feat: league tier system + retrained V25 models (48 quality leagues)

- Add LeagueTier DB model and Prisma schema
- Add league-tiers service (CRUD, sync, retrain trigger)
- Add league-tiers controller with admin API endpoints
- Add /v1/admin/retrain endpoint in AI engine (extract→train→reload pipeline)
- Retrain V25 Pro with 48 quality leagues (MS accuracy: 26.9%→51.4%)
- Update qualified_leagues.json (443→48 leagues)
- Include V25 model files in repo for Docker deployment

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

ai-engine/main.py

@@ -7,11 +7,14 @@ import time
 from contextlib import asynccontextmanager
 from typing import Any
 
+from datetime import datetime
+
 import uvicorn
 from dotenv import load_dotenv
 from fastapi import FastAPI, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
+import subprocess
 from pydantic import BaseModel
 
 try:
@@ -38,6 +41,23 @@ class CouponRequest(BaseModel):
     min_confidence: float | None = None
 
 
+class RetrainRequest(BaseModel):
+    reason: str | None = "manual"
+    markets: str | None = None  # comma-separated, e.g. "MS,OU25,BTTS"
+    trials: int | None = 50
+
+
+# ─── Retrain state tracking ──────────────────────────────────
+_retrain_state: dict[str, Any] = {
+    "running": False,
+    "last_started": None,
+    "last_completed": None,
+    "last_status": None,
+    "last_error": None,
+    "pid": None,
+}
+
+
 @asynccontextmanager
 async def lifespan(_: FastAPI):
     try:
@@ -115,6 +135,8 @@ def read_root() -> dict[str, Any]:
             "GET /v20plus/reversal-watchlist",
             "POST /v20plus/coupon",
             "GET /v20plus/daily-banker",
+            "POST /v1/admin/retrain",
+            "GET /v1/admin/retrain/status",
         ],
     }
 
@@ -298,6 +320,111 @@ async def get_reversal_watchlist_v20plus(
     )
 
 
+# ─── ADMIN: Retrain Pipeline ─────────────────────────────────
+
+def _run_retrain_pipeline(markets: str | None, trials: int):
+    """Background function: extract data → train model → reload."""
+    global _retrain_state
+    ai_dir = os.path.dirname(os.path.abspath(__file__))
+    scripts_dir = os.path.join(ai_dir, "scripts")
+    python = os.path.join(ai_dir, "venv", "bin", "python3")
+    if not os.path.exists(python):
+        python = sys.executable  # fallback
+
+    try:
+        # Step 1: Extract training data
+        print("🔄 [RETRAIN] Step 1/3: Extracting training data...", flush=True)
+        result = subprocess.run(
+            [python, os.path.join(scripts_dir, "extract_training_data.py")],
+            capture_output=True, text=True, timeout=600, cwd=ai_dir,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(f"Extract failed:\n{result.stderr[-500:]}")
+        print(f"✅ [RETRAIN] Extract done", flush=True)
+
+        # Step 2: Train V25 Pro
+        print("🔄 [RETRAIN] Step 2/3: Training V25 Pro model...", flush=True)
+        train_cmd = [python, os.path.join(scripts_dir, "train_v25_pro.py")]
+        if markets:
+            train_cmd += ["--markets", markets]
+        train_cmd += ["--trials", str(trials)]
+
+        result = subprocess.run(
+            train_cmd, capture_output=True, text=True, timeout=3600, cwd=ai_dir,
+        )
+        if result.returncode != 0:
+            raise RuntimeError(f"Training failed:\n{result.stderr[-500:]}")
+        print(f"✅ [RETRAIN] Training done", flush=True)
+
+        # Step 3: Reload models in memory
+        print("🔄 [RETRAIN] Step 3/3: Reloading models...", flush=True)
+        try:
+            orchestrator = get_single_match_orchestrator()
+            v25 = orchestrator._get_v25_predictor()
+            v25._loaded = False
+            v25.load_models()
+            print("✅ [RETRAIN] Models reloaded in memory", flush=True)
+        except Exception as reload_err:
+            print(f"⚠️ [RETRAIN] Hot reload failed (restart needed): {reload_err}", flush=True)
+
+        _retrain_state.update({
+            "running": False,
+            "last_completed": datetime.now().isoformat(),
+            "last_status": "success",
+            "last_error": None,
+        })
+        print("🎉 [RETRAIN] Pipeline complete!", flush=True)
+
+    except Exception as err:
+        _retrain_state.update({
+            "running": False,
+            "last_completed": datetime.now().isoformat(),
+            "last_status": "failed",
+            "last_error": str(err),
+        })
+        print(f"❌ [RETRAIN] Pipeline failed: {err}", flush=True)
+
+
+@app.post("/v1/admin/retrain")
+async def admin_retrain(request: RetrainRequest) -> dict[str, Any]:
+    """Trigger full retrain pipeline: extract → train → reload."""
+    if _retrain_state["running"]:
+        return {
+            "status": "already_running",
+            "message": f"Retrain in progress since {_retrain_state['last_started']}",
+        }
+
+    _retrain_state.update({
+        "running": True,
+        "last_started": datetime.now().isoformat(),
+        "last_status": "running",
+        "last_error": None,
+    })
+
+    # Run in background thread
+    import threading
+    thread = threading.Thread(
+        target=_run_retrain_pipeline,
+        args=(request.markets, request.trials or 50),
+        daemon=True,
+    )
+    thread.start()
+
+    return {
+        "status": "triggered",
+        "message": "Retrain pipeline started in background",
+        "reason": request.reason,
+        "markets": request.markets or "all",
+        "trials": request.trials or 50,
+    }
+
+
+@app.get("/v1/admin/retrain/status")
+async def admin_retrain_status() -> dict[str, Any]:
+    """Check retrain pipeline status."""
+    return {**_retrain_state}
+
+
 if __name__ == "__main__":
     port = int(os.getenv("PORT", "8000"))
     uvicorn.run("main:app", host="0.0.0.0", port=port, reload=True)