"""
Nexus-Core Inference API - Path Fixed
Model: /app/models/nexus-core.onnx
"""

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
import time
import logging
import os
from typing import Optional

from engine import NexusCoreEngine

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

app = FastAPI(
    title="Nexus-Core Inference API",
    description="Fast chess engine (13M params)",
    version="2.0.0"
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

engine = None


class MoveRequest(BaseModel):
    fen: str
    depth: Optional[int] = Field(4, ge=1, le=6)
    time_limit: Optional[int] = Field(3000, ge=1000, le=10000)


class MoveResponse(BaseModel):
    best_move: str
    evaluation: float
    depth_searched: int
    nodes_evaluated: int
    time_taken: int


class HealthResponse(BaseModel):
    status: str
    model_loaded: bool
    version: str
    model_path: Optional[str] = None


@app.on_event("startup")
async def startup_event():
    global engine
    logger.info("🚀 Starting Nexus-Core API...")
    
    # FIXED: Correct model path with hyphen
    model_path = "/app/models/nexus-core.onnx"
    
    # Debug logging
    logger.info(f"Looking for model at: {model_path}")
    
    if os.path.exists("/app/models"):
        logger.info("📂 Files in /app/models/:")
        for f in os.listdir("/app/models"):
            full_path = os.path.join("/app/models", f)
            if os.path.isfile(full_path):
                size = os.path.getsize(full_path) / (1024*1024)
                logger.info(f"   ✓ {f} ({size:.2f} MB)")
    else:
        logger.error("❌ /app/models/ directory does not exist!")
        raise FileNotFoundError("/app/models/ not found")
    
    if not os.path.exists(model_path):
        logger.error(f"❌ Model not found at: {model_path}")
        logger.error("💡 Available files:", os.listdir("/app/models"))
        raise FileNotFoundError(f"Model file missing: {model_path}")
    
    logger.info(f"✅ Model found: {os.path.getsize(model_path)/(1024*1024):.2f} MB")
    
    try:
        engine = NexusCoreEngine(
            model_path=model_path,
            num_threads=2
        )
        logger.info("✅ Nexus-Core engine loaded successfully!")
        
    except Exception as e:
        logger.error(f"❌ Engine load failed: {e}", exc_info=True)
        raise


@app.get("/health", response_model=HealthResponse)
async def health_check():
    return {
        "status": "healthy" if engine else "unhealthy",
        "model_loaded": engine is not None,
        "version": "2.0.0",
        "model_path": "/app/models/nexus-core.onnx"
    }


@app.post("/get-move", response_model=MoveResponse)
async def get_move(request: MoveRequest):
    if not engine:
        raise HTTPException(status_code=503, detail="Engine not loaded")
    
    if not engine.validate_fen(request.fen):
        raise HTTPException(status_code=400, detail="Invalid FEN string")
    
    start = time.time()
    
    try:
        result = engine.get_best_move(
            fen=request.fen,
            depth=request.depth,
            time_limit=request.time_limit
        )
        
        logger.info(
            f"✓ Move: {result['best_move']} | "
            f"Eval: {result['evaluation']:+.2f} | "
            f"Depth: {result['depth_searched']} | "
            f"Nodes: {result['nodes_evaluated']} | "
            f"Time: {result['time_taken']}ms"
        )
        
        return MoveResponse(**result)
        
    except Exception as e:
        logger.error(f"❌ Search error: {e}", exc_info=True)
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/")
async def root():
    return {
        "name": "Nexus-Core Inference API",
        "version": "2.0.0",
        "model": "13M parameters",
        "architecture": "ResNet CNN",
        "speed": "0.5-1s per move @ depth 4",
        "status": "online" if engine else "starting",
        "endpoints": {
            "POST /get-move": "Get best chess move",
            "GET /health": "API health check",
            "GET /docs": "Interactive API documentation"
        }
    }


if __name__ == "__main__":
    import uvicorn
    uvicorn.run(
        app,
        host="0.0.0.0",
        port=7860,
        log_level="info",
        access_log=True
    )