import os
import warnings
import logging
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
import uvicorn

# Suppress TensorFlow warnings
warnings.filterwarnings('ignore', category=UserWarning)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # Suppress TensorFlow logging
logging.getLogger('tensorflow').setLevel(logging.ERROR)

from pydantic import BaseModel
import pickle
import numpy as np
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
from backend.models.schemas import RecommendationRequest, ProductRecommendation, RecommendationResponse

# Initialize FastAPI app with metadata
app = FastAPI(
    title="Product Recommendation API",
    description="API for getting product recommendations based on user queries",
    version="1.0.0"
)

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

# Load embeddings from the correct path
models_dir = os.path.join(os.path.dirname(__file__), "models")
embeddings_path = os.path.join(models_dir, "product_embeddings.pkl")

# Ensure models directory exists
os.makedirs(models_dir, exist_ok=True)

try:
    # Load embeddings
    if os.path.exists(embeddings_path):
        with open(embeddings_path, "rb") as f:
            data = pickle.load(f)
    else:
        raise FileNotFoundError("Product embeddings file not found")

    # Load model
    model = SentenceTransformer('all-MiniLM-L6-v2')
except Exception as e:
    print(f"Error initializing server: {e}")
    raise

@app.get("/")
def read_root():
    return {
        "message": "Product Recommendation API",
        "status": "active",
        "total_products": len(data["product_ids"]) if "product_ids" in data else 0
    }

@app.post("/recommend", response_model=RecommendationResponse)
def recommend_products(request: RecommendationRequest):
    """Get product recommendations based on user search"""
    try:
        query_embedding = model.encode(request.query).reshape(1, -1)
        
        # Compute similarity scores
        similarities = cosine_similarity(query_embedding, data["embeddings"])
        top_indices = np.argsort(similarities[0])[-request.top_n:][::-1]

        recommendations = []
        for i in top_indices:
            recommendations.append(ProductRecommendation(
                product_id=data["product_ids"][i],
                product_name=data["product_names"][i],
                description=data["descriptions"][i],
                brand=data["brands"][i],
                price=float(data["prices"][i]),
                categories=eval(data["categories"][i]) if isinstance(data["categories"][i], str) else data["categories"][i],
                score=float(similarities[0][i])
            ))

        return RecommendationResponse(recommendations=recommendations)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    try:
        uvicorn.run(
            "backend.server:app",
            host="0.0.0.0",
            port=8000,
            reload=True,
            access_log=True
        )
    except Exception as e:
        print(f"Error starting server: {e}")