"""
CyberForge ML Inference Module
Backend integration for mlService.js
"""

import json
import time
import joblib
import numpy as np
from pathlib import Path
from typing import Dict, List, Any, Optional

class CyberForgeInference:
    """
    ML inference service for CyberForge backend.
    Compatible with mlService.js API contract.
    """

    def __init__(self, models_dir: str):
        self.models_dir = Path(models_dir)
        self.loaded_models = {}
        self.manifest = self._load_manifest()

    def _load_manifest(self) -> Dict:
        manifest_path = self.models_dir / "manifest.json"
        if manifest_path.exists():
            with open(manifest_path) as f:
                return json.load(f)
        return {"models": {}}

    def load_model(self, model_name: str) -> bool:
        """Load a model into memory"""
        if model_name in self.loaded_models:
            return True

        model_dir = self.models_dir / model_name
        model_path = model_dir / "model.pkl"
        scaler_path = model_dir / "scaler.pkl"

        if not model_path.exists():
            return False

        self.loaded_models[model_name] = {
            "model": joblib.load(model_path),
            "scaler": joblib.load(scaler_path) if scaler_path.exists() else None
        }
        return True

    def predict(self, model_name: str, features: Dict) -> Dict:
        """
        Make a prediction.

        Args:
            model_name: Name of the model to use
            features: Feature dictionary

        Returns:
            Response matching mlService.js contract
        """
        if not self.load_model(model_name):
            return {"error": f"Model not found: {model_name}"}

        model_data = self.loaded_models[model_name]
        model = model_data["model"]
        scaler = model_data["scaler"]

        # Convert features to array
        X = np.array([list(features.values())])

        # Scale if scaler available
        if scaler:
            X = scaler.transform(X)

        # Predict
        start_time = time.time()
        prediction = int(model.predict(X)[0])
        inference_time = (time.time() - start_time) * 1000

        # Get confidence
        confidence = 0.5
        if hasattr(model, "predict_proba"):
            proba = model.predict_proba(X)[0]
            confidence = float(max(proba))

        # Determine risk level
        risk_level = (
            "critical" if confidence >= 0.9 else
            "high" if confidence >= 0.7 else
            "medium" if confidence >= 0.5 else
            "low" if confidence >= 0.3 else "info"
        )

        return {
            "prediction": prediction,
            "confidence": confidence,
            "risk_level": risk_level,
            "model_name": model_name,
            "model_version": "1.0.0",
            "inference_time_ms": inference_time
        }

    def batch_predict(self, model_name: str, features_list: List[Dict]) -> List[Dict]:
        """Batch predictions"""
        return [self.predict(model_name, f) for f in features_list]

    def list_models(self) -> List[str]:
        """List available models"""
        return list(self.manifest.get("models", {}).keys())

    def get_model_info(self, model_name: str) -> Dict:
        """Get model information"""
        return self.manifest.get("models", {}).get(model_name, {})


# FastAPI integration
def create_api(models_dir: str):
    """Create FastAPI app for model serving"""
    try:
        from fastapi import FastAPI, HTTPException
        from pydantic import BaseModel
    except ImportError:
        return None

    app = FastAPI(title="CyberForge ML API", version="1.0.0")
    inference = CyberForgeInference(models_dir)

    class PredictRequest(BaseModel):
        model_name: str
        features: Dict

    @app.post("/predict")
    async def predict(request: PredictRequest):
        result = inference.predict(request.model_name, request.features)
        if "error" in result:
            raise HTTPException(status_code=404, detail=result["error"])
        return result

    @app.get("/models")
    async def list_models():
        return {"models": inference.list_models()}

    @app.get("/models/{model_name}")
    async def get_model_info(model_name: str):
        info = inference.get_model_info(model_name)
        if not info:
            raise HTTPException(status_code=404, detail="Model not found")
        return info

    return app


if __name__ == "__main__":
    import sys
    models_dir = sys.argv[1] if len(sys.argv) > 1 else "."

    inference = CyberForgeInference(models_dir)
    print(f"Available models: {inference.list_models()}")