"""
Nexus-Nano Inference API
2.8M parameter ultra-fast engine
"""

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

from engine import NexusNanoEngine

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

# FastAPI
app = FastAPI(
    title="Nexus-Nano Inference API",
    description="Ultra-fast 2.8M parameter chess engine",
    version="1.0.0"
)

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

# Global
engine = None


# Models
class MoveRequest(BaseModel):
    fen: str = Field(..., description="FEN notation")
    depth: Optional[int] = Field(4, ge=1, le=6, description="Search depth (1-6)")
    time_limit: Optional[int] = Field(2000, ge=500, le=10000, description="Time in ms")


class MoveResponse(BaseModel):
    best_move: str
    evaluation: float
    depth_searched: int
    seldepth: int
    nodes_evaluated: int
    time_taken: int
    nps: int
    pv: List[str]
    tt_hit_rate: Optional[float] = None


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


# Startup
@app.on_event("startup")
async def startup_event():
    global engine
    
    logger.info("⚡ Starting Nexus-Nano API v1.0...")
    
    try:
        engine = NexusNanoEngine(
            model_path="/app/models/nexus_nano.onnx",
            num_threads=1  # Single-threaded for speed
        )
        logger.info("✅ Engine loaded")
        
    except Exception as e:
        logger.error(f"❌ Failed: {e}")
        raise


# Health
@app.get("/health", response_model=HealthResponse)
async def health_check():
    return {
        "status": "healthy" if engine else "unhealthy",
        "model_loaded": engine is not None,
        "version": "1.0.0",
        "model_size_mb": engine.get_model_size() if engine else None
    }


# Main
@app.post("/get-move", response_model=MoveResponse)
async def get_move(request: MoveRequest):
    if engine is None:
        raise HTTPException(status_code=503, detail="Engine not loaded")
    
    if not engine.validate_fen(request.fen):
        raise HTTPException(status_code=400, detail="Invalid FEN")
    
    start_time = time.time()
    
    try:
        result = engine.get_best_move(
            fen=request.fen,
            depth=request.depth,
            time_limit=request.time_limit
        )
        
        time_taken = int((time.time() - start_time) * 1000)
        
        logger.info(
            f"⚡ Move: {result['best_move']} | "
            f"Eval: {result['evaluation']:+.2f} | "
            f"Depth: {result['depth_searched']} | "
            f"Time: {time_taken}ms | "
            f"NPS: {result['nps']}"
        )
        
        return MoveResponse(
            best_move=result['best_move'],
            evaluation=result['evaluation'],
            depth_searched=result['depth_searched'],
            seldepth=result['seldepth'],
            nodes_evaluated=result['nodes_evaluated'],
            time_taken=time_taken,
            nps=result['nps'],
            pv=result['pv'],
            tt_hit_rate=result['tt_stats']['hit_rate']
        )
        
    except Exception as e:
        logger.error(f"Error: {e}")
        raise HTTPException(status_code=500, detail=str(e))


# Root
@app.get("/")
async def root():
    return {
        "name": "Nexus-Nano Inference API",
        "version": "1.0.0",
        "model": "2.8M parameters (Lightweight CNN)",
        "tagline": "Ultra-fast chess inference",
        "search": "Alpha-Beta + Quiescence",
        "endpoints": {
            "POST /get-move": "Get best move (fast)",
            "GET /health": "Health check",
            "GET /docs": "API docs"
        }
    }


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