KoalaTrapLord
a5fe8cb3fa
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
280 lines
11 KiB
Python
280 lines
11 KiB
Python
"""
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Commander AI Lab — Coach Service
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═════════════════════════════════
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Main orchestrator: loads deck reports, identifies underperformers,
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finds replacement candidates via embeddings, builds prompts,
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calls the LLM, parses responses, and persists coaching sessions.
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"""
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import json
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import logging
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import uuid
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Optional, List, Dict
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from .config import (
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DECK_REPORTS_DIR, COACH_SESSIONS_DIR,
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MAX_UNDERPERFORMERS, MAX_CANDIDATES_PER_UNDERPERFORMER,
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UNDERPERFORMER_IMPACT_THRESHOLD, ensure_dirs,
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)
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from .models import (
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DeckReport, CoachGoals, CoachSession,
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SuggestedCut, SuggestedAdd, CoachStatus,
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)
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from .llm_client import LMStudioClient
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from .embeddings import MTGEmbeddingIndex
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from .prompt_template import build_system_prompt, build_user_prompt
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logger = logging.getLogger("coach.service")
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class CoachService:
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"""
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Deck coaching service powered by local LLM and card embeddings.
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Usage:
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embeddings = MTGEmbeddingIndex()
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embeddings.load()
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llm = LMStudioClient()
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coach = CoachService(llm, embeddings)
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session = await coach.run_coaching_session("edgar-markov")
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"""
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def __init__(self, llm: LMStudioClient, embeddings: MTGEmbeddingIndex):
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self.llm = llm
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self.embeddings = embeddings
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ensure_dirs()
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# ── Deck Report I/O ────────────────────────────────────────
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def load_deck_report(self, deck_id: str) -> Optional[DeckReport]:
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"""Load a deck report from disk."""
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# Try exact filename first, then case-insensitive search
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report_path = DECK_REPORTS_DIR / f"{deck_id}.json"
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if not report_path.exists():
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# Try case-insensitive match
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for f in DECK_REPORTS_DIR.glob("*.json"):
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if f.stem.lower() == deck_id.lower():
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report_path = f
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break
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if not report_path.exists():
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logger.warning("Deck report not found: %s", deck_id)
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return None
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try:
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with open(report_path, "r") as f:
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data = json.load(f)
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return DeckReport(**data)
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except Exception as e:
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logger.error("Failed to load deck report %s: %s", deck_id, e)
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return None
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def list_deck_reports(self) -> List[str]:
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"""List all available deck report IDs."""
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if not DECK_REPORTS_DIR.exists():
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return []
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return [f.stem for f in DECK_REPORTS_DIR.glob("*.json")]
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# ── Coach Session I/O ──────────────────────────────────────
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def save_session(self, session: CoachSession) -> Path:
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"""Persist a coaching session to disk."""
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COACH_SESSIONS_DIR.mkdir(parents=True, exist_ok=True)
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path = COACH_SESSIONS_DIR / f"{session.sessionId}.json"
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with open(path, "w") as f:
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f.write(session.model_dump_json(indent=2))
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return path
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def load_session(self, session_id: str) -> Optional[CoachSession]:
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"""Load a coaching session from disk."""
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path = COACH_SESSIONS_DIR / f"{session_id}.json"
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if not path.exists():
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return None
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try:
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with open(path, "r") as f:
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data = json.load(f)
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return CoachSession(**data)
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except Exception as e:
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logger.error("Failed to load session %s: %s", session_id, e)
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return None
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def list_sessions(self, deck_id: str = None) -> List[dict]:
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"""List all coaching sessions, optionally filtered by deck."""
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if not COACH_SESSIONS_DIR.exists():
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return []
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sessions = []
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for f in sorted(COACH_SESSIONS_DIR.glob("*.json"), reverse=True):
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try:
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with open(f, "r") as fh:
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data = json.load(fh)
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if deck_id and data.get("deckId", "") != deck_id:
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continue
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sessions.append({
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"sessionId": data.get("sessionId", f.stem),
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"deckId": data.get("deckId", ""),
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"timestamp": data.get("timestamp", ""),
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"summary": data.get("summary", "")[:100],
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"cutsCount": len(data.get("suggestedCuts", [])),
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"addsCount": len(data.get("suggestedAdds", [])),
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})
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except Exception:
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continue
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return sessions
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# ── Main Coaching Pipeline ─────────────────────────────────
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async def run_coaching_session(self, deck_id: str,
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goals: Optional[CoachGoals] = None
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) -> CoachSession:
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"""
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Full coaching pipeline:
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1. Load deck report
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2. Identify underperformers
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3. Find replacement candidates via embeddings
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4. Build prompt
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5. Call LLM
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6. Parse response
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7. Persist session
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"""
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# 0. Auto-load embeddings if not loaded
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if not self.embeddings.loaded:
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logger.info("Embeddings not loaded — attempting auto-load...")
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try:
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loaded = self.embeddings.load()
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if loaded:
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logger.info("Embeddings auto-loaded: %d cards", self.embeddings.card_count)
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else:
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logger.warning("Embeddings auto-load returned False")
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except Exception as e:
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logger.warning("Embeddings auto-load failed: %s", e)
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# 1. Load deck report
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report = self.load_deck_report(deck_id)
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if report is None:
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raise ValueError(f"Deck report not found for: {deck_id}")
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# 2. Get deck card names (for exclusion from candidates)
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deck_card_names = [c.name for c in report.cards]
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# 3. Find replacement candidates for underperformers
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candidates: Dict[str, List[dict]] = {}
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underperformers = report.underperformers[:MAX_UNDERPERFORMERS]
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if not underperformers:
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# If no explicit underperformers, pick lowest impact cards
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sorted_cards = sorted(report.cards, key=lambda c: c.impactScore)
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underperformers = [
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c.name for c in sorted_cards[:MAX_UNDERPERFORMERS]
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if c.impactScore < UNDERPERFORMER_IMPACT_THRESHOLD
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]
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# If still no underperformers and we have cards, pick bottom N by impact
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if not underperformers and report.cards:
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sorted_cards = sorted(report.cards, key=lambda c: c.impactScore)
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# Take the bottom 5 non-land cards as candidates for cuts
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underperformers = [
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c.name for c in sorted_cards[:5]
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if not any(t in (c.tags or []) for t in [])
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][:MAX_UNDERPERFORMERS]
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if self.embeddings.loaded:
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for card_name in underperformers:
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matches = self.embeddings.find_replacements(
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underperformer=card_name,
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deck_colors=report.colorIdentity,
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deck_card_names=deck_card_names,
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top_n=MAX_CANDIDATES_PER_UNDERPERFORMER,
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)
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if matches:
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candidates[card_name] = [m.to_dict() for m in matches]
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else:
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logger.warning("Embeddings not loaded — skipping candidate search")
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# 4. Build prompts
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system_prompt = build_system_prompt(report, goals)
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user_prompt = build_user_prompt(report, candidates)
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# 5. Call LLM
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logger.info("Calling LLM for deck: %s", deck_id)
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llm_response = await self.llm.chat(system_prompt, user_prompt)
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# 6. Parse response into CoachSession
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session_id = f"sess-{datetime.now(timezone.utc).strftime('%Y%m%d-%H%M%S')}-{uuid.uuid4().hex[:6]}"
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session = CoachSession(
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sessionId=session_id,
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deckId=deck_id,
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timestamp=datetime.now(timezone.utc).isoformat(),
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modelUsed=llm_response.model,
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promptTokens=llm_response.prompt_tokens,
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completionTokens=llm_response.completion_tokens,
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goals=goals,
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)
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if llm_response.parsed_json:
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self._populate_session_from_json(session, llm_response.parsed_json)
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else:
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# Fallback: store raw text
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session.rawTextExplanation = llm_response.content
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session.summary = "LLM returned non-JSON response. See raw explanation."
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# 7. Persist
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self.save_session(session)
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logger.info("Coaching session saved: %s", session_id)
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return session
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def _populate_session_from_json(self, session: CoachSession, data: dict):
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"""Parse LLM JSON response into CoachSession fields."""
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session.summary = data.get("summary", "")
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session.rawTextExplanation = data.get("rawTextExplanation", "")
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session.manaBaseAdvice = data.get("manaBaseAdvice")
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# Heuristic hints
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hints = data.get("heuristicHints", [])
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session.heuristicHints = hints if isinstance(hints, list) else [str(hints)]
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# Suggested cuts
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for cut_data in data.get("suggestedCuts", []):
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if isinstance(cut_data, dict):
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session.suggestedCuts.append(SuggestedCut(
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cardName=cut_data.get("cardName", ""),
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reason=cut_data.get("reason", ""),
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replacementOptions=cut_data.get("replacementOptions", []),
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))
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# Suggested adds
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for add_data in data.get("suggestedAdds", []):
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if isinstance(add_data, dict):
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session.suggestedAdds.append(SuggestedAdd(
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cardName=add_data.get("cardName", ""),
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role=add_data.get("role", ""),
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reason=add_data.get("reason", ""),
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synergyWith=add_data.get("synergyWith", []),
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))
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# ── Status Check ───────────────────────────────────────────
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def get_status(self) -> CoachStatus:
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"""Check the health of all coach subsystems."""
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status = CoachStatus()
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# Check LLM
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llm_status = self.llm.check_connection()
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status.llmConnected = llm_status.get("connected", False)
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status.llmModel = llm_status.get("active_model")
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status.llmModels = llm_status.get("models", [])
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if not status.llmConnected:
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status.error = llm_status.get("error", "LM Studio not reachable")
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# Check embeddings
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status.embeddingsLoaded = self.embeddings.loaded
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status.embeddingCards = self.embeddings.card_count
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# Check deck reports
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status.deckReportsAvailable = len(self.list_deck_reports())
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return status
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