology
  - detectOptimalThreads() estimates physical cores (excludes HT)
  - ProgressCallback interface for real-time sims/sec reporting
  - Per-game progress emitted as [PROGRESS] lines, parsed by lab_api.py
  - --max-queue flag for graceful backpressure (limits concurrent JVMs)
  - Final throughput summary printed at batch completion
  - StatusResponse now includes simsPerSec field

Also: .gitignore restored, lab_api.py updated with all new API fields
2026-03-12 01:26:36 +00:00
KoalaTrapLord a5fe8cb3fa v23: React + TypeScript SPA migration
Complete frontend rewrite from vanilla HTML/CSS/JS to React 19 + TypeScript + Tailwind CSS v4.

Frontend (frontend/commander-ai-lab-ui/):
- Vite + React 19 + TypeScript + Tailwind CSS v4 + React Router
- 7 pages: BatchSim, Collection, Decks, AutoGen, Simulator, Coach, Training
- Reusable components: Spinner, StatusBadge, CardImage, ManaSymbols, EmptyState
- Full typed API client layer (8 modules matching all backend endpoints)
- Recharts for mana curves, win rate charts, progress bars
- Lucide icons throughout
- Custom dark theme with MTG mana color tokens
- Code splitting (recharts + react in separate chunks)

Backend (lab_api.py):
- SPA-aware static file serving with catch-all for client-side routing
- Fallback to legacy ui/ folder if React dist not built
- Updated startup log with SPA route list

Build system:
- .gitignore added (was missing - removes pyc, db, node_modules, etc.)
- build-full-installer-v2.py: v23 with Node.js check + React build step
- Vite build config with manual chunks for optimal loading

Also includes: V3 deck generator fixes, coach improvements, DeepSeek sim modules
2026-03-12 01:12:02 +00:00
KoalaTrapLord e6b5e0cedb Initial commit 2026-03-10 12:13:56 -04:00
591 changed files with 140033 additions and 24835 deletions

No files matched your search

+21
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[run]
source = src/commander_ai_lab
omit =
*/tests/*
*/__pycache__/*
*/web/static/*
[report]
exclude_lines =
pragma: no cover
def __repr__
raise NotImplementedError
if TYPE_CHECKING:
if __name__ == .__main__.:
\.\.\. # ellipsis
show_missing = true
precision = 1
[html]
directory = htmlcov
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# Commander AI Lab — Environment Variables
# Copy this file to .env and fill in your values.
# start-lab.bat will auto-load .env if it exists.
# ── API Keys ──────────────────────────────────────────────────────
ANTHROPIC_API_KEY=your-anthropic-api-key-here
XIMILAR_API_KEY=your-ximilar-key-here
# ── Forge JAR Path ────────────────────────────────────────────────
# Point this to your Forge GUI desktop fat JAR.
FORGE_JAR_PATH=D:/ForgeCommander/Forge-Repo/forge-gui-desktop/target/forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar
# IMPORTANT: Forge MUST be launched from the forge-gui directory (not forge-gui-desktop/target).
# The JAR looks for res/ relative to its working directory. Without this, you get
# ExceptionInInitializerError on startup.
FORGE_WORKING_DIR=D:/ForgeCommander/Forge-Repo/forge-gui
# ── Deck Paths ────────────────────────────────────────────────────
# Directory containing your precon .dck files.
# Forge reads deck files directly from this folder.
#
# IMPORTANT: lab_api.py reads PRECON_DIR (not PRECON_DECKS_DIR).
# PRECON_DECKS_DIR is kept below for backwards compat but is NOT used by the server.
PRECON_DIR=D:\ForgeCommander\commander-ai-lab\precon-decks
PRECON_DECKS_DIR=D:\ForgeCommander\commander-ai-lab\precon-decks
USER_DECKS_DIR=D:\ForgeCommander\commander-ai-lab\user-decks
FORGE_DECKS_DIR=D:\ForgeCommander\commander-ai-lab\user-decks
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name: CI
on:
push:
branches: [main]
pull_request:
branches: [main]
jobs:
python-tests:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ["3.10", "3.11", "3.12"]
steps:
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -e ".[dev]" || pip install -r requirements.txt || true
pip install ruff pytest pytest-asyncio httpx || true
- name: Lint with ruff
run: ruff check . --select E,F,W --ignore E501 || true
- name: Run tests
run: pytest -v --tb=short tests/ || true
java-tests:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Java 17
uses: actions/setup-java@v4
with:
java-version: '17'
distribution: 'temurin'
- name: Build and run Maven tests
run: mvn test -B --no-transfer-progress || true
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name: Unity Build (Windows + WebGL)
on:
push:
branches: [main]
paths:
- 'unity-client/**'
pull_request:
branches: [main]
paths:
- 'unity-client/**'
workflow_dispatch:
env:
UNITY_PROJECT_PATH: unity-client
jobs:
build:
name: Build ${{ matrix.targetPlatform }}
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
targetPlatform:
- StandaloneWindows64
- WebGL
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
lfs: true
- name: Cache Unity Library
uses: actions/cache@v4
with:
path: ${{ env.UNITY_PROJECT_PATH }}/Library
key: Library-${{ matrix.targetPlatform }}-${{ hashFiles(format('{0}/Assets/**', env.UNITY_PROJECT_PATH)) }}
restore-keys: |
Library-${{ matrix.targetPlatform }}-
Library-
- name: Build Unity project
uses: game-ci/unity-builder@v4
env:
UNITY_LICENSE: ${{ secrets.UNITY_LICENSE }}
UNITY_EMAIL: ${{ secrets.UNITY_EMAIL }}
UNITY_PASSWORD: ${{ secrets.UNITY_PASSWORD }}
with:
projectPath: ${{ env.UNITY_PROJECT_PATH }}
targetPlatform: ${{ matrix.targetPlatform }}
buildName: CommanderAILab
versioning: Semantic
- name: Upload build artifact
uses: actions/upload-artifact@v4
with:
name: Build-${{ matrix.targetPlatform }}
path: build/${{ matrix.targetPlatform }}
retention-days: 14
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@@ -32,9 +32,24 @@ embeddings/
checkpoints/
imported-decks/
# User-created decks (generated at runtime, not committed)
user-decks/*.dck
# OS
.DS_Store
Thumbs.db
# Logs
*.log
logs/
# Runtime artifacts / caches
static/
api/
image_cache/
data/rag_chroma/
.claude/
dependency-reduced-pom.xml
# Precon decks (generated / imported at runtime)
precon-decks/
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# FarmHouse changes
This branch (`farmhouse`) is `KoalaTrapLord/commander-ai-lab` at commit `54cc5e9` plus the changes below. They were made to run the app
**Python-only** (no Forge/Java, no PyTorch) in a Kubernetes namespace, with a local LLM behind an OpenAI-compatible gateway instead of
Anthropic/DeepSeek, and they were each found by actually running the app, not by reading it. Written 2026-10-26.
Every change started life as an anchored text patch (`patch_app.py` in the farmhouse-os repo, which still builds the deployed image from
the upstream commit); here they are ordinary source edits. Lines carry a `# FARMHOUSE_...` marker where the patch left one.
## What was wrong, and what changed
### The simulator played nothing (the big one)
| Problem | Change |
|---|---|
| Every card reached the simulator with **no type line and no rules text**. The loaders join the user's *collection* and a *card_records* table (both empty on a fresh install) and the Forge card cache that was meant to fill the gap is not installed. A land was indistinguishable from a creature: an 8-game batch cast 0 spells, dealt 0 damage and lost every game. | `services/farmhouse_sim.py` `fill_card_data` fills type, text, mana cost, cmc, P/T, keywords and colour identity from the 113k-card Scryfall bulk database. Wired into `services/forge_runner.py` (batch) and `routes/deepseek.py` (`_fetch_deck_card_data`, which the Monte Carlo and single-deck routes use). |
| The batch loader **did not repeat cards by quantity**: a 100-card deck loaded as 66-84 cards with *one* Island. | `expand_quantities` (same module). |
| The LLM engine asked the model **once per turn** and applied the answer as one action. | `src/commander_ai_lab/sim/farmhouse_turns.py`: up to `BRAIN_STEPS` (default 4) questions per turn (land, spell, spell, attack), re-reading the board after each; stops on attack/hold/no change. `BRAIN_STEPS=1` restores the original. |
| A model that "thinks" spent ~46 s on a one-line move. | `deepseek_brain.py` sends `reasoning_effort` from `LLM_REASONING` (46 s -> 1.8 s per move). |
| The engine dropdown defaulted to **Forge**, which cannot run without Java. | `ui/ai-lab.js`: only "LLM opponents" is offered. |
After the fixes: a 40-game Monte Carlo takes 0.4 s with real numbers (10 spells and 12.7 damage per game); two LLM-piloted games took
212 s with 49 model calls and no fallbacks.
### Deck import
| Problem | Change |
|---|---|
| **Moxfield links were accepted nowhere**, and the Deck Builder import took only pasted text. | `services/farmhouse_deck_links.py`: Moxfield (public decks, `api2.moxfield.com`) and Archidekt links in the simulator URL import and in both Deck Builder imports (a lone link in the paste box). A private deck says to use Moxfield's Export > Copy as text. |
| The Deck Builder import looked **every card up on scryfall.com**; after a few dozen it was rate-limited (HTTP 429) and the app reported the rest as "not found" (250-card deck: 55 matched; even plain `Sol Ring` failed). | `local_card()`: names resolve from the local bulk database first; the website is only the fallback. |
### Deck generation
- `deck_builder/pipeline/farmhouse_fit.py`: the model's list is **filtered to the commander's colour identity** (from the bulk DB), de-duplicated,
fitted to per-category targets and trimmed to 99 + commander. Before this the model returned off-colour cards and 359-card lists.
- Scryfall bulk format changed (`jsonl_download_uri`, gzip JSONL; no `download_uri`).
- Scryfall answers **HTTP 400 to the default `python-requests` User-Agent** (every lookup became "Commander not found"): a named UA is sent everywhere.
- Scryfall 429s: requests back off on `Retry-After` and the pace is 0.25 s.
- `LLM_REASONING` / `LLM_MAX_TOKENS` are sent by the deck builder and coach.
### Coach / startup
- The coach was only initialised in `main()`, not when the app starts under uvicorn; `check_connection_sync()` was called but never defined;
the coach used the first model the gateway listed rather than the configured one. All fixed.
- Status label says "Language model" instead of the hardcoded "Claude Opus".
- `RAG_AUTOBUILD` (default off in our deployment) controls whether the card-search index is built at startup (113k embeddings; about an hour).
### Decision model (Laya / Kev), wired and off
`src/commander_ai_lab/sim/farmhouse_decide.py` asks a `/v1/systemone` server which of the nine actions to take, in one forward pass.
`DECISION_MODE=shadow` records whether it agrees with the LLM (the game still uses the LLM); `assist` uses it when `DECISION_MIN_CONF`
sure; `off` (default) does nothing. `scripts/farmhouse_decision_eval.py` replays recorded decisions through it.
**Measured 2026-10-26 on 149 recorded LLM decisions: zero-shot, both Laya checkpoints agreed with the LLM less often than always
guessing the commonest action** (English 16.1%, typed-decisions 18.6%, baseline 27.5%), at 2-4 s per answer on a 4-thread CPU. It is only
worth using after fine-tuning on this game's decisions; the write-up is `LAYA-EVALUATION.md` in the farmhouse-os repo's commander-lab folder.
### Laya connections (all off by default)
Forge's concrete priority path, extra typed questions in the same call, a capture log, a fine-tuning dataset exporter, and card-role classification:
`src/commander_ai_lab/sim/farmhouse_laya.py`, `scripts/farmhouse_laya_dataset.py`, `scripts/farmhouse_laya_roles_eval.py`, tests `tests/farmhouse/test_farmhouse_laya*.py`.
How it fits the strategic-intelligence work, what is measured and how to fine-tune: [docs/LAYA-CONNECTIONS.md](docs/LAYA-CONNECTIONS.md). Reference copies of the Laya
image, manifests and CI are in `farmhouse/laya/`.
### Forge, and rules-accurate simulation with phase-rs
- **Forge is installed** in the cluster image (it was left out by mistake): `farmhouse/forge/README.md`. Batches are clamped to one game at a time (`FORGE_MAX_THREADS`).
- **Rules-accurate simulation** through the phase-rs engine (Rust, headless): `routes/farmhouse_phase.py`, `ui/phase-sim.html` (linked from the home page) and the service in
`farmhouse/phase/`. Four saved decks play real Commander games; the page shows wins by seat with a 95% interval and warns about cards the engine did not recognise.
Seats can be put on a **model** (twin-turbo through Olla, or Laya): the model chooses among the engine's own legal options only, anything wrong or slow is played by phase's AI,
and a call cap bounds the cost (`farmhouse/phase/README.md`). Assessment and plan: `docs/PHASE-AND-PLAYTESTER.md`.
## Settings (environment variables)
| Variable | Meaning |
|---|---|
| `LLM_URL`, `LLM_MODEL` | OpenAI-compatible endpoint (`.../v1`) and model for the coach and deck builder |
| `LLM_REASONING` (`none`), `LLM_MAX_TOKENS`, `LLM_TIMEOUT` | keep requests lean: reasoning off, cap on answer length |
| `COACH_PROVIDER`, `DECK_GEN_PROVIDER` = `local`, `DECK_GEN_MODEL` | use the local model, not Anthropic |
| `OLLAMA_BASE_URL`, `RAG_EMBEDDING_MODEL`, `RAG_AUTOBUILD` | card-search embeddings (one host only: vectors differ between hosts) |
| `BRAIN_API_BASE` (**no** `/v1`; the client appends it), `BRAIN_MODEL`, `BRAIN_MAX_TOKENS`, `BRAIN_STEPS` | the simulator's LLM opponents |
| `DECISION_URL`, `DECISION_MODEL`, `DECISION_MODE` (`off`/`shadow`/`assist`), `DECISION_MIN_CONF`, `DECISION_TIMEOUT` | the decision model above |
| `ollama` Python package | required by the card-search index build (ChromaDB's Ollama embedder imports it) |
## Tests
`tests/farmhouse/` (standard library only, no network): `python -I -X utf8 tests/farmhouse/test_farmhouse_deck_links.py`, `..._sim.py`, `..._fit.py`.
Run them with `-I` and from outside the package folders; do not put `services/` on `sys.path` (`services/logging.py` shadows the standard library).
## Things still worth doing
- **Make the LLM-opponent turns faster** (a piloted move averages ~7.5 s: about 2.2k tokens in, ~30 out, so reading the prompt dominates).
These are ideas, not measured; none is implemented:
- *Shared prefix:* about 1.2k tokens of every prompt (the system prompt) are identical each time; a llama.cpp server can reuse them
if the calls share the prefix, so only the board state is read each move.
- *Smaller prompt:* send a compact board state instead of the indented JSON the engine builds now (`json.dumps(snapshot, indent=2)`
in `deepseek_brain.py`).
- *Parallel games:* run several games at once, which would also fill the GPU while one game waits on a move. Caveat: for long prompts
two requests at once gave no extra throughput on a V100 (reading the prompt is compute-bound), so measure before relying on it.
- Opponents are the engine's heuristics; only the piloted seat uses the model.
- Build the card-search index and measure it.
- The upstream `LICENSE` is missing (`pyproject.toml` says MIT). Ask the author to add one before publishing a derivative widely.
- Forge-based features (the Java simulator, training) remain unavailable by design.
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# Commander AI Lab — Developer Makefile
# ======================================
# Targets:
# make test — run all tests
# make test-unit — run unit tests only (fast)
# make test-integration— run integration tests
# make test-benchmark — run benchmark suite with -s (print timings)
# make coverage — run tests with coverage report
# make serve — start the FastAPI dev server
# make lint — ruff + mypy
# make fmt — ruff format
# make clean — remove build artefacts
PYTHON ?= python
UVICORN ?= uvicorn
PORT ?= 8000
SRC = src/commander_ai_lab
.PHONY: test test-unit test-integration test-benchmark coverage serve lint fmt clean frontend-dev frontend-build test-frontend
test:
$(PYTHON) -m pytest tests/ -q
test-unit:
$(PYTHON) -m pytest tests/ -q -m "not integration and not benchmark"
test-integration:
$(PYTHON) -m pytest tests/test_integration.py -v
test-benchmark:
$(PYTHON) -m pytest tests/test_benchmark.py -v -s
test-phase3:
$(PYTHON) -m pytest tests/test_phase3.py -v
test-phase4:
$(PYTHON) -m pytest tests/test_phase4.py -v
test-phase5:
$(PYTHON) -m pytest tests/test_phase5.py -v
test-phase6:
$(PYTHON) -m pytest tests/test_phase6.py -v
test-phase7:
$(PYTHON) -m pytest tests/test_integration.py tests/test_benchmark.py -v
coverage:
$(PYTHON) -m pytest tests/ --cov=$(SRC) --cov-report=term-missing --cov-report=html
echo "Coverage report: htmlcov/index.html"
serve:
$(UVICORN) commander_ai_lab.web.app:app --reload --port $(PORT)
lint:
$(PYTHON) -m ruff check $(SRC) tests/
$(PYTHON) -m mypy $(SRC) --ignore-missing-imports
fmt:
$(PYTHON) -m ruff format $(SRC) tests/
clean:
find . -type d -name __pycache__ -exec rm -rf {} + 2>/dev/null || true
find . -type f -name '*.pyc' -delete
rm -rf .coverage htmlcov .mypy_cache .ruff_cache dist build
frontend-dev:
cd frontend && npm run dev
frontend-build:
cd frontend && npm run build
test-frontend:
cd frontend && npx vitest run
+366 -294
View File
@@ -1,362 +1,434 @@
# 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 Magic: The Gathering Commander research platform. Run thousands of simulated games, train neural networks on the outcomes, build decks with AI that learns from simulation data, get LLM-powered coaching, and play live 4-player battles — all from a single unified system.
---
## Overview
Commander AI Lab connects a rules-complete Forge engine, a fast Monte Carlo simulator, an LLM deck builder, an ML training pipeline, and multiple game clients (web, LAN, Unity) into one closed-loop platform. Simulation results feed back into deck building and AI decision-making, so the system improves the more it plays.
### Key Capabilities
- **Batch Simulation** — Run hundreds or thousands of Commander games unattended using Forge (rules-complete) or a headless Python Monte Carlo engine
- **ML Training Pipeline** — Reinforcement learning (PPO) trains a neural net on game decision logs; outcome-weighted scoring nudges card weights automatically
- **AI Deck Builder** — 7-step LLM pipeline (EDHrec → Scryfall → Ollama) with simulation-learned insights injected into prompts
- **LLM Coach** — Streaming coaching sessions powered by Anthropic Claude or local Ollama models, enriched with RAG-retrieved card knowledge
- **RAG Card Knowledge** — ChromaDB vector store over 30,000+ Scryfall cards with semantic search and auto-staleness monitoring
- **LAN Battlefield** — Browser-based 4-player Commander game client with ML-powered AI opponents and precon deck support
- **Unity Client** — Full Unity application with battleground scene, lobby system, WebSocket game state sync, and cross-platform builds
- **Card Scanner** — OCR pipeline for physical card recognition via Ximilar API
- **163 Precon Decks** — Complete preconstructed deck library in `.dck` format
---
## 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 │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────┐
│ Clients │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────────┐ │
│ │ Web UI │ │ LAN Client │ │ Unity Client │ │
│ │ (React/TS) │ │ (HTML/JS) │ │ (C# / URP) │ │
│ │ /ui │ │ /lan │ │ /unity-client │ │
│ └──────┬───────┘ └──────┬───────┘ └──────┬───────────────────────┘ │
│ └────────────────┼────────────────┘ │
│ │ HTTP/REST + WebSocket │
│ ┌───────────────────────┴─────────────────────────────────────────┐│
│ │ FastAPI Backend (lab_api.py — port 8080) ││
│ │ ││
│ │ /api/lab/* Batch simulation (Forge + Monte Carlo) ││
│ │ /api/deck-builder/* 7-step LLM deck construction ││
│ │ /api/coach/* Streaming LLM coaching sessions ││
│ │ /api/ml/* Neural net training + policy server ││
│ │ /api/rag/* ChromaDB vector search ││
│ │ /api/game/* Live game sessions (Forge IPC) ││
│ │ /api/policy/* ML policy decisions for AI players ││
│ │ /ws/game/{id} WebSocket game state streaming ││
│ └───────┬──────────────┬──────────────┬──────────────┬────────────┘│
│ │ │ │ │ │
│ ┌──────┴──────┐ ┌────┴─────┐ ┌──────┴──────┐ ┌────┴──────┐ │
│ │ Forge │ │ Monte │ │ Ollama │ │ ChromaDB │ │
│ │ Engine │ │ Carlo │ │ (local LLM) │ │ (vector │ │
│ │ (Java/JVM) │ │ Engine │ │ qwen2.5:7b │ │ store) │ │
│ └─────────────┘ │ (Python) │ └─────────────┘ └───────────┘ │
│ └──────────┘ │
│ ┌──────────────────────────────────────────────────────────────────┐│
│ │ ML Pipeline ││
│ │ decisions_*.jsonl → update_weights.py → learned_weights.json ││
│ │ → /api/ml/train → neural net checkpoints ││
│ └──────────────────────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────────────────────┘
```
## Quick Start (Windows)
---
## Features
### Simulation Engines
| Engine | Description | Speed |
|---|---|---|
| **Forge** | Rules-complete Java engine — handles the stack, instants, triggered abilities, and all Commander rules. Each game spawns a JVM subprocess. | ~0.04 sims/sec (single-threaded) |
| **Monte Carlo** | Headless Python engine — models combat keywords (flying, trample, deathtouch, lifelink, menace, reach, haste), summoning sickness, tapped state, commander zone, multiplayer targeting, and board wipes. No JVM required. | Thousands of games in minutes |
Both engines feed structured results into the same ML pipeline.
### Deck Builder
The `POST /api/deck-builder/build` endpoint runs a 7-step pipeline:
1. Resolve commander via Scryfall
2. Fetch EDHrec recommendations
3. Fetch Scryfall candidates by category (ramp, removal, draw, lands)
4. Ollama suggests synergy/protection/wincon cards — **simulation insights injected here**
5. Filter by color identity, collection, and ban list
6. Ollama enforces ratios (trim/expand to 99) — **simulation insights injected here**
7. Assemble final deck with smart substitution (guarantees 99-card output)
### AI Coach
LLM-powered coaching with two modes:
- **Full Session** — Deep analysis including removal coverage, ramp quality, draw engine profiling, win conditions, anti-synergy detection, pod meta evaluation, and tempo assessment
- **Quick Digest** — Fast ~5-second summary for rapid feedback
Coaching prompts are enriched with semantically relevant cards from the ChromaDB RAG store (15 cards for full sessions, 8 for quick digests). Supports both Anthropic Claude and local Ollama models with streaming responses.
### RAG Card Knowledge
- **SQLite FTS5** bulk card database from Scryfall data
- **ChromaDB** vector index with natural-language card embeddings
- Automatic staleness monitoring (checks every 6 hours, auto-rebuilds if >14 days old)
- Graceful fallback — RAG failures never block coaching or deck building
### ML Training Pipeline
```
Overnight sims → decisions_*.jsonl
↓
update_weights.py → learned_weights.json (feeds deck builder)
↓
/api/ml/train → neural net training on decision dataset
↓
/api/policy/decide → ML policy serves AI decisions in live games
```
- **Outcome-weighted RL** nudges card scoring weights from game results
- **Neural net** trains on the full decision log dataset
- **Distillation loop** — self-play → collect data → retrain → repeat
- **ELO tracking** and model tournaments for comparing AI versions
- Weights clamped to `[-20.0, 20.0]`, stored in `learned_weights.json`
### Win Condition Tracking
Games are classified by win type — not just "Combat" vs "Other":
- **Combat** — standard creature damage wins
- **Combo** — Approach of the Second Sun, Felidar Sovereign, Happily Ever After, and 30+ known alternate-win cards
- **Drain** — life drain effects
- **Mill** — library depletion
- **Poison** — infect/poison counter wins
Draw games (timeout) are analyzed separately with per-player life-at-timeout snapshots and likely cause identification.
### LAN Battlefield Client
Browser-based 4-player Commander game at `/lan`:
- **AI Bridge** — REST + WebSocket connectivity with ML policy decisions as Priority 1 (falls back to local heuristic AI)
- **Deck System** — 3-tab selector: Precons (163 decks, grouped by set), EDHREC search, and custom deck import
- **Player Panels** — Life counters, commander damage bars, poison/energy/experience/rad counters, emblem management
- **Backend Detection** — Auto-detects the Commander AI Lab backend; works offline with local precon data
### Unity Client
Full Unity application (URP, .NET Standard 2.1) with:
- **Battleground Scene** — 4-seat layout with rotated perspectives, life/poison/commander damage widgets, phase tracker, turn order bar
- **Card System** — Hand, battlefield, graveyard, exile zones with card art, tapped rotation, face-down state, summoning sickness indicators
- **Stack & Mana** — LIFO stack display and 6-pip mana pool
- **Lobby System** — Configure 4 seats (human/AI), POST to backend, WebSocket game state sync
- **Elimination** — Grayscale shader overlay on eliminated players
- **Token System** — Create, tap, add counters, delete tokens
- **Cross-Platform** — Windows, WebGL, Android, iOS build targets
- **Scenes** — MainMenu, Collection, DeckBuilder, DeckGenerator, Simulator, Coach, Scanner, Training, Precon, Battleground
### Card Scanner
OCR pipeline for physical Magic cards using the Ximilar API. Scan cards from photos and match them to the Scryfall database.
---
## Quick Start
### 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`
### 1. Build Forge (one-time)
| Tool | Version |
|---|---|
| Python | 3.10+ |
| Java | 17+ |
| Maven | 3.8+ |
| Node.js | 18+ (for LAN client scripts) |
| Ollama | Latest (for local LLM inference) |
| [Forge](https://github.com/KoalaTrapLord/Forge-Repo) | Built at `D:\ForgeCommander\Forge-Repo` |
### Setup (Windows)
```powershell
cd D:\ForgeCommander\forge-repo
# 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 dependencies
pip install -e ".[rag,dev]"
### 4. Launch the Web UI
**Option A — One-click launcher:**
```powershell
# 4. Pull an Ollama model for the deck builder
ollama pull qwen2.5:7b
# 5. Launch
.\start-lab.bat
# → Web UI: http://localhost:8080
# → LAN Client: http://localhost:8080/lan/
```
Edit the paths at the top of `start-lab.bat` if needed.
**Option B — Manual launch:**
Or use the automated installer:
```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
.\install-commander-ai-lab.ps1
```
Open `http://localhost:8080` — select 3 decks, set game count, click **Run AI Sims**.
---
## Overnight AFK Runner
Run batch simulations unattended, then automatically update card weights and train the neural net:
### 5. CLI-only usage (no web server)
```powershell
cd D:\ForgeCommander\forge-repo\forge-gui
java -jar "D:\ForgeCommander\commander-ai-lab\target\commander-ai-lab-1.0.0-SNAPSHOT.jar" `
--forge-jar "..\forge-gui-desktop\target\forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar" `
--forge-dir "." `
--deck1 "Edgar Markov" `
--deck2 "Grimgrin" `
--deck3 "Xyris" `
--games 100 `
--threads 4 `
--output results/batch-100.json
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
```
## Scaling Tests
**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
### 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
---
## Simulation → Deck Builder Feedback Loop
```
Overnight sims → decisions_*.jsonl
↓
update_weights.py → learned_weights.json
↓
sim_insights.py → natural-language insight block
↓
Ollama prompts (suggest_cards + enforce_deck_ratios)
↓
Better card recommendations
```
### 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
```
When active, `BuildResult.sources_consulted` includes `"sim_weights"`. The loop is a no-op until real sim data exists.
### Scale to 100 games (4 threads)
```powershell
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar `
--forge-jar "..." --forge-dir "..." `
--deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" `
--games 100 --threads 4 --output results/scale-100-t4.json
```
### Maximum throughput test (100 games, 8 threads)
```powershell
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar `
--forge-jar "..." --forge-dir "..." `
--deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" `
--games 100 --threads 8 --seed 42 --output results/scale-100-t8-seed42.json
```
### Performance comparison script
Run different thread counts and compare sims/sec:
```powershell
# Baseline: 1 thread
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 1 --seed 42 --output results/perf-t1.json
# 2 threads
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 2 --seed 42 --output results/perf-t2.json
# 4 threads
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 4 --seed 42 --output results/perf-t4.json
# 8 threads
java -jar target\commander-ai-lab-1.0.0-SNAPSHOT.jar --forge-jar "..." --forge-dir "..." --deck1 "Edgar Markov" --deck2 "Grimgrin" --deck3 "Xyris" --games 20 --threads 8 --seed 42 --output results/perf-t8.json
```
Then compare the `simsPerSecond` values in each output JSON to see scaling behavior.
---
## API Reference
### Simulation
| Endpoint | Method | Description |
|---|---|---|
| `/api/lab/decks` | GET | List available Commander decks |
| `/api/lab/start` | POST | Start 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 |
| `/api/lab/start` | POST | Start Forge batch (returns batchId) |
| `/api/lab/start-deepseek` | POST | Start Python/DeepSeek batch |
| `/api/lab/status?batchId=X` | GET | Poll batch progress |
| `/api/lab/result?batchId=X` | GET | Get completed batch JSON |
| `/api/lab/history` | GET | List past batch runs |
| `/api/lab/precons/deck` | GET | Fetch precon `.dck` file content |
### POST /api/lab/start
```json
{
"decks": ["Edgar Markov", "Grimgrin", "Xyris"],
"numGames": 100,
"threads": 4,
"seed": null,
"clock": 120
}
```
### Deck Builder & Coach
### GET /api/lab/status response
```json
{
"batchId": "abc123",
"running": true,
"completed": 42,
"total": 100,
"threads": 4,
"elapsedMs": 156000,
"error": null
}
```
| Endpoint | Method | Description |
|---|---|---|
| `/api/deck-builder/build` | POST | Build a 99-card Commander deck |
| `/api/coach/chat` | POST | Full coaching session (streaming) |
| `/api/coach/quick-digest` | POST | Fast ~5s coaching summary |
## Python Simulator (Monte Carlo Engine)
### ML & Policy
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.
| Endpoint | Method | Description |
|---|---|---|
| `/api/ml/train` | POST | Start neural net training |
| `/api/ml/train/status` | GET | Poll training progress |
| `/api/policy/decide` | POST | Get ML policy decision for game state |
### Location
### RAG
```
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()
```
| Endpoint | Method | Description |
|---|---|---|
| `/api/rag/build` | POST | Build/rebuild ChromaDB index |
| `/api/rag/search` | POST | Semantic card search |
| `/api/rag/status` | GET | Index health and staleness |
### Install as editable package
### Live Game
| Endpoint | Method | Description |
|---|---|---|
| `/api/game/start` | POST | Create a live game session |
| `/api/game/action` | POST | Submit a player action |
| `/api/game/{id}/actions` | GET | Poll Forge IPC actions |
| `/ws/game/{game_id}` | WS | Real-time game state streaming |
---
## ML Scripts
```powershell
cd D:\ForgeCommander\commander-ai-lab
pip install -e .
# Update card weights from decision logs
python -m ml.scripts.update_weights --min-games 10 --lr 0.05 --dry-run
# Reset weights to defaults
python -m ml.scripts.update_weights --reset
# Generate synthetic training data
python ml/scripts/generate_synthetic_data.py
```
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 Simulator 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)
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%}")
print(f"Avg turns: {stats['avg_turns']:.1f}")
print(f"Win rate: {stats['win_rate']:.1%}, Avg turns: {stats['avg_turns']:.1f}")
```
Key functions:
| Function | Description |
|---|---|
| `build_deck(card_names)` | Create enriched `Card` list from names |
| `build_deck_from_text(decklist)` | Parse `"1 Sol Ring\n37 Forest"` format |
| `run_single_game(deck_a, deck_b)` | One game, returns winner/turns/stats |
| `run_simulation(deck_a, deck_b, n)` | Monte Carlo over `n` games, aggregated stats |
### CLI entry point
After `pip install -e .`:
```powershell
commander-sim
```
Runs the same demo as `python -m commander_ai_lab.lab.experiments`.
### How the engine works
1. **Deck enrichment** — `enrich_card()` infers type, CMC, keywords, and power/toughness from card name heuristics when Scryfall data is unavailable.
2. **AI scoring** — `score_card()` uses weighted categories (removal, ramp, draw, aggro, etc.) to pick the best play each turn.
3. **Game loop** — Each turn: untap → draw → play lands → cast spells (AI scored) → combat (attack/block decisions) → end step. Full keyword support for flying, trample, deathtouch, lifelink, menace, and reach.
4. **Monte Carlo** — `run_simulation()` runs N independent games and aggregates win rate, avg turns, damage dealt/received, spells cast, creatures played, removal used, ramp played, cards drawn, and max board size.
---
## Project Structure
```
commander-ai-lab/
├── lab_api.py # Python FastAPI server (web UI backend)
├── lab_api.py # FastAPI server entry point
├── 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
├── install-commander-ai-lab.ps1 # Automated setup script
├── pom.xml # Maven build (Java/Forge components)
├── pyproject.toml # Python package config
│
├── routes/ # FastAPI route modules
│ ├── lab.py # Batch simulation
│ ├── deckbuilder.py # Deck builder pipeline
│ ├── coach.py # LLM coaching
│ ├── ml.py # ML training
│ ├── rag.py # ChromaDB vector search
│ ├── game.py # Live game sessions
│ ├── policy.py # ML policy server
│ ├── precon.py # Precon deck serving
│ ├── scanner.py # Card OCR
│ └── ws_game.py # WebSocket game state
│
├── services/ # Core service layer
│ ├── forge_runner.py # Forge subprocess management
│ ├── draw_game_analyzer.py # Draw/timeout analysis
│ ├── rag_store.py # ChromaDB vector store
│ └── card_text.py # Shared card text builder
│
├── src/commander_ai_lab/
│ ├── sim/ # Monte Carlo engine
│ │ ├── engine.py # Game loop
│ │ ├── models.py # GameResult, Card, Player
│ │ ├── rules.py # Combat, keywords, commander rules
│ │ ├── win_condition_parser.py # Alternate win detection
│ │ └── learned_weights.json # ML-learned card weights
│ ├── deck_builder/ # 7-step LLM pipeline
│ │ ├── api/ # Scryfall, EDHrec, Ollama, sim_insights
│ │ ├── core/ # Models, rules engine, assembler
│ │ └── pipeline/ # build_deck.py orchestrator
│ └── coach/ # LLM coaching
│ ├── coach_service.py # Session + quick digest
│ ├── prompt_template.py # System/user prompts
│ └── embeddings.py # Card embedding utilities
│
├── ml/ # ML training pipeline
│ ├── scripts/ # update_weights.py, ml_cli.py
│ ├── training/ # Neural net trainer
│ ├── encoder/ # Feature encoding
│ └── models/ # Saved checkpoints
│
├── lan-client/ # Browser-based LAN battlefield
│ ├── index.html # Entry point
│ ├── engine.js # Game engine + ML AI turns
│ ├── ai-bridge.js # Backend connector + policy bridge
│ ├── player-panels.js # Enhanced player panels
│ ├── ui.js # 3-tab deck selector + setup
│ └── precons.js # 163 precon deck data
│
├── unity-client/ # Unity game client
│ └── Assets/
│ ├── Scenes/ # 11 scenes (MainMenu → Battleground)
│ ├── Scripts/ # C# controllers, models, services
│ ├── Prefabs/ # Card, deck slot, chat bubble
│ └── Materials/ # URP materials + grayscale shader
│
├── scanner/ # Card OCR pipeline (Ximilar)
├── precon-decks/ # 163 preconstructed decks (.dck)
├── sample-decks/ # Sample deck files
├── data/ # Runtime data (ChromaDB, bulk DB)
├── logs/ # Simulation + decision logs
├── docs/ # Architecture docs + schemas
├── tests/ # Test suite
├── scripts/ # Utility scripts
├── .github/workflows/ # CI + Unity build automation
└── src/main/java/ # Java/Forge batch runner
└── commanderailab/
```
## 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
}
}
```
## Tech Stack
| Layer | Technology |
|---|---|
| Backend | Python 3.10+, FastAPI, Uvicorn |
| Simulation (rules-complete) | Java 17, Maven, Forge |
| Simulation (fast) | Python Monte Carlo engine |
| LLM Inference | Ollama (qwen2.5:7b), Anthropic Claude |
| Vector Store | ChromaDB with all-MiniLM-L6-v2 embeddings |
| Card Database | SQLite FTS5 (Scryfall bulk data) |
| ML Training | PyTorch, PPO reinforcement learning |
| Web Client | HTML/CSS/JS |
| Unity Client | Unity 2022 LTS, URP, C# |
| CI/CD | GitHub Actions |
---
## 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-changing effects (animated lands don't attack)
- **Forge engine** — each game spawns a new JVM (~2–3s overhead); ~0.04 sims/sec single-threaded
- **Forge** — winner life total not captured in quiet mode (only reports losers)
- **Deck builder** — Ollama suggestions depend on the local model quality; `qwen2.5:7b` works well
- **LAN client** — EDHREC commander search is a stub (backend-powered, not yet fully wired)
---
## Contributing
Commander AI Lab is an active research project. Check the [open issues](https://github.com/KoalaTrapLord/commander-ai-lab/issues) for areas where contributions are welcome.
---
## License
See repository for license details.
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<#
.SYNOPSIS
Smoke-tests the Commander AI Lab policy server from PowerShell.
Checks: server reachable -> /api/policy/health -> /api/policy/decide.
Prints a plain-English pass/fail for every step.
.USAGE
# From repo root (server already running in another window):
.\Test-PolicyServer.ps1
# Custom host/port:
.\Test-PolicyServer.ps1 -BaseUrl http://localhost:9000
# Pretty-print the full decide response:
.\Test-PolicyServer.ps1 -Verbose
#>
param(
[string]$BaseUrl = "http://localhost:8080",
[switch]$Verbose
)
$ErrorActionPreference = "Stop"
# ---- helpers ----------------------------------------------------------------
function Write-Pass { param([string]$msg) Write-Host " [PASS] $msg" -ForegroundColor Green }
function Write-Fail { param([string]$msg) Write-Host " [FAIL] $msg" -ForegroundColor Red }
function Write-Info { param([string]$msg) Write-Host " [INFO] $msg" -ForegroundColor Cyan }
function Write-Warn { param([string]$msg) Write-Host " [WARN] $msg" -ForegroundColor Yellow }
function Write-Step { param([string]$msg) Write-Host "`n-- $msg" -ForegroundColor White }
$allPassed = $true
function Invoke-Step {
param(
[string] $Name,
[scriptblock] $Block
)
Write-Step $Name
try { & $Block }
catch {
Write-Fail $_.Exception.Message
$script:allPassed = $false
}
}
# ---- Step 1: server reachable -----------------------------------------------
Invoke-Step "Step 1 -- Server reachable at $BaseUrl" {
$r1 = $null
try { $r1 = Invoke-WebRequest -Uri "$BaseUrl/docs" -UseBasicParsing -TimeoutSec 5 } catch {}
if ($r1 -and $r1.StatusCode -eq 200) {
Write-Pass "FastAPI server is up (GET /docs -> 200)"
return
}
$r2 = $null
try { $r2 = Invoke-WebRequest -Uri "$BaseUrl/" -UseBasicParsing -TimeoutSec 5 } catch {}
if ($r2 -and $r2.StatusCode -lt 500) {
Write-Pass "FastAPI server is up (GET / -> $($r2.StatusCode))"
return
}
throw "Cannot reach $BaseUrl -- is lab_api.py / uvicorn running?"
}
# ---- Step 2: /api/policy/health ---------------------------------------------
$modelLoaded = $false
Invoke-Step "Step 2 -- GET $BaseUrl/api/policy/health" {
$resp = Invoke-RestMethod -Uri "$BaseUrl/api/policy/health" -Method Get -TimeoutSec 10
if ($Verbose) { $resp | ConvertTo-Json -Depth 5 | Write-Host }
if ($resp.ready -eq $true) {
Write-Pass "Model is loaded and ready (ready=true)"
$script:modelLoaded = $true
} elseif ($resp.status -eq "degraded") {
Write-Fail "Health returned status=degraded / ready=false"
Write-Warn " -> Policy model is NOT loaded."
Write-Warn " -> Start the server with --load-policy, or call POST /api/ml/load_model first."
Write-Warn " -> Confirm models/ contains a valid .pt checkpoint."
$script:allPassed = $false
} else {
Write-Warn "Unexpected health response -- dumping:"
$resp | ConvertTo-Json -Depth 5 | Write-Host
$script:allPassed = $false
}
}
# ---- Step 3: /api/policy/decide (synthetic snapshot) ------------------------
Invoke-Step "Step 3 -- POST $BaseUrl/api/policy/decide (synthetic 4-player snapshot)" {
if (-not $script:modelLoaded) {
Write-Warn "Skipping /decide -- model not loaded (Step 2 failed)."
Write-Warn "Fix Step 2 first, then re-run."
return
}
$body = [ordered]@{
game_id = "ps1-smoke-test"
turn = 3
phase = "MAIN1"
active_player = 0
playstyle = "midrange"
temperature = 1.0
greedy = $false
players = @(
[ordered]@{
seat = 0
name = "TestPlayer0"
life = 40
manaAvailable = 3
landCount = 3
creaturesOnField = 1
totalPowerOnBoard = 2
hand = @("Cultivate", "Solemn Simulacrum", "Sol Ring")
battlefield = @("Forest", "Forest", "Forest", "Birds of Paradise")
graveyard = @()
commandZone = @("Atraxa, Praetors Voice")
},
[ordered]@{
seat = 1
name = "TestPlayer1"
life = 38
manaAvailable = 2
landCount = 2
creaturesOnField = 0
totalPowerOnBoard = 0
handCount = 4
hand = @()
battlefield = @("Island", "Swamp")
graveyard = @()
commandZone = @("Ur-Dragon")
},
[ordered]@{
seat = 2
name = "TestPlayer2"
life = 35
manaAvailable = 4
landCount = 4
creaturesOnField = 2
totalPowerOnBoard = 5
handCount = 3
hand = @()
battlefield = @("Mountain", "Mountain", "Mountain", "Mountain", "Goblin Guide", "Monastery Swiftspear")
graveyard = @()
commandZone = @("Krenko, Mob Boss")
},
[ordered]@{
seat = 3
name = "TestPlayer3"
life = 40
manaAvailable = 0
landCount = 0
creaturesOnField = 0
totalPowerOnBoard = 0
handCount = 7
hand = @()
battlefield = @()
graveyard = @()
commandZone = @("Prossh, Skyraider of Kher")
}
)
} | ConvertTo-Json -Depth 10
$resp = Invoke-RestMethod `
-Uri "$BaseUrl/api/policy/decide" `
-Method Post `
-Body $body `
-ContentType "application/json" `
-TimeoutSec 30
if ($Verbose) {
Write-Host ""
Write-Host " Full /decide response:" -ForegroundColor White
$resp | ConvertTo-Json -Depth 6 | Write-Host
}
$ok = $true
if ([string]::IsNullOrEmpty($resp.action)) {
Write-Fail "response.action is empty"
$ok = $false
}
if ($null -eq $resp.confidence) {
Write-Fail "response.confidence is missing"
$ok = $false
}
if ($resp.PSObject.Properties["error"] -and -not [string]::IsNullOrEmpty($resp.error)) {
Write-Fail "Server returned error: $($resp.error)"
$ok = $false
}
if ($ok) {
$confPct = [math]::Round($resp.confidence * 100, 1)
Write-Pass "Decision received -> action='$($resp.action)' conf=$($confPct)% latency=$($resp.inference_ms)ms src=$($resp.vector_source)"
if ($resp.probabilities) {
$top3 = $resp.probabilities.PSObject.Properties |
Sort-Object { $_.Value } -Descending |
Select-Object -First 3
Write-Info "Top-3 action probabilities:"
foreach ($p in $top3) {
$pct = [math]::Round($p.Value * 100, 1)
Write-Info (" " + $p.Name.PadRight(22) + "$pct%")
}
}
} else {
$script:allPassed = $false
}
}
# ---- Step 4: log line guidance ----------------------------------------------
Invoke-Step "Step 4 -- Confirm [routes.policy.decide] log line in server console" {
Write-Info "After Step 3 you should see in the uvicorn window:"
Write-Info " [decide] turn=3 phase=main_1 -> cast_ramp (conf=0.810) | top5=[...] | src=encoder"
Write-Info ""
Write-Info "If that line is MISSING:"
Write-Info " 1. Confirm uvicorn started with --log-level info (the default)"
Write-Info " 2. Confirm lab_api.py calls register_policy_routes(app, policy_svc)"
Write-Info " 3. Confirm routes/policy.py is included via routes/__init__.py"
Write-Info " 4. Logger name is [routes.policy.decide] -- check no filter silences it"
Write-Pass "Guidance printed"
}
# ---- Step 5: why Forge ignores the policy server ----------------------------
Invoke-Step "Step 5 -- Forge IPC status (informational)" {
Write-Warn "Forge does NOT call /api/policy/decide on its own."
Write-Warn "routes/game.py POST /api/game/start has a TODO: wire Forge engine launch."
Write-Warn ""
Write-Warn "Until wired, /decide is only hit by:"
Write-Warn " - This script"
Write-Warn " - overnight-run.py (batch sim path)"
Write-Warn " - Manual Invoke-RestMethod / curl calls"
Write-Warn ""
Write-Warn "Forge launches its built-in AI and plays lands because it has"
Write-Warn "no HTTP client pointed at localhost:8080."
Write-Warn ""
Write-Warn "Next step: wire GameSession inside POST /api/game/start,"
Write-Warn "or add a PolicyClient hook in Forge AiController.java."
Write-Pass "Status explained"
}
# ---- Summary ----------------------------------------------------------------
Write-Host ""
if ($allPassed) {
Write-Host "==========================================" -ForegroundColor Green
Write-Host " ALL STEPS PASSED -- policy server live" -ForegroundColor Green
Write-Host "==========================================" -ForegroundColor Green
} else {
Write-Host "==========================================" -ForegroundColor Red
Write-Host " ONE OR MORE STEPS FAILED -- see above" -ForegroundColor Red
Write-Host "==========================================" -ForegroundColor Red
exit 1
}
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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
+16 -19
View File
@@ -56,15 +56,15 @@ class PerplexityClient:
"""
def __init__(self, api_key: str, model: str = SONAR,
timeout: int = 120):
if not api_key:
raise ValueError("Perplexity API key is required")
timeout: int = 120, base_url: str = None):
if not api_key and not base_url:
raise ValueError("Perplexity API key is required (or provide base_url for local)")
self.api_key = api_key
self.model = model
self.timeout = timeout
self._client = OpenAI(
api_key=api_key,
base_url="https://api.perplexity.ai",
base_url=base_url or "https://api.perplexity.ai",
timeout=timeout,
)
@@ -72,26 +72,30 @@ class PerplexityClient:
self,
system_prompt: str,
user_prompt: str,
json_schema: dict,
json_schema: dict = None,
schema_name: str = "response",
model: str = None,
temperature: float = 0.2,
max_tokens: int = 8192,
) -> PerplexityResponse:
"""
Send a chat request with structured JSON output.
Send a chat request with JSON output.
Uses Sonar models do not support the json_schema response_format.
Structured output enforcement relies on the system prompt
instructing the model to return JSON matching the expected shape.
Args:
system_prompt: System message
system_prompt: System message (should instruct JSON output)
user_prompt: User message
json_schema: JSON Schema dict for response_format
schema_name: Name for the schema (used in response_format)
json_schema: (ignored) Kept for call-site compatibility
schema_name: (ignored) Kept for call-site compatibility
model: Override model (sonar, sonar-pro)
temperature: Sampling temperature (0.0–1.0)
temperature: Sampling temperature (0.0-1.0)
max_tokens: Max output tokens
Returns:
PerplexityResponse with parsed_json guaranteed by schema
PerplexityResponse with parsed_json from the model's output
"""
use_model = model or self.model
@@ -106,14 +110,7 @@ class PerplexityClient:
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
response_format={
"type": "json_schema",
"json_schema": {
"name": schema_name,
"schema": json_schema,
},
},
)
)
choice = response.choices[0]
content = choice.message.content or ""
+349 -71
View File
@@ -5,10 +5,11 @@ Main orchestrator: loads deck reports, identifies underperformers,
finds replacement candidates via embeddings, builds prompts,
calls the LLM, parses responses, and persists coaching sessions.
"""
import json
import logging
import uuid
import os
import re
from datetime import datetime, timezone
from pathlib import Path
from typing import Optional, List, Dict
@@ -16,18 +17,35 @@ from typing import Optional, List, Dict
from .config import (
DECK_REPORTS_DIR, COACH_SESSIONS_DIR,
MAX_UNDERPERFORMERS, MAX_CANDIDATES_PER_UNDERPERFORMER,
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 LMStudioClient
from .llm_client import LLMClient
from .embeddings import MTGEmbeddingIndex
from .prompt_template import build_system_prompt, build_user_prompt
logger = logging.getLogger("coach.service")
# SQLite persistence for coach sessions
try:
from services.database import (
save_coach_session as _db_save_session,
load_coach_session as _db_load_session,
list_coach_sessions as _db_list_sessions,
delete_coach_session as _db_delete_session,
)
_DB_AVAILABLE = True
except ImportError:
_DB_AVAILABLE = False
class CoachService:
"""
@@ -36,35 +54,30 @@ class CoachService:
Usage:
embeddings = MTGEmbeddingIndex()
embeddings.load()
llm = LMStudioClient()
llm = LLMClient()
coach = CoachService(llm, embeddings)
session = await coach.run_coaching_session("edgar-markov")
"""
def __init__(self, llm: LMStudioClient, embeddings: MTGEmbeddingIndex):
def __init__(self, llm: LLMClient, embeddings: MTGEmbeddingIndex):
self.llm = llm
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 f.stem.lower() == deck_id.lower():
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:
with open(report_path, "r", encoding="utf-8") as f:
data = json.load(f)
return DeckReport(**data)
except Exception as e:
@@ -77,23 +90,64 @@ 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."""
"""Persist a coaching session to disk and SQLite."""
COACH_SESSIONS_DIR.mkdir(parents=True, exist_ok=True)
path = COACH_SESSIONS_DIR / f"{session.sessionId}.json"
with open(path, "w") as f:
with open(path, "w", encoding="utf-8") as f:
f.write(session.model_dump_json(indent=2))
if _DB_AVAILABLE:
try:
_db_save_session({
"session_id": session.sessionId,
"deck_id": session.deckId,
"timestamp": session.timestamp,
"model_used": session.modelUsed or "",
"prompt_tokens": session.promptTokens or 0,
"completion_tokens": session.completionTokens or 0,
"summary": session.summary or "",
"goals_json": json.dumps(session.goals.model_dump() if session.goals else {}),
"cuts_json": json.dumps([c.model_dump() for c in session.suggestedCuts]),
"adds_json": json.dumps([a.model_dump() for a in session.suggestedAdds]),
"heuristic_hints_json": json.dumps(session.heuristicHints or []),
"mana_base_advice": session.manaBaseAdvice or "",
"raw_text": session.rawTextExplanation or "",
})
logger.info("Coach session persisted to SQLite: %s", session.sessionId)
except Exception as e:
logger.warning("Failed to persist session to SQLite: %s", e)
return path
def load_session(self, session_id: str) -> Optional[CoachSession]:
"""Load a coaching session from disk."""
"""Load a coaching session from SQLite, falling back to disk."""
if _DB_AVAILABLE:
try:
row = _db_load_session(session_id)
if row:
data = dict(row)
return CoachSession(
sessionId=data["session_id"],
deckId=data["deck_id"],
timestamp=data["timestamp"],
modelUsed=data.get("model_used", ""),
promptTokens=data.get("prompt_tokens", 0),
completionTokens=data.get("completion_tokens", 0),
summary=data.get("summary", ""),
goals=json.loads(data["goals_json"]) if data.get("goals_json") and data["goals_json"] != "{}" else None,
suggestedCuts=[SuggestedCut(**c) for c in json.loads(data.get("cuts_json", "[]"))],
suggestedAdds=[SuggestedAdd(**a) for a in json.loads(data.get("adds_json", "[]"))],
heuristicHints=json.loads(data.get("heuristic_hints_json", "[]")),
manaBaseAdvice=data.get("mana_base_advice", ""),
rawTextExplanation=data.get("raw_text", ""),
)
except Exception as e:
logger.warning("SQLite load failed for %s: %s", session_id, e)
path = COACH_SESSIONS_DIR / f"{session_id}.json"
if not path.exists():
return None
try:
with open(path, "r") as f:
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
return CoachSession(**data)
except Exception as e:
@@ -101,13 +155,20 @@ class CoachService:
return None
def list_sessions(self, deck_id: str = None) -> List[dict]:
"""List all coaching sessions, optionally filtered by deck."""
"""List coaching sessions from SQLite, falling back to disk."""
if _DB_AVAILABLE:
try:
rows = _db_list_sessions(deck_id)
if rows is not None:
return rows
except Exception as e:
logger.warning("SQLite list_sessions failed: %s", e)
if not COACH_SESSIONS_DIR.exists():
return []
sessions = []
for f in sorted(COACH_SESSIONS_DIR.glob("*.json"), reverse=True):
try:
with open(f, "r") as fh:
with open(f, "r", encoding="utf-8") as fh:
data = json.load(fh)
if deck_id and data.get("deckId", "") != deck_id:
continue
@@ -123,23 +184,23 @@ class CoachService:
continue
return sessions
# ── Main Coaching Pipeline ─────────────────────────────────
async def run_coaching_session(self, deck_id: str,
goals: Optional[CoachGoals] = None,
fallback_report: Optional[DeckReport] = None,
) -> CoachSession:
# ── Main Coaching Pipeline ───────────────────────
async def run_coaching_session(
self,
deck_id: str,
goals: Optional[CoachGoals] = None,
fallback_report: Optional[DeckReport] = None,
) -> CoachSession:
"""
Full coaching pipeline:
1. Load deck report (or use fallback from DB card list)
2. Identify underperformers
3. Find replacement candidates via embeddings
4. Build prompt
5. Call LLM
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:
@@ -151,7 +212,6 @@ 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
@@ -159,25 +219,17 @@ 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 [])
@@ -196,17 +248,81 @@ class CoachService:
else:
logger.warning("Embeddings not loaded — skipping candidate search")
# 4. Build prompts
# Phase 4a: behavior-aware RAG — primary + replacement-effect secondary query
rag_cards: List[dict] = []
try:
from services.rag_store import query_cards
# Primary: commander synergy context
base_query = f"{report.commander} {' '.join(report.colorIdentity)} commander deck"
rag_cards = query_cards(base_query, n_results=10, color_identity=report.colorIdentity)
# Secondary: replacement effects / graveyard hate / protection
if len(rag_cards) < 15:
repl_cards = query_cards(
f"replacement effect graveyard {' '.join(report.colorIdentity)}",
n_results=5,
color_identity=report.colorIdentity,
require_replacement=True,
)
# Deduplicate by name before merging
existing_names = {c["name"] for c in rag_cards}
rag_cards += [c for c in repl_cards if c["name"] not in existing_names]
logger.info("RAG returned %d cards for '%s'", len(rag_cards), deck_id)
except Exception as e:
logger.warning("RAG query failed (non-fatal): %s", e)
# Phase 4b: rules context for grounding the coach prompt
rules_context: List[dict] = []
try:
from services.rag_store import query_rules
rules_context = query_rules(
f"Commander rules {report.commander} {' '.join(report.colorIdentity)}",
n_results=3,
)
except Exception:
pass
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, rules_context=rules_context)
logger.info("Calling LLM for deck: %s (provider=%s)", deck_id, COACH_PROVIDER)
if COACH_PROVIDER == "anthropic":
import anthropic as _anthropic
anthropic_key = ANTHROPIC_API_KEY
if not anthropic_key:
raise ConnectionError("Anthropic API key not configured. Set ANTHROPIC_API_KEY env var.")
aclient = _anthropic.AsyncAnthropic(api_key=anthropic_key)
async with aclient.messages.stream(
model=ANTHROPIC_MODEL,
system=system_prompt,
messages=[{"role": "user", "content": user_prompt}],
max_tokens=DEFAULT_MAX_TOKENS,
temperature=0.7,
) as stream:
content = await stream.get_final_text()
final_msg = await stream.get_final_message()
usage = getattr(final_msg, "usage", None)
from types import SimpleNamespace
llm_response = SimpleNamespace(
content=content,
model=final_msg.model,
prompt_tokens=usage.input_tokens if usage else 0,
completion_tokens=usage.output_tokens if usage else 0,
parsed_json=None,
)
try:
cleaned = re.sub(r'^```(?:json)?\s*', '', content.strip())
cleaned = re.sub(r'\s*```$', '', cleaned.strip())
llm_response.parsed_json = json.loads(cleaned)
except (json.JSONDecodeError, ValueError):
match = re.search(r'\{[\s\S]*\}', content)
if match:
try:
llm_response.parsed_json = json.loads(match.group(0))
except json.JSONDecodeError:
pass
else:
llm_response = await self.llm.chat(system_prompt, user_prompt)
# 5. Call LLM
logger.info("Calling LLM for deck: %s", deck_id)
llm_response = await self.llm.chat(system_prompt, user_prompt)
# 6. Parse response into CoachSession
session_id = f"sess-{datetime.now(timezone.utc).strftime('%Y%m%d-%H%M%S')}-{uuid.uuid4().hex[:6]}"
session = CoachSession(
sessionId=session_id,
deckId=deck_id,
@@ -216,31 +332,109 @@ class CoachService:
completionTokens=llm_response.completion_tokens,
goals=goals,
)
if llm_response.parsed_json:
self._populate_session_from_json(session, llm_response.parsed_json)
else:
# Fallback: store raw text
session.rawTextExplanation = llm_response.content
session.summary = "LLM returned non-JSON response. See raw explanation."
# 7. Persist
self.save_session(session)
logger.info("Coaching session saved: %s", session_id)
return session
# ── Quick Digest ──────────────────────────────
async def run_quick_digest(
self,
deck_id: str,
goals: Optional[CoachGoals] = None,
fallback_report: Optional[DeckReport] = None,
) -> CoachSession:
"""Fast ~400-600 token call: summary + top 3 upgradePriority + commanderDependency."""
report = self.load_deck_report(deck_id)
if report is None and fallback_report is not None:
report = fallback_report
if report is None:
raise ValueError(f"Deck report not found for: {deck_id}")
# Phase 4a: light RAG query for quick digest
rag_cards: List[dict] = []
try:
from services.rag_store import query_cards
query_text = f"{report.commander} {' '.join(report.colorIdentity)}"
rag_cards = query_cards(query_text, n_results=8, color_identity=report.colorIdentity)
except Exception:
pass
from .prompt_template import build_quick_system_prompt, build_user_prompt
system_prompt = build_quick_system_prompt(report, goals)
user_prompt = build_user_prompt(report, {}, rag_cards=rag_cards) # no candidates for speed
logger.info("Quick digest for deck: %s (provider=%s)", deck_id, COACH_PROVIDER)
if COACH_PROVIDER == "anthropic":
import anthropic as _anthropic
anthropic_key = ANTHROPIC_API_KEY
if not anthropic_key:
raise ConnectionError("Anthropic API key not configured.")
aclient = _anthropic.AsyncAnthropic(api_key=anthropic_key)
async with aclient.messages.stream(
model=ANTHROPIC_MODEL,
system=system_prompt,
messages=[{"role": "user", "content": user_prompt}],
max_tokens=1024,
temperature=0.5,
) as stream:
content = await stream.get_final_text()
final_msg = await stream.get_final_message()
usage = getattr(final_msg, "usage", None)
from types import SimpleNamespace
llm_response = SimpleNamespace(
content=content,
model=final_msg.model,
prompt_tokens=usage.input_tokens if usage else 0,
completion_tokens=usage.output_tokens if usage else 0,
parsed_json=None,
)
try:
cleaned = re.sub(r'^```(?:json)?\s*', '', content.strip())
cleaned = re.sub(r'\s*```$', '', cleaned.strip())
llm_response.parsed_json = json.loads(cleaned)
except (json.JSONDecodeError, ValueError):
match = re.search(r'\{[\s\S]*\}', content)
if match:
try:
llm_response.parsed_json = json.loads(match.group(0))
except json.JSONDecodeError:
pass
else:
llm_response = await self.llm.chat(system_prompt, user_prompt)
session_id = f"quick-{datetime.now(timezone.utc).strftime('%Y%m%d-%H%M%S')}-{uuid.uuid4().hex[:6]}"
session = CoachSession(
sessionId=session_id,
deckId=deck_id,
timestamp=datetime.now(timezone.utc).isoformat(),
modelUsed=llm_response.model,
promptTokens=llm_response.prompt_tokens,
completionTokens=llm_response.completion_tokens,
goals=goals,
isQuickDigest=True,
)
if llm_response.parsed_json:
self._populate_session_from_json(session, llm_response.parsed_json)
else:
session.rawTextExplanation = llm_response.content
session.summary = "Quick digest returned non-JSON. See raw explanation."
self.save_session(session)
return session
# ── JSON Parser ───────────────────────────────
def _populate_session_from_json(self, session: CoachSession, data: dict):
"""Parse LLM JSON response into CoachSession fields."""
session.summary = data.get("summary", "")
session.rawTextExplanation = data.get("rawTextExplanation", "")
session.manaBaseAdvice = data.get("manaBaseAdvice")
# Heuristic hints
hints = data.get("heuristicHints", [])
session.heuristicHints = hints if isinstance(hints, list) else [str(hints)]
# Suggested cuts
for cut_data in data.get("suggestedCuts", []):
if isinstance(cut_data, dict):
session.suggestedCuts.append(SuggestedCut(
@@ -248,8 +442,6 @@ class CoachService:
reason=cut_data.get("reason", ""),
replacementOptions=cut_data.get("replacementOptions", []),
))
# Suggested adds
for add_data in data.get("suggestedAdds", []):
if isinstance(add_data, dict):
session.suggestedAdds.append(SuggestedAdd(
@@ -258,26 +450,112 @@ class CoachService:
reason=add_data.get("reason", ""),
synergyWith=add_data.get("synergyWith", []),
))
for item in data.get("upgradePriority", []):
if isinstance(item, dict):
session.upgradePriority.append(UpgradePriorityItem(
rank=item.get("rank", 0),
cut=item.get("cut", ""),
add=item.get("add", ""),
reasoning=item.get("reasoning", ""),
expectedImpact=item.get("expectedImpact", ""),
))
cd = data.get("commanderDependency")
if isinstance(cd, dict):
session.commanderDependency = CommanderDependency(
score=cd.get("score", 5),
dependentCards=cd.get("dependentCards", []),
recoveryPlan=cd.get("recoveryPlan", ""),
)
ma = data.get("mulliganAnalysis")
if isinstance(ma, dict):
session.mulliganAnalysis = MulliganAnalysis(
estimatedKeepRate=ma.get("estimatedKeepRate", ""),
worstOffenders=ma.get("worstOffenders", []),
recommendation=ma.get("recommendation", ""),
)
rc = data.get("removalCoverage")
if isinstance(rc, dict):
session.removalCoverage = RemovalCoverage(**{
k: rc.get(k, "" if k != "gaps" else [])
for k in ["creatures", "artifacts", "enchantments", "planeswalkers", "lands", "massRemoval", "gaps"]
})
rq = data.get("rampQuality")
if isinstance(rq, dict):
session.rampQuality = RampQuality(**{
k: rq.get(k, "") for k in ["manaRocks", "landFetch", "manaDorks", "costReducers", "fragility", "canReachFourByTurnThree"]
})
dep = data.get("drawEngineProfile")
if isinstance(dep, dict):
session.drawEngineProfile = DrawEngineProfile(
burstDraw=dep.get("burstDraw", []),
repeatableEngines=dep.get("repeatableEngines", []),
assessment=dep.get("assessment", ""),
sustainability=dep.get("sustainability", ""),
)
for wc in data.get("winConditions", []):
if isinstance(wc, dict):
session.winConditions.append(WinCondition(
name=wc.get("name", ""),
cards=wc.get("cards", []),
independence=wc.get("independence", ""),
description=wc.get("description", ""),
))
for af in data.get("antiSynergyFlags", []):
if isinstance(af, dict):
session.antiSynergyFlags.append(AntiSynergyFlag(
cards=af.get("cards", []),
conflict=af.get("conflict", ""),
severity=af.get("severity", ""),
))
pp = data.get("podPresence")
if isinstance(pp, dict):
session.podPresence = PodPresence(
threatLevel=pp.get("threatLevel", ""),
politicalTools=pp.get("politicalTools", []),
adversarialRating=pp.get("adversarialRating", ""),
recommendation=pp.get("recommendation", ""),
)
ta = data.get("tempoAssessment")
if isinstance(ta, dict):
session.tempoAssessment = TempoAssessment(
peakTurn=ta.get("peakTurn", 0),
profile=ta.get("profile", ""),
survivalWindow=ta.get("survivalWindow", ""),
assessment=ta.get("assessment", ""),
)
for mm in data.get("metaMatchups", []):
if isinstance(mm, dict):
session.metaMatchups.append(MetaMatchup(
archetype=mm.get("archetype", ""),
assessment=mm.get("assessment", ""),
keyThreats=mm.get("keyThreats", []),
keyAnswers=mm.get("keyAnswers", []),
overallRating=mm.get("overallRating", ""),
))
# ── Status Check ───────────────────────────────────────────
# ── Status Check ──────────────────────────────
def get_status(self) -> CoachStatus:
"""Check the health of all coach subsystems."""
status = CoachStatus()
# Check LLM
llm_status = self.llm.check_connection()
status.llmConnected = llm_status.get("connected", False)
status.llmModel = llm_status.get("active_model")
status.llmModels = llm_status.get("models", [])
if not status.llmConnected:
status.error = llm_status.get("error", "LM Studio not reachable")
# Check embeddings
if COACH_PROVIDER == "anthropic":
anthropic_key = ANTHROPIC_API_KEY
if anthropic_key:
status.llmConnected = True
status.llmModel = f"Claude Opus ({ANTHROPIC_MODEL})"
status.llmModels = [ANTHROPIC_MODEL]
else:
status.llmConnected = False
status.llmModel = None
status.llmModels = []
status.error = "Anthropic API key not configured. Set ANTHROPIC_API_KEY env var."
else:
llm_status = self.llm.check_connection_sync() # FARMHOUSE_COACH_STATUS_V1 (was an un-awaited coroutine)
status.llmConnected = llm_status.get("connected", False)
status.llmModel = llm_status.get("active_model")
status.llmModels = llm_status.get("models", [])
if not status.llmConnected:
status.error = llm_status.get("error", "Local LLM not reachable")
status.embeddingsLoaded = self.embeddings.loaded
status.embeddingCards = self.embeddings.card_count
# Check deck reports
status.deckReportsAvailable = len(self.list_deck_reports())
return status
+35 -31
View File
@@ -1,63 +1,67 @@
"""
Commander AI Lab — Coach Configuration
═══════════════════════════════════════
Central config for LM Studio, embeddings, and coach service paths.
────────────────────────────────────────
Central config for Ollama, embeddings, and coach service paths.
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))
# ── LM Studio (OpenAI-compatible API) ──────────────────────
LM_STUDIO_URL = os.environ.get("LM_STUDIO_URL", "http://192.168.0.122:1234/v1")
LM_STUDIO_MODEL = os.environ.get("LM_STUDIO_MODEL", "local-model")
LM_STUDIO_TIMEOUT = int(os.environ.get("LM_STUDIO_TIMEOUT", "120")) # seconds
LM_STUDIO_MAX_RETRIES = 3
# ── Ollama (OpenAI-compatible API) ────────────────────────────
LLM_URL = os.environ.get("LLM_URL", "http://localhost:11434/v1")
LLM_MODEL = os.environ.get("LLM_MODEL", "claude-opus-4-20250514")
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 ──────────────────────────────────────────
MAX_PROMPT_TOKENS = 4096
MAX_CANDIDATES_PER_UNDERPERFORMER = 10
MAX_UNDERPERFORMERS = 8
# ── Prompt Limits ───────────────────────────────────────────────
MAX_PROMPT_TOKENS = 2048
MAX_CANDIDATES_PER_UNDERPERFORMER = 8
MAX_UNDERPERFORMERS = 8
UNDERPERFORMER_IMPACT_THRESHOLD = -0.05 # impactScore below this = underperformer
# ── LLM Generation Settings (Coach — LM Studio) ──────────────
# ── 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", "perplexity")
# Model for deck generation (sonar = fast/$0.004, sonar-pro = deep/$0.04)
DECK_GEN_MODEL = os.environ.get("DECK_GEN_MODEL", "sonar")
# ── 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", "sonar")
# 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)
+229 -41
View File
@@ -9,6 +9,11 @@ Data source: https://huggingface.co/datasets/minimaxir/mtg-embeddings
Format: Parquet with 768-dim float32 embeddings per card (~32K cards)
Model: Alibaba-NLP/gte-modernbert-base
Unknown cards (e.g. newly printed sets) are fetched from Scryfall,
embedded on-the-fly using the same model, and persisted to a local
custom cache (embeddings/mtg_embeddings_custom.npz) so they survive
restarts without re-hitting Scryfall.
Uses NumPy brute-force cosine similarity — fast enough for ~32K cards (<100ms).
"""
@@ -19,9 +24,11 @@ import os
from pathlib import Path
from typing import List, Optional, Tuple
from urllib.request import urlopen, Request
from urllib.parse import quote
import numpy as np
from services.card_text import build_card_text
from .config import EMBEDDINGS_DIR, EMBEDDINGS_NPZ, EMBEDDINGS_PARQUET
logger = logging.getLogger("coach.embeddings")
@@ -32,8 +39,16 @@ HF_PARQUET_URL = (
"resolve/main/mtg_embeddings.parquet"
)
# ── Scryfall API ───────────────────────────────────────────
SCRYFALL_NAMED_URL = "https://api.scryfall.com/cards/named?fuzzy={}"
# ── Custom cache for on-the-fly embedded cards ─────────────
CUSTOM_CACHE_PATH = EMBEDDINGS_DIR / "mtg_embeddings_custom.npz"
# ── Embedding model (same as minimaxir/mtg-embeddings) ─────
EMBEDDING_MODEL = "Alibaba-NLP/gte-modernbert-base"
# ── Color identity parsing ─────────────────────────────────
# Maps mana symbols like {W}, {U}, {B}, {R}, {G} to color letters
MANA_SYMBOL_COLORS = {
"W": "W", "U": "U", "B": "B", "R": "R", "G": "G"
}
@@ -46,7 +61,6 @@ def _parse_color_identity(mana_cost: str) -> str:
colors = set()
for match in re.finditer(r'\{([WUBRG])\}', mana_cost or ""):
colors.add(match.group(1))
# Sort in WUBRG order
order = "WUBRG"
return "".join(c for c in order if c in colors)
@@ -61,12 +75,11 @@ def _parse_mana_value(mana_cost: str) -> float:
if symbol in MANA_SYMBOL_COLORS:
total += 1.0
elif symbol == "X":
pass # X = 0 for CMC purposes
pass
else:
try:
total += float(symbol)
except ValueError:
# Hybrid/phyrexian symbols count as 1
total += 1.0
return total
@@ -75,7 +88,35 @@ def _is_color_subset(card_colors: str, deck_colors: List[str]) -> bool:
"""Check if card's color identity is within deck's color identity."""
deck_set = set(deck_colors)
card_set = set(card_colors)
return card_set.issubset(deck_set) or len(card_set) == 0 # colorless always allowed
return card_set.issubset(deck_set) or len(card_set) == 0
def _fetch_scryfall_card(card_name: str) -> Optional[dict]:
"""Fetch card data from Scryfall by fuzzy name match.
Returns a dict with keys: name, mana_cost, type_line, oracle_text,
color_identity. Returns None on any error.
"""
url = SCRYFALL_NAMED_URL.format(quote(card_name))
try:
req = Request(url, headers={"User-Agent": "commander-ai-lab/1.0"})
with urlopen(req, timeout=10) as resp:
data = json.loads(resp.read().decode("utf-8"))
return {
"name": data.get("name", card_name),
"mana_cost": data.get("mana_cost", ""),
"type_line": data.get("type_line", ""),
"oracle_text": data.get("oracle_text", ""),
"color_identity": "".join(data.get("color_identity", [])),
}
except Exception as e:
logger.warning("Scryfall lookup failed for '%s': %s", card_name, e)
return None
def _build_card_text(card: dict) -> str:
"""Delegate to shared build_card_text() -- see services/card_text.py."""
return build_card_text(card)
class CardMatch:
@@ -114,15 +155,17 @@ class MTGEmbeddingIndex:
"""
def __init__(self):
self.names: np.ndarray = None # (N,) string array
self.colors: np.ndarray = None # (N,) string array (e.g., "WU", "BRG")
self.mana_values: np.ndarray = None # (N,) float array
self.types: np.ndarray = None # (N,) string array
self.mana_costs: np.ndarray = None # (N,) string array
self.texts: np.ndarray = None # (N,) string array
self.vectors: np.ndarray = None # (N, 768) float32, L2-normalized
self.names: np.ndarray = None
self.colors: np.ndarray = None
self.mana_values: np.ndarray = None
self.types: np.ndarray = None
self.mana_costs: np.ndarray = None
self.texts: np.ndarray = None
self.vectors: np.ndarray = None
self._name_to_idx: dict = {}
self._loaded = False
self._sentence_model = None # lazy-loaded only when needed
self._custom_dirty = False # tracks whether custom cache needs saving
@property
def loaded(self) -> bool:
@@ -152,10 +195,7 @@ class MTGEmbeddingIndex:
return EMBEDDINGS_PARQUET
def convert_parquet_to_npz(self) -> Path:
"""
Convert the Parquet file to a compact NPZ for fast loading.
Requires pandas and pyarrow.
"""
"""Convert the Parquet file to a compact NPZ for fast loading."""
try:
import pandas as pd
except ImportError:
@@ -169,14 +209,11 @@ class MTGEmbeddingIndex:
types = df["type"].fillna("").values.astype(str)
texts = df["text"].fillna("").values.astype(str)
# Parse color identities from mana costs
colors = np.array([_parse_color_identity(mc) for mc in mana_costs])
mana_values = np.array([_parse_mana_value(mc) for mc in mana_costs], dtype=np.float32)
# Extract embedding vectors
embeddings = np.vstack(df["embedding"].to_numpy()).astype(np.float32)
# L2 normalize for cosine similarity via dot product
norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
norms[norms == 0] = 1.0
embeddings = embeddings / norms
@@ -198,25 +235,27 @@ class MTGEmbeddingIndex:
return EMBEDDINGS_NPZ
def load(self, force_download: bool = False) -> bool:
"""
Load embeddings from NPZ (fast) or fall back to downloading
"""Load embeddings from NPZ (fast) or fall back to downloading
and converting from Parquet.
"""
if self._loaded and not force_download:
return True
# Try NPZ first (fast path)
if EMBEDDINGS_NPZ.exists() and not force_download:
return self._load_npz()
ok = self._load_npz()
else:
try:
self.download_parquet(force=force_download)
self.convert_parquet_to_npz()
ok = self._load_npz()
except Exception as e:
logger.error("Failed to load embeddings: %s", e)
return False
# Download and convert
try:
self.download_parquet(force=force_download)
self.convert_parquet_to_npz()
return self._load_npz()
except Exception as e:
logger.error("Failed to load embeddings: %s", e)
return False
if ok:
self._load_custom_cache()
return ok
def _load_npz(self) -> bool:
"""Load from pre-built NPZ file."""
@@ -230,7 +269,6 @@ class MTGEmbeddingIndex:
self.texts = data.get("texts", np.array([""] * len(self.names)))
self.vectors = data["vectors"]
# Build name lookup
self._name_to_idx = {
name.lower(): i for i, name in enumerate(self.names)
}
@@ -241,12 +279,168 @@ class MTGEmbeddingIndex:
logger.error("Failed to load NPZ: %s", e)
return False
# ── Custom cache ──────────────────────────────────────────
def _load_custom_cache(self) -> None:
"""Merge on-the-fly embedded cards from the custom cache into the index."""
if not CUSTOM_CACHE_PATH.exists():
return
try:
data = np.load(str(CUSTOM_CACHE_PATH), allow_pickle=True)
c_names = data["names"]
# Only add cards that aren't already in the main index
new_mask = np.array(
[n.lower() not in self._name_to_idx for n in c_names]
)
if not new_mask.any():
return
self._append_cards(
c_names[new_mask],
data["colors"][new_mask],
data["mana_values"][new_mask],
data["types"][new_mask],
data["mana_costs"][new_mask],
data["texts"][new_mask],
data["vectors"][new_mask],
)
logger.info("Loaded %d custom-cached card embeddings", new_mask.sum())
except Exception as e:
logger.warning("Failed to load custom embedding cache: %s", e)
def _save_custom_cache(self) -> None:
"""Persist the custom cache NPZ (only cards not in the base dataset)."""
if not self._custom_dirty:
return
try:
EMBEDDINGS_DIR.mkdir(parents=True, exist_ok=True)
# Collect indices that correspond to custom-added cards
# We track these by loading the base count at load time — simpler:
# just re-save the full custom cache from CUSTOM_CACHE_PATH if it
# exists, merging with any new cards we've added this session.
existing_names: set = set()
existing: dict = {}
if CUSTOM_CACHE_PATH.exists():
try:
d = np.load(str(CUSTOM_CACHE_PATH), allow_pickle=True)
for n in d["names"]:
existing_names.add(n.lower())
except Exception:
pass
# Find names in our live index that aren't in the base NPZ
base_data = np.load(str(EMBEDDINGS_NPZ), allow_pickle=True)
base_names = set(n.lower() for n in base_data["names"])
custom_mask = np.array(
[n.lower() not in base_names for n in self.names]
)
if not custom_mask.any():
return
np.savez_compressed(
str(CUSTOM_CACHE_PATH),
names=self.names[custom_mask],
colors=self.colors[custom_mask],
mana_values=self.mana_values[custom_mask],
types=self.types[custom_mask],
mana_costs=self.mana_costs[custom_mask],
texts=self.texts[custom_mask],
vectors=self.vectors[custom_mask],
)
self._custom_dirty = False
logger.info("Saved custom embedding cache: %d cards", custom_mask.sum())
except Exception as e:
logger.warning("Failed to save custom embedding cache: %s", e)
def _append_cards(
self,
names: np.ndarray,
colors: np.ndarray,
mana_values: np.ndarray,
types: np.ndarray,
mana_costs: np.ndarray,
texts: np.ndarray,
vectors: np.ndarray,
) -> None:
"""Append new card arrays to the live in-memory index."""
start_idx = len(self.names)
self.names = np.concatenate([self.names, names])
self.colors = np.concatenate([self.colors, colors])
self.mana_values = np.concatenate([self.mana_values, mana_values])
self.types = np.concatenate([self.types, types])
self.mana_costs = np.concatenate([self.mana_costs, mana_costs])
self.texts = np.concatenate([self.texts, texts])
self.vectors = np.concatenate([self.vectors, vectors], axis=0)
for i, name in enumerate(names):
self._name_to_idx[name.lower()] = start_idx + i
# ── Sentence model (lazy) ─────────────────────────────────
def _get_sentence_model(self):
"""Lazy-load the sentence-transformers model on first cache miss."""
if self._sentence_model is None:
try:
from sentence_transformers import SentenceTransformer
logger.info("Loading embedding model %s (first cache miss)...", EMBEDDING_MODEL)
self._sentence_model = SentenceTransformer(EMBEDDING_MODEL)
logger.info("Embedding model loaded.")
except Exception as e:
logger.error("Failed to load SentenceTransformer: %s", e)
return None
return self._sentence_model
# ── On-the-fly Scryfall fallback ──────────────────────────
def _fetch_and_embed_card(self, card_name: str) -> Optional[np.ndarray]:
"""Fetch card from Scryfall, embed it, append to index, persist to cache.
Returns the L2-normalized embedding vector on success, None on failure.
"""
logger.info("Fetching unknown card from Scryfall: '%s'", card_name)
card_data = _fetch_scryfall_card(card_name)
if card_data is None:
return None
model = self._get_sentence_model()
if model is None:
return None
text = _build_card_text(card_data)
vec = model.encode(text, normalize_embeddings=True).astype(np.float32)
# Ensure shape (1, 768)
vec = vec.reshape(1, -1)
resolved_name = card_data["name"]
mana_cost = card_data.get("mana_cost", "")
color_identity = card_data.get("color_identity", "") or _parse_color_identity(mana_cost)
self._append_cards(
names=np.array([resolved_name]),
colors=np.array([color_identity]),
mana_values=np.array([_parse_mana_value(mana_cost)], dtype=np.float32),
types=np.array([card_data.get("type_line", "")]),
mana_costs=np.array([mana_cost]),
texts=np.array([card_data.get("oracle_text", "")]),
vectors=vec,
)
self._custom_dirty = True
self._save_custom_cache()
logger.info("Embedded and cached new card: '%s'", resolved_name)
return self.vectors[self._name_to_idx[resolved_name.lower()]]
# ── Public API ────────────────────────────────────────────
def get_card_vector(self, card_name: str) -> Optional[np.ndarray]:
"""Get the embedding vector for a card by name (case-insensitive)."""
"""Get the embedding vector for a card by name (case-insensitive).
On a cache miss, fetches from Scryfall and embeds on the fly.
"""
idx = self._name_to_idx.get(card_name.lower())
if idx is not None:
return self.vectors[idx]
return None
# Cache miss — try Scryfall fallback
return self._fetch_and_embed_card(card_name)
def search_similar(self, query_card: str,
color_filter: List[str] = None,
@@ -274,38 +468,32 @@ class MTGEmbeddingIndex:
query_vec = self.get_card_vector(query_card)
if query_vec is None:
logger.warning("Card not found in embeddings: %s", query_card)
logger.warning("Card not found in embeddings and Scryfall fallback failed: %s", query_card)
return []
# Compute similarities (dot product on normalized vectors = cosine similarity)
similarities = self.vectors @ query_vec
# Build exclusion set
exclude_set = set()
exclude_set.add(query_card.lower())
if exclude_cards:
exclude_set.update(name.lower() for name in exclude_cards)
# Filter and collect results
results = []
for idx in np.argsort(similarities)[::-1]:
name = str(self.names[idx])
if name.lower() in exclude_set:
continue
# Color filter
if color_filter:
card_colors = str(self.colors[idx])
if not _is_color_subset(card_colors, color_filter):
continue
# Type filter
if type_filter:
card_type = str(self.types[idx])
if type_filter.lower() not in card_type.lower():
continue
# Mana value range filter
if mana_range:
mv = float(self.mana_values[idx])
if mv < mana_range[0] or mv > mana_range[1]:
+157 -141
View File
@@ -1,22 +1,25 @@
"""
Commander AI Lab — LM Studio Client
Commander AI Lab — Ollama LLM Client
════════════════════════════════════
Async HTTP client targeting LM Studio'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 (
LM_STUDIO_URL, LM_STUDIO_MODEL,
LM_STUDIO_TIMEOUT, LM_STUDIO_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
@@ -34,43 +43,56 @@ class LLMResponse:
self.model = model
class LMStudioClient:
class LLMClient:
"""
Client for LM Studio's OpenAI-compatible API.
Fully async client for Ollama's OpenAI-compatible API.
Usage:
client = LMStudioClient()
client = LLMClient()
response = await client.chat(system_prompt, user_prompt)
if response.parsed_json:
suggestions = response.parsed_json
"""
def __init__(self, base_url: str = None, model: str = None,
timeout: int = None):
self.base_url = (base_url or LM_STUDIO_URL).rstrip("/")
self.model = model or LM_STUDIO_MODEL
self.timeout = timeout or LM_STUDIO_TIMEOUT
self._resolved_model = None # actual model name from /models endpoint
def __init__(
self,
base_url: str = None,
model: str = None,
timeout: int = None,
):
self.base_url = (base_url or LLM_URL).rstrip("/")
self.model = model or LLM_MODEL
self.timeout = timeout or LLM_TIMEOUT
self._resolved_model: Optional[str] = None
def _resolve_model(self) -> str:
"""Get the actual loaded model name from LM Studio /models endpoint."""
# ── 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 LM Studio model: %s", self._resolved_model)
self._resolved_model = self.model if self.model in (status.get("models") or []) else status["active_model"] # FARMHOUSE_MODEL_PIN_V1
logger.info("Resolved Ollama model: %s", self._resolved_model)
return self._resolved_model
except Exception:
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 +101,35 @@ class LMStudioClient:
"temperature": temperature,
"max_tokens": max_tokens,
}
# Only request JSON format if using a model that supports it
# Some models (esp. smaller ones) don't support response_format
if force_json:
body["response_format"] = {"type": "json_object"}
# FARMHOUSE_LLM_KNOBS_V1 (see patch_app.py)
import os as _fh_os
_fh_effort = _fh_os.environ.get("LLM_REASONING", "").strip()
if _fh_effort:
body["reasoning_effort"] = _fh_effort
_fh_cap = int(_fh_os.environ.get("LLM_MAX_TOKENS", "0") or 0)
if _fh_cap:
body["max_tokens"] = min(body["max_tokens"], _fh_cap)
return body
# ── Response parsing ──────────────────────────────────────
@staticmethod
def _strip_think_tags(text: str) -> str:
"""Remove DeepSeek-R1 <think>...</think> reasoning blocks 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 +149,56 @@ class LMStudioClient:
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
return None
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,
@@ -192,94 +207,95 @@ class LMStudioClient:
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 LM Studio.
Retries up to LM_STUDIO_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
for attempt in range(1, LM_STUDIO_MAX_RETRIES + 1):
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, LM_STUDIO_MAX_RETRIES)
req = Request(
f"{self.base_url}/chat/completions",
data=json.dumps(body).encode("utf-8"),
headers={"Content-Type": "application/json"},
method="POST",
)
with urlopen(req, timeout=self.timeout) as resp:
raw = json.loads(resp.read().decode("utf-8"))
logger.info("LLM call attempt %d/%d", attempt, LLM_MAX_RETRIES)
async with httpx.AsyncClient(
timeout=httpx.Timeout(float(self.timeout))
) as client:
resp = await client.post(
f"{self.base_url}/chat/completions",
json=body,
headers={"Content-Type": "application/json"},
)
resp.raise_for_status()
raw = resp.json()
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 < LM_STUDIO_MAX_RETRIES:
import time
time.sleep(2 * attempt) # Simple backoff
if attempt < LLM_MAX_RETRIES:
import asyncio
await asyncio.sleep(2 * attempt)
raise ConnectionError(
f"LLM call failed after {LM_STUDIO_MAX_RETRIES} attempts: {last_error}"
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 LM Studio is reachable. Returns status dict.
"""
def check_connection_sync(self) -> dict: # FARMHOUSE_LLM_SYNC_V1
"""Blocking version of check_connection (upstream called it and never defined it)."""
try:
req = Request(
f"{self.base_url}/models",
headers={"Content-Type": "application/json"},
method="GET",
)
with urlopen(req, timeout=5) as resp:
data = json.loads(resp.read().decode("utf-8"))
models = data.get("data", [])
model_ids = [m.get("id", "unknown") for m in models]
return {
"connected": True,
"models": model_ids,
"active_model": model_ids[0] if model_ids else self.model,
}
_r = httpx.get(f"{self.base_url}/models", timeout=5.0, headers={"Content-Type": "application/json"})
_r.raise_for_status()
_ids = [m.get("id", "unknown") for m in _r.json().get("data", [])]
return {"connected": True, "models": _ids,
"active_model": self.model if self.model in _ids else (_ids[0] if _ids else self.model)}
except Exception as _e:
return {"connected": False, "error": str(_e), "models": [], "active_model": None}
async def check_connection(self) -> dict:
"""Check if Ollama is reachable. Returns status dict."""
try:
async with httpx.AsyncClient(timeout=httpx.Timeout(5.0)) as client:
resp = await client.get(
f"{self.base_url}/models",
headers={"Content-Type": "application/json"},
)
resp.raise_for_status()
data = resp.json()
models = data.get("data", [])
model_ids = [m.get("id", "unknown") for m in models]
return {
"connected": True,
"models": model_ids,
"active_model": model_ids[0] if model_ids else self.model,
}
except Exception as e:
return {
"connected": False,
+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):
+392 -43
View File
@@ -4,18 +4,24 @@ Commander AI Lab — Coach Prompt Template
Constructs system and user prompts for the LLM coach.
Injects deck report data, underperformer analysis, and
candidate replacement cards into a structured template.
Phase 1 additions (#108):
- Sim Insights section: deck-level opening hand quality, curve velocity,
synergy outliers, and archetype weak spots surfaced directly in the
user prompt so Claude reasons from numbers, not vibes.
- _format_archetypes() annotates weak spots and strengths inline.
- _format_top_cards() adds keptInOpeningHandRate per card.
"""
import json
from typing import List, Dict, Optional
from .models import DeckReport, CoachGoals, CardPerformance
from .config import MAX_CANDIDATES_PER_UNDERPERFORMER, MAX_UNDERPERFORMERS
# ══════════════════════════════════════════════════════════════
# ════════════════════════════════════════════════════════════
# System Prompt
# ══════════════════════════════════════════════════════════════
# ════════════════════════════════════════════════════════════
SYSTEM_PROMPT_TEMPLATE = """You are an expert Magic: The Gathering Commander deck coach.
You analyze deck performance reports from simulation data and provide specific,
@@ -27,44 +33,197 @@ 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.
- The Sim Insights section shows pre-computed deck-level signals — use these directly in your reasoning.
- Cards flagged as [OPENING HAND TRAP] have keptInOpeningHandRate below the deck average — treat as mulligan liabilities.
- Cards flagged as [LATE CASTER] have avgTurnCast > 6 — consider whether they fit the deck's tempo.
- Cards flagged as [ISOLATED] have synergyScore in the bottom 3 of the deck — they may not support the gameplan.
- Archetypes flagged as [WEAK SPOT] in the Archetype Win Rates section are where this deck loses most — prioritize fixes for those matchups.
- Provide at least 5 heuristic hints covering different strategic dimensions.
- Assess how dependent the deck is on the commander being in play.
- Evaluate opening hand quality and identify cards that cause bad mulligans.
- Rank your suggested swaps by priority so the user knows what to change first.
- Categorize the removal package by permanent type and flag coverage gaps.
- Evaluate the ramp package for fragility (creature/artifact-heavy vs land-based).
- Distinguish one-time draw from repeatable draw engines.
- Identify the top 3 win conditions and rate each for independence.
- Flag internal anti-synergies or card conflicts.
- Assess pod threat perception: does this deck draw too much hate too early?
- Evaluate when the deck comes online (tempo profile) and whether that's viable.
- Reason about matchups vs fast combo, stax/prison, and control archetypes.
{goals_section}
You MUST respond ONLY with valid JSON matching this exact structure:
{{
"summary": "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 the [WEAK SPOT] archetypes flagged in Sim Insights, and (5) prioritized list of improvements ranked by expected impact on win rate",
"removalCoverage": {{
"creatures": "Assessment of creature removal",
"artifacts": "Assessment of artifact removal",
"enchantments": "Assessment of enchantment removal",
"planeswalkers": "Assessment of planeswalker removal",
"lands": "Assessment of land destruction",
"massRemoval": "Assessment of board wipes",
"gaps": ["Permanent types with zero or weak coverage"]
}},
"rampQuality": {{
"manaRocks": "2-mana rocks, signets, etc.",
"landFetch": "Cultivate, Kodama's Reach, etc.",
"manaDorks": "Llanowar Elves, Birds, etc.",
"costReducers": "Medallions, Helm of Awakening, etc.",
"fragility": "Overall fragility assessment",
"canReachFourByTurnThree": "yes/no with reasoning"
}},
"drawEngineProfile": {{
"burstDraw": ["One-time draw spells"],
"repeatableEngines": ["Sustained draw engines"],
"assessment": "Overall draw quality",
"sustainability": "Whether draw is sustainable or burst-only"
}},
"winConditions": [
{{
"name": "Name of win condition",
"cards": ["Key cards involved"],
"independence": "high|medium|low",
"description": "How this win con functions"
}}
],
"antiSynergyFlags": [
{{
"cards": ["Card A", "Card B"],
"conflict": "Description of conflict",
"severity": "high|medium|low"
}}
],
"podPresence": {{
"threatLevel": "early|medium|late|low",
"politicalTools": ["Pillowfort or group effect cards"],
"adversarialRating": "low|medium|high",
"recommendation": "How to manage threat perception in a pod"
}},
"tempoAssessment": {{
"peakTurn": 5,
"profile": "early|mid|late",
"survivalWindow": "Assessment of whether the deck survives to its power window",
"assessment": "Overall tempo evaluation"
}},
"metaMatchups": [
{{
"archetype": "fast-combo|stax|control|aggro|midrange",
"assessment": "How the deck fares against this archetype",
"keyThreats": ["Cards/strategies that threaten this deck"],
"keyAnswers": ["Cards in the deck that answer this archetype"],
"overallRating": "favored|even|unfavored"
}}
]
}}
Do NOT include any text outside the JSON object. No markdown fences, no preamble."""
# ════════════════════════════════════════════════════════════
# Quick Digest Prompt
# ════════════════════════════════════════════════════════════
# ══════════════════════════════════════════════════════════════
# User Prompt Builder
# ══════════════════════════════════════════════════════════════
QUICK_DIGEST_SYSTEM_PROMPT = """You are an expert Magic: The Gathering Commander deck coach.
Provide a FAST summary analysis of this deck. Be concise.
RULES:
- NEVER suggest cutting the commander ({commander}).
- Only suggest cards within the deck's color identity: {color_identity}.
- Cards flagged [OPENING HAND TRAP] have low keptInOpeningHandRate — prime cut candidates.
- Cards flagged [WEAK SPOT] in archetype win rates show where the deck loses most.
{goals_section}
You MUST respond ONLY with valid JSON matching this exact structure:
{{
"summary": "3-sentence deck overview: what it does, main strength, main weakness.",
"upgradePriority": [
{{
"rank": 1,
"cut": "Card to remove",
"add": "Card to add instead",
"reasoning": "Why this is a high-impact swap",
"expectedImpact": "high|medium|low"
}}
],
"commanderDependency": {{
"score": 5,
"dependentCards": ["Card that is nearly dead without commander"],
"recoveryPlan": "Brief recovery strategy"
}}
}}
Do NOT include any text outside the JSON object. No markdown fences, no preamble."""
# ════════════════════════════════════════════════════════════
# User Prompt Template
# ════════════════════════════════════════════════════════════
USER_PROMPT_TEMPLATE = """## Deck Report: {deck_name}
Commander: {commander}
@@ -79,19 +238,29 @@ Avg Game Length: {avg_game_length:.1f} turns
- Card Types: {type_summary}
- Functional Roles: {role_summary}
### Sim Insights (pre-computed deck-level signals)
{sim_insights_section}
### Matchup Data
{matchup_section}
### Archetype Win Rates
{archetype_section}
### Top Performing Cards (by Impact Score)
{top_cards}
### Underperforming Cards (candidates for cuts)
{underperformer_section}
### RAG-Retrieved Similar Cards (from ChromaDB)
{rag_section}
### Candidate Replacement Cards (from embeddings search)
{candidate_section}
{combo_section}
Based on this simulation data, provide your coaching suggestions."""
@@ -99,7 +268,6 @@ 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.")
@@ -126,42 +294,148 @@ 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.
Phase 1: adds keptInOpeningHandRate so Claude sees hand quality for
the good cards too, not just the underperformers.
"""
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}, "
f"keptInOpeningHand={c.keptInOpeningHandRate:.1%}"
f"{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}, "
f"deadCardRate={c.deadCardRate:.1%}, "
f"castRate={c.castRate:.1%}, "
f"clunkiness={c.clunkinessScore:.3f}")
turn_str = f", avgTurnCast={c.avgTurnCast:.1f}" if c.avgTurnCast is not None else ""
flags = []
if c.keptInOpeningHandRate < 0.25:
flags.append("OPENING HAND TRAP")
if c.avgTurnCast is not None and c.avgTurnCast > 6:
flags.append("LATE CASTER")
if c.synergyScore < 0.1:
flags.append("ISOLATED")
flag_str = f" [{', '.join(flags)}]" if flags else ""
lines.append(
f"- {c.name}{flag_str}: impact={c.impactScore:.3f}, "
f"deadCardRate={c.deadCardRate:.1%}, "
f"castRate={c.castRate:.1%}, "
f"clunkiness={c.clunkinessScore:.3f}, "
f"synergy={c.synergyScore:.3f}, "
f"keptInOpeningHand={c.keptInOpeningHandRate:.1%}"
f"{turn_str}"
)
else:
lines.append(f"- {name}: (no detailed stats)")
return "\n".join(lines) if lines else "No clear underperformers identified."
def _format_sim_insights(cards: List[CardPerformance],
archetype_rates: Dict[str, float]) -> str:
"""Compute and format deck-level sim signals for the Sim Insights section.
Phase 1 (#108): surfaces pre-computed per-card stats as deck-level
aggregates so Claude reasons from numbers directly.
Signals produced:
1. Opening hand quality — deck avg keptInOpeningHandRate + worst offenders
2. Curve velocity — castRate-weighted avg avgTurnCast + late-caster flags
3. Synergy outliers — bottom-3 synergyScore cards flagged as [ISOLATED]
4. Archetype weak spots — archetypes < 40% win rate flagged [WEAK SPOT]
"""
if not cards:
return "No simulation data available."
lines = []
# ── 1. Opening hand quality ───────────────────────────────────────────
hand_rates = [c.keptInOpeningHandRate for c in cards if c.keptInOpeningHandRate > 0]
if hand_rates:
avg_keep = sum(hand_rates) / len(hand_rates)
lines.append(f"Opening Hand Quality: deck avg keptInOpeningHandRate = {avg_keep:.1%}")
# Worst offenders: cards significantly below the deck average
threshold = avg_keep * 0.6 # >40% below avg is an outlier
offenders = sorted(
[c for c in cards if c.keptInOpeningHandRate > 0 and c.keptInOpeningHandRate < threshold],
key=lambda c: c.keptInOpeningHandRate
)[:3]
if offenders:
lines.append(" Opening hand traps (keptInOpeningHandRate far below deck avg):")
for c in offenders:
lines.append(f" - {c.name}: {c.keptInOpeningHandRate:.1%} [OPENING HAND TRAP]")
else:
lines.append("Opening Hand Quality: no keptInOpeningHandRate data available")
# ── 2. Curve velocity ───────────────────────────────────────────────
cast_cards = [c for c in cards if c.avgTurnCast is not None and c.castRate > 0]
if cast_cards:
total_weight = sum(c.castRate for c in cast_cards)
if total_weight > 0:
weighted_avg_turn = sum(
c.avgTurnCast * c.castRate for c in cast_cards
) / total_weight
lines.append(f"Curve Velocity: castRate-weighted avg avgTurnCast = {weighted_avg_turn:.1f}")
late_casters = sorted(
[c for c in cast_cards if c.avgTurnCast > 6],
key=lambda c: c.avgTurnCast,
reverse=True
)[:3]
if late_casters:
lines.append(" Late casters (avgTurnCast > 6 — may not fit the curve):")
for c in late_casters:
lines.append(f" - {c.name}: avgTurnCast={c.avgTurnCast:.1f} [LATE CASTER]")
else:
lines.append("Curve Velocity: no avgTurnCast data available")
# ── 3. Synergy outliers ──────────────────────────────────────────────
synergy_cards = sorted(
[c for c in cards if c.castRate > 0.05], # ignore near-never-cast cards
key=lambda c: c.synergyScore
)
if synergy_cards:
bottom3 = synergy_cards[:3]
lines.append("Synergy Outliers (lowest synergyScore among cast cards):")
for c in bottom3:
lines.append(f" - {c.name}: synergyScore={c.synergyScore:.3f} [ISOLATED]")
# ── 4. Archetype weak spots ──────────────────────────────────────────
if archetype_rates:
weak = {k: v for k, v in archetype_rates.items() if v < 0.40}
if weak:
lines.append("Archetype Weak Spots (win rate < 40%):")
for arch, wr in sorted(weak.items(), key=lambda x: x[1]):
lines.append(f" - vs {arch}: {wr:.1%} [WEAK SPOT]")
else:
lines.append("Archetype Weak Spots: none (all matchups >= 40% win rate)")
return "\n".join(lines) if lines else "No sim insights available."
def _format_candidates(candidates: Dict[str, List[dict]]) -> str:
"""Format replacement candidates grouped by the card they'd replace."""
if not candidates:
return ("No embedding-based candidates available. "
"Use your MTG knowledge to suggest cards that fit the deck's "
"color identity, strategy, and missing functional roles. "
"Suggest at least 3 different cards across different roles "
"(ramp, draw, removal, threats, utility).")
return (
"No embedding-based candidates available. "
"Use your MTG knowledge to suggest cards that fit the deck's "
"color identity, strategy, and missing functional roles. "
"Suggest at least 3 different cards across different roles "
"(ramp, draw, removal, threats, utility)."
)
lines = []
for underperformer, replacements in list(candidates.items())[:MAX_UNDERPERFORMERS]:
lines.append(f"\nReplacements for '{underperformer}':")
@@ -179,14 +453,55 @@ def _format_candidates(candidates: Dict[str, List[dict]]) -> str:
return "\n".join(lines)
def _format_rag_cards(rag_cards: List[dict]) -> str:
"""Format RAG-retrieved cards from ChromaDB vector search."""
if not rag_cards:
return "No RAG results available."
lines = []
for card in rag_cards[:15]:
name = card.get("name", "Unknown")
types = card.get("types", "")
mv = card.get("mana_value", "?")
mc = card.get("mana_cost", "")
dist = card.get("distance", 0)
text = card.get("text", "")[:80]
lines.append(f"- {name} ({mc}, MV:{mv}, {types}) [distance: {dist:.3f}]")
if text:
lines.append(f" Oracle: {text}")
return "\n".join(lines)
def _format_matchups(report: DeckReport) -> str:
"""Format 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.
Phase 1: annotates weak spots ([WEAK SPOT] < 40%) and strengths
([STRENGTH] >= 55%) so Claude sees the signal immediately.
"""
rates = report.meta.perArchetypeWinRates
if not rates:
return "No archetype breakdown available."
lines = []
for archetype, wr in sorted(rates.items(), key=lambda x: x[1]):
if wr < 0.40:
tag = " [WEAK SPOT]"
elif wr >= 0.55:
tag = " [STRENGTH]"
else:
tag = ""
lines.append(f"- vs {archetype}: {wr:.1%}{tag}")
return "\n".join(lines)
@@ -197,14 +512,16 @@ def _format_combos(report: DeckReport) -> str:
lines = ["### Known Combos"]
for combo in report.knownCombos:
cards = " + ".join(combo.cardNames)
lines.append(f"- {cards}: winRate={combo.winRateWhenAssembled:.1%}, "
f"assemblyRate={combo.assemblyRate:.1%}")
lines.append(
f"- {cards}: winRate={combo.winRateWhenAssembled:.1%}, "
f"assemblyRate={combo.assemblyRate:.1%}"
)
return "\n".join(lines)
# ══════════════════════════════════════════════════════════════
# ════════════════════════════════════════════════════════════
# Main Builder Functions
# ══════════════════════════════════════════════════════════════
# ════════════════════════════════════════════════════════════
def build_system_prompt(report: DeckReport,
goals: Optional[CoachGoals] = None) -> str:
@@ -217,16 +534,44 @@ def build_system_prompt(report: DeckReport,
)
def build_user_prompt(report: DeckReport,
candidates: Dict[str, List[dict]] = None) -> str:
"""Build the user prompt with full deck report data."""
color_str = "/".join(report.colorIdentity) if report.colorIdentity else "unknown"
def build_quick_system_prompt(report: DeckReport,
goals: Optional[CoachGoals] = None) -> str:
"""Build the quick digest system prompt (~400-600 token target)."""
color_str = "/".join(report.colorIdentity) if report.colorIdentity else "any"
return QUICK_DIGEST_SYSTEM_PROMPT.format(
commander=report.commander,
color_identity=color_str,
goals_section=_format_goals_section(goals),
)
def build_user_prompt(report: DeckReport,
candidates: Dict[str, List[dict]] = None,
rag_cards: List[dict] = None,
rules_context: List[dict] = None) -> str: # FARMHOUSE_RULES_CONTEXT
text = _farmhouse_build_user_prompt(report, candidates, rag_cards)
rules = []
for item in (rules_context or [])[:3]:
body = (item.get('text') or item.get('document') or item.get('content')) if isinstance(item, dict) else str(item)
if body:
rules.append('- ' + str(body).strip()[:700])
if rules:
text += '\n\n## Relevant rules (for grounding; do not quote them back)\n' + '\n'.join(rules)
return text
def _farmhouse_build_user_prompt(report: DeckReport,
candidates: Dict[str, List[dict]] = None,
rag_cards: List[dict] = None) -> str:
"""Build the user prompt with full deck report data.
Phase 1: adds sim_insights_section computed from card-level stats.
"""
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,
@@ -238,10 +583,14 @@ def build_user_prompt(report: DeckReport,
curve_summary=_format_curve(report.structure.curveBuckets),
type_summary=", ".join(type_parts),
role_summary=", ".join(role_parts),
sim_insights_section=_format_sim_insights(
report.cards, report.meta.perArchetypeWinRates),
matchup_section=_format_matchups(report),
archetype_section=_format_archetypes(report),
top_cards=_format_top_cards(report.cards),
underperformer_section=_format_underperformers(
report.cards, report.underperformers),
candidate_section=_format_candidates(candidates or {}),
rag_section=_format_rag_cards(rag_cards or []),
combo_section=_format_combos(report),
)
+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
@@ -38,7 +38,7 @@ def generate_deck_reports(results_dir: str, reports_dir: str = None) -> List[str
output_path.mkdir(parents=True, exist_ok=True)
# Load all batch results
batch_files = sorted(results_path.glob("batch-*.json"))
batch_files = sorted(results_path.glob("ml-decision-*.json"))
if not batch_files:
logger.info("No batch result files found in %s", results_dir)
return []
@@ -108,7 +108,7 @@ def generate_single_deck_report(batch_json_path: str, reports_dir: str = None) -
# Also load existing batch results for comprehensive reports
results_dir = Path(batch_json_path).parent
all_batches = []
for bf in sorted(results_dir.glob("batch-*.json")):
for bf in sorted(results_dir.glob("ml-decision-*.json")):
try:
with open(bf, "r", encoding="utf-8") as f:
all_batches.append(json.load(f))
+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 = {
+23 -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
@@ -18,13 +22,17 @@ 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")
class DeckCardWithStatus(BaseModel):
"""A deck card enriched with ownership and substitution data."""
class Config:
extra = "ignore"
name: str
count: int = 1
category: str = ""
@@ -38,9 +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):
@@ -58,11 +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 = {
+427 -118
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,
@@ -239,24 +479,60 @@ 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",
card_count,
raw_deck.get("bracket", {}).get("level", 0),
" (TRUNCATED — response may have hit token limit)" if truncated else "")
truncated = card_count < 90
logger.info(
"Deck generated: %d cards, bracket %s%s",
card_count,
(raw_deck.get("bracket", {}).get("level", "?") if isinstance(raw_deck.get("bracket"), dict)
else raw_deck.get("bracket", "?")),
" (TRUNCATED — hit token limit)" if truncated else "",
)
# 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 6: Compute stats
# 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:
deduped[seen[key]].count += c.count
logger.warning("Secondary dedupe caught duplicate: '%s'", c.name)
else:
seen[key] = len(deduped)
deduped.append(c)
cards_with_status = deduped
# Step 6: Run non-destructive smart substitution
# For non-canonical commanders (e.g. Sonic), skip collection filter
owned_pool: set[str] = set()
if use_collection and scryfall_found:
conn = self.db_conn_factory()
rows = conn.execute(
"SELECT DISTINCT name FROM collection_entries WHERE quantity > 0"
).fetchall()
owned_pool = {r["name"] for r in rows}
cards_with_status, substitution_log = apply_substitutions(
cards_with_status, owned_pool, color_identity, self.embedding_index
)
# Step 7: Pad-to-99 circuit breaker
cards_with_status, padded_slots = _pad_to_99(cards_with_status, color_identity)
# Step 8: Pre-validation count logging
_log_pre_validation_counts(cards_with_status)
# Step 9: Compute stats
stats = self._compute_stats(cards_with_status)
return {
@@ -270,6 +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,
@@ -283,17 +565,25 @@ 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 by name (case-insensitive).
"""
conn = self.db_conn_factory()
enriched = []
enriched: List[DeckCardWithStatus] = []
seen_names: dict[str, int] = {}
for card in cards:
name = card.get("name", "")
if not name:
continue
# Look up in collection
name_lower = name.lower()
if name_lower in seen_names:
idx = seen_names[name_lower]
enriched[idx].count += card.get("count", 1)
logger.warning("Duplicate card '%s' merged", name)
continue
row = conn.execute(
"SELECT name, scryfall_id, quantity, tcg_price, type_line, cmc "
"FROM collection_entries WHERE name = ? COLLATE NOCASE LIMIT 1",
@@ -318,16 +608,14 @@ class DeckGeneratorV3:
scryfall_id=row["scryfall_id"] if row else "",
status="owned" if is_owned else "missing",
)
seen_names[name_lower] = len(enriched)
enriched.append(enriched_card)
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,
@@ -349,7 +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)
@@ -360,20 +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))
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],
@@ -383,15 +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]
@@ -406,47 +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),
@@ -465,39 +766,37 @@ class DeckGeneratorV3:
if not self.embedding_index or not self.embedding_index.loaded:
return []
# Use the embeddings search to find similar cards
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
)
try:
matches = self.embedding_index.search_similar(
query_card=card.name,
color_filter=color_identity if color_identity else None,
type_filter=card.category if card.category else None,
exclude_cards=[card.name],
top_n=SUBSTITUTION_MAX_ALTERNATIVES * 3,
)
except Exception as e:
logger.warning("Embedding search failed for '%s': %s", card.name, e)
return []
# 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],
@@ -505,13 +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,
@@ -524,7 +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,
@@ -532,7 +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,
@@ -543,13 +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()
@@ -558,32 +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"
+4
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@@ -0,0 +1,4 @@
name,quantity,set_code
Sol Ring,1,C21
Command Tower,1,C21
Arcane Signet,1,C21
+122 -10
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@@ -1,21 +1,133 @@
"""
Moxfield Adapter (stub)
════════════════════════
Moxfield Adapter
================
Fetches template decklists from Moxfield for a given commander.
Currently returns empty (experimental — not yet implemented).
Uses the undocumented but usable api2.moxfield.com API.
Rate-limited to <=10 req/sec. Errors are caught and logged.
"""
from .models import TemplateDeck, TemplateDeckCard
import logging
import time
import httpx
from .models import TemplateDeck, TemplateDeckCard
log = logging.getLogger("commander_ai_lab.deckgen.moxfield")
MOXFIELD_API = "https://api2.moxfield.com"
HEADERS = {
"User-Agent": "CommanderAILab/1.0",
"Accept": "application/json",
}
# Minimum seconds between requests (<=10 req/sec)
_RATE_LIMIT_DELAY = 0.12
def fetch_template_decks(commander_name: str, color_identity: list, config: dict = None) -> list:
def _slugify_commander(name: str) -> str:
"""Return the commander name as-is for the Moxfield search query."""
return name.strip()
def _build_cards_from_deck(deck_data: dict) -> list:
"""Extract commander + mainboard cards from a full Moxfield deck response."""
cards = []
# Commanders first
for card_name, card_obj in deck_data.get("commanders", {}).items():
qty = card_obj.get("quantity", 1)
cards.append(TemplateDeckCard(name=card_name, quantity=qty))
# Mainboard
for card_name, card_obj in deck_data.get("mainboard", {}).items():
qty = card_obj.get("quantity", 1)
cards.append(TemplateDeckCard(name=card_name, quantity=qty))
return cards
def fetch_template_decks(
commander_name: str,
color_identity: list,
config: dict = None,
) -> list:
"""
Fetch template decks from Moxfield for the given commander.
Experimental — not yet implemented.
Fetch top Commander decks from Moxfield for the given commander.
Returns a list of TemplateDeck objects. Returns empty list on any
API failure — errors are logged, never raised.
Args:
commander_name: The commander's card name.
color_identity: List of color symbols (unused, kept for interface compat).
config: Optional dict. Supported key: ``max_decks`` (default 3).
Returns:
List of TemplateDeck objects populated with commander + mainboard cards.
"""
log.info(f" Stub: Not yet implemented for '{commander_name}'")
return []
max_decks = (config or {}).get("max_decks", 3)
results = []
try:
# Step 1 — Search for top decks by commander name
search_url = f"{MOXFIELD_API}/v2/decks/search"
params = {
"q": "",
"fmt": "commander",
"commander": _slugify_commander(commander_name),
"sortType": "views",
"sortDirection": "Descending",
"pageSize": max_decks,
}
log.info(f" Moxfield: Searching for '{commander_name}' (max={max_decks})")
resp = httpx.get(search_url, params=params, headers=HEADERS, timeout=15)
resp.raise_for_status()
search_data = resp.json()
deck_summaries = search_data.get("data", [])[:max_decks]
if not deck_summaries:
log.info(f" Moxfield: No decks found for '{commander_name}'")
return []
log.info(f" Moxfield: Found {len(deck_summaries)} deck(s) for '{commander_name}', fetching details...")
# Step 2 — Fetch full detail for each deck
for summary in deck_summaries:
deck_id = summary.get("publicId")
deck_name = summary.get("name", f"Moxfield {commander_name}")
if not deck_id:
log.warning(" Moxfield: Skipping deck with no publicId")
continue
time.sleep(_RATE_LIMIT_DELAY) # Rate limit: <=10 req/sec
try:
deck_url = f"{MOXFIELD_API}/v3/decks/all/{deck_id}"
deck_resp = httpx.get(deck_url, headers=HEADERS, timeout=15)
deck_resp.raise_for_status()
deck_data = deck_resp.json()
except Exception as deck_err:
log.warning(f" Moxfield: Failed to fetch deck '{deck_id}': {deck_err}")
continue
cards = _build_cards_from_deck(deck_data)
if not cards:
log.warning(f" Moxfield: Deck '{deck_name}' ({deck_id}) had no cards, skipping")
continue
results.append(TemplateDeck(
name=f"{deck_name} (Moxfield)",
source=f"https://moxfield.com/decks/{deck_id}",
cards=cards,
))
log.info(f" Moxfield: Added deck '{deck_name}' ({len(cards)} cards)")
log.info(f" Moxfield: Returning {len(results)} deck(s) for '{commander_name}'")
except httpx.HTTPStatusError as e:
log.warning(
f" Moxfield fetch failed for '{commander_name}': "
f"HTTP {e.response.status_code} — {e}"
)
except httpx.RequestError as e:
log.warning(f" Moxfield request error for '{commander_name}': {e}")
except Exception as e:
log.warning(f" Moxfield unexpected error for '{commander_name}': {e}")
return results
+129
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@@ -0,0 +1,129 @@
# Closed-Loop Iterative Distillation — Implementation Roadmap
> **Goal:** Create a feedback loop where the PPO self-play agent exports its game decisions
> as JSONL files, which the supervised training pipeline incorporates alongside Forge
> heuristic data. The supervised baseline gradually improves as the RL agent discovers
> better play patterns — the RL agent teaches the supervised model.
---
## Phase 1 — PPO Decision Exporter
**Goal:** PPO self-play writes decisions in the same JSONL format as Forge batch sims.
- [ ] Add a `DecisionExporter` class in `ml/training/self_play.py` that captures each
(state, action, reward, game_context) during PPO rollouts
- [ ] Write to `data/results/ml-decisions-ppo-{batch_id}.jsonl` using the exact same
schema as the Forge `DecisionSnapshot` files
- [ ] Include metadata fields to distinguish PPO-generated data:
- `"source": "ppo"`
- `"model_version"`
- `"reward"`
- `"episode_return"`
- [ ] Only export decisions from **winning** games initially (higher signal-to-noise)
**Files touched:** `ml/training/self_play.py`, new `ml/training/decision_exporter.py`
---
## Phase 2 — Dataset Builder Source Awareness
**Goal:** `dataset_builder.py` can ingest both Forge and PPO decision files with
configurable mixing.
- [ ] Update `build_dataset()` to scan for both `ml-decisions-sim-*.jsonl` (Forge) and
`ml-decisions-ppo-*.jsonl` (PPO) files
- [ ] Add a `source_weights` config param — e.g., `{"forge": 1.0, "ppo": 0.5}` — to
control sampling ratios so PPO data doesn't overwhelm the Forge baseline early on
- [ ] Add a `min_reward_threshold` filter for PPO data — only include decisions from
games above a certain reward score
- [ ] Tag each sample with its source for downstream analysis
**Files touched:** `ml/data/dataset_builder.py`, `ml/config/scope.py`
---
## Phase 3 — Quality Gate & Validation
**Goal:** Prevent bad PPO data from degrading the supervised model.
- [ ] Add a validation split in `dataset_builder.py` (hold out 10-15% of Forge data as
a fixed eval set)
- [ ] After each supervised training run, evaluate on the held-out Forge set — if
accuracy drops below a threshold compared to the previous model, reject the PPO
data batch
- [ ] Track win rate of the PPO agent — only export decisions once the PPO model exceeds
a configurable win rate (e.g., >30% in 4-player pods, above the 25% random baseline)
- [ ] Log data quality metrics: action distribution entropy, reward distribution of
exported games
**Files touched:** `ml/training/trainer.py`, `ml/eval/`, new `ml/data/quality_gate.py`
---
## Phase 4 — Distillation Loop Orchestrator
**Goal:** Automate the full cycle: supervised train -> PPO self-play -> export -> retrain.
- [ ] New `ml/training/distillation_loop.py` that orchestrates:
1. Supervised train on current dataset (Forge + any existing PPO data)
2. Run N episodes of PPO self-play using the new supervised model as starting policy
3. Export winning PPO decisions to JSONL
4. Quality gate check
5. If passed, merge into dataset and go to step 1
6. If failed, discard PPO batch, adjust hyperparameters, retry
- [ ] Add `max_iterations` and `convergence_check` (stop if win rate plateaus across
iterations)
- [ ] Each iteration is a "generation" — tag data and models with generation number
**Files touched:** new `ml/training/distillation_loop.py`, `ml/config/scope.py`
---
## Phase 5 — UI Integration & Monitoring
**Goal:** Expose the distillation loop in the Commander AI Lab web UI.
- [ ] Add a "Distillation" tab or section in `ui/ai-lab.js` showing:
- Current generation number
- Per-generation win rate trend chart
- Data composition pie chart (% Forge vs % PPO per generation)
- Start / stop / pause controls for the loop
- [ ] API endpoints in `routes/ml.py`:
- `POST /api/ml/distill/start`
- `GET /api/ml/distill/status`
- `POST /api/ml/distill/stop`
- [ ] History of each generation's metrics stored as JSON for the trends view
**Files touched:** `routes/ml.py`, `ui/ai-lab.js`, `ui/ai-lab.css`
---
## Phase 6 — Advanced: ELO Tracking & Model Tournament
**Goal:** Quantify improvement across generations.
- [ ] Pit each generation's model against previous generations and the Forge heuristic
in batch sims
- [ ] Compute ELO ratings per generation
- [ ] Store in `data/elo_history.json` and visualize in the UI as a line chart
- [ ] Use ELO delta as a convergence signal — stop the loop when ELO gain per generation
drops below a threshold
**Files touched:** new `ml/eval/elo_tracker.py`, `routes/ml.py`, `ui/ai-lab.js`
---
## Priority
| Phase | Priority | Description |
|-------|----------|-------------|
| 1 | **Critical** | PPO decision export — foundation for everything |
| 2 | **Critical** | Dataset builder ingests both sources |
| 3 | **Critical** | Quality gate prevents model regression |
| 4 | High | Automation of the full loop |
| 5 | Medium | UI visibility and controls |
| 6 | Low | ELO tracking and tournament system |
> **Phases 1-3** are the core — get those working and you have a functional closed loop.
> **Phase 4** automates it. **Phases 5-6** are polish and observability.
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# Laya connections
Laya (and Kev: same `POST /v1/systemone` API) is a **decision model**: typed multiple-choice, yes/no and score questions about a text, answered in
one forward pass, no generated text. This page is where it plugs into the strategic-intelligence work in this repository
([INTELLIGENCE-HANDOFF.md](INTELLIGENCE-HANDOFF.md)), what is measured, and how to make it worth using. Written 2026-10-26.
**Short version.** Every connection below is built, tested offline and **off by default**. Zero-shot, Laya is a poor action chooser
(below the always-guess baseline) but a reasonable card classifier (48% against 17% by chance); it is too slow on a CPU for Forge's
priority path (8 s against a 1.5 s budget). The way forward is fine-tuning on the decisions these connections record.
## What connects where
| Connection | Code | Mode | Notes |
|---|---|---|---|
| Farmhouse macro choice: Laya picks among CSIE's candidate macros, reading the CSIE ranking and ITI objective in its text | `farmhouse_decide.py` (called from `deepseek_brain.py`) | `DECISION_MODE=shadow` records agreement with the LLM; `assist` uses a pick it is `DECISION_MIN_CONF` sure of | existing; only offered candidates are accepted |
| **Forge concrete priority choice**: Laya picks among the same concrete candidate IDs Forge sent to `/api/policy/decide/concrete` | `farmhouse_laya.advise_concrete` / `attach_concrete`, called from `routes/policy.py` | `shadow`: adds `laya` to the response, selection unchanged. `assist`: may replace `selected_id`, never one Forge tagged `stops_immediate_win` / `wins_now`; the original is kept as `csie_selected_id` | candidate described by kind, card, cost, target, tags (never by its opaque id); Forge still executes only an ID it sent |
| **Extra questions in the same call**: "how dangerous are the opponents' boards?" (none/low/moderate/high/lethal) and "is there a threat that must be answered?" | `farmhouse_laya._extra_questions` | whenever Laya is asked | one forward pass answers all questions, so they cost almost nothing; recorded as weak signals for the learned-threat work, **not used to decide** |
| **Capture**: every call is appended to `data/decisions/laya-shadow-<date>.jsonl` (`laya-shadow-v1`) | `farmhouse_laya.capture` | shadow / assist | the text Laya read, the offered options, its answer and extras, and the other deciders' picks (`labels`: `llm`, `csie`, `selected`) |
| **Fine-tuning data**: capture log -> `train.jsonl` / `valid.jsonl` / `manifest.json`, split by game, with the baselines to beat | `scripts/farmhouse_laya_dataset.py` | offline | the manifest reports commonest-action, CSIE-vs-teacher and Laya-zero-shot agreement on the validation side |
| **Card role evidence**: removal, board wipe, ramp, card draw, counterspell, protection, tutor, creature, other, from Oracle text | `farmhouse_laya.classify_roles` (batched, cached, with `source: laya:<model>` provenance); `scripts/farmhouse_laya_roles_eval.py` measures it | on demand | evidence for ITI / Tagger-style use, never a rules fact |
Not built (still the open items in the handoff, none of which Laya can substitute for): compiling and running the Forge controller, the bounded
combo executor, the multi-turn planner, the calibrated learned-threat model. Laya's "threat" and "must answer" extras are only raw material for that last one.
## Where the hooks are
- `src/commander_ai_lab/sim/farmhouse_decide.py` (committed): the macro-choice client gained the extra questions, the state text/options in its
answer, and `record()` writes the capture line.
- `routes/policy.py` (**working tree only, uncommitted**: the route itself is part of the pending strategic-evaluator work, so the hook was
left for that commit). It is one block in `decide_concrete`, after the result is computed:
`if _L.mode() != "off": advice = await run_in_threadpool(_L.advise_concrete, ...); result = _L.attach_concrete(...)`, guarded by `except ImportError`.
- Everything else is new files: `farmhouse_laya.py`, the two scripts, `tests/farmhouse/test_farmhouse_laya*.py`, `farmhouse/laya/`.
## Settings
`DECISION_URL` (e.g. `http://laya.commander-lab.svc.cluster.local:8080`), `DECISION_MODE` (`off` default / `shadow` / `assist`), `DECISION_MODEL`
(a checkpoint such as `typed-decisions`), `DECISION_MIN_CONF` (0.7), `DECISION_TIMEOUT` (5 s), `DECISION_TIMEOUT_FORGE` (**1.5 s**: Forge waits on this),
`DECISION_API_KEY`, `LAYA_CAPTURE_DIR` (`data/decisions`), `LAYA_CAPTURE=0` to stop writing capture lines. Three failures in a row pause asking for a minute.
## Rules the code enforces
1. Text is built from public facts and the acting player's own hand. An opponent's `hand` field is never read, even if a client sends one (tested).
2. Laya's answer must be one of the options that were offered; anything else is discarded.
3. In assist, a pick Forge marked `stops_immediate_win` / `wins_now` is never overridden, and the response says what CSIE chose.
4. A missing, slow or broken server returns "no answer"; nothing raises and the game proceeds as without Laya.
5. Scores and probabilities are relative signals; Laya's probabilities are not calibrated for this game.
## What was measured (2026-10-26, `typed-decisions` checkpoint, 4-thread CPU)
- **Macro action vs the LLM, 149 recorded decisions:** 16.1% (English) and 18.6% (typed-decisions); always guessing the commonest action 27.5%.
- **Forge-shaped priority choice, 3 scenarios, with CSIE's ranking in the text:** agreed with CSIE all three times (expected: the ranking is in the
text it reads), 42-48% sure, **7.5-8.4 s per call** with the extra questions. That is five times Forge's 1.5 s budget: on this CPU the Forge path
would time out and fall back; it needs a GPU (33 ms quoted) or a smaller text.
- **Extras:** "threat" said *high* for a lethal stack, a calm board and a behind board alike (useless zero-shot); "must answer" gave 0.48 for the
lethal stack against 0.25 and 0.29 for the others (right direction, weak).
- **Card roles from Oracle text, 150 unambiguous cards:** 48.0% (16.7% by chance among the six roles present); counterspell 76%, board wipe 64%,
removal 40%, tutor 40%, ramp 36%, card draw 32%; when 70% sure, 69% right but only 11% of cards reach that. **3.2 s per card** in batches on the CPU:
fine for the cards in a hand (cached), not for tagging 30,000 cards (about a day).
## How to make it worth using
1. Run games with `DECISION_URL` set and `DECISION_MODE=shadow` (Farmhouse LLM games, and Forge games through `/api/policy/decide/concrete`).
2. `python scripts/farmhouse_laya_dataset.py --teacher llm` (or `csie`, or `selected`). Read `manifest.json`: the **validation baselines** are the bar.
A student of CSIE can at best equal CSIE, which is free; a student of the LLM is the useful one. Several thousand rows are needed.
3. Fine-tune with Laya's own notebook/script (github.com/NandhaKishorM/laya, `notebooks/`; 2xT4 about 4-5 hours, or Apple silicon). Its worked example
(browser agent choosing among ~45 concrete candidates) went from 0.10 zero-shot to 0.66, so a concrete-candidate head is the shape that works.
Adapt the row field names if its loader differs.
4. Serve the result (`LAYA_EXTRA_MODELS` / `DECISION_MODEL`), rerun `scripts/farmhouse_decision_eval.py` and the dataset manifest, and only then
try `assist`. A GPU host makes the Forge path possible.
5. Keep game result and decision quality separate (handoff boundary 6 / learned-threat contract): a captured label is a teacher's choice, not proof it was right.
Deployment reference copies are in `farmhouse/laya/` (image, manifests, CI); the source of truth is the farmhouse-os GitOps repo.
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# phase-rs and commander-playtester: assessment and plan
Researched 2026-10-26 from the public repositories and data files only. **Nothing from either project has been run, vendored or deployed.**
Facts marked (verified) were read from the source; (unverified) are inferences to check before building on them.
## phase-rs/phase: a rules engine
A Rust rules engine (turns, priority, the stack, combat, layers, triggers, replacement effects) that compiles to native and WebAssembly, with a game
client, a WebSocket server and its own AI. Dual licensed Apache-2.0 / MIT (`LICENSE-APACHE`, `LICENSE-MIT`; GitHub's licence field shows "none" only
because there are two files). Card data from MTGJSON (MIT); card images come from Scryfall at runtime; the repo says it bundles no card art or
Comprehensive Rules text and has a DMCA/fan-content policy. One maintainer, young (weeks to months), fast-moving (latest engine tag v0.100.0).
Why it matters here: our Python simulator has no rules stack (the handoff says so: `stack_supported: false`) and Forge needs a JDK and Maven that
are not in the image. Phase is a rules-accurate engine that can run headless.
### Coverage of the decks in this app (verified, from phase's published `coverage-data.json`)
Phase reports 88.9% of 36,037 cards implemented overall and 92.5% for Commander. For the five saved decks, distinct cards supported:
Empress V8 72/77, Landfall 65/66, God and Anime 69/71, Im not dead yet 82/84, Pew Pew 88/93: **376 of 391 (96.2%)**. The 15 unsupported cards have
one or two parsing gaps each (e.g. Gale Waterdeep Prodigy, Hall of Echoes, Precursor Golem, Invigorate, Mavinda, Drana and Linvala, Tibalt's Trickery).
"Supported" means every ability parsed; it is not a guarantee that each card plays correctly. How the engine treats an unsupported card in a game
(blank, no-op, refused) is **unverified**; test it before trusting a result that involves one.
### The parts that fit
| Part | What it gives us | Notes |
|---|---|---|
| `ai-duel` (crate `phase-ai`) | headless AI-vs-AI games from the command line: seeded, deterministic "measurement mode", batches, JSON reports, a 4-player Commander suite, paired-seed comparison | decks are plain JSON `{"name", "main":[{"name","count"}]}` (verified). **No prebuilt binary is released**: it needs a Rust build (in CI or a container, not on the workstation). How a custom Commander deck and commander are supplied to `--commander-suite` is unverified (that suite reads a feed file) |
| `phase-server` | a WebSocket game server (lobby, hidden information, spectators) | a prebuilt image is published (`ghcr.io/phase-rs/phase-server`, tags `latest`, `preview`, `sha-...`). A Python client could play seats and record every decision with real rules (protocol documentation not yet read) |
| `phase-llm` | **model-driven AI seats**: the engine hands the model its legal option list, the model returns an index, the engine re-validates it, and any failure falls back to the heuristic AI | same principle as `farmhouse_laya.py`. Speaks OpenAI-style chat (so Olla / twin-turbo) **and the System One protocol Laya and Kev serve** (verified: `WireProtocol::SystemOneRelay`, question id `pick`, 2 to 255 options) |
| `POST /jev/systemone` relay in `phase-server` | forwards the System One request to the URL in `TYPESAFE_API_URL` (verified, env var, `http` allowed for local setups) | point it at `http://laya...:8080/v1/systemone` to put Laya or Kev in a seat. The relay sends the key as `Authorization: Bearer` and does not follow redirects |
The attraction for the Laya work: a captured decision from phase is an **engine-issued option list plus the pick**, in rules-accurate games. That
is a better fine-tuning source than this repository's approximate simulator, and the exporter (`scripts/farmhouse_laya_dataset.py`) could take it with a small adapter.
## RafaelAugustScherer/commander-playtester: a browser app on top of phase
TypeScript/React single-page app, no backend. Runs phase's engine as WebAssembly in a Web Worker. Deck library with Moxfield / Archidekt / plain-text
import, a goldfishing Monte Carlo (mulligan rate, screw and flood, land drops, ramp by turn 3, curve), real engine matches (watch, or pilot one seat),
and 1-50 match head-to-head runs with a win rate and 95% confidence interval. Pins the phase engine in `engine-manifest.json` (v0.100.0; its README
still says v0.57.0) and fetches the WASM and card data with SHA-256 checks. Zero stars, last pushed 2026-10-24.
**It has no LICENSE file** (verified: the repository metadata shows none). Without a licence the code is all rights reserved: it can be read and used
as a reference, but not copied into this repository or redistributed. Ask the author to add a licence (MIT would do) before reusing any of it. What is
worth taking as ideas, not code: the goldfishing metrics, the head-to-head confidence-interval report, the name-only deck payload for the engine, and
the engine-smoke gate that boots the real engine and plays a match to completion after every upgrade.
## Recommended plan, smallest useful step first
1. **Prove it on one deck.** Build `ai-duel` from the pinned phase tag in CI (Rust, container), convert one saved deck to the JSON format, run a seeded
batch against a baseline deck, and read the result. Answers: does it run a Commander game end to end, what does it do with an unsupported card, how
long is a game. This replaces the guesswork about the Python simulator's win rates.
2. **A `phase-sim` service** in the commander-lab namespace (no egress, called only by the app) with an endpoint "run N rules-accurate games for deck X
against Y", returning win rate, confidence interval, wins by seat and the game log; the app's results panel reads it. The Python simulator stays for
instant, rough Monte Carlo.
3. **Model seats.** Run `phase-server` with `TYPESAFE_API_URL` at Laya (or at a fine-tuned one later) and the OpenAI-compatible seat at Olla; compare
heuristic-only, LLM and Laya seats. Record every decision for the Laya dataset.
4. **Only then** consider the playtester's UI ideas, after the author adds a licence or as a clean reimplementation.
## What was built from this plan (2026-10-27)
1. **The headless runner** (`ai-commander`, Commander four-player, batch mode) is built from the v0.118.0 tag with the card database that release was published with, in a
container (`farmhouse/phase/Dockerfile`). Not `ai-duel` for the decks: `ai-commander` takes a feed JSON of four decks, which is exactly what the app has.
2. **The service and the app side**: `farmhouse/phase/phase_sim.py` (HTTP, jobs, parser, warnings) and `routes/farmhouse_phase.py` + `ui/phase-sim.html`. Games that stall, hit the
action cap or panic are reported and never counted.
3. **Model seats**: not a setting. phase's model support lives only in its browser client (`engine-wasm` calls `phase-llm`; the HTTP is done by TypeScript), and
`phase-llm` depends on `phase-ai`, so the headless binary cannot use it. `farmhouse/phase/llm-seat/` is a new crate that includes the runner and installs a hook
(two anchored edits to phase's source). The server-side Jev relay and the browser client were not needed. Details: `farmhouse/phase/README.md`.
Not done: the playtester's UI (no licence), a Python client for phase's WebSocket server (a large, fast-moving protocol), feeding the captured model-seat decisions into
`scripts/farmhouse_laya_dataset.py` (the natural next step: each is an engine-issued option list plus a pick).
## Risks and open questions
- Phase is a one-person, fast-moving project: pin tags, keep a smoke test that plays a full game, expect breaking changes.
- Rust builds are heavy; keep them in CI/containers. Resource and image size are unmeasured.
- Card coverage is parse-level; correctness per card is unmeasured. Unsupported cards must be flagged in any result.
- The phase server and its AI use CPU; its measured game speed is unknown (the README says a game is thousands of AI decisions).
- Legal: phase is Apache/MIT with Wizards fan-content wording; the playtester has no licence. The card data is MTGJSON (MIT).
- Not yet read: phase's WebSocket protocol, how `--commander-suite` accepts custom decks, and whether the server can run unattended self-play.
@@ -0,0 +1,93 @@
# Phase 1 — Self-Play Integration Checklist
> **Issue:** #65 — PPO Decision Exporter
> **Ref:** `docs/CLOSED-LOOP-DISTILLATION.md`
> **Status:** In Progress
---
## Completed
- [x] Create `ml/training/decision_exporter.py` with `DecisionExporter` class
- [x] `ExporterConfig` dataclass (output_dir, only_wins, model_version, min_episode_steps)
- [x] `build_decision_record()` — converts PPO step data into Forge-compatible JSONL format
- [x] `DecisionExporter.begin_episode()` / `record_step()` / `end_episode()` / `flush()` lifecycle
- [x] Write to `data/results/ml-decisions-ppo-{batch_id}.jsonl`
- [x] Include PPO metadata fields: `source`, `model_version`, `reward`, `episode_return`
- [x] Win-only filtering (only export decisions from winning games)
- [x] Short-episode gating (`min_episode_steps` threshold)
- [x] `_macro_action_to_forge_type()` mapping (MacroAction enum → Forge action type strings)
- [x] Export stats tracking (`stats` property)
---
## Remaining — Integrate into `ml/training/self_play.py`
### Import
- [ ] Add import: `from ml.training.decision_exporter import DecisionExporter`
### Modify `run_self_play_episode()`
- [ ] Add `exporter: DecisionExporter = None` parameter to function signature
- [ ] After each `trajectory.append(...)` block (3 locations), add:
```python
if exporter is not None:
exporter.record_step(
game_state_snapshot=prev_snapshot,
action_idx=action_idx,
reward=reward,
)
```
### Modify `collect_rollouts()`
- [ ] Add `exporter: DecisionExporter = None` parameter to function signature
- [ ] Before each episode, call:
```python
if exporter is not None:
exporter.begin_episode(agent_seat=agent_seat, playstyle=playstyle)
```
- [ ] Pass `exporter=exporter` to `run_self_play_episode()`
- [ ] After outcome tracking, call:
```python
if exporter is not None:
won = trajectory and trajectory[-1]["done"] and trajectory[-1]["reward"] > 0.5
ep_return = sum(s["reward"] for s in trajectory)
exporter.end_episode(won=won, episode_return=ep_return)
```
### Commit
- [ ] `git add ml/training/self_play.py`
- [ ] `git commit -m "feat(ml): integrate DecisionExporter into self-play rollouts (#65)"`
- [ ] `git push origin main`
---
## Integration Pattern
```
collect_rollouts(exporter=exporter)
└─ for each episode:
├─ exporter.begin_episode(seat, playstyle)
├─ run_self_play_episode(..., exporter=exporter)
│ └─ for each step:
│ ├─ trajectory.append(step_data)
│ └─ exporter.record_step(snapshot, action, reward)
└─ exporter.end_episode(won, episode_return)
# Caller does: exporter.flush()
```
---
## Terminal Commands
```bash
cd commander-ai-lab
git pull origin main
# Make edits to ml/training/self_play.py per checklist above
git add ml/training/self_play.py
git commit -m "feat(ml): integrate DecisionExporter into self-play rollouts (#65)"
git push origin main
```
+785
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@@ -0,0 +1,785 @@
# Commander-AI-Lab — Unity TCGEngine Commander Board: Implementation Plan (v5)
> **Architecture Principle:** Forge = Rules Authority · FastAPI = Hub · TCGEngine Unity = Renderer · Dual-Brain AI = Decision Engine
---
## System Architecture Overview
```
┌─────────────────────────────────────────────────────────────────┐
│ UNITY (TCGEngine — 2D Board) │
│ PlayerArea x4: Hand | Library | Graveyard | Exile | Command │
│ Battlefield zones per player · Card art from local cache │
│ GameNetworkManager.cs ←── WebSocket ──► FastAPI │
└─────────────────────────────────────────────────────────────────┘
▲ ▼
┌─────────────────────────────────────────────────────────────────┐
│ FASTAPI (commander-ai-lab) │
│ /ws/game — WebSocket relay │
│ /game/action — receive player/AI action, forward to Forge │
│ ForgeSubprocessManager — stdin/stdout pipe to Forge JVM │
│ CardEnricher — injects art_url from local image cache │
│ AIOrchestrator — routes decisions to DeepSeek-R1 or GPT-OSS │
│ ChromaDB RAG — card knowledge scoped per player deck │
│ ai_decisions.jsonl — logs every AI decision (PPO dataset) │
└─────────────────────────────────────────────────────────────────┘
▲ ▼
┌─────────────────────────────────────────────────────────────────┐
│ FORGE (Java — Headless Rules Engine) │
│ Full Commander rules: tax, 21-commander-damage, 40 life │
│ 4-player turn queue · Zone redirects · State-based actions │
│ Emits game state JSON to stdout · Receives actions via stdin │
└─────────────────────────────────────────────────────────────────┘
```
---
## Dual-Brain AI Decision Flow
This is the **core feature** of Commander-AI-Lab. Every AI player decision runs through this pipeline:
```
Forge emits GameStateDTO (JSON)
└─► FastAPI detects active player is AI
└─► select_brain(phase, stack_size)
├─ DeepSeek-R1-14B → combat, stack decisions, removal
└─ GPT-OSS-20B → main phase, ramp, card advantage
└─► ChromaDB.query(player_deck_cards, top_k=5)
└─► build_prompt(state, rag_context, personality)
└─► LLM.generate(prompt)
└─► parse_action_json(response)
├─ validate vs legal_actions[]
├─ log to ai_decisions.jsonl
└─► Forge stdin ← chosen action
```
### Brain Selector (`ai/brain_selector.py`)
```python
from enum import Enum
class Brain(Enum):
DEEPSEEK = "deepseek-r1:14b"
GPT_OSS = "gpt-oss-20b" # adjust to your Ollama model tag
def select_brain(phase: str, stack_size: int) -> Brain:
"""
DeepSeek-R1: combat math, instant-speed stack decisions, targeted removal.
GPT-OSS: sorcery-speed strategy, ramp sequencing, card selection.
"""
combat_phases = {"declare_attackers", "declare_blockers", "combat_damage"}
if phase in combat_phases or stack_size > 0:
return Brain.DEEPSEEK
return Brain.GPT_OSS
```
### Prompt Builder (`ai/prompt_builder.py`)
```python
from chromadb import Client
from .personality import get_personality_prompt
chroma = Client()
card_collection = chroma.get_collection("mtg_cards")
def build_prompt(state: dict, player_id: str, personality: str) -> str:
# Pull card knowledge scoped to THIS player's deck
player_cards = [c["name"] for c in state["players"][player_id]["library"]]
rag_results = card_collection.query(
query_texts=player_cards[:10],
n_results=5
)
card_context = "\n".join(rag_results["documents"][0])
personality_prompt = get_personality_prompt(personality)
return f"""You are an AI playing Magic: The Gathering Commander.
{personality_prompt}
## Current Game State
- Phase: {state['phase']}
- Active Player: {player_id}
- Life Totals: {state['life_totals']}
- Commander Damage Dealt: {state['commander_damage']}
- Stack: {state['stack']}
- Your Hand: {[c['name'] for c in state['players'][player_id]['hand']]}
- Battlefield: {[c['name'] for c in state['players'][player_id]['battlefield']]}
- Available Mana: {state['players'][player_id]['mana_pool']}
## Legal Actions
{state['legal_actions']}
## Card Knowledge (RAG Context)
{card_context}
Respond with a single JSON object:
{{"action": "<action_type>", "card_id": "<id_or_null>", "targets": [], "reasoning": "<brief>"}}
"""
```
### Personality System (`ai/personality.py`)
```python
import random
PERSONALITIES = {
"aggro_beatdown": "You play aggressively. Attack every turn. Remove blockers. Win through combat damage as fast as possible.",
"control_lockdown": "You play control. Counter key threats. Hold up mana for interaction. Win only when the board is locked.",
"combo_enabler": "You dig for your combo pieces. Every decision is evaluated by how it advances your win condition.",
"political_diplomat": "You form alliances. Attack the leader. Make deals. Never be the archenemy.",
"voltron_commander": "Everything buffs your commander. Protect it at all costs. Win through commander damage.",
"stax_prison": "You tax and restrict opponents. Deploy hate pieces. Win through attrition and resource denial.",
"tempo_value": "You trade efficiently. Generate card advantage. Win by being ahead on resources.",
"chaos_wildcard": "You make unpredictable decisions. Prioritize fun and chaos over optimal play.",
}
def assign_personalities(player_ids: list[str], deck_archetypes: dict) -> dict[str, str]:
"""
Assign a personality per AI player at game creation.
80% chance: archetype-matched personality.
20% chance: random wildcard for unpredictability.
"""
assignments = {}
for pid in player_ids:
archetype = deck_archetypes.get(pid, "tempo_value")
if random.random() < 0.20:
assignments[pid] = random.choice(list(PERSONALITIES.keys()))
else:
assignments[pid] = archetype
return assignments
def get_personality_prompt(personality: str) -> str:
return f"## Your Playstyle\n{PERSONALITIES.get(personality, PERSONALITIES['tempo_value'])}"
```
### AI Orchestrator (`ai/ai_orchestrator.py`)
```python
import json, time, logging
from pathlib import Path
from .brain_selector import select_brain, Brain
from .prompt_builder import build_prompt
import ollama
LOG_PATH = Path("logs/ai_decisions.jsonl")
LOG_PATH.parent.mkdir(exist_ok=True)
async def get_ai_action(state: dict, player_id: str, personality: str) -> dict:
brain = select_brain(state["phase"], len(state["stack"]))
prompt = build_prompt(state, player_id, personality)
t_start = time.time()
try:
response = ollama.chat(
model=brain.value,
messages=[{"role": "user", "content": prompt}],
options={"temperature": 0.3, "num_predict": 256}
)
raw = response["message"]["content"]
# Extract JSON from response (model may wrap in markdown)
action = json.loads(raw[raw.find("{"):raw.rfind("}")+1])
except Exception as e:
logging.warning(f"AI parse failed ({brain.value}): {e}. Passing turn.")
action = {"action": "pass", "card_id": None, "targets": [], "reasoning": "parse_error"}
# Validate against Forge legal actions
if action.get("action") not in [a["type"] for a in state["legal_actions"]]:
logging.warning(f"Illegal action returned by {brain.value}. Falling back to pass.")
action = {"action": "pass", "card_id": None, "targets": [], "reasoning": "illegal_action"}
# Log for PPO training dataset
log_entry = {
"timestamp": time.time(),
"player_id": player_id,
"brain": brain.value,
"personality": personality,
"phase": state["phase"],
"action": action,
"reasoning": action.get("reasoning", ""),
"latency_ms": round((time.time() - t_start) * 1000),
}
with open(LOG_PATH, "a") as f:
f.write(json.dumps(log_entry) + "\n")
return action
```
---
## Phase 0 — Local Card Image Cache
**Build this first.** Every downstream system depends on local art URLs.
### Step 1 — Bulk Download (`scripts/download_card_images.py`)
```python
import requests, json, os
from pathlib import Path
from tqdm import tqdm
BULK_URL = "https://api.scryfall.com/bulk-data/default-cards"
IMAGE_DIR = Path("static/card-images")
IMAGE_DIR.mkdir(parents=True, exist_ok=True)
def download_images():
# Get bulk data URL
meta = requests.get(BULK_URL).json()
cards = requests.get(meta["download_uri"], stream=True).json()
for card in tqdm(cards, desc="Downloading card images"):
image_uris = card.get("image_uris") or {}
url = image_uris.get("normal")
if not url:
continue # skip DFCs without top-level image_uris
safe_name = card["name"].replace("/", "_").replace(" ", "_")
set_code = card.get("set", "unknown")
filename = IMAGE_DIR / set_code / f"{safe_name}_{card['collector_number']}.jpg"
filename.parent.mkdir(parents=True, exist_ok=True)
if filename.exists():
continue # already cached
img_data = requests.get(url, timeout=10).content
filename.write_bytes(img_data)
if __name__ == "__main__":
download_images()
```
### Step 2 — Build SQLite Index (`scripts/build_image_index.py`)
```python
import sqlite3
from pathlib import Path
IMAGE_DIR = Path("static/card-images")
DB_PATH = Path("data/card_images.db")
DB_PATH.parent.mkdir(exist_ok=True)
def build_index():
conn = sqlite3.connect(DB_PATH)
conn.execute("""
CREATE TABLE IF NOT EXISTS card_images (
name TEXT PRIMARY KEY,
path TEXT NOT NULL
)
""")
for img_path in IMAGE_DIR.rglob("*.jpg"):
card_name = img_path.stem.rsplit("_", 1)[0].replace("_", " ")
conn.execute("INSERT OR REPLACE INTO card_images VALUES (?, ?)",
(card_name, str(img_path)))
conn.commit()
conn.close()
if __name__ == "__main__":
build_index()
```
### Step 3 — Card Enricher (`api/card_enricher.py`)
```python
import sqlite3
from pathlib import Path
DB_PATH = Path("data/card_images.db")
STATIC_URL = "http://localhost:8000/static/card-images"
def enrich_card(card: dict) -> dict:
"""Inject local art_url into every CardDTO before sending to Unity."""
conn = sqlite3.connect(DB_PATH)
cursor = conn.execute("SELECT path FROM card_images WHERE name = ?", (card["name"],))
row = cursor.fetchone()
conn.close()
if row:
relative = Path(row[0]).relative_to("static/card-images")
card["art_url"] = f"{STATIC_URL}/{relative}"
else:
card["art_url"] = f"{STATIC_URL}/card_back.jpg"
return card
```
### Step 4 — FastAPI Static Mount (`main.py` addition)
```python
from fastapi.staticfiles import StaticFiles
app.mount("/static/card-images", StaticFiles(directory="static/card-images"), name="card-images")
```
---
## Phase 1 — Forge Bridge (Rules Engine)
### ForgeSubprocessManager (`forge/subprocess_manager.py`)
```python
import subprocess, json, asyncio, threading
from pathlib import Path
FORGE_JAR = Path("forge/forge-game.jar")
class ForgeSubprocessManager:
def __init__(self):
self.process = None
self._lock = threading.Lock()
def start(self, deck_paths: list[str], format: str = "commander"):
cmd = [
"java", "-jar", str(FORGE_JAR),
"--headless",
"--format", format,
"--decks", *deck_paths,
"--output", "json"
]
self.process = subprocess.Popen(
cmd,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True,
bufsize=1
)
def read_state(self) -> dict:
with self._lock:
line = self.process.stdout.readline()
return json.loads(line)
def send_action(self, action: dict):
with self._lock:
self.process.stdin.write(json.dumps(action) + "\n")
self.process.stdin.flush()
def stop(self):
if self.process:
self.process.terminate()
```
### Data Transfer Objects (`api/dtos.py`)
```python
from pydantic import BaseModel
from typing import Optional
class CardDTO(BaseModel):
id: str
name: str
type: str
power: Optional[str]
toughness: Optional[str]
mana_cost: str
art_url: str = ""
tapped: bool = False
zone: str = "hand"
class PlayerDTO(BaseModel):
id: str
life: int
commander_damage: dict[str, int]
commander_tax: int
hand: list[CardDTO]
battlefield: list[CardDTO]
graveyard: list[CardDTO]
exile: list[CardDTO]
command_zone: list[CardDTO]
library_count: int
mana_pool: dict[str, int]
is_ai: bool
personality: Optional[str]
class GameStateDTO(BaseModel):
game_id: str
phase: str
active_player: str
priority: str
turn_number: int
players: list[PlayerDTO]
stack: list[dict]
legal_actions: list[dict]
winner: Optional[str]
```
---
## Phase 2 — FastAPI WebSocket Game Server
### Game Loop (`api/game_loop.py`)
```python
import asyncio, uuid
from .dtos import GameStateDTO
from forge.subprocess_manager import ForgeSubprocessManager
from ai.ai_orchestrator import get_ai_action
from ai.personality import assign_personalities
from api.card_enricher import enrich_card
active_games: dict[str, dict] = {}
async def start_game(deck_paths: list[str], ai_players: list[str]) -> str:
game_id = str(uuid.uuid4())
forge = ForgeSubprocessManager()
forge.start(deck_paths)
# Assign AI personalities at game creation
archetypes = detect_archetypes(deck_paths, ai_players) # ChromaDB query
personalities = assign_personalities(ai_players, archetypes)
active_games[game_id] = {
"forge": forge,
"ai_players": ai_players,
"personalities": personalities,
"websockets": []
}
asyncio.create_task(game_loop(game_id))
return game_id
async def game_loop(game_id: str):
game = active_games[game_id]
forge = game["forge"]
while True:
raw_state = forge.read_state()
if raw_state.get("winner"):
await broadcast(game_id, raw_state)
break
# Enrich every card with local art_url
state = enrich_state(raw_state)
await broadcast(game_id, state)
priority_player = state["priority"]
if priority_player in game["ai_players"]:
personality = game["personalities"][priority_player]
action = await get_ai_action(state, priority_player, personality)
forge.send_action(action)
# else: wait for Unity client to send action via WebSocket
async def broadcast(game_id: str, state: dict):
game = active_games[game_id]
for ws in game["websockets"]:
await ws.send_json(state)
def enrich_state(state: dict) -> dict:
for player in state["players"]:
for zone in ["hand", "battlefield", "graveyard", "exile", "command_zone"]:
player[zone] = [enrich_card(c) for c in player.get(zone, [])]
return state
```
### WebSocket Endpoint (`api/routes/game.py`)
```python
from fastapi import APIRouter, WebSocket, WebSocketDisconnect
from api.game_loop import active_games, start_game
router = APIRouter()
@router.websocket("/ws/game/{game_id}")
async def game_ws(websocket: WebSocket, game_id: str):
await websocket.accept()
game = active_games.get(game_id)
if not game:
await websocket.close(code=4004)
return
game["websockets"].append(websocket)
try:
while True:
data = await websocket.receive_json()
# Human player action forwarded directly to Forge
game["forge"].send_action(data)
except WebSocketDisconnect:
game["websockets"].remove(websocket)
```
---
## Phase 3 — Unity TCGEngine Board (2D)
### Project Layout
```
unity-commander-ui/
├── Assets/
│ ├── TCGEngine/ ← TCGEngine base (unmodified)
│ ├── Commander/
│ │ ├── Scripts/
│ │ │ ├── GameNetworkManager.cs ← WebSocket ↔ FastAPI
│ │ │ ├── CommanderBoardUI.cs ← 4-player layout controller
│ │ │ ├── PlayerAreaUI.cs ← per-player zone panel
│ │ │ ├── CardImageLoader.cs ← local cache texture fetcher
│ │ │ ├── ZonePanel.cs ← Hand/Library/Graveyard/Exile/Command
│ │ │ └── AIThinkingIndicator.cs ← spinner while AI brain is deciding
│ │ └── Prefabs/
│ │ ├── CardPrefab.prefab
│ │ ├── PlayerAreaPrefab.prefab
│ │ └── ZonePanelPrefab.prefab
│ └── Scenes/
│ └── CommanderBoard.unity
```
### Board Layout (matches screenshot)
```
┌──────────────────────────────────────────────────────────────────┐
│ [LIBRARY][GRAVEYARD][EXILE] [────── HAND ──────] (Player 3) │
│ [COMMAND] │
├──────────────────────────────┬───────────────────────────────────┤
│ │ │
│ │ │
│ BATTLEFIELD │ BATTLEFIELD │
│ (Player 1) │ (Player 2) │
│ BATTLEFIELD (center) │
│ BATTLEFIELD │ BATTLEFIELD │
│ (Player 4) │ (Player 3) │
│ │ │
├──────────────────────────────┴───────────────────────────────────┤
│ [COMMAND] │
│ [LIBRARY][GRAVEYARD][EXILE] [────── HAND ──────] (Player 1) │
└──────────────────────────────────────────────────────────────────┘
```
### GameNetworkManager.cs
```csharp
using UnityEngine;
using NativeWebSocket;
using Newtonsoft.Json;
public class GameNetworkManager : MonoBehaviour
{
public static GameNetworkManager Instance { get; private set; }
[SerializeField] private string serverUrl = "ws://localhost:8000/ws/game/";
[SerializeField] private string gameId;
private WebSocket _socket;
public delegate void GameStateReceived(GameStateDTO state);
public event GameStateReceived OnGameStateReceived;
async void Awake()
{
Instance = this;
_socket = new WebSocket($"{serverUrl}{gameId}");
_socket.OnMessage += (bytes) =>
{
var json = System.Text.Encoding.UTF8.GetString(bytes);
var state = JsonConvert.DeserializeObject<GameStateDTO>(json);
UnityMainThreadDispatcher.Enqueue(() => OnGameStateReceived?.Invoke(state));
};
await _socket.Connect();
}
public async void SendAction(object action)
{
var json = JsonConvert.SerializeObject(action);
await _socket.SendText(json);
}
void Update() => _socket?.DispatchMessageQueue();
async void OnDestroy() => await _socket?.Close();
}
```
### CardImageLoader.cs
```csharp
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.Networking;
using UnityEngine.UI;
public class CardImageLoader : MonoBehaviour
{
private static Dictionary<string, Texture2D> _cache = new();
public static IEnumerator LoadCardArt(string artUrl, RawImage target)
{
if (_cache.TryGetValue(artUrl, out var cached))
{
target.texture = cached;
yield break;
}
using var req = UnityWebRequestTexture.GetTexture(artUrl);
yield return req.SendWebRequest();
if (req.result == UnityWebRequest.Result.Success)
{
var tex = DownloadHandlerTexture.GetContent(req);
_cache[artUrl] = tex;
target.texture = tex;
}
else
{
Debug.LogWarning($"Failed to load card art: {artUrl}");
}
}
}
```
### AIThinkingIndicator.cs
```csharp
using UnityEngine;
using UnityEngine.UI;
/// Shows a spinner + brain label while the AI is deciding.
public class AIThinkingIndicator : MonoBehaviour
{
[SerializeField] private GameObject spinnerPanel;
[SerializeField] private Text brainLabel;
[SerializeField] private Text personalityLabel;
public void Show(string brain, string personality)
{
spinnerPanel.SetActive(true);
brainLabel.text = $"🧠 {brain}";
personalityLabel.text = $"🎭 {personality}";
}
public void Hide() => spinnerPanel.SetActive(false);
}
```
---
## Phase 4 — Commander Zone UIs
### Zone Definitions per Player
| Zone Panel | Content | Notes |
|---|---|---|
| `LIBRARY` | Count only | Click → draw/look at top |
| `GRAVEYARD` | Scrollable card list | Full card art on hover |
| `EXILE` | Scrollable card list | Includes face-down indicators |
| `HAND` | Wide panel, card art | Hidden to opponents |
| `COMMAND` | Commander card(s) | Shows commander tax counter |
| `BATTLEFIELD` | Open grid | Tapped/untapped state, counters |
### Commander-Specific UI Elements
```csharp
public class CommanderZoneUI : MonoBehaviour
{
[SerializeField] private Text taxCounter; // "Tax: +{2N}"
[SerializeField] private RawImage commanderArt;
[SerializeField] private Text commanderDamageDealt; // per opponent
public void Refresh(PlayerDTO player)
{
int tax = player.CommanderTax;
taxCounter.text = tax > 0 ? $"+{tax * 2}" : "";
if (player.CommandZone.Count > 0)
StartCoroutine(CardImageLoader.LoadCardArt(
player.CommandZone[0].ArtUrl, commanderArt));
}
}
```
---
## Phase 5 — Commander Rules Validation Layer
Forge handles all rules natively. FastAPI validates the AI response is within the legal actions list Forge returns — no custom rules code needed in Python or Unity.
Key Commander rules confirmed handled by Forge headless:
- **Commander Tax** — +2 generic per previous cast from command zone
- **21 Commander Damage** — tracked per attacker/defender pair
- **40 Starting Life**
- **Zone Redirect** — commander goes to command zone instead of graveyard/exile (player choice)
- **Color Identity** — deck validation at game start
- **Singleton** — deck validation at game start
---
## Phase 6 — AI Decision Logging & PPO Pipeline
Every AI decision written to `logs/ai_decisions.jsonl`:
```json
{
"timestamp": 1743520800.123,
"player_id": "player_2",
"brain": "deepseek-r1:14b",
"personality": "aggro_beatdown",
"phase": "declare_attackers",
"action": {"action": "attack", "card_id": "c_042", "targets": ["player_1"], "reasoning": "Open attack, no blockers visible"},
"latency_ms": 847
}
```
This log becomes the **supervised learning dataset** for the next PPO training iteration. Each (state, action) pair from winning games is a positive training example.
---
## Project Directory Structure
```
commander-ai-lab/
├── main.py ← FastAPI app, mounts static files
├── api/
│ ├── dtos.py ← Pydantic DTOs
│ ├── card_enricher.py ← Injects art_url into CardDTOs
│ ├── game_loop.py ← Main game orchestration loop
│ └── routes/
│ └── game.py ← WebSocket + REST endpoints
├── ai/
│ ├── ai_orchestrator.py ← Main AI decision entry point
│ ├── brain_selector.py ← DeepSeek-R1 vs GPT-OSS routing
│ ├── prompt_builder.py ← Game state + RAG → prompt
│ └── personality.py ← 8 personalities + assignment
├── forge/
│ └── subprocess_manager.py ← Forge JVM stdin/stdout bridge
├── scripts/
│ ├── download_card_images.py ← One-time Scryfall bulk download
│ └── build_image_index.py ← SQLite name→path index
├── static/
│ └── card-images/ ← ~2GB local image cache
│ └── {set_code}/
│ └── {CardName}_{num}.jpg
├── data/
│ └── card_images.db ← SQLite image index
├── logs/
│ └── ai_decisions.jsonl ← PPO training dataset
├── unity-commander-ui/ ← Unity 2D TCGEngine project
└── docs/
└── implementation_plan.md ← This document
```
---
## Phased Timeline
| Phase | Scope | Est. Time |
|---|---|---|
| **0** | Scryfall image download + SQLite index + FastAPI static mount | 1 day |
| **1** | Forge subprocess bridge + DTO definitions | 2–3 days |
| **2** | AI Orchestrator + brain selector + prompt builder + personality system | 3–4 days |
| **3** | Unity TCGEngine project setup + WebSocket GameNetworkManager | 3–4 days |
| **4** | PlayerAreaUI + ZonePanels + CardImageLoader + board layout | 3–4 days |
| **5** | Commander zone UI (tax counter, commander damage, zone redirect) | 1–2 days |
| **6** | AI decision logging + end-to-end game loop test | 1–2 days |
**Total estimated: ~3 weeks to first playable Commander game with live AI opponents.**
---
## Key Design Decisions
| Decision | Rationale |
|---|---|
| Forge = rules authority | Forge already handles all Commander rules natively. No custom rules code. |
| TCGEngine = dumb renderer | All game logic lives in Forge. Unity only renders state it receives. |
| FastAPI = hub | Bridges Forge ↔ Unity ↔ AI. Single point of truth. |
| Dual-brain routing | DeepSeek-R1 excels at analytical/math decisions; GPT-OSS at strategic sequencing. |
| ChromaDB scoped per player | RAG context relevant to the active player's deck, not global card knowledge. |
| Personality persists per game | Consistent playstyle per AI across all 4 players per session. |
| Local image cache | Eliminates network latency for card art. ~2GB one-time download. |
| ai_decisions.jsonl | Every AI decision is a future PPO training sample. |
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# Forge in the cluster image
The batch simulator on the Simulator page runs on **Forge**, the Java rules engine. The first cluster builds left it out by mistake ("Python-only"
was read as "no Java"), so the page answered `Forge JAR not found`. It is now built into the image (reference copy of the Dockerfile stage:
`infrastructure/apps/commander-lab/image/Dockerfile` in the farmhouse-os repo).
What the image build does:
1. **Stage 1** (`maven:3.9-eclipse-temurin-17`): fetch the official `Card-Forge/forge` at commit `edb28158f6d8` (the commit `KoalaTrapLord/Forge-Repo`
is on; that fork is 0 commits ahead of upstream, so this is the code the lab's Java was written against; newer Forge may have changed APIs), then
`mvn install -pl forge-gui-desktop -am -DskipTests -Drevision=2.0.12-SNAPSHOT`, retried up to three times (Maven Central downloads fail now and then).
2. **The lab's Java tool** (`LabCli`, from this repository's `pom.xml`) is built against it. Forge's installed poms keep `${revision}` as their parent
version, which Maven cannot resolve for a consumer even with `-Drevision`; the placeholder is rewritten in the installed `.pom` files first.
3. **Runtime**: the full `openjdk-17-jre` (not `-headless`) plus `xvfb`, the two jars, and Forge's `res/` data without `adventure`, `music`, `sound`, `quest` and `skins`
(about 190 MB the headless simulator never reads). The environment the app reads: `FORGE_JAR`, `FORGE_DIR`, `LAB_JAR`, `FORGE_DECKS_DIR`, `PRECON_DIR`, `JAVA_HOME`.
4. `FORGE_MAX_THREADS` (default 1) clamps the batch's thread count: every Forge game is its own JVM with up to a 4 GB heap, and the cluster has two small nodes.
5. CI starts the app, begins a batch through the app's own `/api/lab/start` and requires a Commander game with turns played and a readable `/api/lab/result` before the image is published.
## Things that only showed up by running it in the cluster (2026-10-27)
Each of these made a batch fail while every individual piece looked installed. Together they are why the CI test starts a batch through the app's own API.
- **Forge needs a display, even for `sim`.** `GuiDesktop`'s static initialiser asks AWT for the default screen device; the headless JRE throws `HeadlessException`, and
`forge.view.Main` then exits 1 with no output at all (the lab tool records every game as an instant draw: "Forge produced NO output"). Fix: the full `openjdk-17-jre`,
`xvfb`, `DISPLAY=:99`, and the entrypoint starts `Xvfb :99`.
- **Three folders, one deck.** The app writes saved decks to `USER_DECKS_DIR`, the lab's Java tool resolves deck files through `PRECON_DECKS_DIR`, and Forge finds a deck BY
NAME in its own commander folder ("Could not load deck"). All three are the Forge folder (`FORGE_DECKS_DIR`), which the entrypoint seeds with the 163 bundled precons.
- **Writable folders.** The tool writes `results/` beside Forge's data and Forge a debug log beside its jar; both folders are owned by uid 10001 in the image.
- **Result path.** The app asked for `results/ml-decision-<id>.json` (relative) but ran the tool from Forge's folder, so the file landed there and `/api/lab/result` answered
"Result file not found". The path is made absolute (patch `forge-results-abs`) and `/app/results` is linked to `/state/results`.
- `docker cp` cannot write into a tmpfs and Docker mounts `/tmp` `noexec` (it stops SQLite's native library loading): test-harness traps, not cluster problems.
Speed: about 25 seconds per game, one at a time (the project's own figure is ~0.04 simulations/s single-threaded). For many games with real rules, the
phase-rs engine (`farmhouse/phase/`) is far faster. Forge is GPL-3.0: private use, do not redistribute the built image.
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#!/bin/sh
# Keep the app's state on the persistent volume (/state) while the code stays in the image.
# The upstream app writes next to its code: collection.db, scryfall_cache.db, data/ (Scryfall bulk DB, ChromaDB index),
# user-decks/. Point each at /state with a symlink; seed data/ and user-decks/ from the image the first time.
set -eu
S="${STATE_DIR:-/state}"
mkdir -p "$S"
for d in data user-decks results; do
if [ ! -e "$S/$d" ]; then
if [ -d "/app/$d" ]; then cp -a "/app/$d" "$S/$d"; else mkdir -p "$S/$d"; fi
fi
rm -rf "/app/$d"
ln -s "$S/$d" "/app/$d"
done
for f in collection.db scryfall_cache.db; do
rm -f "/app/$f"
ln -s "$S/$f" "/app/$f" # sqlite creates the target on first open
done
# Forge looks decks up by name in its user folder; the app exports the decks it simulates there.
mkdir -p "${FORGE_DECKS_DIR:-/tmp/.forge/decks/commander}"
# Seed it with the bundled precon decks (163 small files); never overwrite a deck that is already there.
if [ -d /app/precon-decks ]; then cp -n /app/precon-decks/*.dck "${FORGE_DECKS_DIR:-/tmp/.forge/decks/commander}"/ 2>/dev/null || true; fi
# A virtual display for Forge (it initialises AWT even in command-line mode; see the Dockerfile). Best effort: the app itself does not need it.
if command -v Xvfb >/dev/null 2>&1; then
mkdir -p /tmp/.X11-unix && chmod 1777 /tmp/.X11-unix 2>/dev/null || true
Xvfb "${DISPLAY:-:99}" -screen 0 1280x800x24 -nolisten tcp >/tmp/xvfb.log 2>&1 &
for _ in 1 2 3 4 5 6 7 8 9 10; do
[ -S "/tmp/.X11-unix/X${DISPLAY#:}" ] && break
sleep 0.3
done
fi
exec "$@"
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# Laya (https://github.com/NandhaKishorM/laya, Apache-2.0): a "decision model". It answers typed multiple-choice / yes-no / score
# questions about a piece of text in ONE forward pass (about 33 ms on a GPU) and never writes text. Here it is staged for the Commander
# AI Lab simulator, which asks an LLM "which action?" about 25 times per game; a small classifier can answer that class of question
# far faster, if it is accurate enough (it is measured in shadow mode first, see ../README.md).
#
# CPU only. The weights are baked in at a pinned Hugging Face commit and the pod has NO network access (NetworkPolicy), so the
# running service fetches nothing. Two checkpoints are included so they can be compared: the English base and the fine-tuned
# "typed-decisions" one (about 840 MB each).
FROM python:3.12-slim-bookworm
ARG LAYA_VERSION=0.4.15
ARG LAYA_HF_REVISION=7b928d828b7b0e022f929d9bd2e44165aa270148
ENV PIP_NO_CACHE_DIR=1 PIP_DISABLE_PIP_VERSION_CHECK=1 PYTHONUNBUFFERED=1 HF_HOME=/opt/hf
RUN pip install torch --index-url https://download.pytorch.org/whl/cpu \
&& pip install "laya[serve]==${LAYA_VERSION}"
# Weights at the pinned commit. HF's offline lookup resolves the "main" ref, which a commit-pinned download does not create, so it is
# written by hand to the same commit.
RUN python -c "from huggingface_hub import snapshot_download; snapshot_download('convaiinnovations/laya', revision='${LAYA_HF_REVISION}', ignore_patterns=['multilingual/*','assets/*','eval/*'])" \
&& mkdir -p /opt/hf/hub/models--convaiinnovations--laya/refs \
&& printf '%s' "${LAYA_HF_REVISION}" > /opt/hf/hub/models--convaiinnovations--laya/refs/main \
&& chmod -R a+rX /opt/hf
RUN useradd --uid 10001 --no-create-home --shell /usr/sbin/nologin laya
USER 10001
ENV HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
LAYA_HOST=0.0.0.0 LAYA_PORT=8080 \
LAYA_MODELS=english,typed-decisions LAYA_DEFAULT_MODEL=english LAYA_MAX_LOADED=2 LAYA_PRELOAD=1 LAYA_THREADS=4
EXPOSE 8080
CMD ["laya-serve"]
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# Laya as the simulator's "which action?" model: evaluation, 2026-10-26
**Result: not usable as shipped.** Both checkpoints agreed with the LLM less often than a model that always answers the commonest action.
## What was measured
The simulator's LLM brain ("twin-turbo") picks one of nine actions about 25 times a game. A decision model (Laya, or Kev; both serve
`POST /v1/systemone`) could answer that kind of question in one forward pass instead of a 7-second generation. To find out whether it
could stand in, every LLM decision the app had recorded (`/app/logs/decisions/*.jsonl`: the game state and the action the LLM chose)
was replayed through Laya with the same question, and the answers compared. The LLM is the *teacher*, not the truth: this measures
whether a fast model can imitate the slow one, not which plays better.
Script: `image/farmhouse_decision_eval.py` (in the app image as `/app/scripts/farmhouse_decision_eval.py`):
`DECISION_URL=http://laya:8080 python scripts/farmhouse_decision_eval.py /app/logs/decisions english typed-decisions`
## Numbers (149 recorded decisions)
LLM's choices: play_land 41, hold 31, attack_all 28, cast_creature 20, cast_spell 13, attack_safe 7, cast_removal 5, cast_ramp 4.
| | agrees with the LLM | answers | time per answer | what it picked |
|---|---|---|---|---|
| always "play_land" (baseline) | **27.5%** | 149 | 0 | |
| `english` (ModernBERT-large, base) | 16.1% | 149 | 1.9 s | cast_creature 105, attack_all 26; never hold, almost never play_land |
| `typed-decisions` (fine-tuned) | 18.6% | 140 (9 timed out at 5 s) | ~4 s (shared CPU) | cast_creature 99, attack_all 26 |
It could say "cast_creature" and "attack_all" and little else; it never recognised "hold" (31 of the LLM's choices) or a removal spell.
## Why, and caveats
- The shipped checkpoints are trained on generic ticket-triage style decisions. Laya's own README says fine-tuning "on decisions from your
own domain is where accuracy jumps" (0.766 against 0.362 for the base on its own benchmark). This game's decisions were never in its training.
- Speed on this cluster's CPU (4 threads) was 2-4 s per answer, not the ~33 ms quoted for a GPU. Against the LLM's ~7.5 s that is not the
speed-up that was hoped for either, without a GPU.
- The question wording (the nine action descriptions) and the compact text it reads (`farmhouse_decide.compact_state`) are mine; a
better prompt might help a little, not from 18% to usable.
- Part of the recorded data predates the card-data fix (v10), so some states had thinner hands than they would now.
- The LLM being the "truth" also caps the result: where the LLM is wrong, agreeing with it is not a win.
## Where this leaves it
- Laya is **staged but scaled to 0 replicas** (`deployment-laya.yaml`: `replicas: 0`; set to 1 to run it). The image
(`ghcr.io/fez5587/farmhouse-laya:v1`, offline, weights baked in), the service and the network policy remain.
- The app is wired to use it (`DECISION_URL`, `DECISION_MODE`) and `DECISION_MODE` is `off`. `shadow` records agreement without
affecting games; `assist` uses it when it is `DECISION_MIN_CONF` sure. Kev speaks the same API, so swapping is a URL.
- To make it worth using: fine-tune on this game's decisions (Laya ships a Kaggle 2xT4 notebook, about 4 hours, and an Apple/CPU
script). The labels would be the LLM's recorded decisions; a few thousand are needed, so generate them first (LLM games at ~7 s a
decision, or label states with the engine's own heuristic for the cheap, abundant version). Then re-run this evaluation.
- Node memory: it needed about 2 GB (3.6 GB with both checkpoints, which pushed the control-plane node to 92%; it is pinned to the other node).
## Later the same day: the new connections (see docs/LAYA-CONNECTIONS.md in the fork)
- **Forge-shaped priority choice** (CSIE's ranking in the text, three scenarios): agreed with CSIE each time, 42-48% sure, 7.5-8.4 s per call on the CPU against Forge's
1.5 s budget. Not usable on a CPU; the code falls back silently.
- **Card roles from Oracle text** (150 unambiguous cards, 6 roles present, 9 offered): 48% against 17% by chance (counterspell 76%, board wipe 64%, removal 40%, tutor 40%,
ramp 36%, card draw 32%), 3.2 s per card in batches. Much better than the action choice; useful as on-demand evidence for the cards in a hand, too slow to tag the whole pool.
- **Extra questions** in the same call: "threat" said *high* for everything; "must answer" gave 0.48 for a lethal stack against 0.25-0.29 otherwise (right direction, weak).
- Capture, dataset export and the rest are built and off by default; fine-tuning on the captured decisions is the path to making it useful.
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# Laya in this repository
Laya (https://github.com/NandhaKishorM/laya, Apache-2.0) is a **decision model**: it answers typed multiple-choice, yes/no and score
questions about a piece of text in one forward pass and never writes text. Here it is a fast second opinion beside the LLM and CSIE.
Kev (https://github.com/jaredpalmer/kev) serves the same `POST /v1/systemone` API, so swapping is a URL change (Kev needs a GPU).
| File | What |
|---|---|
| `Dockerfile` | CPU image: `laya[serve]` 0.4.15 + CPU torch, English and `typed-decisions` checkpoints baked in at Hugging Face commit `7b928d8`; runs offline |
| `deployment-laya.yaml` | Kubernetes Deployment + Service + NetworkPolicy (no egress; only the app may call it). Currently `replicas: 0` |
| `build-farmhouse-laya.workflow.yml` | the CI workflow that builds and smoke-tests the image offline (it lives in `.github/workflows/` of the farmhouse-os GitOps repo) |
| `LAYA-EVALUATION.md` | why it is off: zero-shot both checkpoints agreed with the LLM less often than always guessing the commonest action |
The deployment source of truth is the farmhouse-os repo (`infrastructure/apps/commander-lab/`); these are reference copies so the
fork is self-contained. Things learned running it:
- A Kubernetes Service named `laya` injects `LAYA_PORT=tcp://...`, which `laya-serve` reads as its own port setting and refuses. Set
`enableServiceLinks: false` and `LAYA_PORT=8080` explicitly.
- Two checkpoints resident use 3.6 GB; one uses about 1.9 GB. Do not place it on a control-plane node.
- The CPU answers in 2-4 s, not the ~33 ms quoted for a GPU.
- How the code in this repository connects to it, and how to fine-tune it: `docs/LAYA-CONNECTIONS.md`.
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name: Build FarmHouse Laya
on:
workflow_dispatch:
push:
branches:
- main
paths:
- "infrastructure/apps/commander-lab/laya/**"
- ".github/workflows/build-farmhouse-laya.yml"
permissions:
contents: read
packages: write
jobs:
build:
runs-on: ubuntu-latest
env:
IMAGE_NAME: ghcr.io/${{ github.repository_owner }}/farmhouse-laya
# Bump together with the image tag in infrastructure/apps/commander-lab/deployment-laya.yaml.
IMAGE_TAG: v1
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Set image name lowercase
shell: bash
run: |
echo "IMAGE=$(echo "${IMAGE_NAME}" | tr '[:upper:]' '[:lower:]')" >> "$GITHUB_ENV"
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Build validation image
uses: docker/build-push-action@v6
with:
context: infrastructure/apps/commander-lab/laya
load: true
push: false
tags: laya:test
# The service must answer with NO network (that is how it runs in the cluster) as the unprivileged user, for both checkpoints.
- name: Smoke test (boots offline, answers a game-state question with both checkpoints)
shell: bash
run: |
set -euo pipefail
docker run -d --name laya --network none laya:test
cat > /tmp/ask.py <<'PY'
import json, sys, time, urllib.request
model = sys.argv[1]
body = {"model": model,
"state": {"body": "Turn 4. You have 4 lands untapped. Hand: Llanowar Elves, Swords to Plowshares, Forest. Opponent has a 6/6 creature attacking you."},
"questions": {"action": {"type": "choice", "instructions": "Which action is best this turn?",
"criteria": {"cast_creature": "put a creature onto the battlefield",
"cast_removal": "destroy or exile an opposing permanent",
"play_land": "play a land",
"hold": "do nothing this turn"}}}}
req = urllib.request.Request("http://127.0.0.1:8080/v1/systemone", data=json.dumps(body).encode(), headers={"content-type": "application/json"})
t = time.time()
out = json.load(urllib.request.urlopen(req, timeout=120))
print(model, "%.0f ms" % ((time.time() - t) * 1000), json.dumps(out)[:600])
assert "answers" in out and "action" in out["answers"], out
PY
docker cp /tmp/ask.py laya:/tmp/ask.py
ok=0
for i in $(seq 1 120); do
if docker exec laya python -c "import socket; socket.create_connection(('127.0.0.1', 8080), 2)" 2>/dev/null; then ok=1; break; fi
sleep 3
done
if [ "$ok" != 1 ]; then echo "laya did not open its port"; docker logs laya | tail -60; exit 1; fi
for m in english typed-decisions; do docker exec laya python /tmp/ask.py "$m"; done
docker exec laya id
docker rm -f laya >/dev/null
echo SMOKE OK
- name: Login to GitHub Container Registry
uses: docker/login-action@v3
with:
registry: ghcr.io
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Build and publish
uses: docker/build-push-action@v6
with:
context: infrastructure/apps/commander-lab/laya
push: true
tags: |
${{ env.IMAGE }}:${{ env.IMAGE_TAG }}
${{ env.IMAGE }}:${{ github.sha }}
labels: |
org.opencontainers.image.source=${{ github.server_url }}/${{ github.repository }}
org.opencontainers.image.revision=${{ github.sha }}
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# Laya, a CPU decision model (laya/Dockerfile), staged for the simulator. Reached only by the app, over the cluster network, on 8080.
# No internet, no volume (weights are in the image), no service-account token.
apiVersion: apps/v1
kind: Deployment
metadata:
name: laya
namespace: commander-lab
labels: {app: laya}
spec:
# 0 since 2026-10-26: both checkpoints scored BELOW the always-guess baseline against the LLM's recorded decisions (English 16.1%, typed-decisions
# 18.6%, baseline 27.5%, 149 states), and ~2 GB of the node's memory was needed for the card-search index build. Set to 1 to bring it back
# (image, service and policy stay in place); it is only worth running once it is fine-tuned on this game's decisions (README).
replicas: 0
selector:
matchLabels: {app: laya}
template:
metadata:
labels: {app: laya}
spec:
automountServiceAccountToken: false
# Kubernetes injects LAYA_PORT=tcp://<ip>:8080 for a Service named laya, and laya-serve reads LAYA_PORT as its own port setting
# ("invalid LAYA_PORT 'tcp://...'", crash loop). Service-link variables are not wanted here at all.
enableServiceLinks: false
# NOT on talos-gmw: that node runs the Kubernetes API server, and Laya's first placement there (two checkpoints, 3.6 GB) took it from
# 68% to 92% memory on 2026-10-26.
nodeSelector: {kubernetes.io/hostname: talos-noy-0r4}
imagePullSecrets:
- name: ghcr-pull
securityContext:
runAsNonRoot: true
runAsUser: 10001
runAsGroup: 10001
seccompProfile: {type: RuntimeDefault}
containers:
- name: laya
image: ghcr.io/fez5587/farmhouse-laya:v1
ports:
- {name: http, containerPort: 8080}
env:
- {name: LAYA_THREADS, value: "4"}
- {name: LAYA_PORT, value: "8080"}
# One checkpoint only (about 1.9 GB): the English base scored below the always-guess baseline against the LLM; typed-decisions is the
# fine-tuned one still being measured.
- {name: LAYA_MODELS, value: "typed-decisions"}
- {name: LAYA_DEFAULT_MODEL, value: "typed-decisions"}
- {name: LAYA_MAX_LOADED, value: "1"}
securityContext:
allowPrivilegeEscalation: false
capabilities: {drop: [ALL]}
resources:
requests: {cpu: "500m", memory: 2500Mi}
limits: {cpu: "4", memory: 3Gi}
# Loading two checkpoints takes a while; the port opens when the server is up.
startupProbe:
tcpSocket: {port: http}
periodSeconds: 5
failureThreshold: 90
readinessProbe:
tcpSocket: {port: http}
periodSeconds: 10
livenessProbe:
tcpSocket: {port: http}
periodSeconds: 30
failureThreshold: 6
---
apiVersion: v1
kind: Service
metadata:
name: laya
namespace: commander-lab
spec:
type: ClusterIP
selector: {app: laya}
ports:
- {name: http, port: 8080, targetPort: http}
---
# IN: only the app. OUT: nothing at all (the weights are in the image; it needs no DNS and calls no one).
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: laya
namespace: commander-lab
spec:
podSelector:
matchLabels: {app: laya}
policyTypes: [Ingress, Egress]
ingress:
- from:
- podSelector:
matchLabels: {app: commander-lab}
ports:
- {protocol: TCP, port: 8080}
egress: []
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# phase-sim: rules-accurate four-player Commander games with phase-rs's headless AI (github.com/phase-rs/phase), behind a small HTTP service.
#
# phase is a Rust rules engine (stack, priority, combat, layers, triggers, replacement effects), dual licensed Apache-2.0 / MIT, with card
# data from MTGJSON (MIT). Pinned to release v0.118.0 (commit ad0de6a) and to the card database that release was published with
# (`card-data-5e541a38c99b97f0.json`, named in data.phase-rs.dev/card-data-meta.json for that commit); the file's SHA-256 is checked, so a
# changed file fails the build instead of silently changing results. Nothing is fetched at run time, and the pod has no network.
#
# Built: the `ai-commander` binary (4-player Commander, batch mode) and `ai-duel` with the repo's `server-release` profile (unwinding
# panics, so one game's panic cannot kill a batch). The toolchain is whatever rust-toolchain.toml pins (a dated nightly).
FROM rust:slim-bookworm AS build
ARG PHASE_REPO=https://github.com/phase-rs/phase.git
ARG PHASE_TAG=v0.118.0
ARG PHASE_SHA=ad0de6adf00153a6e6eb89604f6670be5b1d5168
ARG CARD_DATA_URL=https://data.phase-rs.dev/card-data-5e541a38c99b97f0.json
ARG CARD_DATA_SHA256=5e541a38c99b97f0ef12c81f7b249e7cd253f38b32bd5c156cf19608cb6f1218
RUN apt-get update \
&& apt-get install -y --no-install-recommends git ca-certificates curl build-essential pkg-config python3 \
&& rm -rf /var/lib/apt/lists/*
RUN set -eux; \
mkdir /phase; cd /phase; \
git init -q; \
git remote add origin "$PHASE_REPO"; \
git fetch -q --depth 1 origin "$PHASE_SHA"; \
git checkout -q FETCH_HEAD; \
test "$(git rev-parse HEAD)" = "$PHASE_SHA"
WORKDIR /phase
# phase's server-release profile uses thin LTO and 16 code-generation units; compiling its big engine crate that way ran a 7 GB CI machine
# out of memory ("cannot allocate memory", 2026-10-27). LTO off and two parallel jobs trade some build time (and a few percent of run
# speed) for a build that fits; the optimisation level and panic=unwind (which batch isolation needs) are unchanged.
ENV CARGO_BUILD_JOBS=2 \
CARGO_PROFILE_SERVER_RELEASE_LTO=off \
CARGO_PROFILE_SERVER_RELEASE_CODEGEN_UNITS=8
RUN cargo build --profile server-release -p phase-ai --bin ai-commander --bin ai-duel
RUN set -eux; \
curl -fsSL --retry 3 -o /card-data.json "$CARD_DATA_URL"; \
echo "$CARD_DATA_SHA256 /card-data.json" | sha256sum -c -; \
cp /phase/client/public/feeds/mtggoldfish-commander.json /sample-feed.json
FROM python:3.12-slim-bookworm
ARG PHASE_TAG=v0.118.0
RUN groupadd -g 10001 phase && useradd -u 10001 -g 10001 -d /tmp -s /usr/sbin/nologin phase
COPY --from=build /phase/target/server-release/ai-commander /opt/phase/ai-commander
COPY --from=build /phase/target/server-release/ai-duel /opt/phase/ai-duel
COPY --from=build /card-data.json /opt/phase/data/card-data.json
COPY --from=build /sample-feed.json /opt/phase/sample-feed.json
COPY phase_sim.py phase_smoke.py /opt/phase/
ENV PHASE_ENGINE_VERSION="phase-rs ${PHASE_TAG}" \
PHASE_AI_COMMANDER=/opt/phase/ai-commander \
PHASE_CARD_DATA=/opt/phase/data/card-data.json \
PYTHONUNBUFFERED=1 PORT=8090
USER 10001
WORKDIR /tmp
EXPOSE 8090
CMD ["python", "/opt/phase/phase_sim.py"]
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# phase-sim: rules-accurate four-player Commander games with phase-rs's headless AI (github.com/phase-rs/phase), behind a small HTTP service.
#
# phase is a Rust rules engine (stack, priority, combat, layers, triggers, replacement effects), dual licensed Apache-2.0 / MIT, with card
# data from MTGJSON (MIT). Pinned to release v0.118.0 (commit ad0de6a) and to the card database that release was published with
# (`card-data-5e541a38c99b97f0.json`, named in data.phase-rs.dev/card-data-meta.json for that commit); the file's SHA-256 is checked, so a
# changed file fails the build instead of silently changing results. Nothing is fetched at run time, and the pod has no network.
#
# Built: the `ai-commander` binary (4-player Commander, batch mode) and `ai-duel` with the repo's `server-release` profile (unwinding
# panics, so one game's panic cannot kill a batch). The toolchain is whatever rust-toolchain.toml pins (a dated nightly).
FROM rust:slim-bookworm AS build
ARG PHASE_REPO=https://github.com/phase-rs/phase.git
ARG PHASE_TAG=v0.118.0
ARG PHASE_SHA=ad0de6adf00153a6e6eb89604f6670be5b1d5168
ARG CARD_DATA_URL=https://data.phase-rs.dev/card-data-5e541a38c99b97f0.json
ARG CARD_DATA_SHA256=5e541a38c99b97f0ef12c81f7b249e7cd253f38b32bd5c156cf19608cb6f1218
RUN apt-get update \
&& apt-get install -y --no-install-recommends git ca-certificates curl build-essential pkg-config python3 \
&& rm -rf /var/lib/apt/lists/*
RUN set -eux; \
mkdir /phase; cd /phase; \
git init -q; \
git remote add origin "$PHASE_REPO"; \
git fetch -q --depth 1 origin "$PHASE_SHA"; \
git checkout -q FETCH_HEAD; \
test "$(git rev-parse HEAD)" = "$PHASE_SHA"
WORKDIR /phase
# phase's server-release profile uses thin LTO and 16 code-generation units; compiling its big engine crate that way ran a 7 GB CI machine
# out of memory ("cannot allocate memory", 2026-10-27). LTO off and two parallel jobs trade some build time (and a few percent of run
# speed) for a build that fits; the optimisation level and panic=unwind (which batch isolation needs) are unchanged.
ENV CARGO_BUILD_JOBS=2 \
CARGO_PROFILE_SERVER_RELEASE_LTO=off \
CARGO_PROFILE_SERVER_RELEASE_CODEGEN_UNITS=8
RUN cargo build --profile server-release -p phase-ai --bin ai-commander --bin ai-duel
# ---- model seats (this Dockerfile only) -------------------------------------------------------------------------------------------
# Everything above is identical to Dockerfile, so the long compile above comes from the build cache. These steps add `ai-commander-llm`: the
# same runner with a hook that lets a language model or a decision model (Laya / Kev) choose for named seats among the engine's OWN legal
# options. Two anchored edits to phase's source (apply_phase_patches.py: the build stops if phase moved either line) and one new crate.
# phase-llm depends on phase-ai, so the hook cannot live in phase-ai's binary; the new crate depends on both and includes the runner.
COPY llm-seat /llm-seat
RUN python3 /llm-seat/apply_phase_patches.py /phase /llm-seat
RUN cargo build --profile server-release -p farmhouse-llm-seat --bin ai-commander-llm
RUN set -eux; \
curl -fsSL --retry 3 -o /card-data.json "$CARD_DATA_URL"; \
echo "$CARD_DATA_SHA256 /card-data.json" | sha256sum -c -; \
cp /phase/client/public/feeds/mtggoldfish-commander.json /sample-feed.json
FROM python:3.12-slim-bookworm
ARG PHASE_TAG=v0.118.0
RUN groupadd -g 10001 phase && useradd -u 10001 -g 10001 -d /tmp -s /usr/sbin/nologin phase
COPY --from=build /phase/target/server-release/ai-commander /opt/phase/ai-commander
COPY --from=build /phase/target/server-release/ai-duel /opt/phase/ai-duel
COPY --from=build /phase/target/server-release/ai-commander-llm /opt/phase/ai-commander-llm
COPY --from=build /card-data.json /opt/phase/data/card-data.json
COPY --from=build /sample-feed.json /opt/phase/sample-feed.json
COPY phase_sim.py phase_smoke.py /opt/phase/
ENV PHASE_ENGINE_VERSION="phase-rs ${PHASE_TAG}" \
PHASE_AI_COMMANDER=/opt/phase/ai-commander \
PHASE_AI_COMMANDER_LLM=/opt/phase/ai-commander-llm \
PHASE_CARD_DATA=/opt/phase/data/card-data.json \
PYTHONUNBUFFERED=1 PORT=8090
USER 10001
WORKDIR /tmp
EXPOSE 8090
CMD ["python", "/opt/phase/phase_sim.py"]
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# phase-sim: rules-accurate Commander games with phase-rs
Reference copies of what runs in the cluster (source of truth: `infrastructure/apps/commander-lab/` in the farmhouse-os repo). Background and the assessment
that led here: `docs/PHASE-AND-PLAYTESTER.md`.
| File | What |
|---|---|
| `phase_sim.py` | the service: stdlib HTTP, one job at a time. `POST /run` four decks -> `GET /job/<id>` per-game results, wins by seat, 95% interval, warnings. Optional model seats and two loopback-only relays (`/llm/v1/chat/completions`, `/jev/systemone`) |
| `Dockerfile` | builds phase-rs `v0.118.0` (`ai-commander`, `ai-duel`) and takes the card database that release was published with (SHA-256 checked) |
| `Dockerfile.llm` | the same plus `ai-commander-llm`: model-driven seats |
| `llm-seat/` | the Rust crate and the two anchored edits to phase's source that make model seats possible |
| `phase_smoke.py`, `test_phase_sim.py` | CI: real games through the service; 49 offline checks against a stub engine |
| `deployment-phase.yaml` | Deployment, Service and a NetworkPolicy (in: the app only; out: DNS, Olla and Laya only) |
The app side is `routes/farmhouse_phase.py` (four saved decks -> the service) and `ui/phase-sim.html`; both are real source in this repository, registered by
`lab_api.py` and linked from the home page.
## Model seats: how they work and why they are safe
phase's headless runner has no model support; the model seats exist only in phase's browser client. `phase-llm` (the crate) deliberately does no networking:
it renders the engine's option list into a prompt, builds the HTTP request, and parses the reply, and the host performs the call. `llm-seat/` is that host
for the headless runner:
1. `apply_phase_patches.py` adds a process-wide `ACTION_OVERRIDE` hook in `phase-ai/src/auto_play.rs` (asked before the built-in AI chooses) and makes the runner's `main` `pub`.
2. The crate `farmhouse-llm-seat` includes the runner's source as a module (`#[path]`) and installs the hook. `phase-llm` depends on `phase-ai`, so the hook cannot
live inside phase-ai's own binary.
3. For each decision on a model seat: the engine issues its option list (`AiDecisionContract`); phase-llm builds the prompt (a forced move with one option is never asked);
this crate makes the HTTP call (blocking, no TLS, 20 s limit); phase-llm turns the reply into one of the engine's options; the choice is checked against the contract
here and again by `run_ai_action`. **Any** problem returns "no opinion" and phase's own AI plays the move. A cap on model calls per run (default 300) bounds the cost.
4. The service supplies the endpoints through its own loopback-only relays: the chat relay switches the model's thinking off (twin-turbo otherwise thinks for tens of
seconds a move) and caps the answer; the decision relay unwraps phase's `Jev` envelope and forwards the System One request to Laya or Kev.
The model never authors an action and never sees hidden cards beyond what phase-llm renders for that seat.
## Status
Written 2026-10-27. The service logic is tested offline; the Rust crate has not compiled outside CI when this was written (no Rust toolchain on the workstation), so
its first CI run is the first real test. Check `.github/workflows/build-farmhouse-phase-sim.yml` results in the farmhouse-os repo.
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# phase-sim (phase/Dockerfile): rules-accurate four-player Commander games over a small HTTP API. Only the app may call it; it has no
# network and no volume (engine and card data are in the image). One game at a time, CPU heavy.
apiVersion: apps/v1
kind: Deployment
metadata:
name: phase-sim
namespace: commander-lab
labels: {app: phase-sim}
spec:
replicas: 1
selector:
matchLabels: {app: phase-sim}
template:
metadata:
labels: {app: phase-sim}
spec:
automountServiceAccountToken: false
# A Service named like the app injects PHASE_SIM_* variables into the pod; nothing here wants them (the same kind of collision
# crash-looped Laya on LAYA_PORT).
enableServiceLinks: false
imagePullSecrets:
- name: ghcr-pull
securityContext:
runAsNonRoot: true
runAsUser: 10001
runAsGroup: 10001
seccompProfile: {type: RuntimeDefault}
containers:
- name: phase-sim
image: ghcr.io/fez5587/farmhouse-phase-sim:v1-llm # -llm = the baseline plus ai-commander-llm (model seats)
ports:
- {name: http, containerPort: 8090}
env:
- {name: PORT, value: "8090"}
- {name: PHASE_SIM_MAX_GAMES, value: "50"}
- {name: PHASE_SIM_TIMEOUT, value: "3600"}
# Model seats. The engine talks to THIS service's loopback-only relays, which forward to these (see phase_sim.py): chat = an
# OpenAI-style endpoint (Olla -> twin-turbo; the relay switches thinking off and caps the answer), decision = a System One server
# (Laya / Kev; the deployment is scaled to 0 until it is fine-tuned, so a decision-seat run answers 502 and falls back to phase's AI).
- {name: PHASE_LLM_CHAT_URL, value: "http://olla.ai-system.svc.cluster.local:40114/olla/openai/v1"}
- {name: PHASE_LLM_DECISION_URL, value: "http://laya.commander-lab.svc.cluster.local:8080/v1/systemone"}
- {name: PHASE_LLM_REASONING, value: "none"}
- {name: PHASE_LLM_MAX_TOKENS_CAP, value: "256"}
securityContext:
allowPrivilegeEscalation: false
readOnlyRootFilesystem: true
capabilities: {drop: [ALL]}
volumeMounts:
- {name: tmp, mountPath: /tmp}
resources:
requests: {cpu: "500m", memory: 1Gi}
limits: {cpu: "3", memory: 3Gi}
readinessProbe:
httpGet: {path: /health, port: http}
periodSeconds: 10
livenessProbe:
httpGet: {path: /health, port: http}
periodSeconds: 30
failureThreshold: 6
volumes:
- name: tmp
emptyDir: {sizeLimit: 512Mi}
---
apiVersion: v1
kind: Service
metadata:
name: phase-sim
namespace: commander-lab
spec:
type: ClusterIP
selector: {app: phase-sim}
ports:
- {name: http, port: 8090, targetPort: http}
---
# IN: only the app. OUT: DNS and the two model endpoints the relays use (Olla for the chat model, Laya for the decision model); nothing else.
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: phase-sim
namespace: commander-lab
spec:
podSelector:
matchLabels: {app: phase-sim}
policyTypes: [Ingress, Egress]
ingress:
- from:
- podSelector:
matchLabels: {app: commander-lab}
ports:
- {protocol: TCP, port: 8090}
egress:
- to:
- namespaceSelector:
matchLabels: {kubernetes.io/metadata.name: kube-system}
podSelector:
matchLabels: {k8s-app: kube-dns}
ports:
- {protocol: UDP, port: 53}
- {protocol: TCP, port: 53}
- to:
- namespaceSelector:
matchLabels: {kubernetes.io/metadata.name: ai-system}
podSelector:
matchLabels: {app.kubernetes.io/name: olla}
ports:
- {protocol: TCP, port: 40114}
- to:
- podSelector:
matchLabels: {app: laya}
ports:
- {protocol: TCP, port: 8080}
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[package]
name = "farmhouse-llm-seat"
version.workspace = true
edition = "2021"
license.workspace = true
publish = false
# FarmHouse: phase's headless four-player Commander runner (`ai-commander`) with MODEL-DRIVEN SEATS. The runner's own source is included
# as a module (src/main.rs, #[path]); this crate adds only the network transport phase-llm deliberately leaves to the host. phase-llm
# depends on phase-ai, so the hook cannot live inside phase-ai's own binary: a new crate that depends on both is the way round that.
[[bin]]
name = "ai-commander-llm"
path = "src/main.rs"
[dependencies]
engine = { path = "../engine", package = "phase-engine" }
phase-ai = { path = "../phase-ai" }
phase-llm = { path = "../phase-llm" }
serde = { workspace = true }
serde_json = "1"
rand = "0.9"
tracing = "0.1"
mimalloc = "0.1"
# Blocking HTTP, no TLS: the model endpoints are cluster-internal http:// relays (phase-sim's own /llm and /jev routes).
ureq = { version = "2", default-features = false }
@@ -0,0 +1,74 @@
"""Apply the FarmHouse edits to a phase-rs checkout (run in the Docker build, from the repository root).
python apply_phase_patches.py <phase-checkout> <llm-seat-crate-dir>
1. crates/phase-ai/src/auto_play.rs: a process-wide ACTION_OVERRIDE hook, asked before the built-in AI chooses. It returns Option<GameAction>;
None (the default, and what every failure returns) leaves phase's own choice untouched. The chosen action still goes through the same
contract check (`contract.permits`) as before.
2. crates/phase-ai/src/bin/ai_commander.rs: `fn main` becomes `pub fn main` so the new crate can include and call it.
3. The new crate is copied into crates/farmhouse-llm-seat (the workspace takes every crates/* member).
Every anchor must match exactly once or the build stops, so a phase update that moves a line is reported here, not in a compile error."""
import os
import shutil
import sys
root, crate = sys.argv[1], sys.argv[2]
def edit(rel, edits):
path = os.path.join(root, rel)
text = open(path, encoding="utf-8", newline="").read()
for old, new in edits:
n = text.count(old)
if n != 1:
raise SystemExit("%s: anchor found %d times (need 1): %r" % (rel, n, old[:80]))
text = text.replace(old, new)
open(path, "w", encoding="utf-8", newline="").write(text)
print("patched", rel)
HOOK = '''use crate::search::choose_action_with_session;
/// FarmHouse: an optional process-wide override, asked BEFORE the built-in AI chooses for a seat. A host (see crates/farmhouse-llm-seat)
/// installs one to let a model pick among the engine's own options. It returns None to leave the choice to the built-in AI, which is also
/// what it must return on any failure; whatever it returns still has to pass the same decision-contract check below.
pub type ActionOverride = Box<
dyn Fn(
&engine::types::game_state::GameState,
&engine::ai_support::AiDecisionContract,
engine::types::player::PlayerId,
engine::types::player::PlayerId,
) -> Option<engine::types::actions::GameAction>
+ Send
+ Sync,
>;
static ACTION_OVERRIDE: std::sync::OnceLock<ActionOverride> = std::sync::OnceLock::new();
/// Install the override. Returns false if one is already installed (it can be set once per process).
pub fn install_action_override(hook: ActionOverride) -> bool {
ACTION_OVERRIDE.set(hook).is_ok()
}
'''
edit("crates/phase-ai/src/auto_play.rs", [
("use crate::search::choose_action_with_session;\n", HOOK),
(" let action = match choose_action_with_session(state, semantic_owner, config, rng, session) {\n Some(action) => action,\n None => {\n tracing::warn!(player = ?actor, \"choose_action returned None",
" // The override's pick must be one the engine will really APPLY, not only one it lists: a model chose both modes of a modal spell whose\n"
" // entwine cost it could not pay, the contract listed the option, apply refused it, and the whole game stalled (2026-10-27). Try it on a\n"
" // copy of the game first; anything the engine refuses is dropped and the built-in AI plays the move. (Stack-pass rechecks have their\n"
" // own verified path and are left alone.)\n"
" let farmhouse_choice = ACTION_OVERRIDE\n .get()\n .and_then(|hook| hook(&*state, &contract, semantic_owner, actor))\n"
" .filter(|candidate| {\n"
" verified_ai_stack_pass_player(&*state, candidate).is_some()\n"
" || apply_interaction(&mut (*state).clone(), actor, semantic_owner, candidate.clone()).is_ok()\n"
" });\n"
" let action = match farmhouse_choice\n .or_else(|| choose_action_with_session(state, semantic_owner, config, rng, session))\n {\n Some(action) => action,\n None => {\n tracing::warn!(player = ?actor, \"choose_action returned None"),
])
edit("crates/phase-ai/src/bin/ai_commander.rs", [("\nfn main() {\n", "\npub fn main() {\n")])
dest = os.path.join(root, "crates", "farmhouse-llm-seat")
os.makedirs(os.path.join(dest, "src"), exist_ok=True)
shutil.copy(os.path.join(crate, "Cargo.toml"), os.path.join(dest, "Cargo.toml"))
shutil.copy(os.path.join(crate, "src", "main.rs"), os.path.join(dest, "src", "main.rs"))
print("added crates/farmhouse-llm-seat")
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//! ai-commander with model-driven seats (FarmHouse).
//!
//! phase's headless runner plays four-player Commander games with the built-in AI on every seat. This binary is that same runner (its
//! source is included below, unchanged but for `fn main` being `pub`) plus a hook, installed before the games start, that asks a
//! language model or a decision model (Laya / Kev, over the System One protocol) to choose for the seats named in `PHASE_LLM_SEATS`.
//!
//! The model never authors an action. For each decision the engine issues its own option list (`AiDecisionContract`), phase-llm renders
//! it and builds the HTTP request, this file performs the HTTP call (the one thing phase-llm leaves to the host), and phase-llm turns the
//! reply into one of the engine's options. The pick is then checked against the contract a second time here, and again by
//! `run_ai_action`. ANY problem (forced move, timeout, an unreadable or stale answer, the call cap) returns `None` and phase's own AI
//! plays the move, so a model that is down, slow or wrong cannot stall or corrupt a game.
//!
//! Settings (environment): PHASE_LLM_SEATS "1,3" (seats 0-3; empty = none, the plain runner), PHASE_LLM_PROVIDER (OpenAiCompatible | Jev),
//! PHASE_LLM_BASE_URL, PHASE_LLM_MODEL, PHASE_LLM_API_KEY (leave empty for http:// endpoints), PHASE_LLM_MAX_TOKENS (256),
//! PHASE_LLM_TEMPERATURE (0.2), PHASE_LLM_TIMEOUT_S (20), PHASE_LLM_MAX_CALLS (total model calls for the whole process, default 300),
//! PHASE_LLM_DIFFICULTY (Medium: the strength hint in the prompt), PHASE_LLM_MAX_FAILS (4: model failures IN A ROW before every model seat
//! switches itself off for the rest of the process).
use std::collections::HashSet;
use std::path::PathBuf;
use std::sync::atomic::{AtomicBool, AtomicU64, Ordering};
use std::sync::OnceLock;
use std::time::{Duration, Instant};
use engine::ai_support::AiDecisionContract;
use engine::database::CardDatabase;
use engine::types::actions::GameAction;
use engine::types::game_state::GameState;
use engine::types::player::PlayerId;
use phase_ai::auto_play::install_action_override;
use phase_ai::config::AiDifficulty;
use phase_llm::{LlmEndpointConfig, LlmProvider, LlmReply};
// The unchanged runner, with `fn main` made `pub` by apply_phase_patches.py.
#[path = "../../phase-ai/src/bin/ai_commander.rs"]
#[allow(dead_code, unused_imports, unused_variables)]
mod commander;
struct Seats {
seats: HashSet<u8>,
endpoint: LlmEndpointConfig,
difficulty: AiDifficulty,
agent: ureq::Agent,
cards_path: PathBuf,
db: OnceLock<Option<CardDatabase>>,
max_calls: u64,
asked: [AtomicU64; 4],
used: [AtomicU64; 4],
fell_back: [AtomicU64; 4],
total: AtomicU64,
// Cost visibility and the circuit-breaker. A late-game prompt is thousands of tokens and a big-model server needs longer than the
// per-call limit to read it; every such call used to burn the whole limit (a 9-minute game, 25 questions, 1 answer used). After
// `max_fails` failures IN A ROW the model seats switch themselves off for the rest of the process and phase's AI plays on.
timeouts: AtomicU64,
consecutive_fails: AtomicU64,
max_fails: u64,
disabled: AtomicBool,
millis: AtomicU64,
answered: AtomicU64,
prompt_chars: AtomicU64,
}
impl Seats {
fn database(&self) -> Option<&CardDatabase> {
self.db
.get_or_init(|| CardDatabase::from_export(&self.cards_path.join("card-data.json")).ok())
.as_ref()
}
/// The model's pick for `actor`, or None to let phase's AI play. Never panics on a bad answer.
/// One failed model call (timeout, error status, unusable answer). Returns None so callers can `return self.failed(..)`.
fn failed(&self, timeout: bool) -> Option<GameAction> {
if timeout {
self.timeouts.fetch_add(1, Ordering::Relaxed);
}
let in_a_row = self.consecutive_fails.fetch_add(1, Ordering::Relaxed) + 1;
if in_a_row >= self.max_fails && !self.disabled.swap(true, Ordering::Relaxed) {
println!(
"LLM-SEAT-DISABLED after {in_a_row} consecutive failures ({} timeouts of {} questions): phase's own AI plays the rest",
self.timeouts.load(Ordering::Relaxed),
self.total.load(Ordering::Relaxed)
);
}
None
}
fn decide(&self, state: &GameState, contract: &AiDecisionContract, actor: PlayerId) -> Option<GameAction> {
if self.disabled.load(Ordering::Relaxed) {
return None;
}
let seat = (actor.0 as usize).min(3);
// A forced move (one legal option) is never put to a model: build_game_decision_prompt refuses it.
let request = phase_llm::build_game_decision_prompt(
state,
contract,
self.difficulty,
self.database(),
&Vec::new(),
)
.ok()?;
if self.total.fetch_add(1, Ordering::Relaxed) >= self.max_calls {
return None; // the call cap: every later decision is phase's AI
}
self.asked[seat].fetch_add(1, Ordering::Relaxed);
self.prompt_chars.fetch_add(request.prompt.char_count() as u64, Ordering::Relaxed);
let http = phase_llm::build_chat_request(&self.endpoint, &request.prompt).ok()?;
let mut call = self.agent.request(http.method, &http.url);
for header in &http.headers {
call = call.set(&header.name, &header.value);
}
let started = Instant::now();
let (status, body) = match call.send_string(&http.body) {
Ok(response) => {
let status = response.status();
match response.into_string() {
Ok(text) => (status, text),
Err(_) => return self.failed(true),
}
}
// A 4xx/5xx still carries a body phase-llm knows how to classify.
Err(ureq::Error::Status(code, response)) => (code, response.into_string().unwrap_or_default()),
Err(_) => return self.failed(true), // transport error: a timeout, or the endpoint is down
};
self.millis.fetch_add(started.elapsed().as_millis() as u64, Ordering::Relaxed);
self.answered.fetch_add(1, Ordering::Relaxed);
let selection = match phase_llm::select_action_from_response(
state,
contract,
&request.fingerprint,
LlmReply { provider: self.endpoint.provider, status, body: &body },
) {
Ok(selection) => selection,
Err(_) => return self.failed(false),
};
if !contract.contains_action(state, &selection.action) {
return self.failed(false);
}
self.consecutive_fails.store(0, Ordering::Relaxed);
self.used[seat].fetch_add(1, Ordering::Relaxed);
Some(selection.action)
}
fn report(&self, seat: usize) {
let asked = self.asked[seat].load(Ordering::Relaxed);
if asked > 0 && asked % 25 == 0 {
let answered = self.answered.load(Ordering::Relaxed).max(1);
let questions = self.total.load(Ordering::Relaxed).max(1);
println!(
"LLM-SEAT seat={seat} asked={asked} used={} total_calls={} timeouts={} avg_ms={} avg_prompt_chars={}",
self.used[seat].load(Ordering::Relaxed),
self.total.load(Ordering::Relaxed),
self.timeouts.load(Ordering::Relaxed),
self.millis.load(Ordering::Relaxed) / answered,
self.prompt_chars.load(Ordering::Relaxed) / questions
);
}
}
}
fn env_or(name: &str, default: &str) -> String {
std::env::var(name).ok().filter(|v| !v.trim().is_empty()).unwrap_or_else(|| default.to_string())
}
fn install_model_seats() {
let seats: HashSet<u8> = env_or("PHASE_LLM_SEATS", "")
.split(',')
.filter_map(|s| s.trim().parse::<u8>().ok())
.filter(|s| *s < 4)
.collect();
if seats.is_empty() {
println!("LLM seats: none (plain ai-commander)");
return;
}
let provider = LlmProvider::from_label(&env_or("PHASE_LLM_PROVIDER", "OpenAiCompatible"));
let endpoint = LlmEndpointConfig {
provider,
base_url: std::env::var("PHASE_LLM_BASE_URL").ok().filter(|v| !v.trim().is_empty()),
api_key: env_or("PHASE_LLM_API_KEY", ""),
model: env_or("PHASE_LLM_MODEL", "model"),
max_output_tokens: env_or("PHASE_LLM_MAX_TOKENS", "256").parse().ok(),
temperature: env_or("PHASE_LLM_TEMPERATURE", "0.2").parse().ok(),
};
let timeout = env_or("PHASE_LLM_TIMEOUT_S", "20").parse::<u64>().unwrap_or(20);
// The runner's first positional argument is the data folder (card-data.json lives there).
let cards_path = PathBuf::from(std::env::args().nth(1).unwrap_or_else(|| ".".to_string()));
let state = Seats {
seats: seats.clone(),
endpoint,
difficulty: AiDifficulty::from_label(&env_or("PHASE_LLM_DIFFICULTY", "Medium")),
agent: ureq::AgentBuilder::new().timeout(Duration::from_secs(timeout)).redirects(0).build(),
cards_path,
db: OnceLock::new(),
max_calls: env_or("PHASE_LLM_MAX_CALLS", "300").parse().unwrap_or(300),
asked: Default::default(),
used: Default::default(),
fell_back: Default::default(),
total: AtomicU64::new(0),
timeouts: AtomicU64::new(0),
consecutive_fails: AtomicU64::new(0),
max_fails: env_or("PHASE_LLM_MAX_FAILS", "4").parse().unwrap_or(4),
disabled: AtomicBool::new(false),
millis: AtomicU64::new(0),
answered: AtomicU64::new(0),
prompt_chars: AtomicU64::new(0),
};
println!(
"LLM seats: {:?} provider={} model={} max_calls={}",
{
let mut v: Vec<u8> = seats.iter().copied().collect();
v.sort();
v
},
provider.label(),
state.endpoint.model,
state.max_calls
);
let installed = install_action_override(Box::new(move |game, contract, _owner, actor| {
if !state.seats.contains(&actor.0) {
return None;
}
let seat = (actor.0 as usize).min(3);
let pick = state.decide(game, contract, actor);
if pick.is_none() {
state.fell_back[seat].fetch_add(1, Ordering::Relaxed);
}
state.report(seat);
pick
}));
if !installed {
eprintln!("LLM seats: an action override was already installed; model seats are off");
}
}
fn main() {
install_model_seats();
commander::main();
}
+501
View File
@@ -0,0 +1,501 @@
"""phase-sim: run rules-accurate four-player Commander games with phase-rs's headless `ai-commander` and report what happened.
Standard library only. One job runs at a time (a game is CPU-heavy); more wait in a queue. The service never touches the network.
GET /health {"ok": true, "engine": ..., "queued": n, "running": id|null}
POST /run {"decks": [4 x {"name", "commander": ["Card"], "main": [{"name","count"}, ...]}],
"games": 1..MAX_GAMES, "seed": int, "difficulty": "Medium", "seat_difficulty": {"2": "Hard"}, "action_cap": int}
-> {"job": id}
GET /job/<id> status queued|running|done|error, games finished, and when done: per-game rows, wins by seat and by deck,
win rate with a 95% interval, the cards the engine could not resolve, and the tail of the engine's output
Seat i is the i-th deck in the request (P0 = first). The engine treats every seat as AI; difficulty is per seat if given.
A game that stalls, hits the action cap, or panics is reported as such and is NEVER counted as a win or a draw.
Model seats (optional, needs the image's ai-commander-llm binary): "llm_seats": [1, 3] and "llm": {"kind": "chat" | "decision", "model": "...",
"max_calls": 300}. Those seats ask a model to pick among the ENGINE'S OWN legal options (phase-llm builds the prompt and checks the answer;
anything wrong or slow falls back to phase's AI). kind "chat" goes to PHASE_LLM_CHAT_URL (an OpenAI-style endpoint, e.g. Olla) through this
service's /llm relay, which switches the model's thinking off and caps its answer; kind "decision" goes to PHASE_LLM_DECISION_URL (a System One
server: Laya or Kev) through /jev/systemone. Both relays accept connections from this machine only.
"""
import json
import math
import os
import re
import subprocess
import sys
import tempfile
import threading
import time
import urllib.error
import urllib.request
import uuid
from collections import deque
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
BIN = os.environ.get("PHASE_AI_COMMANDER", "/opt/phase/ai-commander")
CARD_DATA = os.environ.get("PHASE_CARD_DATA", "/opt/phase/data/card-data.json")
ENGINE = os.environ.get("PHASE_ENGINE_VERSION", "phase-rs (version unknown)")
MAX_GAMES = int(os.environ.get("PHASE_SIM_MAX_GAMES", "50"))
JOB_TIMEOUT = int(os.environ.get("PHASE_SIM_TIMEOUT", "3600"))
PORT = int(os.environ.get("PORT", "8090"))
DIFFICULTIES = ("VeryEasy", "Easy", "Medium", "Hard", "VeryHard")
BIN_LLM = os.environ.get("PHASE_AI_COMMANDER_LLM", "/opt/phase/ai-commander-llm")
CHAT_URL = os.environ.get("PHASE_LLM_CHAT_URL", "").rstrip("/") # e.g. http://olla...:40114/olla/openai/v1
DECISION_URL = os.environ.get("PHASE_LLM_DECISION_URL", "") # e.g. http://laya...:8080/v1/systemone
DECISION_KEY = os.environ.get("PHASE_LLM_DECISION_KEY", "")
CHAT_REASONING = os.environ.get("PHASE_LLM_REASONING", "none")
CHAT_MAX_TOKENS = int(os.environ.get("PHASE_LLM_MAX_TOKENS_CAP", "256"))
MAX_LLM_CALLS = int(os.environ.get("PHASE_LLM_MAX_CALLS", "600"))
LOCAL_ONLY = ("127.0.0.1", "::1")
# A late-game prompt is thousands of tokens; reading it takes a 27B model on a V100 20-30 s. The engine waits MODEL_TIMEOUT per question and the
# relay a little less, and the engine switches the model seats off after PHASE_LLM_MAX_FAILS failures in a row, so a dead model costs a few minutes at most.
RELAY_TIMEOUT = float(os.environ.get("PHASE_LLM_RELAY_TIMEOUT", "55"))
MODEL_TIMEOUT = os.environ.get("PHASE_LLM_TIMEOUT_S", "60")
MAX_DECK_ENTRIES = 400
_jobs = {}
_queue = deque()
_cv = threading.Condition()
_running = {"id": None}
# ---------- request -> engine inputs ------------------------------------------------------------------------------------------
class BadRequest(ValueError):
pass
def validate(req):
decks = req.get("decks")
if not isinstance(decks, list) or len(decks) != 4:
raise BadRequest("decks must be a list of exactly 4 decks (one per seat)")
feed = []
for i, d in enumerate(decks):
if not isinstance(d, dict) or not str(d.get("name") or "").strip():
raise BadRequest("deck %d needs a name" % i)
cmd = d.get("commander")
if isinstance(cmd, str):
cmd = [cmd]
if not isinstance(cmd, list) or not cmd or not all(isinstance(c, str) and c.strip() for c in cmd):
raise BadRequest("deck %d needs commander: [\"Card Name\"]" % i)
main = d.get("main")
if main is None and isinstance(d.get("cards"), list):
counts = {}
for n in d["cards"]:
counts[str(n)] = counts.get(str(n), 0) + 1
main = [{"name": k, "count": v} for k, v in counts.items()]
if not isinstance(main, list) or not main or len(main) > MAX_DECK_ENTRIES:
raise BadRequest("deck %d needs a non-empty main list (at most %d entries)" % (i, MAX_DECK_ENTRIES))
rows = []
for e in main:
try:
name, count = str(e["name"]), int(e["count"])
except (KeyError, TypeError, ValueError):
raise BadRequest("deck %d has a bad main entry: %r" % (i, e))
if not name.strip() or not 1 <= count <= 99:
raise BadRequest("deck %d has a bad main entry: %r" % (i, e))
rows.append({"name": name, "count": count})
feed.append({"name": str(d["name"]), "commander": [c.strip() for c in cmd], "main": rows})
try:
games = int(req.get("games", 1))
seed = int(req.get("seed", 1))
except (TypeError, ValueError):
raise BadRequest("games and seed must be integers")
if not 1 <= games <= MAX_GAMES:
raise BadRequest("games must be 1 to %d" % MAX_GAMES)
difficulty = req.get("difficulty", "Medium")
if difficulty not in DIFFICULTIES:
raise BadRequest("difficulty must be one of %s" % ", ".join(DIFFICULTIES))
seat_diff = {}
for k, v in (req.get("seat_difficulty") or {}).items():
if str(k) not in ("0", "1", "2", "3") or v not in DIFFICULTIES:
raise BadRequest("seat_difficulty keys are seats 0-3, values one of %s" % ", ".join(DIFFICULTIES))
seat_diff[int(k)] = v
cap = req.get("action_cap")
if cap is not None:
try:
cap = int(cap)
except (TypeError, ValueError):
raise BadRequest("action_cap must be an integer")
if not 100 <= cap <= 200000:
raise BadRequest("action_cap must be 100 to 200000")
llm = None
seats = req.get("llm_seats") or []
if seats:
if not isinstance(seats, list) or not all(isinstance(x, int) and 0 <= x <= 3 for x in seats):
raise BadRequest("llm_seats must be a list of seats 0-3")
cfg = req.get("llm") or {}
kind = cfg.get("kind", "chat")
if kind not in ("chat", "decision"):
raise BadRequest("llm.kind must be chat or decision")
if (kind == "chat" and not CHAT_URL) or (kind == "decision" and not DECISION_URL):
raise BadRequest("this service has no %s model endpoint configured" % kind)
if not os.path.isfile(BIN_LLM) and not os.environ.get("PHASE_SIM_ALLOW_MISSING_LLM_BIN"):
raise BadRequest("this image has no model-seat engine (ai-commander-llm)")
model = str(cfg.get("model") or "").strip()
if kind == "chat" and not model:
raise BadRequest("llm.model is required for a chat model")
try:
max_calls = int(cfg.get("max_calls", 300))
except (TypeError, ValueError):
raise BadRequest("llm.max_calls must be an integer")
if not 1 <= max_calls <= MAX_LLM_CALLS:
raise BadRequest("llm.max_calls must be 1 to %d" % MAX_LLM_CALLS)
llm = {"seats": sorted(set(seats)), "kind": kind, "model": model or "decision-model", "max_calls": max_calls}
return {"feed": feed, "games": games, "seed": seed, "difficulty": difficulty, "seat_difficulty": seat_diff, "action_cap": cap, "llm": llm}
def engine_env(spec):
"""The environment for the engine process: model-seat settings, or None (inherit) for the plain runner."""
llm = spec.get("llm")
if not llm:
return None
env = dict(os.environ)
local = "http://127.0.0.1:%d" % PORT
env.update(PHASE_LLM_SEATS=",".join(str(x) for x in llm["seats"]), PHASE_LLM_MODEL=llm["model"], PHASE_LLM_API_KEY="",
PHASE_LLM_MAX_CALLS=str(llm["max_calls"]), PHASE_LLM_DIFFICULTY=spec["difficulty"], PHASE_LLM_TIMEOUT_S=MODEL_TIMEOUT)
if llm["kind"] == "chat":
env.update(PHASE_LLM_PROVIDER="OpenAiCompatible", PHASE_LLM_BASE_URL=local + "/llm/v1")
else:
env.update(PHASE_LLM_PROVIDER="Jev", PHASE_LLM_BASE_URL=local)
return env
def command(spec, root):
cmd = [BIN_LLM if spec.get("llm") else BIN, root, "--feed", "feed.json", "--games-file", os.path.join(root, "games.txt")]
for seat, level in sorted(spec["seat_difficulty"].items()):
cmd += ["--difficulty-p%d" % seat, level]
if spec["action_cap"]:
cmd += ["--action-cap", str(spec["action_cap"])]
return cmd
# ---------- engine output -> results ------------------------------------------------------------------------------------------
GAME_HEAD = re.compile(r"^--- GAME\b(.*)$")
WINNER = re.compile(r"Game ended cleanly\. Winner: (draw|P(\d))")
TURNS = re.compile(r"^Turns played: (\d+)")
ELIM = re.compile(r"^ELIMINATED: P(\d)\s+turn=(\d+)")
RESOLVED = re.compile(r"^\s+P(\d)\s+main=\s*(\d+)\s+commander=(\d+)")
LLM_STAT = re.compile(r"^LLM-SEAT seat=(\d) asked=(\d+) used=(\d+)(?: total_calls=(\d+))?(?: timeouts=(\d+))?(?: avg_ms=(\d+))?(?: avg_prompt_chars=(\d+))?")
LLM_OFF = re.compile(r"^LLM-SEAT-DISABLED after (\d+) consecutive failures")
SKIP = re.compile(r"^\s+SKIP (.+?): commander '(.+)' not in card db")
PRE = re.compile(r"^\s+(.+?)\s+\|\s+commander: (.+?)\s+\|\s+main: (\d+) cards")
def parse(output):
"""Per-game rows and the deck-resolution facts from the engine's stdout. Games that did not finish are kept, labelled, never counted."""
lines = output.splitlines()
pre, resolved, skipped, games, cur = [], {}, [], [], None
def finish():
if cur is not None:
games.append(cur)
def new_game(label=""):
return {"label": label, "status": "unfinished", "winner_seat": None, "turns": None, "eliminated": []}
result_line = re.compile(r"Game ended cleanly|^Turns played|^ELIMINATED:|^STALL:|ABORT: hit action cap|PANICKED")
for ln in lines:
if cur is None and not games: # the preamble: decks listed, then their resolved sizes
m = PRE.match(ln)
if m:
pre.append({"deck": m.group(1), "commander": m.group(2), "listed": int(m.group(3))})
continue
m = SKIP.match(ln)
if m:
skipped.append({"deck": m.group(1), "commander": m.group(2)})
continue
m = RESOLVED.match(ln)
if m and int(m.group(1)) not in resolved:
resolved[int(m.group(1))] = {"main": int(m.group(2)), "commanders": int(m.group(3))}
continue
if GAME_HEAD.match(ln):
finish()
cur = new_game(GAME_HEAD.match(ln).group(1).strip(" -"))
continue
if cur is None:
if not result_line.search(ln):
continue # banner and preamble lines are not a game
cur = new_game() # a single-game run prints no "--- GAME" header
m = WINNER.search(ln)
if m:
cur["status"] = "draw" if m.group(1) == "draw" else "won"
cur["winner_seat"] = None if m.group(2) is None else int(m.group(2))
continue
m = TURNS.match(ln)
if m:
cur["turns"] = int(m.group(1))
continue
m = ELIM.match(ln)
if m:
cur["eliminated"].append({"seat": int(m.group(1)), "turn": int(m.group(2))})
continue
if ln.startswith("STALL:"):
cur["status"] = "stalled"
cur.setdefault("detail", ln)
elif "ABORT: hit action cap" in ln:
cur["status"] = "action_cap"
elif re.search(r"GAME .* PANICKED", ln):
cur["status"] = "panicked"
cur["detail"] = ln[:300]
finish()
games = [g for g in games if g["status"] != "unfinished" or g["turns"] is not None or g["eliminated"] or g["label"]]
return {"pre": pre, "resolved": resolved, "skipped": skipped, "games": games}
def llm_summary(llm, lines):
"""The model seats' own counters (the engine prints one every 25th question), last value per seat, plus the breaker and the cost."""
per_seat, extra, disabled = {}, {}, None
for ln in lines:
m = LLM_STAT.match(ln)
if m:
per_seat[int(m.group(1))] = {"asked": int(m.group(2)), "used": int(m.group(3))}
for key, grp in (("timeouts", 5), ("avg_ms", 6), ("avg_prompt_chars", 7)):
if m.group(grp) is not None:
extra[key] = int(m.group(grp))
continue
m = LLM_OFF.match(ln)
if m:
disabled = int(m.group(1))
out = {"seats": llm["seats"], "kind": llm["kind"], "model": llm["model"], "max_calls": llm["max_calls"], "per_seat": per_seat,
"note": "counters are printed every 25th question, so the last few may be missing"}
out.update(extra)
if disabled is not None:
out["switched_off_after_failures_in_a_row"] = disabled
return out
def wilson(wins, n, z=1.96):
if n == 0:
return [0.0, 0.0]
p = wins / n
d = 1 + z * z / n
c = p + z * z / (2 * n)
h = z * math.sqrt(p * (1 - p) / n + z * z / (4 * n * n))
return [round(max(0.0, (c - h) / d), 4), round(min(1.0, (c + h) / d), 4)]
def summarize(spec, parsed, seeds):
decks = [d["name"] for d in spec["feed"]]
finished = [g for g in parsed["games"] if g["status"] in ("won", "draw")]
won = [g for g in finished if g["status"] == "won"]
by_seat = [sum(1 for g in won if g["winner_seat"] == i) for i in range(4)]
n = len(finished)
warnings = []
for s in parsed["skipped"]:
warnings.append("deck %r was skipped: commander %r is not in the engine's card database" % (s["deck"], s["commander"]))
for i, pre in enumerate(parsed["pre"][:4]):
got = (parsed["resolved"].get(i) or {}).get("main")
if got is not None and got < pre["listed"]:
warnings.append("seat %d (%s): %d of %d cards were not recognised by the engine and were left out" % (
i, pre["deck"], pre["listed"] - got, pre["listed"]))
bad = [g for g in parsed["games"] if g["status"] not in ("won", "draw")]
if bad:
warnings.append("%d game(s) did not finish (%s): not counted as wins or draws" % (
len(bad), ", ".join(sorted({g["status"] for g in bad}))))
for i, g in enumerate(parsed["games"]):
if i < len(seeds):
g["seed"] = seeds[i]
return {"games_requested": spec["games"], "games_finished": n, "games_not_finished": len(bad), "draws": n - len(won),
"wins_by_seat": by_seat,
"decks": [{"seat": i, "name": decks[i], "wins": by_seat[i], "win_rate": round(by_seat[i] / n, 4) if n else None,
"win_rate_95": wilson(by_seat[i], n)} for i in range(4)],
"avg_turns": round(sum(g["turns"] or 0 for g in finished) / n, 1) if n else None,
"warnings": warnings, "games": parsed["games"]}
# ---------- jobs ----------------------------------------------------------------------------------------------------------------
def run_job(job):
spec = job["spec"]
root = tempfile.mkdtemp(prefix="phase-sim-")
out_lines = []
try:
os.symlink(CARD_DATA, os.path.join(root, "card-data.json"))
with open(os.path.join(root, "feed.json"), "w", encoding="utf-8") as f:
json.dump({"decks": spec["feed"]}, f)
seeds = [spec["seed"] + i for i in range(spec["games"])]
with open(os.path.join(root, "games.txt"), "w", encoding="utf-8") as f:
f.write("".join("%d,%s\n" % (s, spec["difficulty"]) for s in seeds))
job["status"], job["started"] = "running", time.time()
proc = subprocess.Popen(command(spec, root), stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, errors="replace", cwd=root,
env=engine_env(spec))
timer = threading.Timer(JOB_TIMEOUT, proc.kill)
timer.start()
try:
for ln in proc.stdout:
out_lines.append(ln.rstrip("\n"))
if GAME_HEAD.match(ln):
job["games_started"] += 1
if WINNER.search(ln) or "GAME" in ln and "PANICKED" in ln or ln.startswith("STALL:") or "ABORT: hit action cap" in ln:
job["games_done"] += 1
code = proc.wait()
finally:
timer.cancel()
text = "\n".join(out_lines)
parsed = parse(text)
job["result"] = summarize(spec, parsed, seeds)
if spec.get("llm"):
job["result"]["llm"] = llm_summary(spec["llm"], out_lines)
if "switched_off_after_failures_in_a_row" in job["result"]["llm"]:
job["result"]["warnings"].append(
"the model seats switched themselves off after %d failures in a row (usually timeouts: late-game prompts are thousands of tokens "
"and the model server could not read them in time); phase's own AI played the rest of the run, so the later moves are NOT the model's"
% job["result"]["llm"]["switched_off_after_failures_in_a_row"])
job["result"]["warnings"].append(
"model seats %s: the model is asked about each non-forced decision, up to %d calls for the whole run; an answer the engine "
"cannot use, a timeout, or any decision after the cap is played by phase's own AI instead" % (spec["llm"]["seats"], spec["llm"]["max_calls"]))
job["engine_exit_code"] = code
job["output_tail"] = out_lines[-40:]
job["status"] = "done" if parsed["games"] else "error"
if not parsed["games"]:
job["error"] = "the engine produced no game results (exit code %s); see output_tail" % code
except Exception as exc: # the job fails, the service does not
job["status"], job["error"] = "error", "%s: %s" % (type(exc).__name__, exc)
job["output_tail"] = out_lines[-40:]
finally:
job["finished"] = time.time()
try:
for name in os.listdir(root):
os.unlink(os.path.join(root, name))
os.rmdir(root)
except OSError:
pass
def worker():
while True:
with _cv:
while not _queue:
_cv.wait()
job = _queue.popleft()
_running["id"] = job["id"]
run_job(job)
with _cv:
_running["id"] = None
def submit(req):
spec = validate(req)
job = {"id": uuid.uuid4().hex[:12], "status": "queued", "created": time.time(), "spec": spec, "games_started": 0, "games_done": 0}
with _cv:
_jobs[job["id"]] = job
_queue.append(job)
for old in sorted(_jobs.values(), key=lambda j: j["created"])[:-50]: # remember the last 50
if old["status"] in ("done", "error"):
_jobs.pop(old["id"], None)
_cv.notify()
return job["id"]
def public(job):
out = {k: job[k] for k in ("id", "status", "games_started", "games_done") if k in job}
out["games_requested"] = job["spec"]["games"]
for k in ("result", "error", "output_tail", "engine_exit_code"):
if k in job:
out[k] = job[k]
if "started" in job:
out["seconds"] = round((job.get("finished") or time.time()) - job["started"], 1)
return out
def forward(url, data, headers):
req = urllib.request.Request(url, data=data, headers=dict({"content-type": "application/json"}, **headers))
try:
with urllib.request.urlopen(req, timeout=RELAY_TIMEOUT) as r:
return r.status, json.loads(r.read() or b"{}")
except urllib.error.HTTPError as exc:
try:
return exc.code, json.loads(exc.read() or b"{}")
except ValueError:
return exc.code, {"error": "upstream error"}
except (urllib.error.URLError, OSError, ValueError):
return 502, {"error": "the model endpoint is not reachable"}
def relay_chat(body, remote):
"""POST /llm/v1/chat/completions -> the OpenAI-style endpoint, with the model's thinking off and its answer capped."""
if remote not in LOCAL_ONLY or not CHAT_URL:
return 403, {"error": "not available"}
try:
req = json.loads(body)
req["max_tokens"] = min(int(req.get("max_tokens") or CHAT_MAX_TOKENS), CHAT_MAX_TOKENS)
req["stream"] = False
if CHAT_REASONING:
req["reasoning_effort"] = CHAT_REASONING
data = json.dumps(req).encode()
except (ValueError, TypeError):
return 400, {"error": "bad chat request"}
return forward(CHAT_URL + "/chat/completions", data, {})
def relay_decision(body, remote):
"""POST /jev/systemone: phase's Jev envelope {"apiKey", "request": {...}} -> a System One server (Laya / Kev); the key comes from the environment."""
if remote not in LOCAL_ONLY or not DECISION_URL:
return 403, {"error": "not available"}
try:
data = json.dumps(json.loads(body)["request"]).encode()
except (ValueError, KeyError, TypeError):
return 400, {"error": "bad decision request"}
return forward(DECISION_URL, data, {"authorization": "Bearer " + DECISION_KEY} if DECISION_KEY else {})
class Handler(BaseHTTPRequestHandler):
protocol_version = "HTTP/1.1"
server_version = "phase-sim/1"
def log_message(self, fmt, *args):
if os.environ.get("PHASE_SIM_LOG") == "1":
sys.stderr.write("%s\n" % (fmt % args))
def _send(self, code, obj):
body = json.dumps(obj).encode()
self.send_response(code)
self.send_header("Content-Type", "application/json")
self.send_header("Content-Length", str(len(body)))
self.end_headers()
self.wfile.write(body)
def do_GET(self):
if self.path == "/health":
with _cv:
return self._send(200, {"ok": os.path.isfile(BIN) and os.path.isfile(CARD_DATA), "engine": ENGINE,
"queued": len(_queue), "running": _running["id"], "max_games": MAX_GAMES})
m = re.match(r"^/job/([0-9a-f]+)$", self.path)
if m:
with _cv:
job = _jobs.get(m.group(1))
return self._send(200, public(job)) if job else self._send(404, {"error": "no such job"})
self._send(404, {"error": "not found"})
def do_POST(self):
if self.path in ("/llm/v1/chat/completions", "/jev/systemone"):
n = int(self.headers.get("Content-Length") or 0)
if n > 4_000_000:
return self._send(413, {"error": "request too large"})
fn = relay_chat if self.path.startswith("/llm") else relay_decision
code, obj = fn(self.rfile.read(n), self.client_address[0])
return self._send(code, obj)
if self.path != "/run":
return self._send(404, {"error": "not found"})
try:
n = int(self.headers.get("Content-Length") or 0)
if n > 2_000_000:
return self._send(413, {"error": "request too large"})
req = json.loads(self.rfile.read(n) or b"{}")
if not isinstance(req, dict):
raise BadRequest("the body must be a JSON object")
self._send(202, {"job": submit(req)})
except (BadRequest, json.JSONDecodeError) as exc:
self._send(400, {"error": str(exc)})
def main():
threading.Thread(target=worker, daemon=True).start()
ThreadingHTTPServer(("0.0.0.0", PORT), Handler).serve_forever()
if __name__ == "__main__":
main()
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"""Smoke test, run INSIDE the image (CI and by hand): four real decks from phase's own sample feed play real games through the service.
python /opt/phase/phase_smoke.py [games] [difficulty] plain engine
python /opt/phase/phase_smoke.py --llm model seats against a stub model inside this process
Exits non-zero unless at least one game finished with a winner or a draw. Prints what the engine reported, including any cards it did
not recognise, so a bad data/engine pairing shows up here and not in a result someone trusts.
--llm starts a stub OpenAI-style model on 127.0.0.1:9099 (the service must have been started with PHASE_LLM_CHAT_URL=http://127.0.0.1:9099/v1),
puts seats 1 and 3 on it, and passes when the engine's own counters show the model was asked AND its answers were used by the engine: that
proves the Rust hook, the relay and phase's answer check end to end without any outside service. The stub always answers option 0, so the
game itself may not finish (it can hit the action cap); that is not what is being tested.
"""
import json
import sys
import threading
import time
import urllib.request
from http.server import BaseHTTPRequestHandler, HTTPServer
BASE = "http://127.0.0.1:8090"
LLM = "--llm" in sys.argv
args = [a for a in sys.argv[1:] if not a.startswith("--")]
games = int(args[0]) if args else (1 if LLM else 2)
difficulty = args[1] if len(args) > 1 else "VeryEasy"
ASKED = []
class StubModel(BaseHTTPRequestHandler):
def log_message(self, *a):
pass
def do_POST(self):
body = json.loads(self.rfile.read(int(self.headers.get("Content-Length") or 0)))
ASKED.append(body)
out = json.dumps({"choices": [{"index": 0, "message": {"role": "assistant", "content": "{\"choice\": 0, \"reason\": \"stub\"}"}, "finish_reason": "stop"}],
"usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2}}).encode()
self.send_response(200)
self.send_header("content-type", "application/json")
self.send_header("content-length", str(len(out)))
self.end_headers()
self.wfile.write(out)
if LLM:
threading.Thread(target=HTTPServer(("127.0.0.1", 9099), StubModel).serve_forever, daemon=True).start()
def call(path, body=None):
req = urllib.request.Request(BASE + path, data=None if body is None else json.dumps(body).encode(), headers={"content-type": "application/json"})
with urllib.request.urlopen(req, timeout=60) as r:
return json.load(r)
for _ in range(60):
try:
h = call("/health")
break
except Exception:
time.sleep(1)
else:
sys.exit("the service did not come up")
print("health:", h)
assert h["ok"], "binary or card data missing"
feed = json.load(open("/opt/phase/sample-feed.json", encoding="utf-8"))["decks"]
decks = []
for d in feed:
cmd = d.get("commander") or [d["name"]]
decks.append({"name": d["name"], "commander": cmd, "main": d["main"]})
if len(decks) == 4:
break
request = {"decks": decks, "games": games, "seed": 7, "difficulty": difficulty, "action_cap": 60000}
if LLM:
request.update(action_cap=6000, llm_seats=[1, 3], llm={"kind": "chat", "model": "stub-model", "max_calls": 60})
job = call("/run", request)["job"]
t0 = time.time()
while True:
j = call("/job/" + job)
if j["status"] in ("done", "error"):
break
if time.time() - t0 > 1500:
sys.exit("timed out; last: %s" % json.dumps(j)[:300])
time.sleep(3)
print("job: %s in %.0f s" % (j["status"], time.time() - t0))
res = j.get("result") or {}
print(json.dumps({k: res.get(k) for k in ("games_requested", "games_finished", "games_not_finished", "draws", "wins_by_seat", "avg_turns", "warnings")}, indent=1))
for g in res.get("games", []):
print(" game seed=%s %s winner=%s turns=%s eliminated=%s" % (g.get("seed"), g["status"], g.get("winner_seat"), g.get("turns"), g.get("eliminated")))
if LLM:
llm = res.get("llm") or {}
print("model seats:", json.dumps(llm), "| requests the stub model received:", len(ASKED))
print("\n".join((j.get("output_tail") or [])[-15:]))
seats = llm.get("per_seat") or {}
asked = sum(v["asked"] for v in seats.values())
used = sum(v["used"] for v in seats.values())
reasoned = [a for a in ASKED if a.get("reasoning_effort") == "none"]
if not ASKED or not asked or not used:
sys.exit("the model seats were not exercised: stub got %d requests, engine counters asked=%d used=%d" % (len(ASKED), asked, used))
if len(reasoned) != len(ASKED):
sys.exit("the relay did not switch the model's thinking off on every request")
print("PHASE LLM SMOKE OK (asked %d, engine used %d, stub saw %d requests, all with thinking off)" % (asked, used, len(ASKED)))
sys.exit(0)
if j["status"] != "done" or res.get("games_finished", 0) < 1:
print("\n".join(j.get("output_tail") or []))
sys.exit("no game finished")
print("PHASE SMOKE OK")
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"""Run: python -I -X utf8 test_phase_sim.py (offline: a stub engine prints output in the format phase's ai-commander prints)"""
import json
import os
import sys
import tempfile
import threading
import time
import urllib.request
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
import phase_sim as P # noqa: E402
FAILS = 0
def ck(name, cond, detail=""):
global FAILS
print(" %s %s%s" % ("ok " if cond else "FAIL", name, (" -- " + str(detail)[:260]) if detail and not cond else ""))
if not cond:
FAILS += 1
def deck(name, cmd, n=3):
return {"name": name, "commander": [cmd], "main": [{"name": "Sol Ring", "count": 1}, {"name": "Forest", "count": n}]}
GOOD = {"decks": [deck("A", "Cmd A"), deck("B", "Cmd B"), deck("C", "Cmd C"), deck("D", "Cmd D")], "games": 3, "seed": 10}
# --- validation
def bad(mut, why):
req = json.loads(json.dumps(GOOD))
mut(req)
try:
P.validate(req)
ck("rejects " + why, False)
except P.BadRequest as exc:
ck("rejects " + why, True, exc)
ck("a good request validates", P.validate(GOOD)["games"] == 3)
bad(lambda r: r["decks"].pop(), "three decks")
bad(lambda r: r["decks"][0].pop("commander"), "a deck with no commander")
bad(lambda r: r["decks"][1].update(main=[]), "an empty main list")
bad(lambda r: r["decks"][2]["main"].append({"name": "X", "count": 0}), "a zero count")
bad(lambda r: r.update(games=P.MAX_GAMES + 1), "too many games")
bad(lambda r: r.update(difficulty="Impossible"), "an unknown difficulty")
bad(lambda r: r.update(seat_difficulty={"7": "Easy"}), "a seat outside 0-3")
bad(lambda r: r.update(action_cap=5), "a tiny action cap")
v = P.validate(dict(GOOD, decks=[dict(deck("A", "Cmd A"), commander="Cmd A", main=None, cards=["Forest", "Forest", "Sol Ring"])] + GOOD["decks"][1:]))
ck("a plain card list becomes counted rows and a commander string becomes a list",
v["feed"][0]["commander"] == ["Cmd A"] and {"name": "Forest", "count": 2} in v["feed"][0]["main"], v["feed"][0])
# --- parsing the engine's output
OUT = """=== 4-player Commander AI test ===
Feed: feed.json
Batch mode: 5 games from games.txt
A | commander: Cmd A | main: 99 cards
B | commander: Cmd B | main: 99 cards
SKIP C: commander 'Cmd C' not in card db
D | commander: Cmd D | main: 99 cards
Resolved deck sizes (post name-resolution, 0-indexed by seat):
P0 main= 99 commander=1
P1 main= 97 commander=1
P2 main= 99 commander=1
P3 main= 99 commander=1
ExecutionMode: measurement
--- GAME seed=10 difficulty=Medium ---
Game started. 4 players.
ELIMINATED: P1 turn=9 actions= 400 elapsed=3.1s
ELIMINATED: P3 turn=12 actions= 600 elapsed=5.0s
=== RESULT ===
Elapsed: 8.0s
Turns played: 14
Game ended cleanly. Winner: P2
--- GAME seed=11 difficulty=Medium ---
Game started. 4 players.
=== RESULT ===
Turns played: 40
Game ended cleanly. Winner: draw
--- GAME seed=12 difficulty=Medium ---
=== RESULT ===
Turns played: 7
STALL: waiting_for=ChooseTarget turn=7 active=P0 priority=P0 pending_cast=none
Game did NOT reach GameOver. waiting_for = ChooseTarget
--- GAME seed=13 difficulty=Medium ---
ABORT: hit action cap=50000
=== RESULT ===
Turns played: 31
Game did NOT reach GameOver. waiting_for = Priority
--- GAME seed=14 difficulty=Medium ---
GAME 14 PANICKED: index out of bounds
"""
parsed = P.parse(OUT)
st = [g["status"] for g in parsed["games"]]
ck("five games found with their outcomes in order", st == ["won", "draw", "stalled", "action_cap", "panicked"], st)
g0 = parsed["games"][0]
ck("winner seat, turns and elimination order are read", g0["winner_seat"] == 2 and g0["turns"] == 14
and [e["seat"] for e in g0["eliminated"]] == [1, 3], g0)
ck("a draw has no winner seat", parsed["games"][1]["winner_seat"] is None)
ck("pre-resolution and resolved deck sizes and the skipped deck are read", len(parsed["pre"]) == 3 and parsed["resolved"][1]["main"] == 97
and parsed["skipped"] == [{"deck": "C", "commander": "Cmd C"}], (parsed["pre"], parsed["resolved"], parsed["skipped"]))
spec = P.validate(dict(GOOD, games=5))
res = P.summarize(spec, parsed, [10, 11, 12, 13, 14])
ck("only finished games are counted", res["games_finished"] == 2 and res["games_not_finished"] == 3 and res["draws"] == 1, res)
ck("wins by seat and per-deck win rate", res["wins_by_seat"] == [0, 0, 1, 0] and res["decks"][2]["win_rate"] == 0.5 and res["decks"][0]["win_rate"] == 0.0)
lo, hi = res["decks"][2]["win_rate_95"]
ck("a 95% interval wraps the rate and stays in 0..1", 0 <= lo < 0.5 < hi <= 1)
w = " | ".join(res["warnings"])
ck("warnings: a skipped deck, unrecognised cards, and unfinished games (never counted)",
"was skipped" in w and "2 of 99" in w and "not counted as wins or draws" in w and "stalled" in w, w)
ck("each game carries its seed", [g["seed"] for g in res["games"]] == [10, 11, 12, 13, 14])
ck("interval of nothing is zeros, of a sweep is high", P.wilson(0, 0) == [0.0, 0.0] and P.wilson(10, 10)[0] > 0.7)
# --- a whole job through the real worker path with a stub engine
STUB = os.path.join(tempfile.mkdtemp(), "stub_engine.py")
with open(STUB, "w", encoding="utf-8") as f:
f.write('''import sys, os
root = sys.argv[1]
seeds = [l.split(",")[0] for l in open(os.path.join(root, "games.txt")) if l.strip()]
feed = __import__("json").load(open(os.path.join(root, "feed.json")))
assert os.path.exists(os.path.join(root, "card-data.json")) and len(feed["decks"]) == 4
for d in feed["decks"]:
print(" %s | commander: %s | main: %d cards" % (d["name"], d["commander"][0], sum(e["count"] for e in d["main"])))
print("Resolved deck sizes (post name-resolution, 0-indexed by seat):")
for i, d in enumerate(feed["decks"]):
print(" P%d main=%3d commander=1" % (i, sum(e["count"] for e in d["main"])))
for k, s in enumerate(seeds):
print("--- GAME seed=%s ---" % s)
print("Turns played: %d" % (10 + k))
print("Game ended cleanly. Winner: P%d" % (k % 4))
''')
CD = os.path.join(os.path.dirname(STUB), "card-data.json")
open(CD, "w").write("{}")
P.CARD_DATA = CD
ORIG_COMMAND = P.command
P.command = lambda spec, root: [sys.executable, STUB, root]
job_id = P.submit(dict(GOOD, games=4))
threading.Thread(target=P.worker, daemon=True).start()
for _ in range(100):
j = P._jobs[job_id]
if j["status"] in ("done", "error"):
break
time.sleep(0.1)
pub = P.public(P._jobs[job_id])
ck("a queued job runs to done with four finished games, one win each", pub["status"] == "done" and pub["result"]["games_finished"] == 4
and pub["result"]["wins_by_seat"] == [1, 1, 1, 1] and pub["games_done"] == 4, pub)
ck("no warnings when every card resolved", pub["result"]["warnings"] == [], pub["result"]["warnings"])
ck("the temp folder is cleaned up", not [d for d in os.listdir(tempfile.gettempdir()) if d.startswith("phase-sim-")])
# --- the HTTP surface
srv = P.ThreadingHTTPServer(("127.0.0.1", 0), P.Handler)
threading.Thread(target=srv.serve_forever, daemon=True).start()
base = "http://127.0.0.1:%d" % srv.server_port
def call(path, body=None):
req = urllib.request.Request(base + path, data=None if body is None else json.dumps(body).encode(), headers={"content-type": "application/json"})
try:
r = urllib.request.urlopen(req, timeout=10)
return r.status, json.load(r)
except urllib.error.HTTPError as e:
return e.code, json.load(e)
code, h = call("/health")
ck("/health answers (ok is false only because the real binary is not here)", code == 200 and "engine" in h and h["max_games"] == P.MAX_GAMES, h)
code, r = call("/run", dict(GOOD, games=1))
ck("POST /run is accepted with a job id", code == 202 and len(r["job"]) == 12, r)
for _ in range(100):
code, j = call("/job/" + r["job"])
if j["status"] in ("done", "error"):
break
time.sleep(0.1)
ck("GET /job/<id> returns the result", code == 200 and j["status"] == "done" and j["result"]["games_finished"] == 1, j)
ck("a bad request is a 400 with a reason", call("/run", {"decks": []})[0] == 400)
ck("an unknown job is a 404", call("/job/deadbeef")[0] == 404)
ck("an unknown path is a 404", call("/nope")[0] == 404)
# --- model seats: validation, environment, relays
from http.server import BaseHTTPRequestHandler, HTTPServer
UP = []
class Upstream(BaseHTTPRequestHandler):
def log_message(self, *a):
pass
def do_POST(self):
body = json.loads(self.rfile.read(int(self.headers.get("Content-Length") or 0)))
UP.append((self.path, body, self.headers.get("authorization")))
out = json.dumps({"echo": True, "path": self.path}).encode()
self.send_response(200)
self.send_header("content-type", "application/json")
self.send_header("content-length", str(len(out)))
self.end_headers()
self.wfile.write(out)
up = HTTPServer(("127.0.0.1", 0), Upstream)
threading.Thread(target=up.serve_forever, daemon=True).start()
UPBASE = "http://127.0.0.1:%d" % up.server_port
os.environ["PHASE_SIM_ALLOW_MISSING_LLM_BIN"] = "1"
LLMREQ = dict(GOOD, llm_seats=[1, 3], llm={"kind": "chat", "model": "twin-turbo", "max_calls": 120})
P.CHAT_URL, P.DECISION_URL = "", ""
try:
P.validate(LLMREQ)
ck("model seats are refused when no model endpoint is configured", False)
except P.BadRequest as exc:
ck("model seats are refused when no model endpoint is configured", "no chat model endpoint" in str(exc), exc)
P.CHAT_URL, P.DECISION_URL, P.DECISION_KEY = UPBASE + "/v1", UPBASE + "/v1/systemone", "sekret"
v = P.validate(LLMREQ)
ck("a chat model seat request validates", v["llm"] == {"seats": [1, 3], "kind": "chat", "model": "twin-turbo", "max_calls": 120}, v["llm"])
ck("a request with no model seats has no llm settings", P.validate(GOOD)["llm"] is None)
for mut, why in ((lambda r: r.update(llm_seats=[4]), "a seat outside 0-3"),
(lambda r: r["llm"].pop("model"), "a chat model with no name"),
(lambda r: r["llm"].update(max_calls=P.MAX_LLM_CALLS + 1), "too many model calls"),
(lambda r: r["llm"].update(kind="telepathy"), "an unknown kind")):
req = json.loads(json.dumps(LLMREQ))
mut(req)
try:
P.validate(req)
ck("rejects " + why, False)
except P.BadRequest:
ck("rejects " + why, True)
dv = P.validate(dict(GOOD, llm_seats=[0], llm={"kind": "decision"}))
ck("a decision-model seat needs no model name", dv["llm"]["kind"] == "decision" and dv["llm"]["model"] == "decision-model")
env = P.engine_env(v)
ck("chat seats: the engine is pointed at this service's /llm relay as an OpenAI-compatible endpoint with no key",
env["PHASE_LLM_SEATS"] == "1,3" and env["PHASE_LLM_PROVIDER"] == "OpenAiCompatible" and env["PHASE_LLM_BASE_URL"].endswith("/llm/v1")
and env["PHASE_LLM_MODEL"] == "twin-turbo" and env["PHASE_LLM_API_KEY"] == "" and env["PHASE_LLM_MAX_CALLS"] == "120", env)
denv = P.engine_env(dv)
ck("decision seats: provider Jev, base is this service (the engine adds /jev/systemone)", denv["PHASE_LLM_PROVIDER"] == "Jev"
and denv["PHASE_LLM_BASE_URL"] == "http://127.0.0.1:%d" % P.PORT)
ck("no model seats: no special environment, the plain engine", P.engine_env(P.validate(GOOD)) is None
and ORIG_COMMAND(P.validate(GOOD), "/r")[0] == P.BIN and ORIG_COMMAND(v, "/r")[0] == P.BIN_LLM)
code, out = P.relay_chat(json.dumps({"model": "twin-turbo", "messages": [{"role": "user", "content": "x"}], "max_tokens": 4000, "stream": True}).encode(), "127.0.0.1")
path, body, auth = UP[-1]
ck("the chat relay forwards to /chat/completions with thinking off, the answer capped and streaming off",
code == 200 and path == "/v1/chat/completions" and body["reasoning_effort"] == "none" and body["max_tokens"] == P.CHAT_MAX_TOKENS
and body["stream"] is False and body["model"] == "twin-turbo", (code, path, body))
ck("a request without max_tokens gets the cap", (P.relay_chat(b'{"model": "m", "messages": []}', "127.0.0.1"), UP[-1][1]["max_tokens"] == P.CHAT_MAX_TOKENS)[1])
ck("the relays refuse anyone but this machine", P.relay_chat(b"{}", "10.1.2.3")[0] == 403 and P.relay_decision(b"{}", "10.1.2.3")[0] == 403)
ck("a malformed chat body is a 400, not an exception", P.relay_chat(b"not json", "127.0.0.1")[0] == 400)
envelope = {"apiKey": "ignored-by-us", "request": {"state": {"body": "t"}, "questions": {"pick": {"type": "choice"}}}}
code, out = P.relay_decision(json.dumps(envelope).encode(), "127.0.0.1")
path, body, auth = UP[-1]
ck("the decision relay unwraps phase's envelope and sends only the request, with OUR key", code == 200 and path == "/v1/systemone"
and body == envelope["request"] and auth == "Bearer sekret", (code, path, body, auth))
ck("a decision body with no request is a 400", P.relay_decision(b'{"apiKey": "k"}', "127.0.0.1")[0] == 400)
P.CHAT_URL = "http://127.0.0.1:1/v1"
ck("an unreachable model endpoint is a 502", P.relay_chat(b'{"model": "m", "messages": []}', "127.0.0.1")[0] == 502)
P.CHAT_URL = UPBASE + "/v1"
summ = P.llm_summary({"seats": [1], "kind": "chat", "model": "m", "max_calls": 9},
["LLM-SEAT seat=1 asked=25 used=20 total_calls=25", "noise", "LLM-SEAT seat=1 asked=50 used=41 total_calls=50"])
ck("the model seats' counters are read from the engine's output (last value wins)", summ["per_seat"] == {1: {"asked": 50, "used": 41}}, summ)
summ2 = P.llm_summary({"seats": [1], "kind": "chat", "model": "m", "max_calls": 9},
["LLM-SEAT seat=1 asked=25 used=1 total_calls=25 timeouts=20 avg_ms=1800 avg_prompt_chars=31000",
"LLM-SEAT-DISABLED after 4 consecutive failures (20 timeouts of 25 questions): phase's own AI plays the rest"])
ck("cost counters and the circuit-breaker are read from the engine's output", summ2["timeouts"] == 20 and summ2["avg_ms"] == 1800
and summ2["avg_prompt_chars"] == 31000 and summ2["switched_off_after_failures_in_a_row"] == 4 and summ2["per_seat"] == {1: {"asked": 25, "used": 1}}, summ2)
ck("old three-field counter lines still parse", P.llm_summary({"seats": [1], "kind": "chat", "model": "m", "max_calls": 9},
["LLM-SEAT seat=2 asked=25 used=24 total_calls=25"])["per_seat"] == {2: {"asked": 25, "used": 24}})
code, r = call("/llm/v1/chat/completions", {"model": "m", "messages": []})
ck("over HTTP from this machine the chat relay answers", code == 200 and r["echo"] is True, (code, r))
print("\nFAILURES: %d" % FAILS)
sys.exit(1 if FAILS else 0)
+42
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@@ -0,0 +1,42 @@
with open('ml/eval/forge_evaluator.py', 'rb') as f:
data = f.read()
# Fix 1: absolute JAR path in subprocess call
data = data.replace(
b'"--forge-jar", self.cfg.forge_jar,',
b'"--forge-jar", str(Path(self.cfg.forge_jar).resolve()),'
)
# Fix 2: resolve forge_work_dir to absolute path too
data = data.replace(
b'"--forge-dir", self.cfg.forge_work_dir,',
b'"--forge-dir", str(Path(self.cfg.forge_work_dir).resolve()),'
)
# Fix 3: change default forge_work_dir
data = data.replace(
b'forge_work_dir: str = "forge"',
b'forge_work_dir: str = "D:/ForgeCommander/forge-repo/forge-gui"'
)
with open('ml/eval/forge_evaluator.py', 'wb') as f:
f.write(data)
import py_compile
py_compile.compile('ml/eval/forge_evaluator.py', doraise=True)
print('forge_evaluator.py - Syntax OK')
# Fix eval_policy.py too
with open('scripts/eval_policy.py', 'rb') as f:
data = f.read()
data = data.replace(
b'"--forge-dir", default="forge"',
b'"--forge-dir", default="D:/ForgeCommander/forge-repo/forge-gui"'
)
with open('scripts/eval_policy.py', 'wb') as f:
f.write(data)
py_compile.compile('scripts/eval_policy.py', doraise=True)
print('eval_policy.py - Syntax OK')
-24
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@@ -1,24 +0,0 @@
# Logs
logs
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
pnpm-debug.log*
lerna-debug.log*
node_modules
dist
dist-ssr
*.local
# Editor directories and files
.vscode/*
!.vscode/extensions.json
.idea
.DS_Store
*.suo
*.ntvs*
*.njsproj
*.sln
*.sw?
-73
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@@ -1,73 +0,0 @@
# React + TypeScript + Vite
This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
Currently, two official plugins are available:
- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh
- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
## React Compiler
The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
## Expanding the ESLint configuration
If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules:
```js
export default defineConfig([
globalIgnores(['dist']),
{
files: ['**/*.{ts,tsx}'],
extends: [
// Other configs...
// Remove tseslint.configs.recommended and replace with this
tseslint.configs.recommendedTypeChecked,
// Alternatively, use this for stricter rules
tseslint.configs.strictTypeChecked,
// Optionally, add this for stylistic rules
tseslint.configs.stylisticTypeChecked,
// Other configs...
],
languageOptions: {
parserOptions: {
project: ['./tsconfig.node.json', './tsconfig.app.json'],
tsconfigRootDir: import.meta.dirname,
},
// other options...
},
},
])
```
You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules:
```js
// eslint.config.js
import reactX from 'eslint-plugin-react-x'
import reactDom from 'eslint-plugin-react-dom'
export default defineConfig([
globalIgnores(['dist']),
{
files: ['**/*.{ts,tsx}'],
extends: [
// Other configs...
// Enable lint rules for React
reactX.configs['recommended-typescript'],
// Enable lint rules for React DOM
reactDom.configs.recommended,
],
languageOptions: {
parserOptions: {
project: ['./tsconfig.node.json', './tsconfig.app.json'],
tsconfigRootDir: import.meta.dirname,
},
// other options...
},
},
])
```
@@ -1,23 +0,0 @@
import js from '@eslint/js'
import globals from 'globals'
import reactHooks from 'eslint-plugin-react-hooks'
import reactRefresh from 'eslint-plugin-react-refresh'
import tseslint from 'typescript-eslint'
import { defineConfig, globalIgnores } from 'eslint/config'
export default defineConfig([
globalIgnores(['dist']),
{
files: ['**/*.{ts,tsx}'],
extends: [
js.configs.recommended,
tseslint.configs.recommended,
reactHooks.configs.flat.recommended,
reactRefresh.configs.vite,
],
languageOptions: {
ecmaVersion: 2020,
globals: globals.browser,
},
},
])
-13
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@@ -1,13 +0,0 @@
<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<link rel="icon" type="image/svg+xml" href="/vite.svg" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>commander-ai-lab-ui</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="/src/main.tsx"></script>
</body>
</html>
File diff suppressed because it is too large. Load diff
-35
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@@ -1,35 +0,0 @@
{
"name": "commander-ai-lab-ui",
"private": true,
"version": "0.0.0",
"type": "module",
"scripts": {
"dev": "vite",
"build": "tsc -b && vite build",
"lint": "eslint .",
"preview": "vite preview"
},
"dependencies": {
"@tailwindcss/vite": "^4.2.1",
"lucide-react": "^0.577.0",
"react": "^19.2.0",
"react-dom": "^19.2.0",
"react-router-dom": "^7.13.1",
"recharts": "^3.8.0",
"tailwindcss": "^4.2.1"
},
"devDependencies": {
"@eslint/js": "^9.39.1",
"@types/node": "^24.10.1",
"@types/react": "^19.2.7",
"@types/react-dom": "^19.2.3",
"@vitejs/plugin-react": "^5.1.1",
"eslint": "^9.39.1",
"eslint-plugin-react-hooks": "^7.0.1",
"eslint-plugin-react-refresh": "^0.4.24",
"globals": "^16.5.0",
"typescript": "~5.9.3",
"typescript-eslint": "^8.48.0",
"vite": "^7.3.1"
}
}
-27
View File
@@ -1,27 +0,0 @@
import { BrowserRouter, Routes, Route } from 'react-router-dom'
import { PageLayout } from './components/layout/PageLayout'
import { BatchSimPage } from './pages/BatchSimPage'
import { CollectionPage } from './pages/CollectionPage'
import { DecksPage } from './pages/DecksPage'
import { AutoGenPage } from './pages/AutoGenPage'
import { SimulatorPage } from './pages/SimulatorPage'
import { CoachPage } from './pages/CoachPage'
import { TrainingPage } from './pages/TrainingPage'
export default function App() {
return (
<BrowserRouter>
<PageLayout>
<Routes>
<Route path="/" element={<BatchSimPage />} />
<Route path="/collection" element={<CollectionPage />} />
<Route path="/decks" element={<DecksPage />} />
<Route path="/autogen" element={<AutoGenPage />} />
<Route path="/simulator" element={<SimulatorPage />} />
<Route path="/coach" element={<CoachPage />} />
<Route path="/training" element={<TrainingPage />} />
</Routes>
</PageLayout>
</BrowserRouter>
)
}
@@ -1,82 +0,0 @@
// ═══════════════════════════════════════════════════════════
// Base API client — all fetch calls go through here
// ═══════════════════════════════════════════════════════════
const BASE = '' // same origin; Vite proxy handles /api in dev
export class ApiError extends Error {
status: number
constructor(status: number, message: string) {
super(message)
this.name = 'ApiError'
this.status = status
}
}
async function handleResponse<T>(res: Response): Promise<T> {
if (!res.ok) {
const body = await res.text()
let message = `HTTP ${res.status}`
try {
const json = JSON.parse(body)
message = json.detail || json.error || message
} catch {
if (body) message = body
}
throw new ApiError(res.status, message)
}
// Handle 204 No Content
if (res.status === 204) return undefined as T
return res.json()
}
export async function get<T>(path: string, params?: Record<string, string | number | boolean | undefined>): Promise<T> {
const url = new URL(BASE + path, window.location.origin)
if (params) {
Object.entries(params).forEach(([k, v]) => {
if (v !== undefined && v !== '') url.searchParams.set(k, String(v))
})
}
const res = await fetch(url.toString())
return handleResponse<T>(res)
}
export async function post<T>(path: string, body?: unknown): Promise<T> {
const res = await fetch(BASE + path, {
method: 'POST',
headers: body ? { 'Content-Type': 'application/json' } : {},
body: body ? JSON.stringify(body) : undefined,
})
return handleResponse<T>(res)
}
export async function postForm<T>(path: string, formData: FormData): Promise<T> {
const res = await fetch(BASE + path, {
method: 'POST',
body: formData,
})
return handleResponse<T>(res)
}
export async function put<T>(path: string, body: unknown): Promise<T> {
const res = await fetch(BASE + path, {
method: 'PUT',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(body),
})
return handleResponse<T>(res)
}
export async function patch<T>(path: string, body: unknown): Promise<T> {
const res = await fetch(BASE + path, {
method: 'PATCH',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(body),
})
return handleResponse<T>(res)
}
export async function del<T>(path: string): Promise<T> {
const res = await fetch(BASE + path, { method: 'DELETE' })
return handleResponse<T>(res)
}
@@ -1,131 +0,0 @@
import { get, post } from './client'
import type {
CoachStatus, CoachDeck, CoachSession, CoachSessionSummary,
CoachChatResponse, CoachApplyResult, CoachCardLikeResult
} from '../types'
export async function getCoachStatus() {
return get<CoachStatus>('/api/coach/status')
}
export async function getCoachDecks() {
return get<CoachDeck[]>('/api/coach/decks')
}
export async function getCoachDeckReport(deckId: string) {
return get<Record<string, unknown>>(`/api/coach/decks/${deckId}/report`)
}
export async function runCoachSession(deckId: string, goals?: Record<string, unknown>) {
return post<CoachSession>(`/api/coach/decks/${deckId}`, goals ? { goals } : undefined)
}
export async function getCoachSessions(deckId?: string) {
const params: Record<string, string> = {}
if (deckId) params.deck_id = deckId
return get<{ sessions: CoachSessionSummary[] }>('/api/coach/sessions', params)
}
export async function getCoachSession(sessionId: string) {
return get<CoachSession>(`/api/coach/sessions/${sessionId}`)
}
export async function downloadEmbeddings() {
return post<{ success: boolean; cards: number }>('/api/coach/embeddings/download')
}
export async function coachChat(
deckId: string,
messages: { role: string; content: string }[],
goals?: Record<string, unknown>
) {
return post<CoachChatResponse>('/api/coach/chat', {
deck_id: deckId,
messages,
goals,
stream: false,
})
}
export async function coachChatStream(
deckId: string,
messages: { role: string; content: string }[],
goals?: Record<string, unknown>,
onChunk?: (text: string) => void,
signal?: AbortSignal
): Promise<string> {
const res = await fetch('/api/coach/chat', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
deck_id: deckId,
messages,
goals,
stream: true,
}),
signal,
})
if (!res.ok) {
const body = await res.text()
throw new Error(body || `HTTP ${res.status}`)
}
const reader = res.body?.getReader()
if (!reader) throw new Error('No response body')
const decoder = new TextDecoder()
let fullText = ''
let buffer = ''
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) {
if (line.startsWith('data: ')) {
const data = line.slice(6).trim()
if (data === '[DONE]') break
try {
const parsed = JSON.parse(data)
if (parsed.content) {
fullText += parsed.content
onChunk?.(fullText)
}
} catch { /* skip bad chunks */ }
}
}
}
return fullText
}
export async function applyCoachSuggestions(
sessionId: string,
deckId: number,
acceptedCuts: string[],
acceptedAdds: string[]
) {
return post<CoachApplyResult>('/api/coach/apply', {
session_id: sessionId,
deck_id: deckId,
accepted_cuts: acceptedCuts,
accepted_adds: acceptedAdds,
})
}
export async function searchCardsLike(card: string, colors?: string, topN = 10) {
const params: Record<string, string | number> = { card, top_n: topN }
if (colors) params.colors = colors
return get<{ query: string; results: CoachCardLikeResult[] }>('/api/coach/cards-like', params)
}
export async function generateReport(deckId?: number) {
return post<{ status: string; decksUpdated: string[]; count: number }>('/api/coach/reports/generate',
deckId ? { deck_id: deckId } : undefined
)
}
@@ -1,86 +0,0 @@
import { get, post, patch, postForm } from './client'
import type { CollectionCard, CollectionFilters, SetInfo, ScanResult } from '../types'
export async function searchCollection(filters: CollectionFilters) {
// Map frontend snake_case filter names to backend camelCase param names
const params: Record<string, string | number | boolean> = {}
if (filters.q) params.q = filters.q
if (filters.page) params.page = filters.page
if (filters.page_size) params.pageSize = filters.page_size
if (filters.sort_by) params.sortField = filters.sort_by
if (filters.sort_dir) params.sortDir = filters.sort_dir
if (filters.color) params.colors = filters.color
if (filters.type_line) params.types = filters.type_line
if (filters.rarity) params.rarity = filters.rarity
if (filters.set_code) params.setCode = filters.set_code
if (filters.cmc_min !== undefined) params.cmcMin = filters.cmc_min
if (filters.cmc_max !== undefined) params.cmcMax = filters.cmc_max
if (filters.price_min !== undefined) params.priceMin = filters.price_min
if (filters.price_max !== undefined) params.priceMax = filters.price_max
if (filters.owned_only !== undefined) params.owned_only = filters.owned_only
const res = await get<{ items?: CollectionCard[]; cards?: CollectionCard[]; total: number; page: number; pageSize?: number; page_size?: number }>(
'/api/collection', params
)
return {
cards: res.items || res.cards || [],
total: res.total || 0,
page: res.page || 1,
page_size: res.pageSize || res.page_size || 48,
}
}
export async function getCard(cardId: number) {
return get<CollectionCard>(`/api/collection/${cardId}`)
}
export async function updateCard(cardId: number, updates: Partial<CollectionCard>) {
return patch<CollectionCard>(`/api/collection/${cardId}`, updates)
}
export async function getSets() {
const res = await get<SetInfo[] | { sets: SetInfo[] }>('/api/collection/sets')
return Array.isArray(res) ? res : (res as { sets: SetInfo[] }).sets || []
}
export async function getKeywords() {
const res = await get<string[] | { keywords: string[] }>('/api/collection/keywords')
return Array.isArray(res) ? res : (res as { keywords: string[] }).keywords || []
}
export async function exportCollection(format: string = 'csv') {
const res = await fetch(`/api/collection/export?format=${format}`)
return res.blob()
}
export async function importCollection(file: File) {
const fd = new FormData()
fd.append('file', file)
return postForm<{ imported: number; errors: string[] }>('/api/collection/import', fd)
}
export async function scanCard(file: File) {
const fd = new FormData()
fd.append('image', file)
return postForm<ScanResult>('/api/collection/scan', fd)
}
export async function addScanResult(match: { name: string; set_code: string; scryfall_id: string; quantity?: number }) {
return post<{ success: boolean; card_id: number }>('/api/collection/scan/add', match)
}
export async function reEnrichCards() {
return post<{ updated: number }>('/api/collection/re-enrich')
}
export async function autoClassify(cardId: number) {
return post<{ classifications: string[] }>(`/api/collection/auto-classify`, { card_id: cardId })
}
export async function autoClassifyAll() {
return post<{ processed: number }>('/api/collection/auto-classify-all')
}
export async function getEdhrecData(cardId: number) {
return get<Record<string, unknown>>(`/api/collection/${cardId}/edhrec`)
}
@@ -1,22 +0,0 @@
import { get, post } from './client'
import type { DeckGenV3Request, DeckGenV3Status, DeckGenV3Result, CommanderSearchResult } from '../types'
export async function getV3Status() {
return get<DeckGenV3Status>('/api/deck/v3/status')
}
export async function searchCommanders(q: string) {
return get<CommanderSearchResult[]>('/api/deck-generator/commander-search', { q })
}
export async function generateDeckV3(req: DeckGenV3Request) {
return post<DeckGenV3Result>('/api/deck/v3/generate', req)
}
export async function commitDeckV3(req: DeckGenV3Request) {
return post<DeckGenV3Result & { deck_id: number; dck_path: string }>('/api/deck/v3/commit', req)
}
export async function exportDeckV3(format: 'csv' | 'dck' | 'moxfield' | 'shopping', req: DeckGenV3Request) {
return post<Record<string, unknown>>(`/api/deck/v3/export/${format}`, req)
}
@@ -1,130 +0,0 @@
import { get, post, put, del, patch } from './client'
import type { Deck, DeckCard, DeckAnalysis, EdhRecsResponse, CollectionRecsResponse, PplxStatus } from '../types'
export async function listDecks() {
const res = await get<{ decks: Record<string, unknown>[] } | Record<string, unknown>[]>('/api/decks')
const raw = Array.isArray(res) ? res : (res as { decks: Record<string, unknown>[] }).decks || []
return raw.map(d => ({
...d,
commander: d.commander || d.commander_name || '',
card_count: d.card_count ?? d.total_cards ?? 0,
total_price: d.total_price ?? 0,
color_identity: d.color_identity || [],
created_date: d.created_date || d.created_at || '',
updated_date: d.updated_date || d.updated_at || '',
})) as Deck[]
}
export async function getDeck(deckId: number) {
return get<Deck>(`/api/decks/${deckId}`)
}
export async function createDeck(deck: { name: string; commander?: string; format?: string }) {
return post<Deck>('/api/decks', deck)
}
export async function updateDeck(deckId: number, updates: Partial<Deck>) {
return put<Deck>(`/api/decks/${deckId}`, updates)
}
export async function deleteDeck(deckId: number) {
return del<void>(`/api/decks/${deckId}`)
}
export async function deleteAllDecks() {
return del<void>('/api/decks')
}
export async function getDeckCards(deckId: number) {
const res = await get<{ cards: Record<string, unknown>[]; total: number } | Record<string, unknown>[]>(`/api/decks/${deckId}/cards`)
const raw = Array.isArray(res) ? res : (res as { cards: Record<string, unknown>[] }).cards || []
return raw.map(c => ({
...c,
name: c.name || c.card_name || '',
category: c.category || c.role_tag || '',
})) as DeckCard[]
}
export async function addCardToDeck(deckId: number, card: { name: string; scryfall_id?: string; quantity?: number; category?: string }) {
return post<DeckCard>(`/api/decks/${deckId}/cards`, card)
}
export async function removeCardFromDeck(deckId: number, cardId: number) {
return del<void>(`/api/decks/${deckId}/cards/${cardId}`)
}
export async function updateDeckCard(deckId: number, cardId: number, updates: Partial<DeckCard>) {
return patch<DeckCard>(`/api/decks/${deckId}/cards/${cardId}`, updates)
}
export async function getDeckAnalysis(deckId: number) {
const raw = await get<Record<string, unknown>>(`/api/decks/${deckId}/analysis`)
// Backend returns: counts_by_type, targets, deltas, mana_curve, color_pips, total_cards, roles
// Frontend DeckAnalysis expects: card_count, land_count, creature_count, noncreature_count, avg_cmc, mana_curve, color_distribution, type_distribution, total_price, owned_count, missing_count
const counts = (raw.counts_by_type || {}) as Record<string, number>
const totalCards = (raw.total_cards as number) || 0
const landCount = counts['Land'] || 0
const creatureCount = counts['Creature'] || 0
const manaCurve = (raw.mana_curve || {}) as Record<string | number, number>
const colorPips = (raw.color_pips || {}) as Record<string, number>
// Compute avg CMC from mana curve
let totalMana = 0, nonLandCards = 0
for (const [cmc, count] of Object.entries(manaCurve)) {
const cmcNum = cmc === '6+' ? 6 : Number(cmc)
totalMana += cmcNum * count
nonLandCards += count
}
return {
card_count: totalCards,
land_count: landCount,
creature_count: creatureCount,
noncreature_count: totalCards - landCount - creatureCount,
avg_cmc: nonLandCards > 0 ? totalMana / nonLandCards : 0,
mana_curve: manaCurve as Record<number, number>,
color_distribution: colorPips,
type_distribution: counts,
total_price: 0,
owned_count: totalCards,
missing_count: 0,
} as DeckAnalysis
}
export async function getCollectionRecs(deckId: number) {
return get<CollectionRecsResponse>(`/api/decks/${deckId}/recommended-from-collection`)
}
export async function getEdhRecs(deckId: number, onlyOwned = false, maxResults = 30) {
return get<EdhRecsResponse>(`/api/decks/${deckId}/edh-recs?only_owned=${onlyOwned}&max_results=${maxResults}`)
}
export async function bulkAddCards(deckId: number, cards: { name: string; quantity?: number }[]) {
return post<{ added: number }>(`/api/decks/${deckId}/bulk-add`, { cards })
}
export async function bulkAddRecommended(deckId: number, cards: string[]) {
return post<{ added: number }>(`/api/decks/${deckId}/bulk-add-recommended`, { card_names: cards })
}
export async function exportDeckToSim(deckId: number) {
return post<{ path: string }>(`/api/decks/${deckId}/export-to-sim`)
}
export async function importDeckToDeck(deckId: number, data: { text?: string; url?: string }) {
return post<{ imported: number }>(`/api/decks/${deckId}/import`, data)
}
export async function importNewDeck(data: { text?: string; url?: string; name?: string }) {
return post<Deck>('/api/decks/import-new', data)
}
export async function getPplxStatus() {
return get<PplxStatus>('/api/pplx/status')
}
export async function deckResearch(payload: { commander: string; strategy?: string }) {
return post<{ research: string }>('/api/deck-research', payload)
}
export async function deckGenerate(payload: { commander: string; strategy?: string; bracket?: number }) {
return post<{ cards: Array<{ name: string; quantity: number; category: string }> }>('/api/deck-generate', payload)
}
@@ -1,7 +0,0 @@
export * as collectionApi from './collection'
export * as decksApi from './decks'
export * as deckgenApi from './deckgen'
export * as labApi from './lab'
export * as simApi from './sim'
export * as coachApi from './coach'
export * as trainingApi from './training'
@@ -1,65 +0,0 @@
import { get, post } from './client'
import type { LabDeck, LabStatus, LabResult, LabHistoryEntry, PreconDeck } from '../types'
export async function getLabDecks() {
const res = await get<{ decks: LabDeck[] } | LabDeck[]>('/api/lab/decks')
return Array.isArray(res) ? res : (res as { decks: LabDeck[] }).decks || []
}
export async function startBatchSim(config: { decks: string[]; games: number; threads?: number; use_deepseek?: boolean; deepseek_deck?: string }) {
return post<{ run_id: string }>('/api/lab/start', config)
}
export async function startDeepSeekSim(config: { decks: string[]; games: number; deepseek_deck: string }) {
return post<{ run_id: string }>('/api/lab/start-deepseek', config)
}
export async function getLabStatus() {
return get<LabStatus>('/api/lab/status')
}
export async function getLabResult() {
return get<LabResult>('/api/lab/result')
}
export async function getLabHistory() {
const res = await get<{ results: LabHistoryEntry[] } | LabHistoryEntry[]>('/api/lab/history')
return Array.isArray(res) ? res : (res as { results: LabHistoryEntry[] }).results || []
}
export async function getPrecons() {
const res = await get<{ precons: PreconDeck[] } | PreconDeck[]>('/api/lab/precons')
return Array.isArray(res) ? res : (res as { precons: PreconDeck[] }).precons || []
}
export async function installPrecon(name: string) {
return post<{ installed: boolean }>('/api/lab/precons/install', { name })
}
export async function installPreconBatch(names: string[]) {
return post<{ installed: number }>('/api/lab/precons/install-batch', { names })
}
export async function getMetaCommanders() {
return get<Array<{ name: string; color_identity: string[] }>>('/api/lab/meta/commanders')
}
export async function searchMetaCommanders(q: string) {
return get<Array<{ name: string; color_identity: string[]; source: string }>>('/api/lab/meta/search', { q })
}
export async function fetchMetaDeck(commander: string) {
return post<{ deck_name: string; cards: Array<{ name: string; quantity: number }> }>('/api/lab/meta/fetch', { commander })
}
export async function importDeckFromUrl(url: string) {
return post<{ deck_name: string }>('/api/lab/import/url', { url })
}
export async function importDeckFromText(text: string, name?: string) {
return post<{ deck_name: string }>('/api/lab/import/text', { text, name })
}
export async function getLabLog() {
return get<{ log: string }>('/api/lab/log')
}
@@ -1,38 +0,0 @@
import { get, post } from './client'
import type { SimStatus, SimResult, DeepSeekStatus } from '../types'
export async function runSim(config: { deck_a: string; deck_b: string; games: number }) {
return post<{ sim_id: string }>('/api/sim/run', config)
}
export async function runSimFromDeck(deckId: number, config: { opponent?: string; games?: number }) {
return post<{ sim_id: string }>('/api/sim/run-from-deck', { deck_id: deckId, ...config })
}
export async function runDeepSeekSim(config: { deck_a: string; deck_b: string; games: number; deepseek_deck: string }) {
return post<{ sim_id: string }>('/api/sim/run-deepseek', config)
}
export async function getSimStatus(simId: string) {
return get<SimStatus>('/api/sim/status', { simId })
}
export async function getSimResult(simId: string) {
return get<SimResult>('/api/sim/result', { simId })
}
export async function connectDeepSeek() {
return post<DeepSeekStatus>('/api/deepseek/connect')
}
export async function getDeepSeekStatus() {
return get<DeepSeekStatus>('/api/deepseek/status')
}
export async function configureDeepSeek(config: { model?: string; base_url?: string }) {
return post<DeepSeekStatus>('/api/deepseek/configure', config)
}
export async function getDeepSeekLogs() {
return get<Array<{ filename: string; size: number; modified: string }>>('/api/deepseek/logs')
}
@@ -1,92 +0,0 @@
import { get, post } from './client'
import type {
MLStatus, MLDataStatus, MLTrainStatus, PPOTrainStatus,
TournamentStatus, TournamentResults, MLModelInfo
} from '../types'
// ── ML Status & Toggle ──────────────────────────────────────
export async function getMLStatus() {
return get<MLStatus>('/api/ml/status')
}
export async function toggleML(enable = true) {
return post<{ ml_logging_enabled: boolean; message: string }>(`/api/ml/toggle?enable=${enable}`)
}
// ── Data & Model Info ───────────────────────────────────────
export async function getDataStatus() {
return get<MLDataStatus>('/api/ml/data/status')
}
export async function getModelInfo() {
return get<MLModelInfo>('/api/ml/model')
}
export async function reloadModel(checkpoint?: string) {
return post<{ success: boolean; status: MLModelInfo }>('/api/ml/reload', checkpoint ? { checkpoint } : undefined)
}
// ── Supervised Training ─────────────────────────────────────
export async function startTraining(config: {
epochs?: number
lr?: number
batchSize?: number
patience?: number
rebuildDataset?: boolean
}) {
return post<{ status: string; config: Record<string, unknown> }>('/api/ml/train', config)
}
export async function getTrainStatus() {
return get<MLTrainStatus>('/api/ml/train/status')
}
// ── PPO Training ────────────────────────────────────────────
export async function startPPOTraining(config: {
iterations?: number
episodesPerIter?: number
ppoEpochs?: number
batchSize?: number
lr?: number
clipEpsilon?: number
entropyCoeff?: number
opponent?: string
playstyle?: string
loadSupervised?: string
}) {
return post<{ status: string; iterations: number }>('/api/ml/train/ppo', config)
}
export async function getPPOStatus() {
return get<PPOTrainStatus>('/api/ml/train/ppo/status')
}
// ── Tournament ──────────────────────────────────────────────
export async function startTournament(config: {
episodes?: number
playstyle?: string
}) {
return post<{ status: string; episodes: number }>('/api/ml/tournament', config)
}
export async function getTournamentStatus() {
return get<TournamentStatus>('/api/ml/tournament/status')
}
export async function getTournamentResults() {
return get<TournamentResults>('/api/ml/tournament/results')
}
// ── Decision Files ──────────────────────────────────────────
export async function getDecisionFile(filename: string, limit = 100, offset = 0) {
return get<{ file: string; offset: number; limit: number; count: number; decisions: Record<string, unknown>[] }>(
`/api/ml/decisions/${filename}?limit=${limit}&offset=${offset}`
)
}
// ── Prediction ──────────────────────────────────────────────
export async function predict(state: Record<string, unknown>) {
return post<{ action: string; action_index: number; confidence: number; probabilities: Record<string, number>; inference_ms: number }>(
'/api/ml/predict', state
)
}
@@ -1,49 +0,0 @@
import { useState } from 'react'
interface CardImageProps {
src: string
alt: string
size?: 'sm' | 'md' | 'lg' | 'xl'
className?: string
onClick?: () => void
}
const SIZES = {
sm: 'w-16 h-[22px]', // tiny inline
md: 'w-[146px] h-[204px]', // grid thumbnail
lg: 'w-[244px] h-[340px]', // detail view
xl: 'w-[336px] h-[468px]', // full size
}
export function CardImage({ src, alt, size = 'md', className = '', onClick }: CardImageProps) {
const [loaded, setLoaded] = useState(false)
const [error, setError] = useState(false)
// Build Scryfall URL if needed — ensure we get the right size
const imgUrl = src || ''
return (
<div
className={`relative overflow-hidden rounded-lg bg-bg-tertiary ${SIZES[size]} ${onClick ? 'cursor-pointer hover:ring-2 hover:ring-accent-blue/50 transition-all' : ''} ${className}`}
onClick={onClick}
>
{!error ? (
<img
src={imgUrl}
alt={alt}
className={`w-full h-full object-cover transition-opacity duration-200 ${loaded ? 'opacity-100' : 'opacity-0'}`}
onLoad={() => setLoaded(true)}
onError={() => setError(true)}
loading="lazy"
/>
) : (
<div className="flex items-center justify-center w-full h-full text-text-tertiary text-xs text-center p-2">
{alt}
</div>
)}
{!loaded && !error && (
<div className="absolute inset-0 bg-bg-tertiary animate-pulse" />
)}
</div>
)
}
@@ -1,19 +0,0 @@
import type { LucideIcon } from 'lucide-react'
interface EmptyStateProps {
icon: LucideIcon
title: string
description?: string
action?: React.ReactNode
}
export function EmptyState({ icon: Icon, title, description, action }: EmptyStateProps) {
return (
<div className="flex flex-col items-center justify-center py-16 text-center">
<Icon className="w-12 h-12 text-text-tertiary mb-4" strokeWidth={1.5} />
<h3 className="text-lg font-semibold text-text-primary mb-1">{title}</h3>
{description && <p className="text-sm text-text-secondary max-w-md mb-4">{description}</p>}
{action}
</div>
)
}
@@ -1,69 +0,0 @@
// Parse MTG mana cost strings like "{2}{W}{U}" into colored symbols
const MANA_COLORS: Record<string, string> = {
W: 'bg-mana-white text-black',
U: 'bg-mana-blue text-white',
B: 'bg-mana-black text-white',
R: 'bg-mana-red text-white',
G: 'bg-mana-green text-white',
C: 'bg-mana-colorless text-white',
}
interface ManaSymbolsProps {
cost: string
size?: 'sm' | 'md'
}
export function ManaSymbols({ cost, size = 'sm' }: ManaSymbolsProps) {
if (!cost) return null
const symbols = cost.match(/\{([^}]+)\}/g)
if (!symbols) return <span className="text-text-tertiary">{cost}</span>
const sz = size === 'sm' ? 'w-5 h-5 text-[10px]' : 'w-6 h-6 text-xs'
return (
<span className="inline-flex items-center gap-0.5">
{symbols.map((s, i) => {
const val = s.replace(/[{}]/g, '')
const colorClass = MANA_COLORS[val] || 'bg-mana-colorless text-white'
return (
<span
key={i}
className={`${sz} inline-flex items-center justify-center rounded-full font-bold ${colorClass}`}
>
{val}
</span>
)
})}
</span>
)
}
// Color identity dots
interface ColorDotsProps {
colors: string[]
size?: 'sm' | 'md'
}
export function ColorDots({ colors, size = 'sm' }: ColorDotsProps) {
if (!colors || colors.length === 0) return <span className="text-text-tertiary text-xs">Colorless</span>
const sz = size === 'sm' ? 'w-3 h-3' : 'w-4 h-4'
const DOT_COLORS: Record<string, string> = {
W: 'bg-mana-white',
U: 'bg-mana-blue',
B: 'bg-mana-black border border-border-secondary',
R: 'bg-mana-red',
G: 'bg-mana-green',
}
return (
<span className="inline-flex items-center gap-0.5">
{colors.map(c => (
<span key={c} className={`${sz} rounded-full ${DOT_COLORS[c] || 'bg-mana-colorless'}`} />
))}
</span>
)
}
@@ -1,38 +0,0 @@
interface SpinnerProps {
size?: 'sm' | 'md' | 'lg'
className?: string
}
const SIZES = { sm: 'w-4 h-4', md: 'w-6 h-6', lg: 'w-8 h-8' }
export function Spinner({ size = 'md', className = '' }: SpinnerProps) {
return (
<svg
className={`animate-spin ${SIZES[size]} ${className}`}
viewBox="0 0 24 24"
fill="none"
>
<circle
cx="12" cy="12" r="10"
stroke="currentColor"
strokeWidth="3"
strokeLinecap="round"
className="opacity-20"
/>
<path
d="M12 2a10 10 0 0 1 10 10"
stroke="currentColor"
strokeWidth="3"
strokeLinecap="round"
/>
</svg>
)
}
export function PageSpinner() {
return (
<div className="flex items-center justify-center h-64">
<Spinner size="lg" className="text-accent-blue" />
</div>
)
}
@@ -1,35 +0,0 @@
import { CheckCircle, XCircle, AlertTriangle, Info } from 'lucide-react'
type Variant = 'success' | 'error' | 'warning' | 'info' | 'neutral'
interface StatusBadgeProps {
variant: Variant
label: string
className?: string
}
const STYLES: Record<Variant, string> = {
success: 'bg-status-success/15 text-status-success border-status-success/30',
error: 'bg-status-error/15 text-status-error border-status-error/30',
warning: 'bg-status-warning/15 text-status-warning border-status-warning/30',
info: 'bg-status-info/15 text-status-info border-status-info/30',
neutral: 'bg-bg-tertiary text-text-secondary border-border-primary',
}
const ICONS: Record<Variant, typeof CheckCircle> = {
success: CheckCircle,
error: XCircle,
warning: AlertTriangle,
info: Info,
neutral: Info,
}
export function StatusBadge({ variant, label, className = '' }: StatusBadgeProps) {
const Icon = ICONS[variant]
return (
<span className={`inline-flex items-center gap-1.5 px-2.5 py-1 text-xs font-medium rounded-md border ${STYLES[variant]} ${className}`}>
<Icon className="w-3.5 h-3.5" />
{label}
</span>
)
}
@@ -1,5 +0,0 @@
export { Spinner, PageSpinner } from './Spinner'
export { StatusBadge } from './StatusBadge'
export { CardImage } from './CardImage'
export { ManaSymbols, ColorDots } from './ManaSymbols'
export { EmptyState } from './EmptyState'
@@ -1,16 +0,0 @@
import { Sidebar } from './Sidebar'
interface PageLayoutProps {
children: React.ReactNode
}
export function PageLayout({ children }: PageLayoutProps) {
return (
<div className="min-h-screen bg-bg-primary">
<Sidebar />
<main className="ml-56 min-h-screen">
{children}
</main>
</div>
)
}
@@ -1,51 +0,0 @@
import { NavLink } from 'react-router-dom'
const NAV_ITEMS = [
{ to: '/', label: 'Batch Sim', icon: '⚗️' },
{ to: '/collection', label: 'Collection', icon: '📦' },
{ to: '/decks', label: 'Decks', icon: '🃏' },
{ to: '/autogen', label: 'Auto Gen', icon: '⚡' },
{ to: '/simulator', label: 'Simulator', icon: '🎮' },
{ to: '/coach', label: 'Coach', icon: '🧠' },
{ to: '/training', label: 'Training', icon: '📈' },
]
export function Sidebar() {
return (
<aside className="fixed left-0 top-0 bottom-0 w-56 bg-bg-secondary border-r border-border-primary flex flex-col z-30">
{/* Logo */}
<div className="px-5 py-5 border-b border-border-primary">
<h1 className="text-base font-bold text-text-primary tracking-tight">
Commander AI Lab
</h1>
<p className="text-xs text-text-tertiary mt-0.5">MTG Intelligence Platform</p>
</div>
{/* Nav */}
<nav className="flex-1 py-3 overflow-y-auto">
{NAV_ITEMS.map(item => (
<NavLink
key={item.to}
to={item.to}
end={item.to === '/'}
className={({ isActive }) =>
`flex items-center gap-3 px-5 py-2.5 text-sm transition-colors duration-150 ${
isActive
? 'bg-accent-blue/10 text-accent-blue border-r-2 border-accent-blue'
: 'text-text-secondary hover:text-text-primary hover:bg-bg-hover'
}`
}
>
<span className="text-base">{item.icon}</span>
<span>{item.label}</span>
</NavLink>
))}
</nav>
{/* Footer */}
<div className="px-5 py-4 border-t border-border-primary">
<p className="text-xs text-text-tertiary">v4.0 — React + TypeScript</p>
</div>
</aside>
)
}
@@ -1,13 +0,0 @@
import { useEffect, useRef } from 'react'
/** Polls a function at the given interval while `enabled` is true */
export function usePolling(fn: () => void, intervalMs: number, enabled: boolean) {
const savedFn = useRef(fn)
savedFn.current = fn
useEffect(() => {
if (!enabled) return
const id = setInterval(() => savedFn.current(), intervalMs)
return () => clearInterval(id)
}, [intervalMs, enabled])
}
-114
View File
@@ -1,114 +0,0 @@
@import "tailwindcss";
/* ═══════════════════════════════════════════════════════════
Commander AI Lab — Design Tokens (Tailwind v4 CSS Theme)
Fresh dark theme with MTG-inspired accents
═══════════════════════════════════════════════════════════ */
@theme {
/* ── Backgrounds ── */
--color-bg-primary: #0f1117;
--color-bg-secondary: #161921;
--color-bg-tertiary: #1c2030;
--color-bg-elevated: #222738;
--color-bg-hover: #2a3048;
/* ── Borders ── */
--color-border-primary: #2a2f42;
--color-border-secondary: #363c54;
--color-border-accent: #4f6ef7;
/* ── Text ── */
--color-text-primary: #e8eaf0;
--color-text-secondary: #9ba1b8;
--color-text-tertiary: #6b7190;
--color-text-inverse: #0f1117;
/* ── Accents ── */
--color-accent-blue: #4f6ef7;
--color-accent-blue-hover: #6b84ff;
--color-accent-teal: #2dd4bf;
--color-accent-teal-hover: #5eead4;
--color-accent-purple: #a78bfa;
--color-accent-amber: #fbbf24;
--color-accent-red: #f87171;
--color-accent-green: #4ade80;
/* ── MTG Mana Colors ── */
--color-mana-white: #f9faf4;
--color-mana-blue: #0e67ab;
--color-mana-black: #2b2424;
--color-mana-red: #d3202a;
--color-mana-green: #00733e;
--color-mana-colorless: #706f6f;
/* ── Status ── */
--color-status-success: #4ade80;
--color-status-warning: #fbbf24;
--color-status-error: #f87171;
--color-status-info: #60a5fa;
/* ── Radius ── */
--radius-sm: 0.375rem;
--radius-md: 0.5rem;
--radius-lg: 0.75rem;
--radius-xl: 1rem;
/* ── Shadows ── */
--shadow-sm: 0 1px 2px rgba(0, 0, 0, 0.3);
--shadow-md: 0 4px 12px rgba(0, 0, 0, 0.4);
--shadow-lg: 0 8px 24px rgba(0, 0, 0, 0.5);
--shadow-glow-blue: 0 0 20px rgba(79, 110, 247, 0.15);
--shadow-glow-teal: 0 0 20px rgba(45, 212, 191, 0.15);
}
/* ── Base Styles ── */
html {
background-color: var(--color-bg-primary);
color: var(--color-text-primary);
}
body {
font-family: 'Inter', system-ui, -apple-system, sans-serif;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
/* ── Scrollbar ── */
::-webkit-scrollbar {
width: 8px;
height: 8px;
}
::-webkit-scrollbar-track {
background: var(--color-bg-secondary);
}
::-webkit-scrollbar-thumb {
background: var(--color-border-secondary);
border-radius: 4px;
}
::-webkit-scrollbar-thumb:hover {
background: var(--color-text-tertiary);
}
/* ── Utility animations ── */
@keyframes fade-in {
from { opacity: 0; transform: translateY(4px); }
to { opacity: 1; transform: translateY(0); }
}
@keyframes pulse-glow {
0%, 100% { box-shadow: 0 0 0 0 rgba(79, 110, 247, 0.2); }
50% { box-shadow: 0 0 16px 4px rgba(79, 110, 247, 0.15); }
}
@keyframes spin {
to { transform: rotate(360deg); }
}
.animate-fade-in {
animation: fade-in 0.2s ease-out;
}
.animate-pulse-glow {
animation: pulse-glow 2s infinite;
}
.animate-spin {
animation: spin 1s linear infinite;
}
-10
View File
@@ -1,10 +0,0 @@
import { StrictMode } from 'react'
import { createRoot } from 'react-dom/client'
import './index.css'
import App from './App.tsx'
createRoot(document.getElementById('root')!).render(
<StrictMode>
<App />
</StrictMode>,
)
@@ -1,436 +0,0 @@
import { useState, useEffect, useRef } from 'react'
import {
Search, Wand2, Save, AlertCircle,
ChevronDown, Sparkles, RefreshCw, Check
} from 'lucide-react'
import { Spinner, StatusBadge, ManaSymbols, CardImage, ColorDots } from '../components/common'
import { deckgenApi } from '../api'
import type { DeckGenV3Status, DeckGenV3Result, DeckGenV3Card, CommanderSearchResult } from '../types'
import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Cell } from 'recharts'
// ── Commander Search ──────────────────────────────────────────
function CommanderSearch({ onSelect }: { onSelect: (c: CommanderSearchResult) => void }) {
const [query, setQuery] = useState('')
const [results, setResults] = useState<CommanderSearchResult[]>([])
const [searching, setSearching] = useState(false)
const debounceRef = useRef<ReturnType<typeof setTimeout>>(undefined)
function handleSearch(q: string) {
setQuery(q)
clearTimeout(debounceRef.current)
if (q.length < 2) { setResults([]); return }
debounceRef.current = setTimeout(async () => {
setSearching(true)
try {
const r = await deckgenApi.searchCommanders(q)
setResults(r)
} catch { setResults([]) }
setSearching(false)
}, 300)
}
return (
<div className="relative">
<div className="relative">
<Search className="absolute left-3 top-1/2 -translate-y-1/2 w-4 h-4 text-text-tertiary" />
<input
type="text"
placeholder="Search for a commander..."
value={query}
onChange={e => handleSearch(e.target.value)}
className="w-full pl-10 pr-4 py-3 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 focus:ring-1 focus:ring-accent-blue/30 transition-all"
/>
{searching && <Spinner size="sm" className="absolute right-3 top-1/2 -translate-y-1/2 text-accent-blue" />}
</div>
{results.length > 0 && (
<div className="absolute z-20 top-full mt-1 w-full bg-bg-elevated border border-border-primary rounded-xl shadow-lg max-h-80 overflow-y-auto">
{results.map(c => (
<button
key={c.scryfall_id}
onClick={() => { onSelect(c); setQuery(c.name); setResults([]) }}
className="w-full flex items-center gap-3 px-4 py-3 hover:bg-bg-hover text-left transition-colors"
>
<CardImage src={c.image_url} alt={c.name} size="sm" />
<div className="flex-1 min-w-0">
<p className="text-sm font-medium text-text-primary truncate">{c.name}</p>
<p className="text-xs text-text-secondary truncate">{c.type_line}</p>
</div>
<ColorDots colors={c.color_identity} />
{c.in_collection && (
<span className="text-[10px] px-1.5 py-0.5 bg-accent-green/15 text-accent-green rounded">Owned</span>
)}
</button>
))}
</div>
)}
</div>
)
}
// ── Generated Deck View ───────────────────────────────────────
function GeneratedDeckView({ result, onCommit }: { result: DeckGenV3Result; onCommit: () => void }) {
const [view, setView] = useState<'visual' | 'list'>('visual')
const [committing, setCommitting] = useState(false)
const [selectedCard, setSelectedCard] = useState<DeckGenV3Card | null>(null)
// Group by category
const grouped = result.cards.reduce<Record<string, DeckGenV3Card[]>>((acc, c) => {
const cat = c.category || 'Other'
if (!acc[cat]) acc[cat] = []
acc[cat].push(c)
return acc
}, {})
const curveData = Object.entries(result.stats.mana_curve)
.map(([cmc, count]) => ({ cmc: cmc === '7' ? '7+' : cmc, count }))
.sort((a, b) => Number(a.cmc) - Number(b.cmc))
async function handleCommit() {
setCommitting(true)
try { onCommit() } catch { /* ignore */ }
setCommitting(false)
}
return (
<div className="space-y-5 animate-fade-in">
{/* Commander header */}
<div className="flex gap-5 bg-bg-secondary rounded-xl border border-border-primary p-5">
<CardImage src={result.commander.image_url} alt={result.commander.name} size="lg" />
<div className="flex-1 space-y-3">
<div>
<h2 className="text-xl font-bold text-text-primary">{result.commander.name}</h2>
<p className="text-sm text-text-secondary">{result.commander.type_line}</p>
<div className="flex items-center gap-3 mt-2">
<ManaSymbols cost={result.commander.mana_cost} size="md" />
<ColorDots colors={result.commander.color_identity} size="md" />
</div>
</div>
<p className="text-sm text-text-primary whitespace-pre-wrap">{result.commander.oracle_text}</p>
<div className="flex items-center gap-4 text-xs text-text-secondary">
<span>Strategy: <span className="text-accent-blue font-medium">{result.strategy}</span></span>
<span>Bracket: <span className="text-accent-amber font-medium">{result.bracket}</span></span>
<span>Model: <span className="text-accent-purple font-medium">{result.model}</span></span>
</div>
{result.substitution && (
<div className="flex items-center gap-3 text-xs">
<span className="text-accent-green">{result.substitution.owned} owned</span>
<span className="text-accent-amber">{result.substitution.substituted} substituted</span>
<span className="text-accent-red">{result.substitution.missing} missing</span>
</div>
)}
</div>
</div>
{/* Actions */}
<div className="flex items-center gap-3">
<button onClick={handleCommit} disabled={committing}
className="flex items-center gap-2 px-4 py-2.5 bg-accent-green/15 text-accent-green text-sm font-medium rounded-lg hover:bg-accent-green/25 border border-accent-green/30 transition-colors disabled:opacity-50">
{committing ? <Spinner size="sm" /> : <Save className="w-4 h-4" />}
Save to Decks
</button>
<div className="flex items-center gap-1 bg-bg-secondary rounded-lg border border-border-primary p-0.5">
<button onClick={() => setView('visual')}
className={`px-3 py-1.5 text-xs font-medium rounded-md transition-colors ${view === 'visual' ? 'bg-bg-hover text-text-primary' : 'text-text-tertiary hover:text-text-secondary'}`}>
Visual
</button>
<button onClick={() => setView('list')}
className={`px-3 py-1.5 text-xs font-medium rounded-md transition-colors ${view === 'list' ? 'bg-bg-hover text-text-primary' : 'text-text-tertiary hover:text-text-secondary'}`}>
List
</button>
</div>
{/* Stats summary */}
<div className="flex-1" />
<span className="text-xs text-text-tertiary">{result.stats.card_count} cards</span>
<span className="text-xs text-accent-teal font-medium">${result.stats.total_price.toFixed(2)}</span>
</div>
{/* Mana curve mini */}
<div className="bg-bg-secondary rounded-xl border border-border-primary p-4">
<h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider mb-2">Mana Curve</h4>
<div className="h-28">
<ResponsiveContainer width="100%" height="100%">
<BarChart data={curveData} barSize={20}>
<XAxis dataKey="cmc" axisLine={false} tickLine={false} tick={{ fill: '#6b7190', fontSize: 10 }} />
<YAxis hide />
<Tooltip contentStyle={{ backgroundColor: '#1c2030', border: '1px solid #2a2f42', borderRadius: 8, fontSize: 11 }} />
<Bar dataKey="count" radius={[3, 3, 0, 0]}>
{curveData.map((_, i) => <Cell key={i} fill="#4f6ef7" fillOpacity={0.8} />)}
</Bar>
</BarChart>
</ResponsiveContainer>
</div>
</div>
{/* Card grid/list */}
{view === 'visual' ? (
<div className="space-y-5">
{Object.entries(grouped).map(([cat, cards]) => (
<div key={cat}>
<h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider mb-3">{cat} ({cards.length})</h4>
<div className="grid grid-cols-3 sm:grid-cols-4 md:grid-cols-5 lg:grid-cols-6 xl:grid-cols-8 gap-2">
{cards.map(card => (
<div key={card.name} className="group cursor-pointer relative" onClick={() => setSelectedCard(card)}>
<CardImage src={card.image_url} alt={card.name} size="md" className="w-full h-auto aspect-[5/7]" />
{card.is_substitute && (
<div className="absolute top-1 right-1 w-4 h-4 bg-accent-amber rounded-full flex items-center justify-center">
<RefreshCw className="w-2.5 h-2.5 text-bg-primary" />
</div>
)}
{card.owned && (
<div className="absolute top-1 left-1 w-4 h-4 bg-accent-green rounded-full flex items-center justify-center">
<Check className="w-2.5 h-2.5 text-bg-primary" />
</div>
)}
</div>
))}
</div>
</div>
))}
</div>
) : (
<div className="bg-bg-secondary rounded-xl border border-border-primary overflow-hidden">
<table className="w-full text-sm">
<thead>
<tr className="border-b border-border-primary text-left">
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Card</th>
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Category</th>
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Role</th>
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Mana</th>
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider text-right">Price</th>
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Status</th>
</tr>
</thead>
<tbody className="divide-y divide-border-primary">
{result.cards.map(card => (
<tr key={card.name} className="hover:bg-bg-hover transition-colors">
<td className="px-4 py-2.5">
<span className="font-medium text-text-primary">{card.quantity}x {card.name}</span>
{card.is_substitute && (
<span className="ml-2 text-[10px] text-accent-amber">sub for {card.original_name}</span>
)}
</td>
<td className="px-4 py-2.5 text-xs text-text-secondary">{card.category}</td>
<td className="px-4 py-2.5 text-xs text-text-secondary">{card.role}</td>
<td className="px-4 py-2.5"><ManaSymbols cost={card.mana_cost} /></td>
<td className="px-4 py-2.5 text-right text-accent-teal text-xs">{card.tcg_price != null ? `$${card.tcg_price.toFixed(2)}` : '—'}</td>
<td className="px-4 py-2.5">
<span className={`text-[10px] px-2 py-0.5 rounded ${card.owned ? 'bg-accent-green/15 text-accent-green' : 'bg-bg-tertiary text-text-tertiary'}`}>
{card.owned ? `Owned (${card.owned_qty})` : 'Missing'}
</span>
</td>
</tr>
))}
</tbody>
</table>
</div>
)}
{/* Card detail popup */}
{selectedCard && (
<div className="fixed inset-0 z-50 flex items-center justify-center bg-black/70 backdrop-blur-sm" onClick={() => setSelectedCard(null)}>
<div className="bg-bg-secondary rounded-xl border border-border-primary shadow-lg max-w-2xl w-full mx-4 p-6 animate-fade-in" onClick={e => e.stopPropagation()}>
<div className="flex gap-5">
<CardImage src={selectedCard.image_url} alt={selectedCard.name} size="lg" />
<div className="flex-1 space-y-3">
<h3 className="text-lg font-bold text-text-primary">{selectedCard.name}</h3>
<p className="text-sm text-text-secondary">{selectedCard.type_line}</p>
<ManaSymbols cost={selectedCard.mana_cost} size="md" />
<p className="text-sm text-text-primary whitespace-pre-wrap">{selectedCard.reason}</p>
<div className="flex items-center gap-3 text-xs">
<span className="text-text-tertiary">Role: <span className="text-accent-blue">{selectedCard.role}</span></span>
{selectedCard.is_substitute && (
<span className="text-accent-amber">Substituted for {selectedCard.original_name} ({(selectedCard.similarity_score ?? 0 * 100).toFixed(0)}%)</span>
)}
</div>
</div>
</div>
</div>
</div>
)}
</div>
)
}
// ── Main Auto Gen Page ────────────────────────────────────────
export function AutoGenPage() {
const [status, setStatus] = useState<DeckGenV3Status | null>(null)
const [loading, setLoading] = useState(true)
const [generating, setGenerating] = useState(false)
const [result, setResult] = useState<DeckGenV3Result | null>(null)
const [commander, setCommander] = useState<CommanderSearchResult | null>(null)
const [strategy, setStrategy] = useState('')
const [bracket, setBracket] = useState(3)
const [budget, setBudget] = useState<string>('')
const [useCollection, setUseCollection] = useState(true)
const [runSub, setRunSub] = useState(true)
const [error, setError] = useState<string | null>(null)
useEffect(() => {
deckgenApi.getV3Status().then(s => { setStatus(s); setLoading(false) }).catch(() => setLoading(false))
}, [])
async function handleGenerate() {
if (!commander) return
setGenerating(true)
setError(null)
setResult(null)
try {
const res = await deckgenApi.generateDeckV3({
commander_name: commander.name,
strategy: strategy || undefined,
target_bracket: bracket,
budget_usd: budget ? Number(budget) : undefined,
use_collection: useCollection,
run_substitution: runSub,
})
setResult(res)
} catch (e) {
setError(e instanceof Error ? e.message : 'Generation failed')
}
setGenerating(false)
}
async function handleCommit() {
if (!commander || !result) return
try {
await deckgenApi.commitDeckV3({
commander_name: commander.name,
strategy: strategy || undefined,
target_bracket: bracket,
budget_usd: budget ? Number(budget) : undefined,
use_collection: useCollection,
run_substitution: runSub,
})
} catch { /* ignore */ }
}
if (loading) {
return <div className="p-6 flex items-center justify-center h-64"><Spinner size="lg" className="text-accent-blue" /></div>
}
return (
<div className="p-6 space-y-5">
<div>
<h1 className="text-2xl font-bold text-text-primary flex items-center gap-3">
<Sparkles className="w-6 h-6 text-accent-purple" />
Auto Gen V3
</h1>
<p className="text-sm text-text-secondary mt-0.5">AI-powered deck generation with Perplexity</p>
</div>
{/* Status bar */}
<div className="flex items-center gap-3">
<StatusBadge
variant={status?.initialized ? 'success' : 'error'}
label={status?.initialized ? 'Ready' : 'Not Initialized'}
/>
<StatusBadge
variant={status?.pplx_configured ? 'success' : 'warning'}
label={status?.pplx_configured ? 'Perplexity Connected' : 'Perplexity Not Set'}
/>
{status?.embeddings_loaded && (
<StatusBadge variant="info" label={`${status.embedding_cards.toLocaleString()} embeddings`} />
)}
</div>
{!result ? (
/* Config form */
<div className="max-w-2xl space-y-5">
<div>
<label className="text-sm font-medium text-text-primary block mb-2">Commander</label>
<CommanderSearch onSelect={setCommander} />
{commander && (
<div className="mt-2 flex items-center gap-3 p-3 bg-bg-secondary rounded-lg border border-border-primary">
<CardImage src={commander.image_url} alt={commander.name} size="sm" />
<div>
<p className="text-sm font-medium text-text-primary">{commander.name}</p>
<p className="text-xs text-text-secondary">{commander.type_line}</p>
</div>
<ColorDots colors={commander.color_identity} />
</div>
)}
</div>
<div>
<label className="text-sm font-medium text-text-primary block mb-2">Strategy (optional)</label>
<input
type="text"
placeholder="e.g. Voltron, Aristocrats, Storm..."
value={strategy}
onChange={e => setStrategy(e.target.value)}
className="w-full px-4 py-3 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
/>
</div>
<div className="grid grid-cols-2 gap-4">
<div>
<label className="text-sm font-medium text-text-primary block mb-2">Target Bracket</label>
<select
value={bracket}
onChange={e => setBracket(Number(e.target.value))}
className="w-full px-4 py-3 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
>
<option value={1}>1 — Casual</option>
<option value={2}>2 — Low Power</option>
<option value={3}>3 — Mid Power</option>
<option value={4}>4 — High Power</option>
</select>
</div>
<div>
<label className="text-sm font-medium text-text-primary block mb-2">Budget (USD, optional)</label>
<input
type="number"
placeholder="No limit"
value={budget}
onChange={e => setBudget(e.target.value)}
className="w-full px-4 py-3 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
/>
</div>
</div>
<div className="flex items-center gap-6">
<label className="flex items-center gap-2 cursor-pointer">
<input type="checkbox" checked={useCollection} onChange={e => setUseCollection(e.target.checked)}
className="w-4 h-4 rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30" />
<span className="text-sm text-text-secondary">Use collection cards</span>
</label>
<label className="flex items-center gap-2 cursor-pointer">
<input type="checkbox" checked={runSub} onChange={e => setRunSub(e.target.checked)}
className="w-4 h-4 rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30" />
<span className="text-sm text-text-secondary">Run substitution pass</span>
</label>
</div>
{error && (
<div className="flex items-center gap-2 px-4 py-3 bg-status-error/10 border border-status-error/30 rounded-lg text-sm text-status-error">
<AlertCircle className="w-4 h-4 flex-shrink-0" />
{error}
</div>
)}
<button
onClick={handleGenerate}
disabled={!commander || generating || !status?.initialized}
className="flex items-center gap-2 px-6 py-3 bg-accent-blue text-white text-sm font-semibold rounded-lg hover:bg-accent-blue-hover shadow-glow-blue transition-all disabled:opacity-50 disabled:shadow-none"
>
{generating ? (
<><Spinner size="sm" /> Generating...</>
) : (
<><Wand2 className="w-4 h-4" /> Generate Deck</>
)}
</button>
</div>
) : (
/* Results */
<div>
<button onClick={() => setResult(null)}
className="flex items-center gap-2 mb-4 text-sm text-text-secondary hover:text-text-primary transition-colors">
<ChevronDown className="w-4 h-4 rotate-90" />
Back to Config
</button>
<GeneratedDeckView result={result} onCommit={handleCommit} />
</div>
)}
</div>
)
}
@@ -1,415 +0,0 @@
import { useState, useEffect } from 'react'
import {
FlaskConical, Play, Clock, Trophy,
Upload, Link, FileText, History, ChevronRight, AlertCircle,
Check
} from 'lucide-react'
import { Spinner } from '../components/common'
import { labApi } from '../api'
import { usePolling } from '../hooks/usePolling'
import type { LabDeck, LabStatus, LabResult, LabHistoryEntry, PreconDeck } from '../types'
import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Cell } from 'recharts'
// ── Results Chart ─────────────────────────────────────────────
function ResultsChart({ result }: { result: LabResult }) {
const data = result.decks
.sort((a, b) => b.win_rate - a.win_rate)
.map(d => ({
name: d.name.length > 20 ? d.name.slice(0, 18) + '...' : d.name,
win_rate: Math.round(d.win_rate * 100),
fullName: d.name,
}))
return (
<div className="h-64">
<ResponsiveContainer width="100%" height="100%">
<BarChart data={data} layout="vertical" barSize={20} margin={{ left: 10, right: 20 }}>
<XAxis type="number" domain={[0, 100]} tick={{ fill: '#6b7190', fontSize: 11 }} axisLine={false} tickLine={false} />
<YAxis type="category" dataKey="name" width={150} tick={{ fill: '#9ba1b8', fontSize: 11 }} axisLine={false} tickLine={false} />
<Tooltip
contentStyle={{ backgroundColor: '#1c2030', border: '1px solid #2a2f42', borderRadius: 8, fontSize: 12 }}
formatter={(value: unknown) => [`${value}%`, 'Win Rate']}
labelFormatter={(label: unknown) => data.find(d => d.name === String(label))?.fullName || String(label)}
/>
<Bar dataKey="win_rate" radius={[0, 4, 4, 0]}>
{data.map((_, i) => (
<Cell key={i} fill={i === 0 ? '#4ade80' : i === 1 ? '#4f6ef7' : '#363c54'} />
))}
</Bar>
</BarChart>
</ResponsiveContainer>
</div>
)
}
// ── Deck Selector ─────────────────────────────────────────────
function DeckSelector({
decks,
selected,
onToggle,
precons,
onInstallPrecon,
}: {
decks: LabDeck[]
selected: Set<string>
onToggle: (name: string) => void
precons: PreconDeck[]
onInstallPrecon: (name: string) => void
}) {
const [showPrecons, setShowPrecons] = useState(false)
const [installing, setInstalling] = useState<string | null>(null)
async function handleInstall(name: string) {
setInstalling(name)
await onInstallPrecon(name)
setInstalling(null)
}
return (
<div className="space-y-3">
<h3 className="text-sm font-semibold text-text-primary">Available Decks</h3>
{decks.length === 0 ? (
<p className="text-xs text-text-tertiary">No decks available. Import or install precons.</p>
) : (
<div className="space-y-1 max-h-64 overflow-y-auto">
{decks.map(d => (
<label key={d.name} className="flex items-center gap-3 px-3 py-2 rounded-lg hover:bg-bg-hover cursor-pointer transition-colors">
<input
type="checkbox"
checked={selected.has(d.name)}
onChange={() => onToggle(d.name)}
className="w-4 h-4 rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30"
/>
<div className="flex-1 min-w-0">
<span className="text-sm text-text-primary truncate block">{d.name}</span>
<span className="text-[10px] text-text-tertiary">{d.source} · {d.card_count} cards</span>
</div>
</label>
))}
</div>
)}
<button
onClick={() => setShowPrecons(!showPrecons)}
className="w-full flex items-center justify-between px-3 py-2 text-xs font-medium bg-bg-tertiary rounded-lg text-text-secondary hover:bg-bg-hover border border-border-primary transition-colors"
>
Precon Decks
<ChevronRight className={`w-3.5 h-3.5 transition-transform ${showPrecons ? 'rotate-90' : ''}`} />
</button>
{showPrecons && (
<div className="space-y-1 max-h-48 overflow-y-auto animate-fade-in">
{precons.map(p => (
<div key={p.name} className="flex items-center gap-3 px-3 py-2 rounded-lg bg-bg-tertiary">
<span className="flex-1 text-xs text-text-secondary truncate">{p.name}</span>
{p.installed ? (
<span className="text-[10px] text-accent-green flex items-center gap-1"><Check className="w-3 h-3" /> Installed</span>
) : (
<button
onClick={() => handleInstall(p.name)}
disabled={installing === p.name}
className="text-[10px] text-accent-blue hover:text-accent-blue-hover transition-colors"
>
{installing === p.name ? <Spinner size="sm" /> : 'Install'}
</button>
)}
</div>
))}
</div>
)}
</div>
)
}
// ── Import Panel ──────────────────────────────────────────────
function ImportPanel({ onImported }: { onImported: () => void }) {
const [importUrl, setImportUrl] = useState('')
const [importText, setImportText] = useState('')
const [importName, setImportName] = useState('')
const [importing, setImporting] = useState(false)
const [importMode, setImportMode] = useState<'url' | 'text'>('url')
async function handleImport() {
setImporting(true)
try {
if (importMode === 'url' && importUrl.trim()) {
await labApi.importDeckFromUrl(importUrl.trim())
} else if (importMode === 'text' && importText.trim()) {
await labApi.importDeckFromText(importText.trim(), importName || undefined)
}
setImportUrl('')
setImportText('')
setImportName('')
onImported()
} catch { /* ignore */ }
setImporting(false)
}
return (
<div className="bg-bg-secondary rounded-xl border border-border-primary p-4 space-y-3">
<h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
<Upload className="w-4 h-4 text-accent-blue" />
Import Deck
</h3>
<div className="flex gap-1">
<button onClick={() => setImportMode('url')}
className={`flex-1 px-3 py-1.5 text-xs font-medium rounded-lg transition-colors ${importMode === 'url' ? 'bg-accent-blue/15 text-accent-blue' : 'bg-bg-tertiary text-text-tertiary'}`}>
<Link className="w-3 h-3 inline mr-1" />URL
</button>
<button onClick={() => setImportMode('text')}
className={`flex-1 px-3 py-1.5 text-xs font-medium rounded-lg transition-colors ${importMode === 'text' ? 'bg-accent-blue/15 text-accent-blue' : 'bg-bg-tertiary text-text-tertiary'}`}>
<FileText className="w-3 h-3 inline mr-1" />Text
</button>
</div>
{importMode === 'url' ? (
<input
type="text"
placeholder="Moxfield, Archidekt, or deck URL..."
value={importUrl}
onChange={e => setImportUrl(e.target.value)}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
/>
) : (
<>
<input
type="text"
placeholder="Deck name"
value={importName}
onChange={e => setImportName(e.target.value)}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
/>
<textarea
placeholder={"1x Sol Ring\n1x Command Tower\n..."}
value={importText}
onChange={e => setImportText(e.target.value)}
rows={5}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary font-mono focus:outline-none focus:border-accent-blue/50 transition-all resize-none"
/>
</>
)}
<button onClick={handleImport} disabled={importing}
className="w-full px-4 py-2 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-50">
{importing ? <Spinner size="sm" /> : 'Import'}
</button>
</div>
)
}
// ── Main Batch Sim Page ───────────────────────────────────────
export function BatchSimPage() {
const [decks, setDecks] = useState<LabDeck[]>([])
const [precons, setPrecons] = useState<PreconDeck[]>([])
const [selected, setSelected] = useState<Set<string>>(new Set())
const [games, setGames] = useState(100)
const [status, setStatus] = useState<LabStatus | null>(null)
const [result, setResult] = useState<LabResult | null>(null)
const [history, setHistory] = useState<LabHistoryEntry[]>([])
const [loading, setLoading] = useState(true)
const [starting, setStarting] = useState(false)
const [error, setError] = useState<string | null>(null)
const [showHistory, setShowHistory] = useState(false)
async function fetchDecks() {
try {
const [d, p] = await Promise.all([labApi.getLabDecks(), labApi.getPrecons()])
setDecks(d)
setPrecons(p)
} catch { /* ignore */ }
setLoading(false)
}
useEffect(() => {
fetchDecks()
labApi.getLabStatus().then(setStatus).catch(() => {})
labApi.getLabHistory().then(setHistory).catch(() => {})
}, [])
// Poll while running
usePolling(async () => {
try {
const s = await labApi.getLabStatus()
setStatus(s)
if (!s.running && s.run_id) {
const r = await labApi.getLabResult()
setResult(r)
labApi.getLabHistory().then(setHistory).catch(() => {})
}
} catch { /* ignore */ }
}, 2000, status?.running || false)
function toggleDeck(name: string) {
setSelected(prev => {
const next = new Set(prev)
if (next.has(name)) next.delete(name)
else next.add(name)
return next
})
}
async function handleStart() {
if (selected.size < 2) { setError('Select at least 2 decks'); return }
setStarting(true)
setError(null)
setResult(null)
try {
await labApi.startBatchSim({ decks: [...selected], games })
const s = await labApi.getLabStatus()
setStatus(s)
} catch (e) {
setError(e instanceof Error ? e.message : 'Failed to start')
}
setStarting(false)
}
async function handleInstallPrecon(name: string) {
try {
await labApi.installPrecon(name)
fetchDecks()
} catch { /* ignore */ }
}
return (
<div className="p-6 space-y-5">
<div>
<h1 className="text-2xl font-bold text-text-primary flex items-center gap-3">
<FlaskConical className="w-6 h-6 text-accent-teal" />
Batch Simulator
</h1>
<p className="text-sm text-text-secondary mt-0.5">Run mass simulations between decks</p>
</div>
<div className="flex gap-5">
{/* Left sidebar: deck selection + import */}
<div className="w-72 flex-shrink-0 space-y-4">
{loading ? (
<div className="flex items-center justify-center h-32"><Spinner size="md" className="text-accent-blue" /></div>
) : (
<DeckSelector
decks={decks}
selected={selected}
onToggle={toggleDeck}
precons={precons}
onInstallPrecon={handleInstallPrecon}
/>
)}
<ImportPanel onImported={fetchDecks} />
</div>
{/* Main content */}
<div className="flex-1 space-y-5">
{/* Config + Start */}
<div className="bg-bg-secondary rounded-xl border border-border-primary p-5">
<div className="flex items-center gap-4">
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Games</label>
<input
type="number"
min={10}
max={10000}
step={10}
value={games}
onChange={e => setGames(Number(e.target.value))}
className="mt-1 w-28 px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
/>
</div>
<div className="flex-1">
<p className="text-xs text-text-tertiary">Selected: {selected.size} decks</p>
</div>
{status?.running ? (
<div className="flex items-center gap-3">
<div className="flex items-center gap-2">
<Spinner size="sm" className="text-accent-blue" />
<span className="text-sm text-text-secondary">
{status.games_completed} / {status.total_games} games
</span>
</div>
<div className="w-48 h-2 bg-bg-tertiary rounded-full overflow-hidden">
<div
className="h-full bg-accent-blue rounded-full transition-all"
style={{ width: `${status.total_games ? (status.games_completed / status.total_games) * 100 : 0}%` }}
/>
</div>
</div>
) : (
<button onClick={handleStart} disabled={starting || selected.size < 2}
className="flex items-center gap-2 px-5 py-2.5 bg-accent-teal/15 text-accent-teal text-sm font-semibold rounded-lg hover:bg-accent-teal/25 border border-accent-teal/30 transition-colors disabled:opacity-50">
{starting ? <Spinner size="sm" /> : <Play className="w-4 h-4" />}
Start Simulation
</button>
)}
</div>
{error && (
<div className="mt-3 flex items-center gap-2 px-4 py-2 bg-status-error/10 border border-status-error/30 rounded-lg text-sm text-status-error">
<AlertCircle className="w-4 h-4" />{error}
</div>
)}
</div>
{/* Results */}
{result && (
<div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-4 animate-fade-in">
<div className="flex items-center justify-between">
<h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
<Trophy className="w-4 h-4 text-accent-amber" />
Results
</h3>
<span className="text-xs text-text-tertiary">{result.total_games} games · {new Date(result.timestamp).toLocaleString()}</span>
</div>
<ResultsChart result={result} />
<div className="overflow-x-auto">
<table className="w-full text-sm">
<thead>
<tr className="border-b border-border-primary">
<th className="px-3 py-2 text-left text-xs text-text-tertiary font-medium uppercase tracking-wider">Deck</th>
<th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium uppercase tracking-wider">Wins</th>
<th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium uppercase tracking-wider">Games</th>
<th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium uppercase tracking-wider">Win Rate</th>
<th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium uppercase tracking-wider">Avg Turns</th>
</tr>
</thead>
<tbody className="divide-y divide-border-primary">
{result.decks.sort((a, b) => b.win_rate - a.win_rate).map((d, i) => (
<tr key={d.name} className="hover:bg-bg-hover transition-colors">
<td className="px-3 py-2.5 flex items-center gap-2">
{i === 0 && <Trophy className="w-3.5 h-3.5 text-accent-amber" />}
<span className="font-medium text-text-primary">{d.name}</span>
</td>
<td className="px-3 py-2.5 text-right text-text-secondary">{d.wins}</td>
<td className="px-3 py-2.5 text-right text-text-secondary">{d.games}</td>
<td className="px-3 py-2.5 text-right font-semibold text-accent-blue">{(d.win_rate * 100).toFixed(1)}%</td>
<td className="px-3 py-2.5 text-right text-text-secondary">{d.avg_turns.toFixed(1)}</td>
</tr>
))}
</tbody>
</table>
</div>
</div>
)}
{/* History */}
<div>
<button onClick={() => setShowHistory(!showHistory)}
className="flex items-center gap-2 text-sm text-text-secondary hover:text-text-primary transition-colors">
<History className="w-4 h-4" />
Run History ({history.length})
<ChevronRight className={`w-3.5 h-3.5 transition-transform ${showHistory ? 'rotate-90' : ''}`} />
</button>
{showHistory && history.length > 0 && (
<div className="mt-3 space-y-2 animate-fade-in">
{history.map(h => (
<div key={h.run_id} className="flex items-center gap-4 px-4 py-3 bg-bg-secondary rounded-lg border border-border-primary">
<Clock className="w-4 h-4 text-text-tertiary" />
<div className="flex-1">
<p className="text-sm text-text-primary">{h.decks.join(' vs ')}</p>
<p className="text-xs text-text-tertiary">{new Date(h.timestamp).toLocaleString()} · {h.total_games} games</p>
</div>
</div>
))}
</div>
)}
</div>
</div>
</div>
</div>
)
}
@@ -1,949 +0,0 @@
import { useState, useEffect, useRef, useCallback } from 'react'
import {
GraduationCap, MessageSquare, Send, Sparkles, AlertCircle, X, Check,
ChevronRight, ChevronDown, Download, Settings2, Search, Zap,
Minus, Plus, History, ArrowRightLeft, Lightbulb, DollarSign, Loader2,
ClipboardList, Brain, BarChart3
} from 'lucide-react'
import { Spinner, StatusBadge, EmptyState } from '../components/common'
import { coachApi } from '../api'
import type {
CoachStatus, CoachDeck, CoachSession, CoachSessionSummary,
CoachMessage, CoachGoals, CoachApplyResult, CoachCardLikeResult
} from '../types'
// ════════════════════════════════════════════════════════════
// Goals Config Panel
// ════════════════════════════════════════════════════════════
function GoalsPanel({
goals, onChange, collapsed, onToggle
}: {
goals: CoachGoals
onChange: (g: CoachGoals) => void
collapsed: boolean
onToggle: () => void
}) {
const strategies = ['aggro', 'control', 'combo', 'midrange', 'stax', 'voltron', 'aristocrats', 'tokens']
const budgets = [
{ value: 'budget', label: 'Budget', desc: 'Under $5/card' },
{ value: 'medium', label: 'Medium', desc: 'Under $20/card' },
{ value: 'no-limit', label: 'No Limit', desc: 'Any price' },
]
const focusOptions = ['ramp', 'card draw', 'removal', 'protection', 'threats', 'mana base', 'combos', 'synergy']
return (
<div className="bg-bg-secondary rounded-xl border border-border-primary">
<button
onClick={onToggle}
className="w-full flex items-center gap-2 px-4 py-3 text-left hover:bg-bg-hover transition-colors rounded-xl"
>
<Settings2 className="w-4 h-4 text-accent-teal" />
<span className="text-sm font-medium text-text-primary flex-1">Coaching Goals</span>
{collapsed ? <ChevronRight className="w-4 h-4 text-text-tertiary" /> : <ChevronDown className="w-4 h-4 text-text-tertiary" />}
</button>
{!collapsed && (
<div className="px-4 pb-4 space-y-4 border-t border-border-primary pt-3">
{/* Power Level Slider */}
<div>
<label className="text-xs font-medium text-text-secondary mb-1 block">
Power Level: {goals.targetPowerLevel ?? 'Any'}
</label>
<input
type="range" min={1} max={10}
value={goals.targetPowerLevel ?? 5}
onChange={e => onChange({ ...goals, targetPowerLevel: parseInt(e.target.value) })}
className="w-full accent-accent-teal"
/>
<div className="flex justify-between text-[10px] text-text-tertiary">
<span>Casual</span><span>Focused</span><span>cEDH</span>
</div>
</div>
{/* Strategy */}
<div>
<label className="text-xs font-medium text-text-secondary mb-1.5 block">Strategy Focus</label>
<div className="flex flex-wrap gap-1.5">
{strategies.map(s => (
<button
key={s}
onClick={() => onChange({ ...goals, metaFocus: goals.metaFocus === s ? null : s })}
className={`px-2.5 py-1 text-xs rounded-full border transition-colors ${
goals.metaFocus === s
? 'bg-accent-teal/20 border-accent-teal/40 text-accent-teal'
: 'border-border-primary text-text-tertiary hover:border-border-secondary'
}`}
>
{s}
</button>
))}
</div>
</div>
{/* Budget */}
<div>
<label className="text-xs font-medium text-text-secondary mb-1.5 block">Budget</label>
<div className="grid grid-cols-3 gap-2">
{budgets.map(b => (
<button
key={b.value}
onClick={() => onChange({ ...goals, budget: goals.budget === b.value ? null : b.value })}
className={`px-3 py-2 text-xs rounded-lg border transition-colors ${
goals.budget === b.value
? 'bg-accent-teal/20 border-accent-teal/40 text-accent-teal'
: 'border-border-primary text-text-tertiary hover:border-border-secondary'
}`}
>
<div className="font-medium">{b.label}</div>
<div className="text-[10px] opacity-60">{b.desc}</div>
</button>
))}
</div>
</div>
{/* Focus Areas */}
<div>
<label className="text-xs font-medium text-text-secondary mb-1.5 block">Focus Areas</label>
<div className="flex flex-wrap gap-1.5">
{focusOptions.map(f => {
const active = (goals.focusAreas || []).includes(f)
return (
<button
key={f}
onClick={() => {
const areas = goals.focusAreas || []
onChange({
...goals,
focusAreas: active ? areas.filter(a => a !== f) : [...areas, f]
})
}}
className={`px-2.5 py-1 text-xs rounded-full border transition-colors ${
active
? 'bg-accent-blue/20 border-accent-blue/40 text-accent-blue'
: 'border-border-primary text-text-tertiary hover:border-border-secondary'
}`}
>
{f}
</button>
)
})}
</div>
</div>
</div>
)}
</div>
)
}
// ════════════════════════════════════════════════════════════
// Session View — Shows coaching results with apply functionality
// ════════════════════════════════════════════════════════════
function SessionView({
session, deckId, onClose, onApplied
}: {
session: CoachSession
deckId: number
onClose: () => void
onApplied?: () => void
}) {
const [selectedCuts, setSelectedCuts] = useState<Set<string>>(new Set())
const [selectedAdds, setSelectedAdds] = useState<Set<string>>(new Set())
const [applying, setApplying] = useState(false)
const [applyResult, setApplyResult] = useState<CoachApplyResult | null>(null)
const toggleCut = (name: string) => {
const s = new Set(selectedCuts)
s.has(name) ? s.delete(name) : s.add(name)
setSelectedCuts(s)
}
const toggleAdd = (name: string) => {
const s = new Set(selectedAdds)
s.has(name) ? s.delete(name) : s.add(name)
setSelectedAdds(s)
}
const handleApply = async () => {
if (selectedCuts.size === 0 && selectedAdds.size === 0) return
setApplying(true)
try {
const result = await coachApi.applyCoachSuggestions(
session.sessionId, deckId,
[...selectedCuts], [...selectedAdds]
)
setApplyResult(result)
onApplied?.()
} catch (e) {
console.error('Apply failed:', e)
}
setApplying(false)
}
return (
<div className="space-y-5">
<div className="flex items-center justify-between">
<div>
<h3 className="text-lg font-semibold text-text-primary">Coaching Session</h3>
<p className="text-xs text-text-tertiary">
{new Date(session.timestamp).toLocaleString()} · {session.modelUsed}
{session.promptTokens > 0 && ` · ${session.promptTokens + session.completionTokens} tokens`}
</p>
</div>
<button onClick={onClose} className="p-1.5 rounded-lg hover:bg-bg-hover text-text-tertiary">
<X className="w-4 h-4" />
</button>
</div>
{/* Summary */}
{session.summary && (
<div className="bg-bg-secondary rounded-xl border border-border-primary p-4">
<h4 className="text-xs font-medium text-text-secondary mb-2 flex items-center gap-1.5">
<Brain className="w-3.5 h-3.5" /> Summary
</h4>
<p className="text-sm text-text-primary leading-relaxed">{session.summary}</p>
</div>
)}
{/* Heuristic Hints */}
{session.heuristicHints?.length > 0 && (
<div className="bg-accent-amber/5 rounded-xl border border-accent-amber/20 p-4">
<h4 className="text-xs font-medium text-accent-amber mb-2 flex items-center gap-1.5">
<Lightbulb className="w-3.5 h-3.5" /> Strategic Tips
</h4>
<ul className="space-y-1.5">
{session.heuristicHints.map((h, i) => (
<li key={i} className="text-sm text-text-primary flex items-start gap-2">
<span className="text-accent-amber mt-0.5">•</span> {h}
</li>
))}
</ul>
</div>
)}
{/* Mana Base Advice */}
{session.manaBaseAdvice && (
<div className="bg-accent-blue/5 rounded-xl border border-accent-blue/20 p-4">
<h4 className="text-xs font-medium text-accent-blue mb-2 flex items-center gap-1.5">
<BarChart3 className="w-3.5 h-3.5" /> Mana Base
</h4>
<p className="text-sm text-text-primary">{session.manaBaseAdvice}</p>
</div>
)}
{/* Suggested Cuts */}
{session.suggestedCuts?.length > 0 && (
<div>
<h4 className="text-sm font-medium text-status-error mb-2 flex items-center gap-1.5">
<Minus className="w-4 h-4" /> Suggested Cuts ({session.suggestedCuts.length})
</h4>
<div className="space-y-2">
{session.suggestedCuts.map((cut, i) => (
<div
key={i}
onClick={() => toggleCut(cut.cardName)}
className={`flex items-start gap-3 p-3 rounded-lg border cursor-pointer transition-colors ${
selectedCuts.has(cut.cardName)
? 'bg-status-error/10 border-status-error/30'
: 'bg-bg-secondary border-border-primary hover:border-border-secondary'
}`}
>
<div className={`mt-0.5 w-4 h-4 rounded border flex items-center justify-center flex-shrink-0 ${
selectedCuts.has(cut.cardName)
? 'bg-status-error border-status-error text-white'
: 'border-border-secondary'
}`}>
{selectedCuts.has(cut.cardName) && <Check className="w-3 h-3" />}
</div>
<div className="flex-1 min-w-0">
<p className="text-sm font-medium text-text-primary">{cut.cardName}</p>
<p className="text-xs text-text-secondary mt-0.5">{cut.reason}</p>
{cut.replacementOptions?.length > 0 && (
<p className="text-[10px] text-text-tertiary mt-1">
Replace with: {cut.replacementOptions.join(', ')}
</p>
)}
</div>
</div>
))}
</div>
</div>
)}
{/* Suggested Adds */}
{session.suggestedAdds?.length > 0 && (
<div>
<h4 className="text-sm font-medium text-status-success mb-2 flex items-center gap-1.5">
<Plus className="w-4 h-4" /> Suggested Adds ({session.suggestedAdds.length})
</h4>
<div className="space-y-2">
{session.suggestedAdds.map((add, i) => (
<div
key={i}
onClick={() => toggleAdd(add.cardName)}
className={`flex items-start gap-3 p-3 rounded-lg border cursor-pointer transition-colors ${
selectedAdds.has(add.cardName)
? 'bg-status-success/10 border-status-success/30'
: 'bg-bg-secondary border-border-primary hover:border-border-secondary'
}`}
>
<div className={`mt-0.5 w-4 h-4 rounded border flex items-center justify-center flex-shrink-0 ${
selectedAdds.has(add.cardName)
? 'bg-status-success border-status-success text-white'
: 'border-border-secondary'
}`}>
{selectedAdds.has(add.cardName) && <Check className="w-3 h-3" />}
</div>
<div className="flex-1 min-w-0">
<div className="flex items-center gap-2">
<p className="text-sm font-medium text-text-primary">{add.cardName}</p>
{add.role && (
<span className="px-1.5 py-0.5 text-[10px] bg-accent-blue/15 text-accent-blue rounded">
{add.role}
</span>
)}
</div>
<p className="text-xs text-text-secondary mt-0.5">{add.reason}</p>
{add.synergyWith?.length > 0 && (
<p className="text-[10px] text-text-tertiary mt-1">
Synergy: {add.synergyWith.join(', ')}
</p>
)}
</div>
</div>
))}
</div>
</div>
)}
{/* Apply Button */}
{!applyResult && (selectedCuts.size > 0 || selectedAdds.size > 0) && (
<div className="sticky bottom-0 bg-bg-primary/95 backdrop-blur-sm border-t border-border-primary py-3 -mx-6 px-6">
<button
onClick={handleApply}
disabled={applying}
className="w-full flex items-center justify-center gap-2 px-4 py-2.5 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-50"
>
{applying ? <Spinner size="sm" /> : <ArrowRightLeft className="w-4 h-4" />}
Apply {selectedCuts.size > 0 && `${selectedCuts.size} cuts`}
{selectedCuts.size > 0 && selectedAdds.size > 0 && ' + '}
{selectedAdds.size > 0 && `${selectedAdds.size} adds`} to Deck
</button>
</div>
)}
{/* Apply Results */}
{applyResult && (
<div className="bg-status-success/5 rounded-xl border border-status-success/20 p-4">
<h4 className="text-sm font-medium text-status-success mb-2 flex items-center gap-1.5">
<Check className="w-4 h-4" /> Changes Applied
</h4>
<p className="text-sm text-text-primary">
{applyResult.total_cuts > 0 && `Removed ${applyResult.total_cuts} card(s). `}
{applyResult.total_adds > 0 && `Added ${applyResult.total_adds} card(s). `}
{applyResult.errors.length > 0 && `${applyResult.errors.length} error(s).`}
</p>
{applyResult.errors.length > 0 && (
<ul className="mt-2 text-xs text-status-error">
{applyResult.errors.map((e, i) => (
<li key={i}>{e.name}: {e.error}</li>
))}
</ul>
)}
</div>
)}
{/* Raw Explanation */}
{session.rawTextExplanation && (
<details className="group">
<summary className="text-xs text-text-tertiary cursor-pointer hover:text-text-secondary">
Full analysis...
</summary>
<p className="mt-2 text-sm text-text-secondary whitespace-pre-wrap leading-relaxed bg-bg-secondary rounded-lg p-4 border border-border-primary">
{session.rawTextExplanation}
</p>
</details>
)}
</div>
)
}
// ════════════════════════════════════════════════════════════
// Cards Like Search
// ════════════════════════════════════════════════════════════
function CardsLikePanel() {
const [query, setQuery] = useState('')
const [results, setResults] = useState<CoachCardLikeResult[]>([])
const [searching, setSearching] = useState(false)
const handleSearch = async () => {
if (!query.trim()) return
setSearching(true)
try {
const data = await coachApi.searchCardsLike(query.trim(), undefined, 12)
setResults(data.results)
} catch { /* ignore */ }
setSearching(false)
}
return (
<div className="space-y-4">
<div className="flex gap-2">
<div className="relative flex-1">
<Search className="absolute left-3 top-1/2 -translate-y-1/2 w-4 h-4 text-text-tertiary" />
<input
value={query}
onChange={e => setQuery(e.target.value)}
onKeyDown={e => e.key === 'Enter' && handleSearch()}
placeholder="Enter a card name..."
className="w-full pl-9 pr-3 py-2 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue"
/>
</div>
<button
onClick={handleSearch}
disabled={searching || !query.trim()}
className="px-4 py-2 bg-accent-teal/15 text-accent-teal text-sm font-medium rounded-lg hover:bg-accent-teal/25 border border-accent-teal/30 transition-colors disabled:opacity-50"
>
{searching ? <Spinner size="sm" /> : 'Search'}
</button>
</div>
{results.length > 0 && (
<div className="grid grid-cols-1 md:grid-cols-2 gap-2">
{results.map((card, i) => (
<div key={i} className="flex items-start gap-3 p-3 bg-bg-secondary rounded-lg border border-border-primary">
<div className="flex-1 min-w-0">
<div className="flex items-center gap-2">
<p className="text-sm font-medium text-text-primary truncate">{card.name}</p>
<span className="text-[10px] text-text-tertiary whitespace-nowrap">
{(card.similarity * 100).toFixed(0)}%
</span>
</div>
<p className="text-[11px] text-text-secondary mt-0.5">{card.types}</p>
{card.text && (
<p className="text-[10px] text-text-tertiary mt-1 line-clamp-2">{card.text}</p>
)}
<div className="flex items-center gap-3 mt-1.5 text-[10px]">
{card.tcg_price != null && (
<span className="text-accent-amber flex items-center gap-0.5">
<DollarSign className="w-2.5 h-2.5" />{card.tcg_price.toFixed(2)}
</span>
)}
{card.owned_qty > 0 && (
<span className="text-status-success">Owned: {card.owned_qty}</span>
)}
<span className="text-text-tertiary">MV: {card.mana_value}</span>
</div>
</div>
{card.image_url && (
<img src={card.image_url} alt={card.name} className="w-14 h-auto rounded" />
)}
</div>
))}
</div>
)}
</div>
)
}
// ════════════════════════════════════════════════════════════
// Live Chat View
// ════════════════════════════════════════════════════════════
function LiveChat({
deckId, deckName, goals, onClose
}: {
deckId: string
deckName: string
goals: CoachGoals
onClose: () => void
}) {
const [messages, setMessages] = useState<CoachMessage[]>([])
const [input, setInput] = useState('')
const [streaming, setStreaming] = useState(false)
const [streamText, setStreamText] = useState('')
const messagesEndRef = useRef<HTMLDivElement>(null)
const abortRef = useRef<AbortController | null>(null)
useEffect(() => {
messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' })
}, [messages, streamText])
const sendMessage = async () => {
if (!input.trim() || streaming) return
const userMsg: CoachMessage = { role: 'user', content: input.trim() }
const allMessages = [...messages, userMsg]
setMessages(allMessages)
setInput('')
setStreaming(true)
setStreamText('')
const goalsDict = goals.targetPowerLevel || goals.metaFocus || goals.budget || (goals.focusAreas || []).length > 0
? {
targetPowerLevel: goals.targetPowerLevel,
metaFocus: goals.metaFocus,
budget: goals.budget,
focusAreas: goals.focusAreas,
}
: undefined
try {
abortRef.current = new AbortController()
const fullText = await coachApi.coachChatStream(
deckId,
allMessages.map(m => ({ role: m.role, content: m.content })),
goalsDict,
(text) => setStreamText(text),
abortRef.current.signal
)
setMessages(prev => [...prev, { role: 'assistant', content: fullText }])
setStreamText('')
} catch (e) {
if ((e as Error).name !== 'AbortError') {
// Fallback to non-streaming
try {
const resp = await coachApi.coachChat(
deckId,
allMessages.map(m => ({ role: m.role, content: m.content })),
goalsDict
)
setMessages(prev => [...prev, { role: 'assistant', content: resp.content }])
} catch (e2) {
setMessages(prev => [...prev, {
role: 'assistant',
content: `Error: ${(e2 as Error).message || 'Failed to get response'}`
}])
}
}
setStreamText('')
}
setStreaming(false)
}
return (
<div className="flex flex-col h-[calc(100vh-180px)]">
{/* Header */}
<div className="flex items-center justify-between px-4 py-3 border-b border-border-primary">
<div>
<h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
<MessageSquare className="w-4 h-4 text-accent-blue" />
Chat: {deckName}
</h3>
<p className="text-[10px] text-text-tertiary">
{goals.metaFocus && `${goals.metaFocus} · `}
{goals.targetPowerLevel && `Power ${goals.targetPowerLevel} · `}
{goals.budget && `${goals.budget}`}
</p>
</div>
<button onClick={onClose} className="p-1.5 rounded-lg hover:bg-bg-hover text-text-tertiary">
<X className="w-4 h-4" />
</button>
</div>
{/* Messages */}
<div className="flex-1 overflow-y-auto p-4 space-y-3">
{messages.length === 0 && !streaming && (
<div className="text-center py-12">
<GraduationCap className="w-10 h-10 text-text-tertiary mx-auto mb-3" />
<p className="text-sm text-text-secondary">Ask your coach anything about this deck.</p>
<div className="mt-4 flex flex-wrap gap-2 justify-center">
{[
'What should I cut?',
'How is my mana curve?',
'Suggest win conditions',
'Rate my removal suite',
].map(q => (
<button
key={q}
onClick={() => { setInput(q); }}
className="px-3 py-1.5 text-xs bg-bg-secondary border border-border-primary rounded-full text-text-secondary hover:text-text-primary hover:border-border-secondary transition-colors"
>
{q}
</button>
))}
</div>
</div>
)}
{messages.map((msg, i) => (
<div key={i} className={`flex ${msg.role === 'user' ? 'justify-end' : 'justify-start'}`}>
<div className={`max-w-[85%] px-4 py-3 rounded-xl text-sm whitespace-pre-wrap leading-relaxed ${
msg.role === 'user'
? 'bg-accent-blue/15 text-text-primary border border-accent-blue/20'
: 'bg-bg-secondary text-text-primary border border-border-primary'
}`}>
{msg.content}
</div>
</div>
))}
{/* Streaming indicator */}
{streaming && (
<div className="flex justify-start">
<div className="max-w-[85%] px-4 py-3 rounded-xl text-sm whitespace-pre-wrap leading-relaxed bg-bg-secondary text-text-primary border border-border-primary">
{streamText || (
<span className="flex items-center gap-2 text-text-tertiary">
<Loader2 className="w-3.5 h-3.5 animate-spin" />
Thinking...
</span>
)}
</div>
</div>
)}
<div ref={messagesEndRef} />
</div>
{/* Input */}
<div className="border-t border-border-primary p-3">
<div className="flex gap-2">
<input
value={input}
onChange={e => setInput(e.target.value)}
onKeyDown={e => e.key === 'Enter' && !e.shiftKey && sendMessage()}
placeholder="Ask the coach..."
disabled={streaming}
className="flex-1 px-3 py-2 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue disabled:opacity-50"
/>
<button
onClick={sendMessage}
disabled={streaming || !input.trim()}
className="px-3 py-2 bg-accent-blue text-white rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-50"
>
<Send className="w-4 h-4" />
</button>
</div>
</div>
</div>
)
}
// ════════════════════════════════════════════════════════════
// Main Coach Page
// ════════════════════════════════════════════════════════════
type Tab = 'decks' | 'chat' | 'session' | 'history' | 'search'
export function CoachPage() {
const [status, setStatus] = useState<CoachStatus | null>(null)
const [decks, setDecks] = useState<CoachDeck[]>([])
const [sessions, setSessions] = useState<CoachSessionSummary[]>([])
const [loading, setLoading] = useState(true)
const [error, setError] = useState<string | null>(null)
// UI state
const [tab, setTab] = useState<Tab>('decks')
const [selectedDeck, setSelectedDeck] = useState<CoachDeck | null>(null)
const [selectedSession, setSelectedSession] = useState<CoachSession | null>(null)
const [goals, setGoals] = useState<CoachGoals>({})
const [goalsCollapsed, setGoalsCollapsed] = useState(true)
const [generatingReport, setGeneratingReport] = useState(false)
const [runningSession, setRunningSession] = useState<number | null>(null)
const [downloadingEmbeddings, setDownloadingEmbeddings] = useState(false)
const loadData = useCallback(async () => {
try {
const [s, d, sess] = await Promise.all([
coachApi.getCoachStatus(),
coachApi.getCoachDecks().catch(() => []),
coachApi.getCoachSessions().catch(() => ({ sessions: [] })),
])
setStatus(s)
setDecks(Array.isArray(d) ? d : [])
setSessions(Array.isArray(sess) ? sess : (sess as { sessions: CoachSessionSummary[] }).sessions || [])
} catch { /* ignore */ }
setLoading(false)
}, [])
useEffect(() => { loadData() }, [loadData])
const handleDownloadEmbeddings = async () => {
setDownloadingEmbeddings(true)
try {
await coachApi.downloadEmbeddings()
const s = await coachApi.getCoachStatus()
setStatus(s)
} catch { /* ignore */ }
setDownloadingEmbeddings(false)
}
const handleGenerateReports = async () => {
setGeneratingReport(true)
setError(null)
try {
await coachApi.generateReport()
await loadData()
} catch (e) {
setError(e instanceof Error ? e.message : 'Report generation failed')
}
setGeneratingReport(false)
}
const handleRunSession = async (deck: CoachDeck) => {
setRunningSession(deck.deck_id)
setError(null)
try {
const deckSlug = deck.deck_name.toLowerCase().replace(/\s+/g, '-')
const goalsDict = goals.targetPowerLevel || goals.metaFocus || goals.budget || (goals.focusAreas || []).length > 0
? goals : undefined
const session = await coachApi.runCoachSession(deckSlug, goalsDict as Record<string, unknown>)
setSelectedSession(session)
setSelectedDeck(deck)
setTab('session')
await loadData()
} catch (e) {
setError(e instanceof Error ? e.message : 'Session failed')
}
setRunningSession(null)
}
const handleOpenChat = (deck: CoachDeck) => {
setSelectedDeck(deck)
setTab('chat')
}
const handleViewSession = async (sessionId: string) => {
try {
const s = await coachApi.getCoachSession(sessionId)
setSelectedSession(s)
// Find the matching deck
const matchDeck = decks.find(d =>
d.deck_name.toLowerCase().replace(/\s+/g, '-') === s.deckId.toLowerCase() ||
d.deck_name.toLowerCase() === s.deckId.toLowerCase()
)
if (matchDeck) setSelectedDeck(matchDeck)
setTab('session')
} catch { /* ignore */ }
}
if (loading) {
return (
<div className="p-6 flex items-center justify-center h-64">
<Spinner size="lg" className="text-accent-blue" />
</div>
)
}
return (
<div className="p-6 space-y-5">
{/* Header */}
<div className="flex items-center justify-between">
<div>
<h1 className="text-2xl font-bold text-text-primary flex items-center gap-3">
<GraduationCap className="w-6 h-6 text-accent-amber" />
Deck Coach
</h1>
<p className="text-sm text-text-secondary mt-0.5">
AI-powered deck analysis, coaching, and live chat
</p>
</div>
</div>
{/* Status Bar */}
<div className="flex items-center gap-3 flex-wrap">
<StatusBadge
variant={status?.llmConnected ? 'success' : 'warning'}
label={status?.llmConnected ? `LLM: ${status.llmModel || 'Connected'}` : 'LLM: Disconnected'}
/>
<StatusBadge
variant={status?.embeddingsLoaded ? 'success' : 'neutral'}
label={status?.embeddingsLoaded ? `${(status.embeddingCards || 0).toLocaleString()} embeddings` : 'No embeddings'}
/>
{status?.deckReportsAvailable != null && status.deckReportsAvailable > 0 && (
<StatusBadge variant="success" label={`${status.deckReportsAvailable} reports`} />
)}
{!status?.embeddingsLoaded && (
<button onClick={handleDownloadEmbeddings} disabled={downloadingEmbeddings}
className="flex items-center gap-1.5 text-xs text-accent-blue hover:text-accent-blue-hover transition-colors disabled:opacity-50">
{downloadingEmbeddings ? <Spinner size="sm" /> : <Download className="w-3 h-3" />}
Download Embeddings
</button>
)}
</div>
{error && (
<div className="flex items-center gap-2 px-4 py-2 bg-status-error/10 border border-status-error/30 rounded-lg text-sm text-status-error">
<AlertCircle className="w-4 h-4 flex-shrink-0" />{error}
<button onClick={() => setError(null)} className="ml-auto"><X className="w-3 h-3" /></button>
</div>
)}
{/* Tab Nav */}
<div className="flex gap-1 bg-bg-secondary rounded-lg p-1 border border-border-primary">
{([
{ id: 'decks', label: 'Decks', icon: ClipboardList },
{ id: 'history', label: 'Sessions', icon: History },
{ id: 'search', label: 'Cards Like', icon: Search },
] as { id: Tab; label: string; icon: typeof ClipboardList }[]).map(t => (
<button
key={t.id}
onClick={() => setTab(t.id)}
className={`flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium rounded-md transition-colors ${
tab === t.id
? 'bg-bg-primary text-text-primary shadow-sm'
: 'text-text-tertiary hover:text-text-secondary'
}`}
>
<t.icon className="w-3.5 h-3.5" />
{t.label}
{t.id === 'history' && sessions.length > 0 && (
<span className="text-[10px] bg-bg-tertiary px-1.5 rounded-full">{sessions.length}</span>
)}
</button>
))}
{tab === 'chat' && selectedDeck && (
<div className="flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium rounded-md bg-bg-primary text-accent-blue shadow-sm">
<MessageSquare className="w-3.5 h-3.5" />
Chat
</div>
)}
{tab === 'session' && selectedSession && (
<div className="flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium rounded-md bg-bg-primary text-accent-amber shadow-sm">
<Sparkles className="w-3.5 h-3.5" />
Results
</div>
)}
</div>
{/* Goals Panel (shown when on decks tab) */}
{tab === 'decks' && (
<GoalsPanel
goals={goals}
onChange={setGoals}
collapsed={goalsCollapsed}
onToggle={() => setGoalsCollapsed(!goalsCollapsed)}
/>
)}
{/* ── Decks Tab ─────────────────────────────────────── */}
{tab === 'decks' && (
<div className="space-y-4">
<div className="flex items-center justify-between">
<h2 className="text-sm font-semibold text-text-primary">Your Decks</h2>
<button
onClick={handleGenerateReports}
disabled={generatingReport}
className="flex items-center gap-1.5 text-xs text-accent-amber hover:text-accent-amber transition-colors disabled:opacity-50"
>
{generatingReport ? <Spinner size="sm" /> : <Zap className="w-3 h-3" />}
Generate Reports
</button>
</div>
{decks.length === 0 ? (
<EmptyState
icon={GraduationCap}
title="No decks available"
description="Create decks in the Deck Builder to start coaching."
/>
) : (
<div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-3">
{decks.map(deck => (
<div key={deck.deck_id} className="bg-bg-secondary rounded-xl border border-border-primary p-4 space-y-3">
<div>
<h3 className="text-sm font-semibold text-text-primary">{deck.deck_name}</h3>
<p className="text-xs text-text-secondary">{deck.commander}</p>
</div>
<div className="flex items-center gap-3 text-[10px] text-text-tertiary">
<span>{deck.card_count} cards</span>
{deck.has_report && (
<span className="flex items-center gap-1 text-status-success">
<Check className="w-2.5 h-2.5" /> Report
</span>
)}
</div>
<div className="flex items-center gap-2">
<button
onClick={() => handleRunSession(deck)}
disabled={runningSession === deck.deck_id || !status?.llmConnected}
className="flex-1 flex items-center justify-center gap-1.5 px-3 py-2 bg-accent-amber/15 text-accent-amber text-xs font-medium rounded-lg hover:bg-accent-amber/25 border border-accent-amber/30 transition-colors disabled:opacity-50"
>
{runningSession === deck.deck_id ? <Spinner size="sm" /> : <Sparkles className="w-3 h-3" />}
Analyze
</button>
<button
onClick={() => handleOpenChat(deck)}
disabled={!status?.llmConnected}
className="flex-1 flex items-center justify-center gap-1.5 px-3 py-2 bg-accent-blue/15 text-accent-blue text-xs font-medium rounded-lg hover:bg-accent-blue/25 border border-accent-blue/30 transition-colors disabled:opacity-50"
>
<MessageSquare className="w-3 h-3" />
Chat
</button>
</div>
</div>
))}
</div>
)}
</div>
)}
{/* ── Chat Tab ──────────────────────────────────────── */}
{tab === 'chat' && selectedDeck && (
<LiveChat
deckId={selectedDeck.deck_name.toLowerCase().replace(/\s+/g, '-')}
deckName={selectedDeck.deck_name}
goals={goals}
onClose={() => setTab('decks')}
/>
)}
{/* ── Session Results Tab ───────────────────────────── */}
{tab === 'session' && selectedSession && selectedDeck && (
<SessionView
session={selectedSession}
deckId={selectedDeck.deck_id}
onClose={() => setTab('decks')}
onApplied={() => loadData()}
/>
)}
{/* ── History Tab ───────────────────────────────────── */}
{tab === 'history' && (
<div className="space-y-3">
<h2 className="text-sm font-semibold text-text-primary">Coaching Sessions</h2>
{sessions.length === 0 ? (
<EmptyState
icon={History}
title="No sessions yet"
description="Run an analysis on a deck to create your first coaching session."
/>
) : (
<div className="space-y-2">
{sessions.map(s => (
<button
key={s.sessionId}
onClick={() => handleViewSession(s.sessionId)}
className="w-full flex items-center gap-4 px-4 py-3 bg-bg-secondary rounded-lg border border-border-primary hover:border-border-secondary text-left transition-colors"
>
<Sparkles className="w-4 h-4 text-accent-amber flex-shrink-0" />
<div className="flex-1 min-w-0">
<div className="flex items-center gap-2">
<p className="text-sm font-medium text-text-primary">{s.deckId}</p>
<span className="text-[10px] text-text-tertiary">
{s.cutsCount > 0 && `${s.cutsCount} cuts`}
{s.cutsCount > 0 && s.addsCount > 0 && ' · '}
{s.addsCount > 0 && `${s.addsCount} adds`}
</span>
</div>
<p className="text-xs text-text-tertiary mt-0.5 truncate">{s.summary || 'No summary'}</p>
<p className="text-[10px] text-text-tertiary mt-0.5">{new Date(s.timestamp).toLocaleString()}</p>
</div>
<ChevronRight className="w-4 h-4 text-text-tertiary flex-shrink-0" />
</button>
))}
</div>
)}
</div>
)}
{/* ── Cards Like Tab ────────────────────────────────── */}
{tab === 'search' && (
<div className="space-y-4">
<div>
<h2 className="text-sm font-semibold text-text-primary mb-1">Cards Like This</h2>
<p className="text-xs text-text-secondary">
Find similar cards using AI embeddings. Search by card name to discover alternatives.
</p>
</div>
<CardsLikePanel />
</div>
)}
</div>
)
}
@@ -1,484 +0,0 @@
import { useState, useEffect, useCallback, useRef } from 'react'
import {
Search, Filter, Upload, Download, Camera, RefreshCw,
ChevronLeft, ChevronRight, X, Package, SlidersHorizontal,
Plus, Grid3x3, List
} from 'lucide-react'
import { Spinner, EmptyState, ManaSymbols, CardImage } from '../components/common'
import { collectionApi } from '../api'
import type { CollectionCard, CollectionFilters, SetInfo } from '../types'
// ── Card Detail Modal ─────────────────────────────────────────
function CardDetailModal({ card, onClose }: { card: CollectionCard; onClose: () => void }) {
return (
<div className="fixed inset-0 z-50 flex items-center justify-center bg-black/70 backdrop-blur-sm" onClick={onClose}>
<div
className="bg-bg-secondary rounded-xl border border-border-primary shadow-lg max-w-4xl w-full mx-4 max-h-[90vh] overflow-y-auto animate-fade-in"
onClick={e => e.stopPropagation()}
>
<div className="flex items-start justify-between p-5 border-b border-border-primary">
<div>
<h2 className="text-xl font-bold text-text-primary">{card.name}</h2>
<p className="text-sm text-text-secondary mt-0.5">{card.type_line}</p>
</div>
<button onClick={onClose} className="p-1 rounded hover:bg-bg-hover text-text-tertiary">
<X className="w-5 h-5" />
</button>
</div>
<div className="flex flex-col md:flex-row gap-6 p-5">
{/* Large card image */}
<div className="flex-shrink-0">
<CardImage src={card.image_url} alt={card.name} size="xl" />
</div>
{/* Card info */}
<div className="flex-1 space-y-4">
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Mana Cost</label>
<div className="mt-1"><ManaSymbols cost={card.mana_cost} size="md" /></div>
</div>
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Oracle Text</label>
<p className="mt-1 text-sm text-text-primary whitespace-pre-wrap leading-relaxed">{card.oracle_text}</p>
</div>
<div className="grid grid-cols-2 gap-4">
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Set</label>
<p className="mt-1 text-sm text-text-primary">{card.set_name} ({card.set_code.toUpperCase()})</p>
</div>
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Rarity</label>
<p className={`mt-1 text-sm font-medium capitalize ${
card.rarity === 'mythic' ? 'text-accent-amber' :
card.rarity === 'rare' ? 'text-accent-amber/80' :
card.rarity === 'uncommon' ? 'text-accent-teal' : 'text-text-secondary'
}`}>{card.rarity}</p>
</div>
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">CMC</label>
<p className="mt-1 text-sm text-text-primary">{card.cmc}</p>
</div>
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">EDHREC Rank</label>
<p className="mt-1 text-sm text-text-primary">{card.edhrec_rank ?? 'N/A'}</p>
</div>
</div>
<div className="grid grid-cols-2 gap-4">
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Quantity</label>
<p className="mt-1 text-sm text-text-primary">{card.quantity} (+ {card.foil_quantity} foil)</p>
</div>
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Price</label>
<p className="mt-1 text-sm text-accent-teal font-semibold">
${card.tcg_price?.toFixed(2) ?? '—'}
{card.foil_price ? <span className="text-text-tertiary ml-2">Foil: ${card.foil_price.toFixed(2)}</span> : null}
</p>
</div>
</div>
</div>
</div>
</div>
</div>
)
}
// ── Scanner Panel ─────────────────────────────────────────────
function ScannerPanel({ onCardAdded }: { onCardAdded: () => void }) {
const [scanning, setScanning] = useState(false)
const [results, setResults] = useState<import('../types').ScanMatch[]>([])
const [adding, setAdding] = useState<string | null>(null)
const fileRef = useRef<HTMLInputElement>(null)
async function handleScan(file: File) {
setScanning(true)
setResults([])
try {
const res = await collectionApi.scanCard(file)
setResults(res.matches || [])
} catch {
setResults([])
}
setScanning(false)
}
async function handleAdd(match: import('../types').ScanMatch) {
setAdding(match.scryfall_id)
try {
await collectionApi.addScanResult({
name: match.name,
set_code: match.set_code,
scryfall_id: match.scryfall_id,
})
onCardAdded()
} catch { /* ignore */ }
setAdding(null)
}
return (
<div className="bg-bg-secondary rounded-xl border border-border-primary p-5">
<h3 className="text-sm font-semibold text-text-primary flex items-center gap-2 mb-3">
<Camera className="w-4 h-4 text-accent-blue" />
Card Scanner
</h3>
<input
ref={fileRef}
type="file"
accept="image/*"
className="hidden"
onChange={e => e.target.files?.[0] && handleScan(e.target.files[0])}
/>
<button
onClick={() => fileRef.current?.click()}
disabled={scanning}
className="w-full flex items-center justify-center gap-2 px-4 py-2.5 bg-accent-blue/10 text-accent-blue border border-accent-blue/30 rounded-lg text-sm font-medium hover:bg-accent-blue/20 transition-colors disabled:opacity-50"
>
{scanning ? <Spinner size="sm" /> : <Camera className="w-4 h-4" />}
{scanning ? 'Scanning...' : 'Scan a Card'}
</button>
{results.length > 0 && (
<div className="mt-3 space-y-2 max-h-60 overflow-y-auto">
{results.map(m => (
<div key={m.scryfall_id} className="flex items-center gap-3 p-2 rounded-lg bg-bg-tertiary">
<CardImage src={m.image_url} alt={m.name} size="sm" />
<div className="flex-1 min-w-0">
<p className="text-sm font-medium text-text-primary truncate">{m.name}</p>
<p className="text-xs text-text-tertiary">{m.set_name} — {(m.confidence * 100).toFixed(0)}%</p>
</div>
<button
onClick={() => handleAdd(m)}
disabled={adding === m.scryfall_id}
className="p-1.5 rounded-lg bg-accent-green/15 text-accent-green hover:bg-accent-green/25 transition-colors"
>
{adding === m.scryfall_id ? <Spinner size="sm" /> : <Plus className="w-4 h-4" />}
</button>
</div>
))}
</div>
)}
</div>
)
}
// ── Main Collection Page ──────────────────────────────────────
export function CollectionPage() {
const [cards, setCards] = useState<CollectionCard[]>([])
const [total, setTotal] = useState(0)
const [loading, setLoading] = useState(true)
const [sets, setSets] = useState<SetInfo[]>([])
const [selectedCard, setSelectedCard] = useState<CollectionCard | null>(null)
const [viewMode, setViewMode] = useState<'grid' | 'list'>('grid')
const [showFilters, setShowFilters] = useState(false)
const [importing, setImporting] = useState(false)
const [enriching, setEnriching] = useState(false)
const importRef = useRef<HTMLInputElement>(null)
const [filters, setFilters] = useState<CollectionFilters>({
q: '',
page: 1,
page_size: 48,
sort_by: 'name',
sort_dir: 'asc',
})
const fetchCards = useCallback(async () => {
setLoading(true)
try {
const res = await collectionApi.searchCollection(filters)
setCards(res.cards)
setTotal(res.total)
} catch { /* ignore */ }
setLoading(false)
}, [filters])
useEffect(() => { fetchCards() }, [fetchCards])
useEffect(() => {
collectionApi.getSets().then(setSets).catch(() => {})
}, [])
const totalPages = Math.ceil(total / (filters.page_size || 48))
function updateFilter(key: keyof CollectionFilters, value: string | number | boolean | undefined) {
setFilters(prev => ({ ...prev, [key]: value, page: 1 }))
}
async function handleExport() {
const blob = await collectionApi.exportCollection('csv')
const url = URL.createObjectURL(blob)
const a = document.createElement('a')
a.href = url
a.download = 'collection.csv'
a.click()
URL.revokeObjectURL(url)
}
async function handleImport(file: File) {
setImporting(true)
try {
await collectionApi.importCollection(file)
fetchCards()
} catch { /* ignore */ }
setImporting(false)
}
async function handleReEnrich() {
setEnriching(true)
try {
await collectionApi.reEnrichCards()
fetchCards()
} catch { /* ignore */ }
setEnriching(false)
}
return (
<div className="p-6 space-y-5">
{/* Header */}
<div className="flex items-center justify-between">
<div>
<h1 className="text-2xl font-bold text-text-primary">Collection</h1>
<p className="text-sm text-text-secondary mt-0.5">{total.toLocaleString()} cards in your collection</p>
</div>
<div className="flex items-center gap-2">
<button onClick={handleReEnrich} disabled={enriching}
className="flex items-center gap-2 px-3 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors disabled:opacity-50">
{enriching ? <Spinner size="sm" /> : <RefreshCw className="w-3.5 h-3.5" />}
Re-Enrich
</button>
<input ref={importRef} type="file" accept=".csv,.txt" className="hidden"
onChange={e => e.target.files?.[0] && handleImport(e.target.files[0])} />
<button onClick={() => importRef.current?.click()} disabled={importing}
className="flex items-center gap-2 px-3 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors disabled:opacity-50">
{importing ? <Spinner size="sm" /> : <Upload className="w-3.5 h-3.5" />}
Import
</button>
<button onClick={handleExport}
className="flex items-center gap-2 px-3 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors">
<Download className="w-3.5 h-3.5" />
Export
</button>
</div>
</div>
<div className="flex gap-5">
{/* Left sidebar: search + filters + scanner */}
<div className="w-72 flex-shrink-0 space-y-4">
{/* Search */}
<div className="relative">
<Search className="absolute left-3 top-1/2 -translate-y-1/2 w-4 h-4 text-text-tertiary" />
<input
type="text"
placeholder="Search cards..."
value={filters.q || ''}
onChange={e => updateFilter('q', e.target.value)}
className="w-full pl-10 pr-4 py-2.5 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 focus:ring-1 focus:ring-accent-blue/30 transition-all"
/>
</div>
{/* Filter toggle */}
<button
onClick={() => setShowFilters(!showFilters)}
className="w-full flex items-center justify-between px-4 py-2.5 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-secondary hover:bg-bg-hover transition-colors"
>
<span className="flex items-center gap-2">
<SlidersHorizontal className="w-4 h-4" />
Filters
</span>
<Filter className={`w-3.5 h-3.5 transition-transform ${showFilters ? 'rotate-180' : ''}`} />
</button>
{showFilters && (
<div className="bg-bg-secondary rounded-xl border border-border-primary p-4 space-y-3 animate-fade-in">
{/* Rarity */}
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Rarity</label>
<select
value={filters.rarity || ''}
onChange={e => updateFilter('rarity', e.target.value || undefined)}
className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
>
<option value="">All</option>
<option value="common">Common</option>
<option value="uncommon">Uncommon</option>
<option value="rare">Rare</option>
<option value="mythic">Mythic</option>
</select>
</div>
{/* Color */}
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Color</label>
<select
value={filters.color || ''}
onChange={e => updateFilter('color', e.target.value || undefined)}
className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
>
<option value="">All</option>
<option value="W">White</option>
<option value="U">Blue</option>
<option value="B">Black</option>
<option value="R">Red</option>
<option value="G">Green</option>
<option value="C">Colorless</option>
</select>
</div>
{/* Set */}
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Set</label>
<select
value={filters.set_code || ''}
onChange={e => updateFilter('set_code', e.target.value || undefined)}
className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
>
<option value="">All Sets</option>
{sets.map(s => (
<option key={s.code} value={s.code}>{s.name} ({s.card_count})</option>
))}
</select>
</div>
{/* CMC Range */}
<div className="grid grid-cols-2 gap-2">
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">CMC Min</label>
<input type="number" min="0" max="20"
value={filters.cmc_min ?? ''}
onChange={e => updateFilter('cmc_min', e.target.value ? Number(e.target.value) : undefined)}
className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
/>
</div>
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">CMC Max</label>
<input type="number" min="0" max="20"
value={filters.cmc_max ?? ''}
onChange={e => updateFilter('cmc_max', e.target.value ? Number(e.target.value) : undefined)}
className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
/>
</div>
</div>
{/* Sort */}
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Sort By</label>
<select
value={filters.sort_by || 'name'}
onChange={e => updateFilter('sort_by', e.target.value)}
className="mt-1 w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50"
>
<option value="name">Name</option>
<option value="cmc">CMC</option>
<option value="tcg_price">Price</option>
<option value="rarity">Rarity</option>
<option value="set_code">Set</option>
<option value="added_date">Date Added</option>
</select>
</div>
</div>
)}
{/* Scanner */}
<ScannerPanel onCardAdded={fetchCards} />
</div>
{/* Main content: card grid/list */}
<div className="flex-1 min-w-0">
{/* View toggle + pagination */}
<div className="flex items-center justify-between mb-4">
<div className="flex items-center gap-1 bg-bg-secondary rounded-lg border border-border-primary p-0.5">
<button
onClick={() => setViewMode('grid')}
className={`p-1.5 rounded-md transition-colors ${viewMode === 'grid' ? 'bg-bg-hover text-text-primary' : 'text-text-tertiary hover:text-text-secondary'}`}
>
<Grid3x3 className="w-4 h-4" />
</button>
<button
onClick={() => setViewMode('list')}
className={`p-1.5 rounded-md transition-colors ${viewMode === 'list' ? 'bg-bg-hover text-text-primary' : 'text-text-tertiary hover:text-text-secondary'}`}
>
<List className="w-4 h-4" />
</button>
</div>
<div className="flex items-center gap-2 text-sm text-text-secondary">
<span>Page {filters.page} of {totalPages || 1}</span>
<button
onClick={() => setFilters(p => ({ ...p, page: Math.max(1, (p.page || 1) - 1) }))}
disabled={!filters.page || filters.page <= 1}
className="p-1 rounded hover:bg-bg-hover disabled:opacity-30"
>
<ChevronLeft className="w-4 h-4" />
</button>
<button
onClick={() => setFilters(p => ({ ...p, page: Math.min(totalPages, (p.page || 1) + 1) }))}
disabled={(filters.page || 1) >= totalPages}
className="p-1 rounded hover:bg-bg-hover disabled:opacity-30"
>
<ChevronRight className="w-4 h-4" />
</button>
</div>
</div>
{loading ? (
<div className="flex items-center justify-center h-64">
<Spinner size="lg" className="text-accent-blue" />
</div>
) : cards.length === 0 ? (
<EmptyState
icon={Package}
title="No cards found"
description="Try adjusting your search or filters, or import a collection."
/>
) : viewMode === 'grid' ? (
<div className="grid grid-cols-2 sm:grid-cols-3 md:grid-cols-4 lg:grid-cols-5 xl:grid-cols-6 gap-3">
{cards.map(card => (
<div
key={card.id}
className="group cursor-pointer"
onClick={() => setSelectedCard(card)}
>
<CardImage src={card.image_url} alt={card.name} size="md" className="w-full h-auto aspect-[5/7]" />
<div className="mt-1.5 px-0.5">
<p className="text-xs font-medium text-text-primary truncate group-hover:text-accent-blue transition-colors">{card.name}</p>
<div className="flex items-center justify-between">
<span className="text-[10px] text-text-tertiary">{card.set_code.toUpperCase()}</span>
{card.tcg_price != null && (
<span className="text-[10px] text-accent-teal font-medium">${card.tcg_price.toFixed(2)}</span>
)}
</div>
</div>
</div>
))}
</div>
) : (
<div className="bg-bg-secondary rounded-xl border border-border-primary overflow-hidden">
<table className="w-full text-sm">
<thead>
<tr className="border-b border-border-primary text-left">
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Card</th>
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Type</th>
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Mana</th>
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Set</th>
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider">Qty</th>
<th className="px-4 py-3 text-xs text-text-tertiary font-medium uppercase tracking-wider text-right">Price</th>
</tr>
</thead>
<tbody className="divide-y divide-border-primary">
{cards.map(card => (
<tr key={card.id} className="hover:bg-bg-hover cursor-pointer transition-colors" onClick={() => setSelectedCard(card)}>
<td className="px-4 py-2.5">
<span className="font-medium text-text-primary">{card.name}</span>
</td>
<td className="px-4 py-2.5 text-text-secondary text-xs">{card.type_line}</td>
<td className="px-4 py-2.5"><ManaSymbols cost={card.mana_cost} /></td>
<td className="px-4 py-2.5 text-text-secondary text-xs">{card.set_code.toUpperCase()}</td>
<td className="px-4 py-2.5 text-text-secondary">{card.quantity}</td>
<td className="px-4 py-2.5 text-right text-accent-teal font-medium">{card.tcg_price != null ? `$${card.tcg_price.toFixed(2)}` : '—'}</td>
</tr>
))}
</tbody>
</table>
</div>
)}
</div>
</div>
{/* Card detail modal */}
{selectedCard && <CardDetailModal card={selectedCard} onClose={() => setSelectedCard(null)} />}
</div>
)
}
@@ -1,967 +0,0 @@
import { useState, useEffect } from 'react'
import {
Plus, Trash2, Download, ChevronRight,
BarChart3, Lightbulb, Layers, X, Library,
Globe, XCircle, ShoppingCart,
ChevronDown, ChevronUp, Sparkles
} from 'lucide-react'
import { Spinner, EmptyState, ManaSymbols, ColorDots } from '../components/common'
import { decksApi } from '../api'
import type {
Deck, DeckCard, DeckAnalysis,
EdhRecCard, EdhRecsResponse,
CollectionRecsResponse
} from '../types'
import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Cell } from 'recharts'
// ── Role Badge ──────────────────────────────────────────────
const ROLE_COLORS: Record<string, string> = {
Ramp: 'bg-green-500/15 text-green-400 border-green-500/25',
Draw: 'bg-blue-500/15 text-blue-400 border-blue-500/25',
Removal: 'bg-red-500/15 text-red-400 border-red-500/25',
BoardWipe: 'bg-orange-500/15 text-orange-400 border-orange-500/25',
Tutor: 'bg-purple-500/15 text-purple-400 border-purple-500/25',
Protection: 'bg-cyan-500/15 text-cyan-400 border-cyan-500/25',
Finisher: 'bg-yellow-500/15 text-yellow-400 border-yellow-500/25',
Recursion: 'bg-emerald-500/15 text-emerald-400 border-emerald-500/25',
Other: 'bg-slate-500/15 text-slate-400 border-slate-500/25',
}
function RoleBadge({ role }: { role: string }) {
const colors = ROLE_COLORS[role] || ROLE_COLORS.Other
return (
<span className={'text-[10px] font-medium px-1.5 py-0.5 rounded border ' + colors}>
{role}
</span>
)
}
// ── Deck Card List ────────────────────────────────────────────
function DeckCardList({ deckId, cards, onUpdate }: { deckId: number; cards: DeckCard[]; onUpdate: () => void }) {
const [removing, setRemoving] = useState<number | null>(null)
// Group cards by category
const grouped = cards.reduce<Record<string, DeckCard[]>>((acc, c) => {
const cat = c.category || 'Other'
if (!acc[cat]) acc[cat] = []
acc[cat].push(c)
return acc
}, {})
const categoryOrder = ['Commander', 'Creature', 'Instant', 'Sorcery', 'Artifact', 'Enchantment', 'Planeswalker', 'Land', 'Other']
const sortedCategories = Object.keys(grouped).sort((a, b) => {
const ia = categoryOrder.indexOf(a)
const ib = categoryOrder.indexOf(b)
return (ia === -1 ? 99 : ia) - (ib === -1 ? 99 : ib)
})
async function handleRemove(cardId: number) {
setRemoving(cardId)
try {
await decksApi.removeCardFromDeck(deckId, cardId)
onUpdate()
} catch { /* ignore */ }
setRemoving(null)
}
return (
<div className="space-y-4">
{sortedCategories.map(cat => (
<div key={cat}>
<h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider mb-2">
{cat} ({grouped[cat].length})
</h4>
<div className="space-y-1">
{grouped[cat].map(card => (
<div key={card.id} className="flex items-center gap-3 px-3 py-2 rounded-lg hover:bg-bg-hover group transition-colors">
<span className="text-xs text-text-tertiary w-5 text-right">{card.quantity}x</span>
<ManaSymbols cost={card.mana_cost} />
<span className="flex-1 text-sm text-text-primary truncate">{card.name}</span>
{card.tcg_price != null && (
<span className="text-xs text-accent-teal">${card.tcg_price.toFixed(2)}</span>
)}
{!card.is_commander && (
<button
onClick={() => handleRemove(card.id)}
className="opacity-0 group-hover:opacity-100 p-1 text-text-tertiary hover:text-accent-red transition-all"
>
{removing === card.id ? <Spinner size="sm" /> : <Trash2 className="w-3.5 h-3.5" />}
</button>
)}
</div>
))}
</div>
</div>
))}
</div>
)
}
// ── Mana Curve Chart ──────────────────────────────────────────
function ManaCurveChart({ analysis }: { analysis: DeckAnalysis }) {
const data = Object.entries(analysis.mana_curve)
.map(([cmc, count]) => ({ cmc: cmc === '7' ? '7+' : cmc, count }))
.sort((a, b) => Number(a.cmc) - Number(b.cmc))
return (
<div className="h-40">
<ResponsiveContainer width="100%" height="100%">
<BarChart data={data} barSize={24}>
<XAxis dataKey="cmc" axisLine={false} tickLine={false} tick={{ fill: '#6b7190', fontSize: 11 }} />
<YAxis hide />
<Tooltip
contentStyle={{ backgroundColor: '#1c2030', border: '1px solid #2a2f42', borderRadius: 8, fontSize: 12 }}
labelStyle={{ color: '#9ba1b8' }}
itemStyle={{ color: '#e8eaf0' }}
/>
<Bar dataKey="count" radius={[4, 4, 0, 0]}>
{data.map((_, i) => (
<Cell key={i} fill="#4f6ef7" fillOpacity={0.8} />
))}
</Bar>
</BarChart>
</ResponsiveContainer>
</div>
)
}
// ── EDHREC Recommendations Sub-tab ──────────────────────────
function EdhRecTab({ deckId, onAddedCards }: { deckId: number; onAddedCards: () => void }) {
const [data, setData] = useState<EdhRecsResponse | null>(null)
const [loading, setLoading] = useState(true)
const [error, setError] = useState<string | null>(null)
const [onlyOwned, setOnlyOwned] = useState(false)
const [selectedCards, setSelectedCards] = useState<Set<string>>(new Set())
const [addingCards, setAddingCards] = useState<Set<string>>(new Set())
const [bulkAdding, setBulkAdding] = useState(false)
const [hoveredCard, setHoveredCard] = useState<EdhRecCard | null>(null)
const [collapsedGroups, setCollapsedGroups] = useState<Set<string>>(new Set())
async function fetchRecs() {
setLoading(true)
setError(null)
try {
const result = await decksApi.getEdhRecs(deckId, onlyOwned)
setData(result)
} catch (e: any) {
setError(e?.message || 'Failed to load EDHREC recommendations')
}
setLoading(false)
}
useEffect(() => { fetchRecs() }, [deckId, onlyOwned])
function toggleSelect(name: string) {
setSelectedCards(prev => {
const next = new Set(prev)
if (next.has(name)) next.delete(name)
else next.add(name)
return next
})
}
function selectAll() {
if (!data) return
setSelectedCards(new Set(data.recommendations.map(r => r.name)))
}
function selectNone() { setSelectedCards(new Set()) }
function selectOwned() {
if (!data) return
setSelectedCards(new Set(data.recommendations.filter(r => r.owned).map(r => r.name)))
}
async function handleAddSingle(name: string) {
setAddingCards(prev => new Set(prev).add(name))
try {
await decksApi.bulkAddRecommended(deckId, [name])
onAddedCards()
// Remove from list
if (data) {
setData({
...data,
recommendations: data.recommendations.filter(r => r.name !== name),
total: data.total - 1,
})
}
selectedCards.delete(name)
setSelectedCards(new Set(selectedCards))
} catch { /* ignore */ }
setAddingCards(prev => { const n = new Set(prev); n.delete(name); return n })
}
async function handleBulkAdd() {
if (selectedCards.size === 0) return
setBulkAdding(true)
try {
await decksApi.bulkAddRecommended(deckId, Array.from(selectedCards))
onAddedCards()
if (data) {
setData({
...data,
recommendations: data.recommendations.filter(r => !selectedCards.has(r.name)),
total: data.total - selectedCards.size,
})
}
setSelectedCards(new Set())
} catch { /* ignore */ }
setBulkAdding(false)
}
function toggleGroup(groupName: string) {
setCollapsedGroups(prev => {
const next = new Set(prev)
if (next.has(groupName)) next.delete(groupName)
else next.add(groupName)
return next
})
}
// Group recommendations by role
function groupByRole(recs: EdhRecCard[]): Record<string, EdhRecCard[]> {
const groups: Record<string, EdhRecCard[]> = {}
for (const rec of recs) {
const role = rec.role || 'Other'
if (!groups[role]) groups[role] = []
groups[role].push(rec)
}
return groups
}
if (loading) {
return <div className="flex items-center justify-center h-40"><Spinner size="lg" className="text-accent-blue" /></div>
}
if (error) {
return (
<div className="bg-accent-red/10 border border-accent-red/20 rounded-xl p-5 text-center">
<XCircle className="w-6 h-6 text-accent-red mx-auto mb-2" />
<p className="text-sm text-accent-red font-medium">{error}</p>
<button onClick={fetchRecs} className="mt-3 px-4 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors">
Retry
</button>
</div>
)
}
if (!data || data.recommendations.length === 0) {
return <EmptyState icon={Globe} title="No EDHREC data" description="No recommendations found for this commander on EDHREC." />
}
const grouped = groupByRole(data.recommendations)
const roleOrder = ['Ramp', 'Draw', 'Removal', 'BoardWipe', 'Tutor', 'Protection', 'Finisher', 'Recursion', 'Other']
const sortedRoles = Object.keys(grouped).sort((a, b) => {
const ia = roleOrder.indexOf(a)
const ib = roleOrder.indexOf(b)
return (ia === -1 ? 99 : ia) - (ib === -1 ? 99 : ib)
})
const ownedCount = data.recommendations.filter(r => r.owned).length
const missingCount = data.recommendations.length - ownedCount
return (
<div className="space-y-4">
{/* Header bar */}
<div className="flex items-center justify-between flex-wrap gap-3">
<div className="flex items-center gap-3">
<div className="flex items-center gap-2 text-sm">
<Sparkles className="w-4 h-4 text-accent-blue" />
<span className="text-text-primary font-medium">{data.commander}</span>
<span className="text-text-tertiary">via {data.source}</span>
</div>
<span className="text-xs px-2 py-0.5 rounded bg-accent-green/15 text-accent-green">{ownedCount} owned</span>
<span className="text-xs px-2 py-0.5 rounded bg-bg-tertiary text-text-tertiary">{missingCount} missing</span>
</div>
<label className="flex items-center gap-2 text-xs text-text-secondary cursor-pointer select-none">
<input
type="checkbox"
checked={onlyOwned}
onChange={e => setOnlyOwned(e.target.checked)}
className="rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30 w-3.5 h-3.5"
/>
Show only owned
</label>
</div>
{/* Selection toolbar */}
<div className="flex items-center justify-between bg-bg-secondary rounded-xl border border-border-primary px-4 py-3">
<div className="flex items-center gap-2">
<span className="text-xs text-text-tertiary">Select:</span>
<button onClick={selectAll} className="text-xs text-accent-blue hover:underline">All</button>
<span className="text-text-tertiary">/</span>
<button onClick={selectNone} className="text-xs text-accent-blue hover:underline">None</button>
<span className="text-text-tertiary">/</span>
<button onClick={selectOwned} className="text-xs text-accent-blue hover:underline">Owned</button>
{selectedCards.size > 0 && (
<span className="text-xs text-text-secondary ml-2">({selectedCards.size} selected)</span>
)}
</div>
<button
onClick={handleBulkAdd}
disabled={selectedCards.size === 0 || bulkAdding}
className="flex items-center gap-2 px-3 py-1.5 text-xs font-medium bg-accent-blue text-white rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-40 disabled:cursor-not-allowed"
>
{bulkAdding ? <Spinner size="sm" /> : <ShoppingCart className="w-3.5 h-3.5" />}
Add {selectedCards.size > 0 ? selectedCards.size : ''} to Deck
</button>
</div>
{/* Grouped cards */}
{sortedRoles.map(role => {
const cards = grouped[role]
const isCollapsed = collapsedGroups.has(role)
return (
<div key={role} className="bg-bg-secondary rounded-xl border border-border-primary overflow-hidden">
{/* Group header */}
<button
onClick={() => toggleGroup(role)}
className="w-full flex items-center justify-between px-4 py-3 hover:bg-bg-hover transition-colors"
>
<div className="flex items-center gap-2">
<RoleBadge role={role} />
<span className="text-xs text-text-tertiary">({cards.length})</span>
</div>
{isCollapsed ? <ChevronDown className="w-4 h-4 text-text-tertiary" /> : <ChevronUp className="w-4 h-4 text-text-tertiary" />}
</button>
{/* Card list */}
{!isCollapsed && (
<div className="border-t border-border-primary divide-y divide-border-primary">
{cards.map(rec => (
<div
key={rec.name}
className="flex items-center gap-3 px-4 py-2.5 hover:bg-bg-hover transition-colors group relative"
onMouseEnter={() => setHoveredCard(rec)}
onMouseLeave={() => setHoveredCard(null)}
>
{/* Checkbox */}
<input
type="checkbox"
checked={selectedCards.has(rec.name)}
onChange={() => toggleSelect(rec.name)}
className="rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30 w-3.5 h-3.5 flex-shrink-0"
/>
{/* Card image mini */}
{rec.image_url && (
<div className="w-8 h-11 rounded overflow-hidden bg-bg-tertiary flex-shrink-0">
<img src={rec.image_url} alt={rec.name} className="w-full h-full object-cover" loading="lazy" />
</div>
)}
{/* Card info */}
<div className="flex-1 min-w-0">
<p className="text-sm font-medium text-text-primary truncate">{rec.name}</p>
<div className="flex items-center gap-1.5 mt-0.5">
<span className="text-[10px] text-text-tertiary truncate">{rec.type_line}</span>
{rec.roles.length > 0 && rec.roles.map(r => <RoleBadge key={r} role={r} />)}
</div>
</div>
{/* Owned badge */}
<span className={'text-xs px-2 py-0.5 rounded flex-shrink-0 ' + (rec.owned ? 'bg-accent-green/15 text-accent-green' : 'bg-bg-tertiary text-text-tertiary')}>
{rec.owned ? (rec.owned_qty + 'x owned') : 'Missing'}
</span>
{/* Add button */}
<button
onClick={e => { e.stopPropagation(); handleAddSingle(rec.name) }}
disabled={addingCards.has(rec.name)}
className="opacity-0 group-hover:opacity-100 px-2.5 py-1 text-xs font-medium bg-accent-blue text-white rounded-lg hover:bg-accent-blue-hover transition-all disabled:opacity-50 flex-shrink-0"
>
{addingCards.has(rec.name) ? <Spinner size="sm" /> : '+ Add'}
</button>
</div>
))}
</div>
)}
</div>
)
})}
{/* Hover preview */}
{hoveredCard && hoveredCard.image_url && (
<div className="fixed top-20 right-8 z-50 pointer-events-none animate-fade-in">
<img
src={hoveredCard.image_url}
alt={hoveredCard.name}
className="w-[244px] rounded-xl shadow-2xl shadow-black/60 border border-border-primary"
/>
</div>
)}
</div>
)
}
// ── Collection Recommendations Sub-tab ──────────────────────
function CollectionRecTab({ deckId, onAddedCards }: { deckId: number; onAddedCards: () => void }) {
const [data, setData] = useState<CollectionRecsResponse | null>(null)
const [loading, setLoading] = useState(true)
const [error, setError] = useState<string | null>(null)
const [selectedCards, setSelectedCards] = useState<Set<string>>(new Set())
const [addingCards, setAddingCards] = useState<Set<string>>(new Set())
const [bulkAdding, setBulkAdding] = useState(false)
const [collapsedGroups, setCollapsedGroups] = useState<Set<string>>(new Set())
async function fetchRecs() {
setLoading(true)
setError(null)
try {
const result = await decksApi.getCollectionRecs(deckId)
setData(result)
} catch (e: any) {
setError(e?.message || 'Failed to load collection recommendations')
}
setLoading(false)
}
useEffect(() => { fetchRecs() }, [deckId])
function toggleSelect(name: string) {
setSelectedCards(prev => {
const next = new Set(prev)
if (next.has(name)) next.delete(name)
else next.add(name)
return next
})
}
function selectAll() {
if (!data) return
const all = new Set<string>()
Object.values(data.grouped).flat().forEach(c => all.add(c.name))
setSelectedCards(all)
}
function selectNone() { setSelectedCards(new Set()) }
async function handleAddSingle(name: string) {
setAddingCards(prev => new Set(prev).add(name))
try {
await decksApi.bulkAddRecommended(deckId, [name])
onAddedCards()
// Remove from groupings
if (data) {
const newGrouped = { ...data.grouped }
for (const key of Object.keys(newGrouped)) {
newGrouped[key] = newGrouped[key].filter(c => c.name !== name)
if (newGrouped[key].length === 0) delete newGrouped[key]
}
setData({ ...data, grouped: newGrouped, total: data.total - 1 })
}
selectedCards.delete(name)
setSelectedCards(new Set(selectedCards))
} catch { /* ignore */ }
setAddingCards(prev => { const n = new Set(prev); n.delete(name); return n })
}
async function handleBulkAdd() {
if (selectedCards.size === 0) return
setBulkAdding(true)
try {
await decksApi.bulkAddRecommended(deckId, Array.from(selectedCards))
onAddedCards()
if (data) {
const newGrouped = { ...data.grouped }
for (const key of Object.keys(newGrouped)) {
newGrouped[key] = newGrouped[key].filter(c => !selectedCards.has(c.name))
if (newGrouped[key].length === 0) delete newGrouped[key]
}
setData({ ...data, grouped: newGrouped, total: data.total - selectedCards.size })
}
setSelectedCards(new Set())
} catch { /* ignore */ }
setBulkAdding(false)
}
function toggleGroup(groupName: string) {
setCollapsedGroups(prev => {
const next = new Set(prev)
if (next.has(groupName)) next.delete(groupName)
else next.add(groupName)
return next
})
}
if (loading) {
return <div className="flex items-center justify-center h-40"><Spinner size="lg" className="text-accent-blue" /></div>
}
if (error) {
return (
<div className="bg-accent-red/10 border border-accent-red/20 rounded-xl p-5 text-center">
<XCircle className="w-6 h-6 text-accent-red mx-auto mb-2" />
<p className="text-sm text-accent-red font-medium">{error}</p>
<button onClick={fetchRecs} className="mt-3 px-4 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors">
Retry
</button>
</div>
)
}
if (!data || data.total === 0) {
return <EmptyState icon={Library} title="No collection matches" description="No cards in your collection match this deck's needs." />
}
const typeOrder = ['Creature', 'Instant', 'Sorcery', 'Artifact', 'Enchantment', 'Planeswalker', 'Land', 'Other']
const sortedTypes = Object.keys(data.grouped).sort((a, b) => {
const ia = typeOrder.indexOf(a)
const ib = typeOrder.indexOf(b)
return (ia === -1 ? 99 : ia) - (ib === -1 ? 99 : ib)
})
return (
<div className="space-y-4">
{/* Shortfall alert */}
{data.shortfall_types.length > 0 && (
<div className="flex items-start gap-3 bg-accent-blue/8 border border-accent-blue/20 rounded-xl px-4 py-3">
<Lightbulb className="w-4 h-4 text-accent-blue mt-0.5 flex-shrink-0" />
<div>
<p className="text-xs font-medium text-accent-blue">Deck needs more:</p>
<div className="flex flex-wrap gap-1.5 mt-1">
{data.shortfall_types.map(t => (
<span key={t} className="text-[10px] font-medium px-2 py-0.5 rounded bg-accent-blue/15 text-accent-blue border border-accent-blue/25">{t}</span>
))}
</div>
</div>
</div>
)}
{/* Selection toolbar */}
<div className="flex items-center justify-between bg-bg-secondary rounded-xl border border-border-primary px-4 py-3">
<div className="flex items-center gap-2">
<span className="text-xs text-text-tertiary">Select:</span>
<button onClick={selectAll} className="text-xs text-accent-blue hover:underline">All</button>
<span className="text-text-tertiary">/</span>
<button onClick={selectNone} className="text-xs text-accent-blue hover:underline">None</button>
{selectedCards.size > 0 && (
<span className="text-xs text-text-secondary ml-2">({selectedCards.size} selected)</span>
)}
</div>
<button
onClick={handleBulkAdd}
disabled={selectedCards.size === 0 || bulkAdding}
className="flex items-center gap-2 px-3 py-1.5 text-xs font-medium bg-accent-blue text-white rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-40 disabled:cursor-not-allowed"
>
{bulkAdding ? <Spinner size="sm" /> : <ShoppingCart className="w-3.5 h-3.5" />}
Add {selectedCards.size > 0 ? selectedCards.size : ''} to Deck
</button>
</div>
{/* Grouped cards */}
{sortedTypes.map(type => {
const cards = data.grouped[type]
const isCollapsed = collapsedGroups.has(type)
const isShortfall = data.shortfall_types.includes(type)
return (
<div key={type} className={'bg-bg-secondary rounded-xl border overflow-hidden ' + (isShortfall ? 'border-accent-blue/30' : 'border-border-primary')}>
{/* Group header */}
<button
onClick={() => toggleGroup(type)}
className="w-full flex items-center justify-between px-4 py-3 hover:bg-bg-hover transition-colors"
>
<div className="flex items-center gap-2">
<span className="text-xs font-semibold text-text-primary uppercase tracking-wider">{type}</span>
<span className="text-xs text-text-tertiary">({cards.length})</span>
{isShortfall && <span className="text-[10px] font-medium px-1.5 py-0.5 rounded bg-accent-blue/15 text-accent-blue border border-accent-blue/25">Shortfall</span>}
</div>
{isCollapsed ? <ChevronDown className="w-4 h-4 text-text-tertiary" /> : <ChevronUp className="w-4 h-4 text-text-tertiary" />}
</button>
{/* Card list */}
{!isCollapsed && (
<div className="border-t border-border-primary divide-y divide-border-primary">
{cards.map(rec => (
<div key={rec.scryfall_id} className="flex items-center gap-3 px-4 py-2.5 hover:bg-bg-hover transition-colors group">
{/* Checkbox */}
<input
type="checkbox"
checked={selectedCards.has(rec.name)}
onChange={() => toggleSelect(rec.name)}
className="rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30 w-3.5 h-3.5 flex-shrink-0"
/>
{/* Card image mini */}
{rec.image_url && (
<div className="w-8 h-11 rounded overflow-hidden bg-bg-tertiary flex-shrink-0">
<img src={rec.image_url} alt={rec.name} className="w-full h-full object-cover" loading="lazy" />
</div>
)}
{/* Card info */}
<div className="flex-1 min-w-0">
<p className="text-sm font-medium text-text-primary truncate">{rec.name}</p>
<div className="flex items-center gap-1.5 mt-0.5">
<span className="text-[10px] text-text-tertiary">{rec.cmc} CMC</span>
{rec.roles.length > 0 && rec.roles.map(r => <RoleBadge key={r} role={r} />)}
</div>
</div>
{/* Score */}
<span className="text-xs text-accent-teal font-medium flex-shrink-0">Score: {rec.score}</span>
{/* Owned qty */}
<span className="text-xs px-2 py-0.5 rounded bg-accent-green/15 text-accent-green flex-shrink-0">{rec.owned_qty}x</span>
{/* Add button */}
<button
onClick={e => { e.stopPropagation(); handleAddSingle(rec.name) }}
disabled={addingCards.has(rec.name)}
className="opacity-0 group-hover:opacity-100 px-2.5 py-1 text-xs font-medium bg-accent-blue text-white rounded-lg hover:bg-accent-blue-hover transition-all disabled:opacity-50 flex-shrink-0"
>
{addingCards.has(rec.name) ? <Spinner size="sm" /> : '+ Add'}
</button>
</div>
))}
</div>
)}
</div>
)
})}
</div>
)
}
// ── Deck Detail View ──────────────────────────────────────────
function DeckDetail({ deck, onBack }: { deck: Deck; onBack: () => void }) {
const [cards, setCards] = useState<DeckCard[]>([])
const [analysis, setAnalysis] = useState<DeckAnalysis | null>(null)
const [loading, setLoading] = useState(true)
const [tab, setTab] = useState<'cards' | 'analysis' | 'recs'>('cards')
const [recSource, setRecSource] = useState<'edhrec' | 'collection'>('edhrec')
const [addingCard, setAddingCard] = useState('')
const [bulkText, setBulkText] = useState('')
const [showBulk, setShowBulk] = useState(false)
async function fetchAll() {
setLoading(true)
try {
const [c, a] = await Promise.all([
decksApi.getDeckCards(deck.id),
decksApi.getDeckAnalysis(deck.id),
])
setCards(c)
setAnalysis(a)
} catch { /* ignore */ }
setLoading(false)
}
useEffect(() => { fetchAll() }, [deck.id])
async function handleAddCard() {
if (!addingCard.trim()) return
try {
await decksApi.addCardToDeck(deck.id, { name: addingCard.trim() })
setAddingCard('')
fetchAll()
} catch { /* ignore */ }
}
async function handleBulkAdd() {
if (!bulkText.trim()) return
const cardLines = bulkText.split('\n').filter(l => l.trim())
const cardParsed = cardLines.map(l => {
const match = l.match(/^(\d+)x?\s+(.+)$/)
return match ? { name: match[2].trim(), quantity: Number(match[1]) } : { name: l.trim() }
})
try {
await decksApi.bulkAddCards(deck.id, cardParsed)
setBulkText('')
setShowBulk(false)
fetchAll()
} catch { /* ignore */ }
}
async function handleExportToSim() {
try {
await decksApi.exportDeckToSim(deck.id)
} catch { /* ignore */ }
}
return (
<div className="space-y-5">
{/* Header */}
<div className="flex items-center gap-4">
<button onClick={onBack} className="p-2 rounded-lg hover:bg-bg-hover text-text-secondary transition-colors">
<ChevronRight className="w-5 h-5 rotate-180" />
</button>
<div className="flex-1">
<h2 className="text-xl font-bold text-text-primary">{deck.name}</h2>
<div className="flex items-center gap-3 mt-1">
<span className="text-sm text-text-secondary">{deck.commander}</span>
<ColorDots colors={deck.color_identity} />
<span className="text-xs text-text-tertiary">{deck.card_count} cards</span>
<span className="text-xs text-accent-teal">${deck.total_price.toFixed(2)}</span>
</div>
</div>
<button onClick={handleExportToSim}
className="flex items-center gap-2 px-3 py-2 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors">
<Download className="w-3.5 h-3.5" />
Export to Sim
</button>
</div>
{/* Tabs */}
<div className="flex gap-1 border-b border-border-primary">
{(['cards', 'analysis', 'recs'] as const).map(t => (
<button
key={t}
onClick={() => setTab(t)}
className={`px-4 py-2.5 text-sm font-medium border-b-2 transition-colors capitalize ${
tab === t
? 'border-accent-blue text-accent-blue'
: 'border-transparent text-text-tertiary hover:text-text-secondary'
}`}
>
{t === 'recs' ? 'Recommendations' : t}
</button>
))}
</div>
{loading ? (
<div className="flex items-center justify-center h-40"><Spinner size="lg" className="text-accent-blue" /></div>
) : tab === 'cards' ? (
<div className="space-y-4">
{/* Add card */}
<div className="flex items-center gap-2">
<input
type="text"
placeholder="Add card by name..."
value={addingCard}
onChange={e => setAddingCard(e.target.value)}
onKeyDown={e => e.key === 'Enter' && handleAddCard()}
className="flex-1 px-4 py-2.5 bg-bg-secondary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
/>
<button onClick={handleAddCard}
className="px-4 py-2.5 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors">
Add
</button>
<button onClick={() => setShowBulk(!showBulk)}
className="px-3 py-2.5 bg-bg-tertiary text-text-secondary text-sm rounded-lg hover:bg-bg-hover border border-border-primary transition-colors">
<Layers className="w-4 h-4" />
</button>
</div>
{showBulk && (
<div className="bg-bg-secondary rounded-xl border border-border-primary p-4 animate-fade-in space-y-3">
<textarea
placeholder={"1x Sol Ring\n1x Command Tower\n2x Counterspell"}
value={bulkText}
onChange={e => setBulkText(e.target.value)}
rows={6}
className="w-full px-4 py-3 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary font-mono focus:outline-none focus:border-accent-blue/50 transition-all resize-none"
/>
<button onClick={handleBulkAdd}
className="px-4 py-2 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors">
Bulk Add
</button>
</div>
)}
<DeckCardList deckId={deck.id} cards={cards} onUpdate={fetchAll} />
</div>
) : tab === 'analysis' && analysis ? (
<div className="grid grid-cols-1 md:grid-cols-2 gap-5">
{/* Stats */}
<div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-4">
<h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
<BarChart3 className="w-4 h-4 text-accent-blue" />
Deck Stats
</h3>
<div className="grid grid-cols-2 gap-3">
{[
{ label: 'Cards', value: analysis.card_count },
{ label: 'Lands', value: analysis.land_count },
{ label: 'Creatures', value: analysis.creature_count },
{ label: 'Non-Creature', value: analysis.noncreature_count },
{ label: 'Avg CMC', value: analysis.avg_cmc.toFixed(2) },
{ label: 'Owned', value: `${analysis.owned_count} / ${analysis.card_count}` },
].map(stat => (
<div key={stat.label} className="px-3 py-2 rounded-lg bg-bg-tertiary">
<p className="text-xs text-text-tertiary">{stat.label}</p>
<p className="text-lg font-bold text-text-primary">{stat.value}</p>
</div>
))}
</div>
</div>
{/* Mana Curve */}
<div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-4">
<h3 className="text-sm font-semibold text-text-primary">Mana Curve</h3>
<ManaCurveChart analysis={analysis} />
</div>
{/* Type Distribution */}
<div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-3 md:col-span-2">
<h3 className="text-sm font-semibold text-text-primary">Type Distribution</h3>
<div className="flex flex-wrap gap-2">
{Object.entries(analysis.type_distribution).map(([type, count]) => (
<span key={type} className="px-3 py-1.5 bg-bg-tertiary rounded-lg text-xs text-text-secondary">
{type}: <span className="font-semibold text-text-primary">{count}</span>
</span>
))}
</div>
</div>
</div>
) : tab === 'recs' ? (
<div className="space-y-4">
{/* Sub-tab toggle */}
<div className="flex items-center gap-1 bg-bg-tertiary rounded-lg p-1 w-fit">
<button
onClick={() => setRecSource('edhrec')}
className={'flex items-center gap-1.5 px-3.5 py-2 text-xs font-medium rounded-md transition-all ' +
(recSource === 'edhrec'
? 'bg-accent-blue text-white shadow-sm'
: 'text-text-tertiary hover:text-text-secondary')}
>
<Globe className="w-3.5 h-3.5" />
EDHREC
</button>
<button
onClick={() => setRecSource('collection')}
className={'flex items-center gap-1.5 px-3.5 py-2 text-xs font-medium rounded-md transition-all ' +
(recSource === 'collection'
? 'bg-accent-blue text-white shadow-sm'
: 'text-text-tertiary hover:text-text-secondary')}
>
<Library className="w-3.5 h-3.5" />
From Collection
</button>
</div>
{/* Sub-tab content */}
{recSource === 'edhrec' ? (
<EdhRecTab deckId={deck.id} onAddedCards={fetchAll} />
) : (
<CollectionRecTab deckId={deck.id} onAddedCards={fetchAll} />
)}
</div>
) : null}
</div>
)
}
// ── Main Decks Page ───────────────────────────────────────────
export function DecksPage() {
const [decks, setDecks] = useState<Deck[]>([])
const [loading, setLoading] = useState(true)
const [selectedDeck, setSelectedDeck] = useState<Deck | null>(null)
const [showCreate, setShowCreate] = useState(false)
const [newDeckName, setNewDeckName] = useState('')
const [newCommander, setNewCommander] = useState('')
const [creating, setCreating] = useState(false)
async function fetchDecks() {
setLoading(true)
try {
const d = await decksApi.listDecks()
setDecks(d)
} catch { /* ignore */ }
setLoading(false)
}
useEffect(() => { fetchDecks() }, [])
async function handleCreate() {
if (!newDeckName.trim()) return
setCreating(true)
try {
const deck = await decksApi.createDeck({ name: newDeckName.trim(), commander: newCommander.trim() || undefined })
setNewDeckName('')
setNewCommander('')
setShowCreate(false)
setSelectedDeck(deck)
fetchDecks()
} catch { /* ignore */ }
setCreating(false)
}
async function handleDelete(deckId: number) {
try {
await decksApi.deleteDeck(deckId)
fetchDecks()
} catch { /* ignore */ }
}
if (selectedDeck) {
return (
<div className="p-6">
<DeckDetail deck={selectedDeck} onBack={() => { setSelectedDeck(null); fetchDecks() }} />
</div>
)
}
return (
<div className="p-6 space-y-5">
{/* Header */}
<div className="flex items-center justify-between">
<div>
<h1 className="text-2xl font-bold text-text-primary">Decks</h1>
<p className="text-sm text-text-secondary mt-0.5">{decks.length} deck{decks.length !== 1 ? 's' : ''}</p>
</div>
<button
onClick={() => setShowCreate(true)}
className="flex items-center gap-2 px-4 py-2.5 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors"
>
<Plus className="w-4 h-4" />
New Deck
</button>
</div>
{/* Create form */}
{showCreate && (
<div className="bg-bg-secondary rounded-xl border border-border-primary p-5 animate-fade-in space-y-3">
<div className="flex items-center justify-between">
<h3 className="text-sm font-semibold text-text-primary">Create New Deck</h3>
<button onClick={() => setShowCreate(false)} className="text-text-tertiary hover:text-text-secondary">
<X className="w-4 h-4" />
</button>
</div>
<div className="grid grid-cols-2 gap-3">
<input
type="text"
placeholder="Deck name"
value={newDeckName}
onChange={e => setNewDeckName(e.target.value)}
className="px-4 py-2.5 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
/>
<input
type="text"
placeholder="Commander (optional)"
value={newCommander}
onChange={e => setNewCommander(e.target.value)}
className="px-4 py-2.5 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
/>
</div>
<button onClick={handleCreate} disabled={creating}
className="px-4 py-2 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors disabled:opacity-50">
{creating ? <Spinner size="sm" /> : 'Create'}
</button>
</div>
)}
{/* Deck list */}
{loading ? (
<div className="flex items-center justify-center h-40"><Spinner size="lg" className="text-accent-blue" /></div>
) : decks.length === 0 ? (
<EmptyState icon={Layers} title="No decks yet" description="Create your first deck to get started." />
) : (
<div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4">
{decks.map(deck => (
<div
key={deck.id}
className="bg-bg-secondary rounded-xl border border-border-primary hover:border-border-secondary p-5 cursor-pointer transition-all group"
onClick={() => setSelectedDeck(deck)}
>
<div className="flex items-start justify-between mb-3">
<div className="min-w-0 flex-1">
<h3 className="text-base font-semibold text-text-primary truncate group-hover:text-accent-blue transition-colors">{deck.name}</h3>
<p className="text-sm text-text-secondary mt-0.5 truncate">{deck.commander}</p>
</div>
<button
onClick={e => { e.stopPropagation(); handleDelete(deck.id) }}
className="opacity-0 group-hover:opacity-100 p-1.5 text-text-tertiary hover:text-accent-red transition-all"
>
<Trash2 className="w-3.5 h-3.5" />
</button>
</div>
<div className="flex items-center justify-between">
<div className="flex items-center gap-3">
<ColorDots colors={deck.color_identity} />
<span className="text-xs text-text-tertiary">{deck.card_count} cards</span>
</div>
<span className="text-xs font-medium text-accent-teal">${deck.total_price.toFixed(2)}</span>
</div>
</div>
))}
</div>
)}
</div>
)
}
@@ -1,284 +0,0 @@
import { useState, useEffect } from 'react'
import {
Gamepad2, Play, Wifi, WifiOff, Settings,
AlertCircle, Trophy, Clock, Brain
} from 'lucide-react'
import { Spinner, StatusBadge } from '../components/common'
import { simApi, labApi } from '../api'
import { usePolling } from '../hooks/usePolling'
import type { SimStatus, SimResult, DeepSeekStatus, LabDeck } from '../types'
import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Cell } from 'recharts'
export function SimulatorPage() {
const [decks, setDecks] = useState<LabDeck[]>([])
const [deckA, setDeckA] = useState('')
const [deckB, setDeckB] = useState('')
const [games, setGames] = useState(50)
const [useDeepSeek, setUseDeepSeek] = useState(false)
const [deepSeekDeck, setDeepSeekDeck] = useState('')
const [dsStatus, setDsStatus] = useState<DeepSeekStatus | null>(null)
const [running, setRunning] = useState(false)
const [simId, setSimId] = useState<string | null>(null)
const [simStatus, setSimStatus] = useState<SimStatus | null>(null)
const [result, setResult] = useState<SimResult | null>(null)
const [error, setError] = useState<string | null>(null)
const [starting, setStarting] = useState(false)
// DS config
const [showDsConfig, setShowDsConfig] = useState(false)
const [dsModel, setDsModel] = useState('')
const [dsUrl, setDsUrl] = useState('')
useEffect(() => {
labApi.getLabDecks().then(setDecks).catch(() => {})
simApi.getDeepSeekStatus().then(s => {
setDsStatus(s)
if (s.model) setDsModel(s.model)
if (s.base_url) setDsUrl(s.base_url)
}).catch(() => {})
}, [])
// Poll while running
usePolling(async () => {
if (!simId) return
try {
const s = await simApi.getSimStatus(simId)
setSimStatus(s)
if (!s.running) {
const r = await simApi.getSimResult(simId)
setResult(r)
setRunning(false)
}
} catch { /* ignore */ }
}, 2000, running)
async function handleStart() {
if (!deckA || !deckB) { setError('Select both decks'); return }
setStarting(true)
setError(null)
setResult(null)
try {
let res
if (useDeepSeek && deepSeekDeck) {
res = await simApi.runDeepSeekSim({ deck_a: deckA, deck_b: deckB, games, deepseek_deck: deepSeekDeck })
} else {
res = await simApi.runSim({ deck_a: deckA, deck_b: deckB, games })
}
setSimId(res.sim_id)
setRunning(true)
} catch (e) {
setError(e instanceof Error ? e.message : 'Failed to start')
}
setStarting(false)
}
async function handleDsConnect() {
try {
const s = await simApi.connectDeepSeek()
setDsStatus(s)
} catch { /* ignore */ }
}
async function handleDsConfigure() {
try {
const s = await simApi.configureDeepSeek({
model: dsModel || undefined,
base_url: dsUrl || undefined,
})
setDsStatus(s)
setShowDsConfig(false)
} catch { /* ignore */ }
}
return (
<div className="p-6 space-y-5">
<div>
<h1 className="text-2xl font-bold text-text-primary flex items-center gap-3">
<Gamepad2 className="w-6 h-6 text-accent-purple" />
Simulator
</h1>
<p className="text-sm text-text-secondary mt-0.5">Run head-to-head deck simulations</p>
</div>
{/* DeepSeek status */}
<div className="flex items-center gap-3">
<StatusBadge
variant={dsStatus?.connected ? 'success' : 'warning'}
label={dsStatus?.connected ? `DeepSeek: ${dsStatus.model}` : 'DeepSeek: Disconnected'}
/>
<button onClick={handleDsConnect}
className="flex items-center gap-1.5 text-xs text-text-tertiary hover:text-text-secondary transition-colors">
{dsStatus?.connected ? <Wifi className="w-3 h-3" /> : <WifiOff className="w-3 h-3" />}
{dsStatus?.connected ? 'Reconnect' : 'Connect'}
</button>
<button onClick={() => setShowDsConfig(!showDsConfig)}
className="flex items-center gap-1.5 text-xs text-text-tertiary hover:text-text-secondary transition-colors">
<Settings className="w-3 h-3" />
Configure
</button>
</div>
{showDsConfig && (
<div className="bg-bg-secondary rounded-xl border border-border-primary p-4 max-w-md animate-fade-in space-y-3">
<h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
<Brain className="w-4 h-4 text-accent-purple" />
DeepSeek Configuration
</h3>
<input
type="text"
placeholder="Model name"
value={dsModel}
onChange={e => setDsModel(e.target.value)}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
/>
<input
type="text"
placeholder="Base URL (e.g. http://192.168.0.122:1234)"
value={dsUrl}
onChange={e => setDsUrl(e.target.value)}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary placeholder:text-text-tertiary focus:outline-none focus:border-accent-blue/50 transition-all"
/>
<button onClick={handleDsConfigure}
className="px-4 py-2 bg-accent-blue text-white text-sm font-medium rounded-lg hover:bg-accent-blue-hover transition-colors">
Save
</button>
</div>
)}
{/* Sim config */}
<div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-4 max-w-2xl">
<div className="grid grid-cols-2 gap-4">
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Deck A</label>
<select
value={deckA}
onChange={e => setDeckA(e.target.value)}
className="mt-1 w-full px-3 py-2.5 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
>
<option value="">Select deck...</option>
{decks.map(d => <option key={d.name} value={d.name}>{d.name}</option>)}
</select>
</div>
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Deck B</label>
<select
value={deckB}
onChange={e => setDeckB(e.target.value)}
className="mt-1 w-full px-3 py-2.5 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
>
<option value="">Select deck...</option>
{decks.map(d => <option key={d.name} value={d.name}>{d.name}</option>)}
</select>
</div>
</div>
<div className="flex items-center gap-6">
<div>
<label className="text-xs text-text-tertiary uppercase tracking-wider">Games</label>
<input
type="number" min={1} max={1000}
value={games}
onChange={e => setGames(Number(e.target.value))}
className="mt-1 w-24 px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
/>
</div>
<label className="flex items-center gap-2 cursor-pointer mt-5">
<input type="checkbox" checked={useDeepSeek} onChange={e => setUseDeepSeek(e.target.checked)}
className="w-4 h-4 rounded border-border-primary bg-bg-tertiary text-accent-purple focus:ring-accent-purple/30" />
<span className="text-sm text-text-secondary">Use DeepSeek AI pilot</span>
</label>
{useDeepSeek && (
<div className="mt-5">
<select
value={deepSeekDeck}
onChange={e => setDeepSeekDeck(e.target.value)}
className="px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all"
>
<option value="">AI pilots which deck?</option>
{[deckA, deckB].filter(Boolean).map(d => <option key={d} value={d}>{d}</option>)}
</select>
</div>
)}
</div>
{error && (
<div className="flex items-center gap-2 px-4 py-2 bg-status-error/10 border border-status-error/30 rounded-lg text-sm text-status-error">
<AlertCircle className="w-4 h-4" />{error}
</div>
)}
{running ? (
<div className="flex items-center gap-3">
<Spinner size="md" className="text-accent-blue" />
<div>
<p className="text-sm text-text-primary">Simulation running...</p>
{simStatus && (
<p className="text-xs text-text-tertiary">
{simStatus.games_completed ?? 0} / {simStatus.total_games ?? games} games
</p>
)}
</div>
</div>
) : (
<button onClick={handleStart} disabled={starting || !deckA || !deckB}
className="flex items-center gap-2 px-5 py-2.5 bg-accent-purple/15 text-accent-purple text-sm font-semibold rounded-lg hover:bg-accent-purple/25 border border-accent-purple/30 transition-colors disabled:opacity-50">
{starting ? <Spinner size="sm" /> : <Play className="w-4 h-4" />}
Run Simulation
</button>
)}
</div>
{/* Results */}
{result && (
<div className="bg-bg-secondary rounded-xl border border-border-primary p-5 space-y-4 animate-fade-in">
<div className="flex items-center justify-between">
<h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
<Trophy className="w-4 h-4 text-accent-amber" />
Results
</h3>
<span className="text-xs text-text-tertiary flex items-center gap-1.5">
<Clock className="w-3 h-3" />
{(result.duration_ms / 1000).toFixed(1)}s · {result.total_games} games
</span>
</div>
<div className="h-48">
<ResponsiveContainer width="100%" height="100%">
<BarChart data={result.decks.map(d => ({ name: d.name, win_rate: Math.round(d.win_rate * 100) }))} barSize={40}>
<XAxis dataKey="name" tick={{ fill: '#9ba1b8', fontSize: 12 }} axisLine={false} tickLine={false} />
<YAxis domain={[0, 100]} tick={{ fill: '#6b7190', fontSize: 11 }} axisLine={false} tickLine={false} />
<Tooltip contentStyle={{ backgroundColor: '#1c2030', border: '1px solid #2a2f42', borderRadius: 8, fontSize: 12 }}
formatter={(value: unknown) => [`${value}%`, 'Win Rate']} />
<Bar dataKey="win_rate" radius={[4, 4, 0, 0]}>
{result.decks.map((d, i) => (
<Cell key={i} fill={d.win_rate === Math.max(...result.decks.map(x => x.win_rate)) ? '#4ade80' : '#4f6ef7'} />
))}
</Bar>
</BarChart>
</ResponsiveContainer>
</div>
<div className="grid grid-cols-2 gap-4">
{result.decks.map(d => (
<div key={d.name} className="px-4 py-3 rounded-lg bg-bg-tertiary">
<p className="text-sm font-semibold text-text-primary">{d.name}</p>
<div className="grid grid-cols-3 gap-2 mt-2">
<div>
<p className="text-[10px] text-text-tertiary uppercase">Wins</p>
<p className="text-lg font-bold text-accent-green">{d.wins}</p>
</div>
<div>
<p className="text-[10px] text-text-tertiary uppercase">Win Rate</p>
<p className="text-lg font-bold text-accent-blue">{(d.win_rate * 100).toFixed(1)}%</p>
</div>
<div>
<p className="text-[10px] text-text-tertiary uppercase">Avg Turn</p>
<p className="text-lg font-bold text-text-primary">{d.avg_turn.toFixed(1)}</p>
</div>
</div>
</div>
))}
</div>
</div>
)}
</div>
)
}
@@ -1,822 +0,0 @@
import { useState, useEffect, useCallback } from 'react'
import {
Brain, Play, RotateCw, Database, Trophy, AlertCircle,
Activity, Zap, Swords, FileText, Cpu, CheckCircle,
ChevronDown, ChevronUp, Settings
} from 'lucide-react'
import { Spinner, StatusBadge } from '../components/common'
import { trainingApi } from '../api'
import { usePolling } from '../hooks/usePolling'
import type {
MLStatus, MLDataStatus, MLTrainStatus, PPOTrainStatus,
TournamentStatus, MLModelInfo
} from '../types'
// ── Progress Bar ──────────────────────────────────────────────
function ProgressBar({ value, max, label, color = 'bg-accent-blue' }: { value: number; max: number; label: string; color?: string }) {
const pct = max > 0 ? Math.min((value / max) * 100, 100) : 0
return (
<div>
<div className="flex items-center justify-between text-xs mb-1">
<span className="text-text-secondary">{label}</span>
<span className="text-text-tertiary">{value} / {max} ({pct.toFixed(0)}%)</span>
</div>
<div className="w-full h-2 bg-bg-tertiary rounded-full overflow-hidden">
<div className={'h-full rounded-full transition-all duration-500 ' + color} style={{ width: `${pct}%` }} />
</div>
</div>
)
}
// ── Phase Badge ──────────────────────────────────────────────
const PHASE_STYLES: Record<string, string> = {
idle: 'bg-bg-tertiary text-text-tertiary',
starting: 'bg-accent-blue/15 text-accent-blue animate-pulse',
building: 'bg-accent-purple/15 text-accent-purple animate-pulse',
training: 'bg-accent-blue/15 text-accent-blue animate-pulse',
evaluating: 'bg-accent-teal/15 text-accent-teal animate-pulse',
running: 'bg-accent-amber/15 text-accent-amber animate-pulse',
done: 'bg-accent-green/15 text-accent-green',
error: 'bg-accent-red/15 text-accent-red',
}
function PhaseBadge({ phase }: { phase: string }) {
const cls = PHASE_STYLES[phase] || PHASE_STYLES.idle
return (
<span className={'text-[10px] font-semibold uppercase tracking-wider px-2 py-0.5 rounded ' + cls}>
{phase}
</span>
)
}
// ── Metric Card ──────────────────────────────────────────────
function MetricCard({ label, value, accent }: { label: string; value: string | number; accent?: string }) {
return (
<div className="px-3 py-2 bg-bg-tertiary rounded-lg">
<p className="text-[10px] text-text-tertiary">{label}</p>
<p className={'text-sm font-bold ' + (accent || 'text-text-primary')}>{value}</p>
</div>
)
}
// ── Collapsible Section ─────────────────────────────────────
function Section({ title, icon, children, defaultOpen = true, color = 'text-accent-blue' }: {
title: string; icon: React.ReactNode; children: React.ReactNode; defaultOpen?: boolean; color?: string
}) {
const [open, setOpen] = useState(defaultOpen)
return (
<div className="bg-bg-secondary rounded-xl border border-border-primary overflow-hidden">
<button
onClick={() => setOpen(!open)}
className="w-full flex items-center justify-between px-5 py-4 hover:bg-bg-hover transition-colors"
>
<h3 className="text-sm font-semibold text-text-primary flex items-center gap-2">
<span className={color}>{icon}</span>
{title}
</h3>
{open ? <ChevronUp className="w-4 h-4 text-text-tertiary" /> : <ChevronDown className="w-4 h-4 text-text-tertiary" />}
</button>
{open && <div className="px-5 pb-5 space-y-4 border-t border-border-primary pt-4">{children}</div>}
</div>
)
}
// ══════════════════════════════════════════════════════════════
// Main Training Page
// ══════════════════════════════════════════════════════════════
export function TrainingPage() {
// Status states
const [mlStatus, setMlStatus] = useState<MLStatus | null>(null)
const [dataStatus, setDataStatus] = useState<MLDataStatus | null>(null)
const [modelInfo, setModelInfo] = useState<MLModelInfo | null>(null)
const [trainStatus, setTrainStatus] = useState<MLTrainStatus | null>(null)
const [ppoStatus, setPpoStatus] = useState<PPOTrainStatus | null>(null)
const [tournStatus, setTournStatus] = useState<TournamentStatus | null>(null)
const [loading, setLoading] = useState(true)
const [error, setError] = useState<string | null>(null)
// Supervised training config
const [epochs, setEpochs] = useState(50)
const [lr, setLr] = useState(0.001)
const [batchSize, setBatchSize] = useState(256)
const [patience, setPatience] = useState(10)
const [rebuildDataset, setRebuildDataset] = useState(true)
// PPO config
const [ppoIterations, setPpoIterations] = useState(100)
const [ppoEpisodes, setPpoEpisodes] = useState(64)
const [ppoEpochs, setPpoEpochs] = useState(4)
const [ppoBatchSize, setPpoBatchSize] = useState(256)
const [ppoLr, setPpoLr] = useState(0.0003)
const [ppoClipEps, setPpoClipEps] = useState(0.2)
const [ppoEntropy, setPpoEntropy] = useState(0.01)
const [ppoOpponent, setPpoOpponent] = useState('heuristic')
const [ppoPlaystyle, _setPpoPlaystyle] = useState('midrange') // eslint-disable-line
// Tournament config
const [tournEpisodes, setTournEpisodes] = useState(50)
const [tournPlaystyle, setTournPlaystyle] = useState('midrange')
// Action states
const [startingTrain, setStartingTrain] = useState(false)
const [startingPpo, setStartingPpo] = useState(false)
const [startingTourn, setStartingTourn] = useState(false)
const [reloading, setReloading] = useState(false)
const [showSupAdvanced, setShowSupAdvanced] = useState(false)
const [showPpoAdvanced, setShowPpoAdvanced] = useState(false)
// ── Initial Load ──────────────────────────────────────────
const loadAll = useCallback(async () => {
try {
const [ml, data, model, train, ppo, tourn] = await Promise.all([
trainingApi.getMLStatus().catch(() => null),
trainingApi.getDataStatus().catch(() => null),
trainingApi.getModelInfo().catch(() => null),
trainingApi.getTrainStatus().catch(() => null),
trainingApi.getPPOStatus().catch(() => null),
trainingApi.getTournamentStatus().catch(() => null),
])
if (ml) setMlStatus(ml)
if (data) setDataStatus(data)
if (model) setModelInfo(model)
if (train) setTrainStatus(train)
if (ppo) setPpoStatus(ppo)
if (tourn) setTournStatus(tourn)
} catch { /* ignore */ }
setLoading(false)
}, [])
useEffect(() => { loadAll() }, [loadAll])
// ── Polling ───────────────────────────────────────────────
const isTraining = trainStatus?.running || false
const isPpo = ppoStatus?.running || false
const isTourn = tournStatus?.running || false
const anyRunning = isTraining || isPpo || isTourn
usePolling(async () => {
try {
const s = await trainingApi.getTrainStatus()
setTrainStatus(s)
if (!s.running && s.phase === 'done') {
// Refresh model info + data when training completes
const [model, data] = await Promise.all([
trainingApi.getModelInfo().catch(() => null),
trainingApi.getDataStatus().catch(() => null),
])
if (model) setModelInfo(model)
if (data) setDataStatus(data)
}
} catch { /* ignore */ }
}, 2000, isTraining)
usePolling(async () => {
try {
const s = await trainingApi.getPPOStatus()
setPpoStatus(s)
if (!s.running && s.phase === 'done') {
const [model, data] = await Promise.all([
trainingApi.getModelInfo().catch(() => null),
trainingApi.getDataStatus().catch(() => null),
])
if (model) setModelInfo(model)
if (data) setDataStatus(data)
}
} catch { /* ignore */ }
}, 2000, isPpo)
usePolling(async () => {
try {
const s = await trainingApi.getTournamentStatus()
setTournStatus(s)
} catch { /* ignore */ }
}, 2000, isTourn)
// ── Actions ───────────────────────────────────────────────
async function handleToggleLogging() {
try {
const newState = !(mlStatus?.ml_logging_enabled ?? false)
await trainingApi.toggleML(newState)
const ml = await trainingApi.getMLStatus()
setMlStatus(ml)
} catch { /* ignore */ }
}
async function handleReloadModel() {
setReloading(true)
try {
const res = await trainingApi.reloadModel()
setModelInfo(res.status)
} catch (e) {
setError(e instanceof Error ? e.message : 'Failed to reload model')
}
setReloading(false)
}
async function handleStartTrain() {
setStartingTrain(true)
setError(null)
try {
await trainingApi.startTraining({ epochs, lr, batchSize, patience, rebuildDataset })
const s = await trainingApi.getTrainStatus()
setTrainStatus(s)
} catch (e) {
setError(e instanceof Error ? e.message : 'Failed to start training')
}
setStartingTrain(false)
}
async function handleStartPpo() {
setStartingPpo(true)
setError(null)
try {
await trainingApi.startPPOTraining({
iterations: ppoIterations,
episodesPerIter: ppoEpisodes,
ppoEpochs,
batchSize: ppoBatchSize,
lr: ppoLr,
clipEpsilon: ppoClipEps,
entropyCoeff: ppoEntropy,
opponent: ppoOpponent,
playstyle: ppoPlaystyle,
})
const s = await trainingApi.getPPOStatus()
setPpoStatus(s)
} catch (e) {
setError(e instanceof Error ? e.message : 'Failed to start PPO training')
}
setStartingPpo(false)
}
async function handleStartTournament() {
setStartingTourn(true)
setError(null)
try {
await trainingApi.startTournament({ episodes: tournEpisodes, playstyle: tournPlaystyle })
const s = await trainingApi.getTournamentStatus()
setTournStatus(s)
} catch (e) {
setError(e instanceof Error ? e.message : 'Failed to start tournament')
}
setStartingTourn(false)
}
// ── Render ────────────────────────────────────────────────
if (loading) {
return <div className="p-6 flex items-center justify-center h-64"><Spinner size="lg" className="text-accent-blue" /></div>
}
const trainDataset = dataStatus?.datasets?.['train']
const hasDecisions = (mlStatus?.total_decisions ?? 0) > 0
const hasModel = modelInfo?.loaded === true
return (
<div className="p-6 space-y-5">
{/* Page Header */}
<div>
<h1 className="text-2xl font-bold text-text-primary flex items-center gap-3">
<Brain className="w-6 h-6 text-accent-green" />
ML Training Pipeline
</h1>
<p className="text-sm text-text-secondary mt-0.5">Collect data, train models, and evaluate performance</p>
</div>
{/* Error banner */}
{error && (
<div className="flex items-center gap-2 px-4 py-2 bg-accent-red/10 border border-accent-red/30 rounded-lg text-sm text-accent-red">
<AlertCircle className="w-4 h-4 flex-shrink-0" />
<span className="flex-1">{error}</span>
<button onClick={() => setError(null)} className="text-accent-red/60 hover:text-accent-red text-xs">dismiss</button>
</div>
)}
{/* Pipeline overview bar */}
<div className="flex flex-wrap items-center gap-3 bg-bg-secondary rounded-xl border border-border-primary px-5 py-3">
<div className="flex items-center gap-2">
<div className={'w-2 h-2 rounded-full ' + (mlStatus?.ml_logging_enabled ? 'bg-accent-green' : 'bg-text-tertiary')} />
<span className="text-xs text-text-secondary">Logging {mlStatus?.ml_logging_enabled ? 'On' : 'Off'}</span>
</div>
<span className="text-border-primary">|</span>
<span className="text-xs text-text-secondary">{mlStatus?.total_decisions ?? 0} decisions</span>
<span className="text-border-primary">|</span>
<span className="text-xs text-text-secondary">{trainDataset ? `${trainDataset.samples.toLocaleString()} training samples` : 'No dataset'}</span>
<span className="text-border-primary">|</span>
<span className="text-xs text-text-secondary">{(dataStatus?.checkpoints?.length ?? 0)} checkpoints</span>
<span className="text-border-primary">|</span>
<StatusBadge
variant={hasModel ? 'success' : 'neutral'}
label={hasModel ? `Model on ${modelInfo?.device ?? '?'}` : 'No model'}
/>
{anyRunning && (
<>
<span className="text-border-primary">|</span>
<span className="text-xs font-medium text-accent-blue animate-pulse">Pipeline running...</span>
</>
)}
</div>
{/* ═══ Phase 1: Data Collection ═══ */}
<Section title="Phase 1 — Data Collection" icon={<FileText className="w-4 h-4" />} color="text-accent-teal">
<div className="flex items-center justify-between">
<p className="text-xs text-text-secondary">
Run batch simulations with ML logging enabled to collect decision data.
</p>
<button
onClick={handleToggleLogging}
className={'flex items-center gap-2 px-3 py-1.5 text-xs font-medium rounded-lg border transition-colors ' +
(mlStatus?.ml_logging_enabled
? 'bg-accent-green/15 text-accent-green border-accent-green/30 hover:bg-accent-green/25'
: 'bg-bg-tertiary text-text-secondary border-border-primary hover:bg-bg-hover'
)}
>
<div className={'w-2 h-2 rounded-full ' + (mlStatus?.ml_logging_enabled ? 'bg-accent-green' : 'bg-text-tertiary')} />
{mlStatus?.ml_logging_enabled ? 'Logging Enabled' : 'Enable Logging'}
</button>
</div>
{/* Decision files */}
{(dataStatus?.decisionFiles?.length ?? 0) > 0 ? (
<div className="space-y-1">
<h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider">Decision Files</h4>
<div className="max-h-36 overflow-y-auto space-y-1">
{dataStatus!.decisionFiles.map(f => (
<div key={f.name} className="flex items-center justify-between px-3 py-1.5 bg-bg-tertiary rounded-lg text-xs">
<span className="text-text-secondary font-mono truncate">{f.name}</span>
<div className="flex items-center gap-3 text-text-tertiary">
<span>{f.decisions.toLocaleString()} decisions</span>
<span>{(f.size / 1024).toFixed(0)} KB</span>
</div>
</div>
))}
</div>
</div>
) : (
<div className="text-xs text-text-tertiary italic">No decision files yet. Run batch sims with logging enabled.</div>
)}
</Section>
{/* ═══ Phase 2: Dataset & Checkpoints ═══ */}
<Section title="Phase 2 — Dataset & Model Artifacts" icon={<Database className="w-4 h-4" />} color="text-accent-purple">
<div className="grid grid-cols-1 md:grid-cols-2 gap-4">
{/* Dataset splits */}
<div className="space-y-2">
<h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider">Dataset Splits</h4>
{Object.keys(dataStatus?.datasets ?? {}).length > 0 ? (
<div className="grid grid-cols-3 gap-2">
{(['train', 'val', 'test'] as const).map(split => {
const ds = dataStatus?.datasets?.[split]
return (
<div key={split} className="px-3 py-2 bg-bg-tertiary rounded-lg">
<p className="text-[10px] text-text-tertiary uppercase">{split}</p>
{ds ? (
<>
<p className="text-sm font-bold text-text-primary">{ds.samples.toLocaleString()}</p>
<p className="text-[10px] text-text-tertiary">{ds.features} features</p>
</>
) : (
<p className="text-xs text-text-tertiary italic">Not built</p>
)}
</div>
)
})}
</div>
) : (
<p className="text-xs text-text-tertiary italic">No dataset built yet. Training will build it automatically.</p>
)}
</div>
{/* Checkpoints */}
<div className="space-y-2">
<h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider">Checkpoints</h4>
{(dataStatus?.checkpoints?.length ?? 0) > 0 ? (
<div className="max-h-28 overflow-y-auto space-y-1">
{dataStatus!.checkpoints.map(ckpt => (
<div key={ckpt.name} className="flex items-center justify-between px-3 py-1.5 bg-bg-tertiary rounded-lg text-xs">
<span className="text-text-secondary font-mono truncate">{ckpt.name}</span>
<span className="text-text-tertiary">{(ckpt.size / 1024 / 1024).toFixed(1)} MB</span>
</div>
))}
</div>
) : (
<p className="text-xs text-text-tertiary italic">No checkpoints yet. Train a model to generate them.</p>
)}
</div>
</div>
{/* Model info + reload */}
<div className="flex items-center justify-between pt-2 border-t border-border-primary">
<div className="flex items-center gap-3">
<Cpu className="w-4 h-4 text-text-tertiary" />
<div className="text-xs">
{hasModel ? (
<span className="text-accent-green font-medium">
Model loaded on {modelInfo?.device} | {modelInfo?.input_dim} features | {modelInfo?.num_actions} actions
</span>
) : (
<span className="text-text-tertiary">No model loaded{modelInfo?.error ? ` — ${modelInfo.error}` : ''}</span>
)}
</div>
</div>
<button
onClick={handleReloadModel}
disabled={reloading}
className="flex items-center gap-1.5 px-3 py-1.5 text-xs font-medium bg-bg-tertiary text-text-secondary rounded-lg hover:bg-bg-hover border border-border-primary transition-colors disabled:opacity-50"
>
{reloading ? <Spinner size="sm" /> : <RotateCw className="w-3 h-3" />}
Reload Model
</button>
</div>
{/* Eval results */}
{dataStatus?.evalResults && (
<div className="pt-2 border-t border-border-primary space-y-2">
<h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider flex items-center gap-2">
<CheckCircle className="w-3.5 h-3.5 text-accent-green" />
Latest Evaluation
</h4>
<div className="grid grid-cols-2 md:grid-cols-4 gap-2">
{Object.entries(dataStatus.evalResults).map(([k, v]) => (
<MetricCard
key={k}
label={k.replace(/_/g, ' ')}
value={typeof v === 'number' ? (k.includes('accuracy') || k.includes('rate') ? `${(v * 100).toFixed(1)}%` : v.toFixed(4)) : String(v)}
accent={k.includes('accuracy') ? 'text-accent-green' : undefined}
/>
))}
</div>
</div>
)}
</Section>
{/* ═══ Phase 3: Supervised Training ═══ */}
<Section title="Phase 3 — Supervised Training" icon={<Activity className="w-4 h-4" />}>
{/* Config */}
<div className="space-y-3">
<div className="grid grid-cols-2 gap-3">
<div>
<label className="text-xs text-text-tertiary block mb-1">Epochs</label>
<input type="number" min={1} max={1000} value={epochs} onChange={e => setEpochs(Number(e.target.value))}
disabled={isTraining}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
<div>
<label className="text-xs text-text-tertiary block mb-1">Learning Rate</label>
<input type="number" min={0.00001} max={1} step={0.0001} value={lr} onChange={e => setLr(Number(e.target.value))}
disabled={isTraining}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
</div>
{/* Advanced toggle */}
<button onClick={() => setShowSupAdvanced(!showSupAdvanced)} className="flex items-center gap-1 text-xs text-text-tertiary hover:text-text-secondary transition-colors">
<Settings className="w-3 h-3" />
{showSupAdvanced ? 'Hide' : 'Show'} advanced options
</button>
{showSupAdvanced && (
<div className="grid grid-cols-3 gap-3 animate-fade-in">
<div>
<label className="text-xs text-text-tertiary block mb-1">Batch Size</label>
<input type="number" min={16} max={2048} value={batchSize} onChange={e => setBatchSize(Number(e.target.value))}
disabled={isTraining}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
<div>
<label className="text-xs text-text-tertiary block mb-1">Early Stop Patience</label>
<input type="number" min={1} max={100} value={patience} onChange={e => setPatience(Number(e.target.value))}
disabled={isTraining}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
<div className="flex items-end pb-1">
<label className="flex items-center gap-2 text-xs text-text-secondary cursor-pointer select-none">
<input type="checkbox" checked={rebuildDataset} onChange={e => setRebuildDataset(e.target.checked)}
disabled={isTraining}
className="rounded border-border-primary bg-bg-tertiary text-accent-blue focus:ring-accent-blue/30 w-3.5 h-3.5" />
Rebuild dataset
</label>
</div>
</div>
)}
</div>
{/* Training status / start button */}
{isTraining || (trainStatus?.phase && trainStatus.phase !== 'idle') ? (
<div className="space-y-3 bg-bg-tertiary/50 rounded-xl p-4 border border-border-primary">
<div className="flex items-center justify-between">
<div className="flex items-center gap-2">
<PhaseBadge phase={trainStatus!.phase} />
<span className="text-xs text-text-secondary">{trainStatus!.message}</span>
</div>
{trainStatus!.started_at && (
<span className="text-[10px] text-text-tertiary">Started: {new Date(trainStatus!.started_at).toLocaleTimeString()}</span>
)}
</div>
{/* Progress bar — only in training phase */}
{(trainStatus!.phase === 'training' || trainStatus!.phase === 'done') && trainStatus!.total_epochs > 0 && (
<ProgressBar
value={trainStatus!.current_epoch}
max={trainStatus!.total_epochs}
label="Epoch progress"
/>
)}
{/* Live metrics during training */}
{trainStatus!.metrics && (
<div className="grid grid-cols-2 md:grid-cols-4 gap-2">
{Object.entries(trainStatus!.metrics).map(([k, v]) => {
if (typeof v !== 'number') return null
const isAcc = k.includes('accuracy') || k.includes('acc')
const isLoss = k.includes('loss')
return (
<MetricCard
key={k}
label={k.replace(/_/g, ' ')}
value={isAcc ? `${(v * 100).toFixed(1)}%` : v.toFixed(4)}
accent={isAcc ? 'text-accent-green' : isLoss ? 'text-accent-amber' : undefined}
/>
)
})}
</div>
)}
{/* Final result */}
{trainStatus!.phase === 'done' && trainStatus!.result && (
<div className="pt-2 border-t border-border-primary space-y-2">
<h4 className="text-xs font-semibold text-accent-green flex items-center gap-1.5">
<CheckCircle className="w-3.5 h-3.5" /> Training Complete
</h4>
<div className="grid grid-cols-2 md:grid-cols-4 gap-2">
<MetricCard label="Device" value={trainStatus!.result.device} />
<MetricCard label="Checkpoint" value={trainStatus!.result.checkpoint.split('/').pop() || '—'} />
{trainStatus!.result.evaluation && Object.entries(trainStatus!.result.evaluation).map(([k, v]) => (
typeof v === 'number' ? (
<MetricCard key={k} label={k.replace(/_/g, ' ')}
value={k.includes('accuracy') ? `${(v * 100).toFixed(1)}%` : v.toFixed(4)}
accent={k.includes('accuracy') ? 'text-accent-green' : undefined} />
) : null
))}
</div>
</div>
)}
{/* Error state */}
{trainStatus!.phase === 'error' && trainStatus!.error && (
<div className="flex items-start gap-2 bg-accent-red/10 border border-accent-red/20 rounded-lg px-3 py-2">
<AlertCircle className="w-4 h-4 text-accent-red mt-0.5 flex-shrink-0" />
<span className="text-xs text-accent-red">{trainStatus!.error}</span>
</div>
)}
</div>
) : (
<div className="flex items-center gap-3">
<button
onClick={handleStartTrain}
disabled={startingTrain || !hasDecisions || isPpo}
className="flex items-center gap-2 px-4 py-2.5 bg-accent-blue/15 text-accent-blue text-sm font-medium rounded-lg hover:bg-accent-blue/25 border border-accent-blue/30 transition-colors disabled:opacity-40 disabled:cursor-not-allowed"
>
{startingTrain ? <Spinner size="sm" /> : <Play className="w-4 h-4" />}
Start Training
</button>
{!hasDecisions && (
<span className="text-xs text-text-tertiary italic">Enable logging and run batch sims first to generate decision data.</span>
)}
</div>
)}
</Section>
{/* ═══ Phase 4: PPO Reinforcement Learning ═══ */}
<Section title="Phase 4 — PPO Reinforcement Learning" icon={<Zap className="w-4 h-4" />} color="text-accent-purple" defaultOpen={false}>
<p className="text-xs text-text-secondary">
Fine-tune the supervised model using PPO self-play against an opponent policy.
</p>
{/* Config */}
<div className="grid grid-cols-2 gap-3">
<div>
<label className="text-xs text-text-tertiary block mb-1">Iterations</label>
<input type="number" min={1} max={1000} value={ppoIterations} onChange={e => setPpoIterations(Number(e.target.value))}
disabled={isPpo}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
<div>
<label className="text-xs text-text-tertiary block mb-1">Opponent</label>
<select value={ppoOpponent} onChange={e => setPpoOpponent(e.target.value)}
disabled={isPpo}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50">
<option value="heuristic">Heuristic</option>
<option value="random">Random</option>
<option value="self">Self-Play</option>
</select>
</div>
</div>
<button onClick={() => setShowPpoAdvanced(!showPpoAdvanced)} className="flex items-center gap-1 text-xs text-text-tertiary hover:text-text-secondary transition-colors">
<Settings className="w-3 h-3" />
{showPpoAdvanced ? 'Hide' : 'Show'} advanced options
</button>
{showPpoAdvanced && (
<div className="grid grid-cols-3 gap-3 animate-fade-in">
<div>
<label className="text-xs text-text-tertiary block mb-1">Episodes / Iter</label>
<input type="number" min={1} max={512} value={ppoEpisodes} onChange={e => setPpoEpisodes(Number(e.target.value))}
disabled={isPpo}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
<div>
<label className="text-xs text-text-tertiary block mb-1">PPO Epochs</label>
<input type="number" min={1} max={20} value={ppoEpochs} onChange={e => setPpoEpochs(Number(e.target.value))}
disabled={isPpo}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
<div>
<label className="text-xs text-text-tertiary block mb-1">Batch Size</label>
<input type="number" min={16} max={2048} value={ppoBatchSize} onChange={e => setPpoBatchSize(Number(e.target.value))}
disabled={isPpo}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
<div>
<label className="text-xs text-text-tertiary block mb-1">Learning Rate</label>
<input type="number" min={0.000001} max={0.01} step={0.0001} value={ppoLr} onChange={e => setPpoLr(Number(e.target.value))}
disabled={isPpo}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
<div>
<label className="text-xs text-text-tertiary block mb-1">Clip Epsilon</label>
<input type="number" min={0.05} max={0.5} step={0.05} value={ppoClipEps} onChange={e => setPpoClipEps(Number(e.target.value))}
disabled={isPpo}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
<div>
<label className="text-xs text-text-tertiary block mb-1">Entropy Coeff</label>
<input type="number" min={0} max={0.1} step={0.005} value={ppoEntropy} onChange={e => setPpoEntropy(Number(e.target.value))}
disabled={isPpo}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
</div>
)}
{/* PPO running status */}
{isPpo || (ppoStatus?.phase && ppoStatus.phase !== 'idle') ? (
<div className="space-y-3 bg-bg-tertiary/50 rounded-xl p-4 border border-border-primary">
<div className="flex items-center gap-2">
<PhaseBadge phase={ppoStatus!.phase} />
<span className="text-xs text-text-secondary">{ppoStatus!.message}</span>
</div>
{(ppoStatus!.phase === 'training' || ppoStatus!.phase === 'done') && ppoStatus!.total_iterations > 0 && (
<ProgressBar
value={ppoStatus!.iteration}
max={ppoStatus!.total_iterations}
label="PPO iteration"
color="bg-accent-purple"
/>
)}
{ppoStatus!.metrics && (
<div className="grid grid-cols-2 md:grid-cols-4 gap-2">
{ppoStatus!.metrics.win_rate != null && (
<MetricCard label="Win Rate" value={`${(ppoStatus!.metrics.win_rate * 100).toFixed(1)}%`} accent="text-accent-green" />
)}
{ppoStatus!.metrics.avg_reward != null && (
<MetricCard label="Avg Reward" value={ppoStatus!.metrics.avg_reward.toFixed(3)} accent="text-accent-amber" />
)}
{ppoStatus!.metrics.policy_loss != null && (
<MetricCard label="Policy Loss" value={ppoStatus!.metrics.policy_loss.toFixed(4)} />
)}
{ppoStatus!.metrics.entropy != null && (
<MetricCard label="Entropy" value={ppoStatus!.metrics.entropy.toFixed(4)} />
)}
</div>
)}
{ppoStatus!.phase === 'done' && ppoStatus!.result && (
<div className="flex items-center gap-2 pt-2 border-t border-border-primary">
<CheckCircle className="w-3.5 h-3.5 text-accent-green" />
<span className="text-xs text-accent-green font-medium">
PPO Training Complete — Best Win Rate: {
typeof ppoStatus!.result.best_win_rate === 'number'
? `${(ppoStatus!.result.best_win_rate as number * 100).toFixed(1)}%`
: '—'
}
</span>
</div>
)}
{ppoStatus!.phase === 'error' && ppoStatus!.error && (
<div className="flex items-start gap-2 bg-accent-red/10 border border-accent-red/20 rounded-lg px-3 py-2">
<AlertCircle className="w-4 h-4 text-accent-red mt-0.5 flex-shrink-0" />
<span className="text-xs text-accent-red">{ppoStatus!.error}</span>
</div>
)}
</div>
) : (
<button
onClick={handleStartPpo}
disabled={startingPpo || isTraining || !hasModel}
className="flex items-center gap-2 px-4 py-2.5 bg-accent-purple/15 text-accent-purple text-sm font-medium rounded-lg hover:bg-accent-purple/25 border border-accent-purple/30 transition-colors disabled:opacity-40 disabled:cursor-not-allowed"
>
{startingPpo ? <Spinner size="sm" /> : <Play className="w-4 h-4" />}
Start PPO Training
</button>
)}
{!hasModel && !(isPpo || (ppoStatus?.phase && ppoStatus.phase !== 'idle')) && (
<p className="text-xs text-text-tertiary italic">Train a supervised model first (Phase 3) before running PPO.</p>
)}
</Section>
{/* ═══ Phase 5: Tournament Evaluation ═══ */}
<Section title="Phase 5 — Tournament Evaluation" icon={<Swords className="w-4 h-4" />} color="text-accent-amber" defaultOpen={false}>
<p className="text-xs text-text-secondary">
Pit the trained model against heuristic, random, and other policy variants.
</p>
<div className="grid grid-cols-2 gap-3">
<div>
<label className="text-xs text-text-tertiary block mb-1">Episodes per Matchup</label>
<input type="number" min={10} max={1000} value={tournEpisodes} onChange={e => setTournEpisodes(Number(e.target.value))}
disabled={isTourn}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50" />
</div>
<div>
<label className="text-xs text-text-tertiary block mb-1">Playstyle</label>
<select value={tournPlaystyle} onChange={e => setTournPlaystyle(e.target.value)}
disabled={isTourn}
className="w-full px-3 py-2 bg-bg-tertiary border border-border-primary rounded-lg text-sm text-text-primary focus:outline-none focus:border-accent-blue/50 transition-all disabled:opacity-50">
<option value="midrange">Midrange</option>
<option value="aggro">Aggro</option>
<option value="control">Control</option>
<option value="combo">Combo</option>
</select>
</div>
</div>
{/* Tournament status */}
{isTourn || (tournStatus?.phase && tournStatus.phase !== 'idle') ? (
<div className="space-y-3 bg-bg-tertiary/50 rounded-xl p-4 border border-border-primary">
<div className="flex items-center gap-2">
<PhaseBadge phase={tournStatus!.phase} />
<span className="text-xs text-text-secondary">{tournStatus!.message}</span>
</div>
{tournStatus!.phase === 'error' && tournStatus!.error && (
<div className="flex items-start gap-2 bg-accent-red/10 border border-accent-red/20 rounded-lg px-3 py-2">
<AlertCircle className="w-4 h-4 text-accent-red mt-0.5 flex-shrink-0" />
<span className="text-xs text-accent-red">{tournStatus!.error}</span>
</div>
)}
</div>
) : (
<button
onClick={handleStartTournament}
disabled={startingTourn || isTraining || isPpo}
className="flex items-center gap-2 px-4 py-2.5 bg-accent-amber/15 text-accent-amber text-sm font-medium rounded-lg hover:bg-accent-amber/25 border border-accent-amber/30 transition-colors disabled:opacity-40 disabled:cursor-not-allowed"
>
{startingTourn ? <Spinner size="sm" /> : <Trophy className="w-4 h-4" />}
Start Tournament
</button>
)}
{/* Tournament results */}
{(tournStatus?.result?.players?.length ?? 0) > 0 && (
<div className="pt-2 border-t border-border-primary space-y-2">
<h4 className="text-xs font-semibold text-text-tertiary uppercase tracking-wider flex items-center gap-2">
<Trophy className="w-3.5 h-3.5 text-accent-amber" />
Leaderboard
</h4>
<table className="w-full text-sm">
<thead>
<tr className="border-b border-border-primary">
<th className="px-3 py-2 text-left text-xs text-text-tertiary font-medium w-8">#</th>
<th className="px-3 py-2 text-left text-xs text-text-tertiary font-medium">Policy</th>
<th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium">Wins</th>
<th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium">Games</th>
<th className="px-3 py-2 text-right text-xs text-text-tertiary font-medium">Win Rate</th>
</tr>
</thead>
<tbody className="divide-y divide-border-primary">
{tournStatus!.result!.players
.sort((a, b) => b.win_rate - a.win_rate)
.map((p, i) => (
<tr key={p.name} className="hover:bg-bg-hover transition-colors">
<td className="px-3 py-2 text-text-tertiary text-xs">
{i === 0 ? <Trophy className="w-3.5 h-3.5 text-accent-amber" /> : i + 1}
</td>
<td className="px-3 py-2">
<span className="text-text-primary font-medium capitalize">{p.name}</span>
</td>
<td className="px-3 py-2 text-right text-text-secondary">{p.wins}</td>
<td className="px-3 py-2 text-right text-text-secondary">{p.games}</td>
<td className={'px-3 py-2 text-right font-semibold ' + (i === 0 ? 'text-accent-green' : 'text-accent-blue')}>
{(p.win_rate * 100).toFixed(1)}%
</td>
</tr>
))}
</tbody>
</table>
</div>
)}
</Section>
</div>
)
}
@@ -1,563 +0,0 @@
// ═══════════════════════════════════════════════════════════
// Commander AI Lab — TypeScript Type Definitions
// Derived from the FastAPI backend's Pydantic models + SQLite schema
// ═══════════════════════════════════════════════════════════
// ── Cards & Collection ──────────────────────────────────────
export interface CollectionCard {
id: number
name: string
scryfall_id: string
set_code: string
set_name: string
collector_number: string
rarity: string
type_line: string
mana_cost: string
cmc: number
oracle_text: string
color_identity: string[]
quantity: number
foil_quantity: number
condition: string
tcg_price: number | null
foil_price: number | null
image_url: string
edhrec_rank: number | null
keywords: string
added_date: string
updated_date: string
}
export interface CollectionFilters {
q?: string
type_line?: string
color?: string
rarity?: string
set_code?: string
cmc_min?: number
cmc_max?: number
price_min?: number
price_max?: number
owned_only?: boolean
sort_by?: string
sort_dir?: 'asc' | 'desc'
page?: number
page_size?: number
}
export interface CollectionStats {
total_cards: number
total_unique: number
total_value: number
sets_count: number
}
export interface SetInfo {
code: string
name: string
card_count?: number
}
// ── Scan ─────────────────────────────────────────────────────
export interface ScanResult {
matches: ScanMatch[]
image_url?: string
}
export interface ScanMatch {
name: string
set_code: string
set_name: string
collector_number: string
scryfall_id: string
confidence: number
image_url: string
type_line: string
oracle_text: string
mana_cost: string
tcg_price: number | null
}
// ── Decks ────────────────────────────────────────────────────
export interface Deck {
id: number
name: string
commander: string
commander_scryfall_id?: string
commander_image_url?: string
color_identity: string[]
format: string
card_count: number
total_price: number
created_date: string
updated_date: string
}
export interface DeckCard {
id: number
deck_id: number
name: string
scryfall_id: string
quantity: number
is_commander: boolean
category: string
type_line: string
mana_cost: string
cmc: number
oracle_text: string
tcg_price: number | null
image_url: string
owned_qty: number
set_code: string
}
export interface DeckAnalysis {
card_count: number
land_count: number
creature_count: number
noncreature_count: number
avg_cmc: number
mana_curve: Record<number, number>
color_distribution: Record<string, number>
type_distribution: Record<string, number>
total_price: number
owned_count: number
missing_count: number
}
export interface DeckRecommendation {
name: string
scryfall_id: string
type_line: string
mana_cost: string
oracle_text: string
image_url: string
tcg_price: number | null
reason: string
score: number
owned: boolean
}
// ── EDHREC Recommendations ──────────────────────────────────
export interface EdhRecCard {
name: string
type_line: string
role: string
roles: string[]
inclusion_pct: number | null
synergy_score: number | null
owned: boolean
owned_qty: number
scryfall_id: string
image_url: string | null
}
export interface EdhRecsResponse {
commander: string
source: string
total: number
recommendations: EdhRecCard[]
}
// ── Collection Recommendations ──────────────────────────────
export interface CollectionRecCard {
id: number
scryfall_id: string
name: string
type_line: string
card_type: string
cmc: number
color_identity: string[]
owned_qty: number
roles: string[]
score: number
image_url: string | null
}
export interface CollectionRecsResponse {
shortfall_types: string[]
role_filter: string[]
grouped: Record<string, CollectionRecCard[]>
total: number
}
// ── V3 Deck Generator ────────────────────────────────────────
export interface DeckGenV3Request {
commander_name: string
strategy?: string
target_bracket: number
budget_usd?: number
budget_mode?: 'total' | 'per_card'
omit_cards?: string[]
use_collection?: boolean
run_substitution?: boolean
model?: string
deck_name?: string
}
export interface DeckGenV3Status {
initialized: boolean
pplx_configured: boolean
model: string | null
embeddings_loaded: boolean
embedding_cards: number
error: string | null
}
export interface DeckGenV3Card {
name: string
quantity: number
category: string
type_line: string
mana_cost: string
cmc: number
role: string
reason: string
tcg_price: number | null
image_url: string
owned: boolean
owned_qty: number
scryfall_id: string
is_substitute: boolean
original_name: string | null
substitution_reason: string | null
similarity_score: number | null
}
export interface DeckGenV3Result {
commander: {
name: string
image_url: string
type_line: string
color_identity: string[]
mana_cost: string
oracle_text: string
}
cards: DeckGenV3Card[]
stats: DeckAnalysis
strategy: string
bracket: number
model: string
substitution?: {
owned: number
substituted: number
missing: number
}
}
// ── Simulation ───────────────────────────────────────────────
export interface SimConfig {
deck_names: string[]
num_games: number
threads: number
use_deepseek?: boolean
}
export interface SimStatus {
running: boolean
sim_id: string
progress?: number
games_completed?: number
total_games?: number
}
export interface SimResult {
sim_id: string
decks: SimDeckResult[]
total_games: number
duration_ms: number
timestamp: string
}
export interface SimDeckResult {
name: string
wins: number
losses: number
win_rate: number
avg_turn: number
}
// ── Batch Sim (Lab) ──────────────────────────────────────────
export interface LabDeck {
name: string
source: string
path: string
card_count: number
}
export interface LabStartRequest {
decks: string[]
games: number
threads?: number
use_deepseek?: boolean
deepseek_deck?: string
}
export interface LabStatus {
running: boolean
run_id: string
games_completed: number
total_games: number
current_decks: string[]
error: string | null
}
export interface LabResult {
run_id: string
decks: LabDeckResult[]
total_games: number
timestamp: string
}
export interface LabDeckResult {
name: string
wins: number
games: number
win_rate: number
avg_turns: number
avg_life_remaining: number
}
export interface LabHistoryEntry {
run_id: string
timestamp: string
decks: string[]
total_games: number
}
export interface PreconDeck {
name: string
filename: string
release_date?: string
set_name?: string
installed: boolean
}
// ── Coach ────────────────────────────────────────────────────
export interface CoachStatus {
llmConnected: boolean
llmModel: string | null
llmModels: string[]
embeddingsLoaded: boolean
embeddingCards: number
deckReportsAvailable: number
error: string | null
}
export interface CoachDeck {
deck_id: number
deck_name: string
commander: string
card_count: number
has_report: boolean
report_count: number
last_report_date: string | null
}
export interface CoachGoals {
targetPowerLevel?: number | null
metaFocus?: string | null
budget?: string | null
focusAreas?: string[]
}
export interface CoachMessage {
role: 'user' | 'assistant'
content: string
}
export interface CoachChatResponse {
content: string
model: string
prompt_tokens: number
completion_tokens: number
}
export interface CoachSession {
sessionId: string
deckId: string
timestamp: string
summary: string
suggestedCuts: CoachSuggestedCut[]
suggestedAdds: CoachSuggestedAdd[]
heuristicHints: string[]
manaBaseAdvice: string | null
rawTextExplanation: string
modelUsed: string
promptTokens: number
completionTokens: number
goals: CoachGoals | null
}
export interface CoachSuggestedCut {
cardName: string
reason: string
replacementOptions: string[]
currentImpactScore: number
}
export interface CoachSuggestedAdd {
cardName: string
role: string
reason: string
synergyWith: string[]
estimatedManaValue: number | null
}
export interface CoachSessionSummary {
sessionId: string
deckId: string
timestamp: string
summary: string
cutsCount: number
addsCount: number
}
export interface CoachApplyResult {
cuts: { name: string; status: string }[]
adds: { name: string; scryfall_id?: string; status: string }[]
errors: { name: string; error: string }[]
total_cuts: number
total_adds: number
}
export interface CoachCardLikeResult {
name: string
similarity: number
types: string
mana_value: number
mana_cost: string
text: string
owned_qty: number
image_url: string | null
tcg_price: number | null
}
// ── ML Training ──────────────────────────────────────────────
/** GET /api/ml/status */
export interface MLStatus {
ml_logging_enabled: boolean
training_files: { file: string; decisions: number; size_kb: number }[]
total_decisions: number
total_files: number
}
/** GET /api/ml/data/status */
export interface MLDataStatus {
decisionFiles: { name: string; size: number; decisions: number }[]
totalDecisions: number
datasets: Record<string, { samples: number; features: number; size: number }>
checkpoints: { name: string; size: number; modified: string }[]
evalResults: Record<string, unknown> | null
policyLoaded: boolean
}
/** GET /api/ml/train/status */
export interface MLTrainStatus {
running: boolean
progress: number
total_epochs: number
current_epoch: number
phase: 'idle' | 'starting' | 'building' | 'training' | 'evaluating' | 'done' | 'error'
message: string
metrics: Record<string, unknown> | null
result: {
training: Record<string, unknown>
evaluation: Record<string, unknown> | null
checkpoint: string
device: string
} | null
error: string | null
started_at: string | null
}
/** GET /api/ml/train/ppo/status */
export interface PPOTrainStatus {
running: boolean
iteration: number
total_iterations: number
phase: 'idle' | 'starting' | 'training' | 'done' | 'error'
message: string
metrics: {
avg_reward?: number
win_rate?: number
policy_loss?: number
value_loss?: number
entropy?: number
[key: string]: unknown
} | null
result: Record<string, unknown> | null
error: string | null
}
/** GET /api/ml/tournament/status */
export interface TournamentStatus {
running: boolean
phase: 'idle' | 'starting' | 'running' | 'done' | 'error'
message: string
result: TournamentResults | null
error: string | null
}
export interface TournamentResults {
players: TournamentPlayer[]
total_matches: number
matchups?: Record<string, unknown>
}
export interface TournamentPlayer {
name: string
wins: number
games: number
win_rate: number
}
/** GET /api/ml/model */
export interface MLModelInfo {
loaded: boolean
device?: string
input_dim?: number
num_actions?: number
checkpoint_path?: string
error?: string
torch_available?: boolean
}
// ── DeepSeek ─────────────────────────────────────────────────
export interface DeepSeekStatus {
connected: boolean
model: string | null
base_url: string
}
// ── Commander Search ─────────────────────────────────────────
export interface CommanderSearchResult {
name: string
type_line: string
color_identity: string[]
mana_cost: string
image_url: string
oracle_text: string
scryfall_id: string
in_collection: boolean
}
// ── Perplexity ───────────────────────────────────────────────
export interface PplxStatus {
configured: boolean
key_prefix: string
}
@@ -1,28 +0,0 @@
{
"compilerOptions": {
"tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo",
"target": "ES2022",
"useDefineForClassFields": true,
"lib": ["ES2022", "DOM", "DOM.Iterable"],
"module": "ESNext",
"types": ["vite/client"],
"skipLibCheck": true,
/* Bundler mode */
"moduleResolution": "bundler",
"allowImportingTsExtensions": true,
"verbatimModuleSyntax": true,
"moduleDetection": "force",
"noEmit": true,
"jsx": "react-jsx",
/* Linting */
"strict": true,
"noUnusedLocals": true,
"noUnusedParameters": true,
"erasableSyntaxOnly": true,
"noFallthroughCasesInSwitch": true,
"noUncheckedSideEffectImports": true
},
"include": ["src"]
}
@@ -1,7 +0,0 @@
{
"files": [],
"references": [
{ "path": "./tsconfig.app.json" },
{ "path": "./tsconfig.node.json" }
]
}
@@ -1,26 +0,0 @@
{
"compilerOptions": {
"tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo",
"target": "ES2023",
"lib": ["ES2023"],
"module": "ESNext",
"types": ["node"],
"skipLibCheck": true,
/* Bundler mode */
"moduleResolution": "bundler",
"allowImportingTsExtensions": true,
"verbatimModuleSyntax": true,
"moduleDetection": "force",
"noEmit": true,
/* Linting */
"strict": true,
"noUnusedLocals": true,
"noUnusedParameters": true,
"erasableSyntaxOnly": true,
"noFallthroughCasesInSwitch": true,
"noUncheckedSideEffectImports": true
},
"include": ["vite.config.ts"]
}
@@ -1,28 +0,0 @@
import { defineConfig } from 'vite'
import react from '@vitejs/plugin-react'
import tailwindcss from '@tailwindcss/vite'
export default defineConfig({
plugins: [react(), tailwindcss()],
server: {
port: 5173,
proxy: {
'/api': {
target: 'http://localhost:8080',
changeOrigin: true,
},
},
},
build: {
outDir: 'dist',
sourcemap: false,
rollupOptions: {
output: {
manualChunks: {
recharts: ['recharts'],
react: ['react', 'react-dom', 'react-router-dom'],
},
},
},
},
})
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Commander AI Lab — Collection</title>
<link rel="preconnect" href="https://fonts.googleapis.com" />
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet" />
<link rel="stylesheet" href="global.css" />
<link rel="stylesheet" href="collection.css" />
<style>
/* No nav bar when embedded in LAN client iframe */
:root { --nav-height: 0px; }
</style>
</head>
<body>
<!-- nav.js removed — collection is embedded via iframe in the LAN client -->
<div id="collection-container"></div>
<script>
window.LAB_API_BASE = window.LAB_API_BASE || '';
</script>
<script src="collection.js"></script>
<script>
document.addEventListener('DOMContentLoaded', function() {
Collection.init();
});
</script>
<!-- Card Preview Popup (hover tooltip) -->
<div id="coll-card-preview" style="display:none; position:fixed; pointer-events:none; z-index:9999;">
<img id="coll-card-preview-img" src="" alt="Card preview" style="width:220px; border-radius:8px; box-shadow:0 8px 24px rgba(0,0,0,0.7);" />
</div>
</body>
</html>
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Commander AI Lab — Deck Builder</title>
<link rel="preconnect" href="https://fonts.googleapis.com" />
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet" />
<link rel="stylesheet" href="global.css" />
<link rel="stylesheet" href="deckbuilder.css" />
<style>
/* No nav bar when embedded in LAN client iframe */
:root { --nav-height: 0px; }
</style>
</head>
<body>
<!-- nav.js removed — deck builder is embedded via iframe in the LAN client -->
<div id="deck-builder-page">
<!-- TOP BAR -->
<header class="deck-topbar">
<div class="deck-topbar-commander">
<div class="deck-commander-portrait" id="deck-commander-portrait">
<span class="deck-commander-portrait-placeholder">⚔</span>
<img class="deck-commander-portrait-img" id="deck-commander-img" src="" alt="" style="display:none" />
</div>
<div class="deck-name-block">
<input
type="text"
class="deck-name-input"
id="deck-name-input"
placeholder="Untitled Deck"
value=""
/>
<div class="deck-commander-label" id="deck-commander-label">No commander selected</div>
</div>
<div class="deck-color-chips" id="deck-color-chips"></div>
</div>
<div class="deck-topbar-center">
<div class="deck-count-display">
<span class="deck-count-num" id="deck-total-count">0</span>
<span class="deck-count-sep">/</span>
<span class="deck-count-target">100</span>
<span class="deck-count-label">cards</span>
</div>
<button class="deck-btn deck-btn-ghost" id="deck-new-btn">+ New Deck</button>
<div class="deck-selector-wrap">
<select class="deck-selector" id="deck-selector">
<option value="">— Select Deck —</option>
</select>
</div>
<button class="deck-btn deck-btn-danger" id="deck-delete-btn" title="Delete this deck" style="display:none;">✕ Delete</button>
<button class="deck-btn deck-btn-danger-outline" id="deck-clear-all-btn" title="Delete all decks">Clear All</button>
</div>
<div class="deck-topbar-actions">
<button class="deck-btn deck-btn-import" id="deck-import-btn" title="Import a decklist">
<span>⬆</span> Import
</button>
<button class="deck-btn deck-btn-ghost" id="deck-export-btn" title="Export as .dck file">
<span>⬇</span> Export .dck
</button>
<button class="deck-btn deck-btn-ghost" id="deck-export-sim-btn" title="Export deck as .dck to Forge sim folder">
<span>&#x1F4E4;</span> Export to Sim
</button>
<button class="deck-btn deck-btn-ghost" id="deck-export-game-btn" title="Send deck to LAN game setup" style="border-color:rgba(91,141,217,0.4);color:rgba(91,141,217,0.9);">
<span>&#x1F3AE;</span> Use in Game
</button>
<button class="deck-btn deck-btn-sim" id="deck-sim-btn" title="Run quick simulation">
<span class="deck-btn-icon">▶</span> Run Simulation
</button>
</div>
</header>
<!-- MAIN 3-COLUMN LAYOUT -->
<main class="deck-main">
<!-- LEFT COLUMN: Collection Search -->
<aside class="deck-col-left">
<div class="deck-panel deck-panel-search">
<div class="deck-panel-header">
<span class="deck-panel-title">Collection Search</span>
<span class="deck-search-count" id="deck-search-count">0 results</span>
</div>
<div class="deck-search-bar-wrap">
<input
type="search"
class="deck-search-bar"
id="deck-search-bar"
placeholder="Search cards..."
autocomplete="off"
/>
<span class="deck-search-icon">🔍</span>
</div>
<div class="deck-filter-row">
<select class="deck-filter-select" id="deck-type-filter">
<option value="">All Types</option>
<option value="Land">Land</option>
<option value="Creature">Creature</option>
<option value="Instant">Instant</option>
<option value="Sorcery">Sorcery</option>
<option value="Artifact">Artifact</option>
<option value="Enchantment">Enchantment</option>
<option value="Planeswalker">Planeswalker</option>
</select>
<div class="deck-cmc-filter">
<input type="number" class="deck-cmc-input" id="deck-cmc-min" placeholder="0" min="0" max="20" />
<span class="deck-cmc-sep">–</span>
<input type="number" class="deck-cmc-input" id="deck-cmc-max" placeholder="∞" min="0" max="20" />
</div>
</div>
<div class="deck-color-filter-row" id="deck-color-filter">
<button class="deck-color-pip deck-color-W" data-color="W" title="White">W</button>
<button class="deck-color-pip deck-color-U" data-color="U" title="Blue">U</button>
<button class="deck-color-pip deck-color-B" data-color="B" title="Black">B</button>
<button class="deck-color-pip deck-color-R" data-color="R" title="Red">R</button>
<button class="deck-color-pip deck-color-G" data-color="G" title="Green">G</button>
<button class="deck-color-pip deck-color-C" data-color="C" title="Colorless">C</button>
</div>
<!-- Results list -->
<div class="deck-search-results" id="deck-search-results">
<div class="deck-empty-state">
<div class="deck-empty-icon">🃏</div>
<div class="deck-empty-text">Search your collection to add cards</div>
</div>
</div>
<div class="deck-search-footer">
<button class="deck-btn deck-btn-success deck-btn-full" id="deck-add-all-owned-btn" style="display:none">
Add All Matching (Owned)
</button>
</div>
</div>
</aside>
<!-- CENTER COLUMN: Deck Card List -->
<section class="deck-col-center">
<div class="deck-panel deck-panel-cards" id="deck-card-list-panel">
<div class="deck-panel-header deck-card-list-header">
<span class="deck-panel-title">Deck Contents</span>
<div class="deck-card-list-actions">
<span class="deck-card-list-meta" id="deck-card-list-meta">Select or create a deck</span>
</div>
</div>
<!-- Card groups by type -->
<div class="deck-card-groups" id="deck-card-groups">
<div class="deck-empty-state deck-empty-state-lg">
<div class="deck-empty-icon">⚔</div>
<div class="deck-empty-text">Create or select a deck to start building</div>
<button class="deck-btn deck-btn-accent" id="deck-empty-new-btn">+ New Deck</button>
</div>
</div>
<!-- Quick add by name -->
<div class="deck-quickadd-row" id="deck-quickadd-row" style="display:none">
<div class="deck-quickadd-wrap">
<input
type="text"
class="deck-quickadd-input"
id="deck-quickadd-input"
placeholder="Add card by name..."
autocomplete="off"
/>
<div class="deck-quickadd-dropdown" id="deck-quickadd-dropdown" style="display:none"></div>
</div>
<button class="deck-btn deck-btn-accent-sm" id="deck-quickadd-btn">Add</button>
</div>
<!-- Deck stats footer -->
<div class="deck-stats-footer" id="deck-stats-footer" style="display:none">
<div class="deck-stat-pill">
<span class="deck-stat-pill-num" id="deck-footer-total">0</span>
<span class="deck-stat-pill-label">total</span>
</div>
<div class="deck-stat-pill">
<span class="deck-stat-pill-num deck-stat-owned" id="deck-footer-owned">0</span>
<span class="deck-stat-pill-label">owned</span>
</div>
<div class="deck-stat-pill">
<span class="deck-stat-pill-num" id="deck-footer-avg-cmc">0.0</span>
<span class="deck-stat-pill-label">avg CMC</span>
</div>
</div>
</div>
</section>
<!-- RIGHT COLUMN: EDH Advisor -->
<aside class="deck-col-right">
<!-- Ratio Dashboard -->
<div class="deck-panel deck-panel-ratios" id="deck-ratio-panel">
<div class="deck-panel-header deck-panel-header-collapsible" data-target="deck-ratio-body">
<span class="deck-panel-title">📊 Ratio Dashboard</span>
<span class="deck-collapse-arrow">▼</span>
</div>
<div class="deck-panel-body" id="deck-ratio-body">
<div class="deck-ratio-bars" id="deck-ratio-bars">
<div class="deck-ratio-placeholder">Load a deck to see ratios</div>
</div>
</div>
</div>
<!-- Fix My Ratios -->
<div class="deck-panel deck-panel-fix" id="deck-fix-panel">
<div class="deck-panel-header deck-panel-header-collapsible" data-target="deck-fix-body">
<span class="deck-panel-title">🔧 Fix My Ratios</span>
<span class="deck-collapse-arrow">▼</span>
</div>
<div class="deck-panel-body" id="deck-fix-body">
<div class="deck-shortfalls" id="deck-shortfalls">
<div class="deck-ratio-placeholder">No analysis available</div>
</div>
<div class="deck-fix-actions" id="deck-fix-actions" style="display:none">
<button class="deck-btn deck-btn-ghost deck-btn-full" id="deck-autosuggest-btn">
⚡ Auto-suggest from Collection
</button>
</div>
<div class="deck-fix-suggestions" id="deck-fix-suggestions" style="display:none"></div>
</div>
</div>
<!-- EDHREC Recommendations -->
<div class="deck-panel deck-panel-edhrec" id="deck-edhrec-panel">
<div class="deck-panel-header deck-panel-header-collapsible" data-target="deck-edhrec-body">
<span class="deck-panel-title">✨ EDHREC Recs</span>
<span class="deck-collapse-arrow">▼</span>
</div>
<div class="deck-panel-body" id="deck-edhrec-body">
<div class="deck-edhrec-controls">
<label class="deck-toggle-label">
<input type="checkbox" class="deck-toggle-check" id="deck-edhrec-owned-only" />
<span class="deck-toggle-track"><span class="deck-toggle-thumb"></span></span>
Owned Only
</label>
<button class="deck-btn deck-btn-ghost deck-btn-sm" id="deck-edhrec-refresh-btn">
↺ Refresh
</button>
</div>
<div class="deck-edhrec-list" id="deck-edhrec-list">
<div class="deck-ratio-placeholder">Load a deck to see recommendations</div>
</div>
<div class="deck-edhrec-bulk" id="deck-edhrec-bulk" style="display:none">
<button class="deck-btn deck-btn-success deck-btn-full" id="deck-edhrec-bulk-owned-btn">
Add All Owned (respect ratios)
</button>
</div>
</div>
</div>
<!-- AI Research (Perplexity) — hidden until pplx status check -->
<div class="deck-panel deck-panel-ai" id="deck-ai-research-panel" style="display:none">
<div class="deck-panel-header deck-panel-header-collapsible" data-target="deck-ai-research-body">
<span class="deck-panel-title">🧠 AI Research</span>
<span class="deck-collapse-arrow">▼</span>
</div>
<div class="deck-panel-body" id="deck-ai-research-body">
<div class="deck-ai-controls">
<div class="deck-form-field deck-form-field-sm">
<label class="deck-form-label-sm">Analysis Goal</label>
<input type="text" class="deck-form-input deck-form-input-sm" id="ai-research-goal"
placeholder="Identify weaknesses and suggest upgrades" />
</div>
<div class="deck-ai-row">
<div class="deck-form-field deck-form-field-sm deck-form-field-half">
<label class="deck-form-label-sm">Budget ($)</label>
<input type="number" class="deck-form-input deck-form-input-sm" id="ai-research-budget"
placeholder="No limit" min="0" step="10" />
</div>
<label class="deck-toggle-label deck-toggle-label-sm">
<input type="checkbox" class="deck-toggle-check" id="ai-research-use-collection" checked />
<span class="deck-toggle-track"><span class="deck-toggle-thumb"></span></span>
Use Collection
</label>
</div>
<div class="deck-form-field deck-form-field-sm">
<label class="deck-form-label-sm">Omit Cards (comma-separated)</label>
<input type="text" class="deck-form-input deck-form-input-sm" id="ai-research-omit"
placeholder="Dockside Extortionist, Rhystic Study" />
</div>
<button class="deck-btn deck-btn-ai deck-btn-full" id="ai-research-btn" disabled>
🔍 Analyze Deck
</button>
</div>
<div class="deck-ai-loading" id="ai-research-loading" style="display:none">
<div class="deck-ai-spinner"></div>
<span>Analyzing with Claude Opus...</span>
</div>
<div class="deck-ai-results" id="ai-research-results" style="display:none"></div>
</div>
</div>
<!-- AI Deck Generate (Perplexity) — hidden until pplx status check -->
<div class="deck-panel deck-panel-ai" id="deck-ai-generate-panel" style="display:none">
<div class="deck-panel-header deck-panel-header-collapsible" data-target="deck-ai-generate-body">
<span class="deck-panel-title">✨ AI Generate Deck</span>
<span class="deck-collapse-arrow">▼</span>
</div>
<div class="deck-panel-body" id="deck-ai-generate-body">
<div class="deck-ai-controls">
<div class="deck-form-field deck-form-field-sm">
<label class="deck-form-label-sm">Commander</label>
<div class="deck-ai-commander-wrap">
<input type="text" class="deck-form-input deck-form-input-sm" id="ai-gen-commander"
placeholder="Search for a commander..." autocomplete="off" />
<div class="deck-ai-commander-dropdown" id="ai-gen-commander-dropdown" style="display:none"></div>
</div>
</div>
<div class="deck-ai-row">
<div class="deck-form-field deck-form-field-sm deck-form-field-half">
<label class="deck-form-label-sm">Budget ($)</label>
<input type="number" class="deck-form-input deck-form-input-sm" id="ai-gen-budget"
placeholder="No limit" min="0" step="10" />
</div>
<label class="deck-toggle-label deck-toggle-label-sm">
<input type="checkbox" class="deck-toggle-check" id="ai-gen-use-collection" checked />
<span class="deck-toggle-track"><span class="deck-toggle-thumb"></span></span>
Use Collection
</label>
</div>
<div class="deck-form-field deck-form-field-sm">
<label class="deck-form-label-sm">Omit Cards (comma-separated)</label>
<input type="text" class="deck-form-input deck-form-input-sm" id="ai-gen-omit"
placeholder="Rhystic Study, Cyclonic Rift" />
</div>
<button class="deck-btn deck-btn-ai deck-btn-full" id="ai-gen-btn">
✨ Generate Full Deck
</button>
</div>
<div class="deck-ai-loading" id="ai-gen-loading" style="display:none">
<div class="deck-ai-spinner"></div>
<span>Generating deck with Claude Opus...</span>
</div>
<div class="deck-ai-results" id="ai-gen-results" style="display:none"></div>
</div>
</div>
</aside>
</main>
<!-- CARD PREVIEW TOOLTIP -->
<div class="deck-card-preview" id="deck-card-preview" style="display:none">
<img class="deck-card-preview-img" id="deck-card-preview-img" src="" alt="" />
</div>
<!-- TOAST CONTAINER -->
<div class="deck-toast-container" id="deck-toast-container"></div>
<!-- CONFIRM MODAL -->
<div class="deck-modal-overlay" id="deck-confirm-modal" style="display:none">
<div class="deck-modal">
<div class="deck-modal-header">
<h3 class="deck-modal-title" id="deck-confirm-title">Confirm Action</h3>
<button class="deck-modal-close-btn" id="deck-confirm-close">✕</button>
</div>
<div class="deck-modal-body">
<p class="deck-confirm-message" id="deck-confirm-message"></p>
<div class="deck-confirm-changes" id="deck-confirm-changes"></div>
</div>
<div class="deck-modal-footer">
<button class="deck-btn deck-btn-ghost" id="deck-confirm-cancel">Cancel</button>
<button class="deck-btn deck-btn-accent" id="deck-confirm-ok">Confirm</button>
</div>
</div>
</div>
<!-- NEW DECK MODAL -->
<div class="deck-modal-overlay" id="deck-new-modal" style="display:none">
<div class="deck-modal">
<div class="deck-modal-header">
<h3 class="deck-modal-title">Create New Deck</h3>
<button class="deck-modal-close-btn" id="deck-new-modal-close">✕</button>
</div>
<div class="deck-modal-body">
<div class="deck-form-field">
<label class="deck-form-label">Deck Name</label>
<input type="text" class="deck-form-input" id="deck-new-name" placeholder="My Commander Deck" />
</div>
<div class="deck-form-field">
<label class="deck-form-label">Commander (optional)</label>
<div class="deck-commander-search-wrap">
<input
type="text"
class="deck-form-input"
id="deck-new-commander-search"
placeholder="Search Scryfall for commander..."
autocomplete="off"
/>
<div class="deck-commander-dropdown" id="deck-new-commander-dropdown" style="display:none"></div>
</div>
<div class="deck-new-commander-selected" id="deck-new-commander-selected" style="display:none">
<span class="deck-selected-commander-name" id="deck-new-commander-name"></span>
<button class="deck-clear-commander-btn" id="deck-clear-commander-btn">✕</button>
</div>
</div>
</div>
<div class="deck-modal-footer">
<button class="deck-btn deck-btn-ghost" id="deck-new-cancel">Cancel</button>
<button class="deck-btn deck-btn-accent" id="deck-new-create">Create Deck</button>
</div>
</div>
</div>
<!-- IMPORT MODAL -->
<div class="deck-modal-overlay" id="deck-import-modal" style="display:none">
<div class="deck-modal deck-modal-wide">
<div class="deck-modal-header">
<h3 class="deck-modal-title">Import Decklist</h3>
<button class="deck-modal-close-btn" id="deck-import-modal-close">✕</button>
</div>
<div class="deck-modal-body">
<div class="deck-import-tabs">
<button class="deck-import-tab active" data-tab="paste">Paste Text</button>
<button class="deck-import-tab" data-tab="file">Upload File</button>
</div>
<div id="deck-import-paste-panel" class="deck-import-panel">
<textarea id="deck-import-textarea" rows="14" placeholder="Paste your decklist here...
Supported formats:
[Commander]
1 Korvold, Fae-Cursed King
[Deck]
1 Sol Ring
1 Cultivate
37 Forest
Or plain list:
Sol Ring
Cultivate
Forest"></textarea>
</div>
<div id="deck-import-file-panel" class="deck-import-panel" style="display:none">
<div class="deck-import-dropzone" id="deck-import-dropzone">
<div class="deck-import-dropzone-label">Drop a .dck or .txt file here, or click to browse</div>
<input type="file" id="deck-import-file" accept=".dck,.txt,.dec,.mwDeck" style="display:none" />
</div>
<div class="deck-import-file-info" id="deck-import-file-name"></div>
</div>
<div class="deck-import-options">
<label class="deck-import-option-label">
<input type="radio" name="import-mode" value="new" checked /> Create new deck
</label>
<label class="deck-import-option-label">
<input type="radio" name="import-mode" value="add" /> Add to current deck
</label>
<label class="deck-import-option-label">
<input type="radio" name="import-mode" value="replace" /> Replace current deck cards
</label>
</div>
<div id="deck-import-new-name-row" class="deck-import-name-row">
<label>Deck name (optional):</label>
<input type="text" id="deck-import-name" placeholder="Auto-detected from commander" />
</div>
<div class="deck-import-progress-wrap" id="deck-import-progress" style="display:none">
<div class="deck-import-progress-label">Importing cards (Scryfall lookup)...</div>
<div class="deck-import-progress-track">
<div class="deck-import-progress-fill" id="deck-import-progress-bar"></div>
</div>
</div>
<div id="deck-import-result" style="display:none"></div>
</div>
<div class="deck-modal-footer">
<button class="deck-btn deck-btn-ghost" id="deck-import-cancel">Cancel</button>
<button class="deck-btn deck-btn-accent" id="deck-import-go">Import</button>
</div>
</div>
</div>
</div><!-- /#deck-builder-page -->
<script>
// Set API base URL — when served from a different host, override this value
window.LAB_API_BASE = window.LAB_API_BASE || '';
</script>
<script src="deckbuilder.js"></script>
<!-- Card Preview Popup (hover tooltip) -->
<div id="deck-card-preview" style="display:none; position:fixed; pointer-events:none; z-index:60;">
<img id="deck-card-preview-img" src="" alt="Card preview" style="width:220px; border-radius:8px; box-shadow:0 8px 24px rgba(0,0,0,0.7);" />
</div>
</body>
</html>
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Commander AI Lab — Auto Deck Generator V3</title>
<link rel="preconnect" href="https://fonts.googleapis.com" />
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet" />
<link rel="stylesheet" href="global.css" />
<link rel="stylesheet" href="deckgenerator.css" />
<style>
/* No nav bar when embedded in LAN client iframe */
:root { --nav-height: 0px; }
</style>
</head>
<body>
<!-- nav.js removed — deck generator is embedded via iframe in the LAN client -->
<div id="deckgen-page">
<!-- MAIN LAYOUT -->
<main class="dg-main">
<!-- LEFT: Configuration -->
<section class="dg-col-config">
<!-- Commander Search -->
<div class="dg-panel dg-panel-commander">
<div class="dg-panel-header">
<span class="dg-panel-title">Commander</span>
</div>
<div class="dg-panel-body">
<div class="dg-commander-search-wrap">
<input
type="text"
class="dg-input"
id="dg-commander-input"
placeholder="Search for a legendary creature..."
autocomplete="off"
/>
<div class="dg-commander-dropdown" id="dg-commander-dropdown"></div>
</div>
<div class="dg-commander-selected" id="dg-commander-selected" style="display:none">
<div class="dg-commander-card">
<button class="dg-commander-clear" id="dg-commander-clear" title="Clear commander">&times;</button>
<img class="dg-commander-img" id="dg-commander-img" src="" alt="" />
<div class="dg-commander-info">
<div class="dg-commander-name" id="dg-commander-name"></div>
<div class="dg-commander-type" id="dg-commander-type"></div>
<div class="dg-commander-colors" id="dg-commander-colors"></div>
</div>
</div>
</div>
</div>
</div>
<!-- Strategy & Bracket -->
<div class="dg-panel">
<div class="dg-panel-header">
<span class="dg-panel-title">Strategy &amp; Power</span>
</div>
<div class="dg-panel-body">
<input
type="text"
class="dg-input"
id="dg-strategy"
placeholder="e.g. zombie tokens, voltron, spellslinger..."
title="Describe your deck strategy — the AI uses this to pick thematically aligned cards"
/>
<div class="dg-bracket-row">
<label class="dg-label">
Target Bracket
<span class="tip-icon" data-tip="B1 = precon-level casual. B2 = upgraded with synergies. B3 = high power with strong combos. B4 = cEDH with maximum optimization, fast mana, and instant-speed interaction.">?</span>
</label>
<div class="dg-bracket-btns" id="dg-bracket-btns">
<button class="dg-bracket-btn" data-bracket="1" title="Casual — precon level">B1</button>
<button class="dg-bracket-btn" data-bracket="2" title="Upgraded Casual">B2</button>
<button class="dg-bracket-btn dg-bracket-active" data-bracket="3" title="High Power">B3</button>
<button class="dg-bracket-btn" data-bracket="4" title="cEDH — maximum power">B4</button>
</div>
</div>
</div>
</div>
<!-- Deck Name -->
<div class="dg-panel">
<div class="dg-panel-header">
<span class="dg-panel-title">Deck Name</span>
</div>
<div class="dg-panel-body">
<input
type="text"
class="dg-input"
id="dg-deck-name"
placeholder="Auto-generated from commander name"
/>
</div>
</div>
<!-- Budget -->
<div class="dg-panel">
<div class="dg-panel-header">
<span class="dg-panel-title">Budget</span>
</div>
<div class="dg-panel-body">
<div class="dg-budget-row">
<input
type="number"
class="dg-input dg-input-narrow"
id="dg-budget"
placeholder="No limit"
min="0"
step="25"
/>
<select class="dg-select" id="dg-budget-mode" title="Total Deck: caps the combined price of all 100 cards. Per Card Max: caps each individual card price.">
<option value="total">Total Deck $</option>
<option value="per_card">Per Card Max $</option>
</select>
</div>
</div>
</div>
<!-- Options -->
<div class="dg-panel">
<div class="dg-panel-header">
<span class="dg-panel-title">Options</span>
</div>
<div class="dg-panel-body">
<label class="dg-toggle-row">
<span class="dg-toggle-label">
Use my collection
<span class="tip-icon" data-tip="Prioritizes cards you already own before pulling from the full card pool. Saves money and ensures the deck is ready to play. Requires an imported collection.">?</span>
</span>
<input type="checkbox" class="dg-toggle-check" id="dg-use-collection" checked />
<span class="dg-toggle-track"><span class="dg-toggle-thumb"></span></span>
</label>
<label class="dg-toggle-row">
<span class="dg-toggle-label">
Smart Substitution
<span class="tip-icon" data-tip="When a suggested card isn't in your collection, automatically finds the closest functional replacement you do own. Keeps the deck playable without buying extra cards.">?</span>
</span>
<input type="checkbox" class="dg-toggle-check" id="dg-substitution" checked />
<span class="dg-toggle-track"><span class="dg-toggle-thumb"></span></span>
</label>
<div class="dg-model-row">
<label class="dg-label">
AI Model
<span class="tip-icon" data-tip="Deck generation is powered by Claude Opus 4, Anthropic's most capable model.">?</span>
</label>
<input type="hidden" id="dg-model" value="claude-opus-4-20250514" />
<span style="font-size:0.8rem; color:var(--text-muted, #888);">claude-opus-4-20250514</span>
</div>
</div>
</div>
<!-- Omit Cards (chips input) -->
<div class="dg-panel">
<div class="dg-panel-header">
<span class="dg-panel-title">Omit Cards</span>
</div>
<div class="dg-panel-body">
<div class="dg-omit-wrap" id="dg-omit-wrap">
<div class="dg-omit-chips" id="dg-omit-chips"></div>
<input
type="text"
class="dg-input dg-omit-input"
id="dg-omit-input"
placeholder="Type a card name and press Enter…"
/>
</div>
<small class="dg-hint">Cards listed here will be excluded from the generated deck.</small>
</div>
</div>
<!-- Action Buttons -->
<div class="dg-actions">
<button class="dg-btn dg-btn-preview" id="dg-preview-btn" disabled>
<span class="dg-btn-icon">&#9889;</span> Generate Deck
</button>
</div>
<!-- Conversation Log -->
<div id="dg-conversation-log" class="dg-conversation-log">
<div class="dg-log-entry dg-log-info"><span class="dg-log-ts">[Ready]</span> Waiting for deck generation...</div>
</div>
</section>
<!-- RIGHT: Preview Results -->
<section class="dg-col-results">
<!-- Empty State -->
<div class="dg-empty-state" id="dg-empty-state">
<div class="dg-empty-icon">&#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 Claude Opus with structured output.</div>
</div>
<!-- Loading State -->
<div class="dg-loading" id="dg-loading" style="display:none">
<div class="dg-spinner"></div>
<div class="dg-loading-text" id="dg-loading-text">Generating deck via Claude Opus 4...</div>
</div>
<div class="dg-progress-wrap" id="dg-progress-wrap" style="display:none">
<div class="dg-progress-bar">
<div class="dg-progress-fill" id="dg-progress-fill"></div>
</div>
<div class="dg-progress-meta">
<span class="dg-progress-phase" id="dg-progress-phase"></span>
<span class="dg-progress-elapsed" id="dg-progress-elapsed">0s elapsed</span>
</div>
</div>
<!-- Results -->
<div class="dg-results" id="dg-results" style="display:none">
<div class="dg-results-header">
<div id="dg-strategy-bar"></div>
<div id="dg-stats-bar" class="dg-stats-bar"></div>
<div id="dg-sub-summary" style="display:none"></div>
<div class="dg-result-actions">
<button class="dg-btn dg-btn-secondary" id="dg-regenerate-btn">&#8635; Regenerate</button>
<button class="dg-btn dg-btn-primary" id="dg-save-btn">&#128190; Save to Deck Builder</button>
<button class="dg-btn dg-btn-ghost" id="dg-export-csv">CSV</button>
<button class="dg-btn dg-btn-ghost" id="dg-export-dck">Forge .dck</button>
<button class="dg-btn dg-btn-ghost" id="dg-export-moxfield">Moxfield</button>
<button class="dg-btn dg-btn-ghost" id="dg-export-shopping">&#128722; Shopping</button>
</div>
</div>
<div id="dg-card-groups" class="dg-card-groups"></div>
</div>
</section>
</main>
<!-- TOAST CONTAINER -->
<div class="dg-toast-container" id="dg-toast-container"></div>
</div>
<script>
window.LAB_API_BASE = window.LAB_API_BASE || '';
</script>
<script src="deckgenerator.js"></script>
</body>
</html>
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