import os
import json
import numpy as np
import faiss
from fastapi import FastAPI, HTTPException, Depends, status
from fastapi.middleware.cors import CORSMiddleware
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from pydantic import BaseModel
from typing import List, Optional
import logging
import time

# Try different import patterns to handle both direct execution and module execution
try:
    from .settings import get_settings
except ImportError:
    try:
        from app.settings import get_settings
    except ImportError:
        from settings import get_settings

# Configure logging
logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO").upper())
logger = logging.getLogger(__name__)

# Security
security = HTTPBearer()

def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)):
    expected_token = os.getenv("API_TOKEN")
    if not expected_token:
        raise HTTPException(status_code=500, detail="API token not configured")
    if credentials.credentials != expected_token:
        raise HTTPException(status_code=401, detail="Invalid token")
    return credentials.credentials

# Pydantic models
class ExploreRequest(BaseModel):
    liked_ids: List[int]
    disliked_ids: List[int] = []
    top_k: int = 400

class MovieResult(BaseModel):
    id: int
    title: str
    year: int
    poster_path: Optional[str]
    genres: List[str]
    coords: List[float]

class ExploreResponse(BaseModel):
    movies: List[MovieResult]
    bary: List[float]
    center: List[float]

# Global variables for loaded data
vectors = None
id_map = None
faiss_index = None
movie_metadata = None

def load_data():
    """Load FAISS index, vectors, and metadata on startup"""
    try:
        # Load vectors
        vectors = np.load("app/data/movies.npy")
        logger.info(f"Loaded {vectors.shape[0]} movie vectors of dimension {vectors.shape[1]}")
        
        # Load ID mapping
        with open("app/data/id_map.json", "r") as f:
            id_map = json.load(f)
        logger.info(f"Loaded ID mapping for {len(id_map)} movies")
        
        # Load FAISS index
        faiss_index = faiss.read_index("app/data/faiss.index")
        logger.info(f"Loaded FAISS index with {faiss_index.ntotal} vectors")
        
        # Load movie metadata
        with open("app/data/movie_metadata.json", "r") as f:
            movie_metadata = json.load(f)
        logger.info(f"Loaded metadata for {len(movie_metadata)} movies")
        
        return vectors, id_map, faiss_index, movie_metadata
        
    except Exception as e:
        logger.error(f"Failed to load data: {e}")
        raise

def build_plane(likes: np.ndarray, dislikes: np.ndarray = None, dim: int = 2):
    """
    Build user subspace from liked/disliked movies
    Returns (axes, center) where axes is 2xD orthonormal matrix
    """
    n_likes = likes.shape[0] if likes is not None else 0
    d = vectors.shape[1]
    
    # Compute composite vector: +liked - 0.5*disliked
    if n_likes == 0:
        # Cold start: use global average
        center = vectors.mean(0)
        # Create random orthonormal basis
        axes = np.random.randn(dim, d)
        axes[0] /= np.linalg.norm(axes[0])
        for i in range(1, dim):
            for j in range(i):
                axes[i] -= np.dot(axes[i], axes[j]) * axes[j]
            axes[i] /= np.linalg.norm(axes[i])
    else:
        # Compute composite from likes and dislikes
        composite = likes.mean(0)
        if dislikes is not None and dislikes.shape[0] > 0:
            composite -= 0.5 * dislikes.mean(0)
        
        if n_likes == 1:
            # One like: use as center, random orthogonal axes
            center = composite
            axis1 = np.random.randn(d)
            axis1 /= np.linalg.norm(axis1)
            axis2 = np.random.randn(d)
            axis2 -= np.dot(axis2, axis1) * axis1
            axis2 /= np.linalg.norm(axis2)
            axes = np.vstack([axis1, axis2])
        elif n_likes == 2:
            # Two likes: line between them
            center = likes.mean(0)
            axis1 = likes[1] - likes[0]
            axis1 /= np.linalg.norm(axis1)
            axis2 = np.random.randn(d)
            axis2 -= np.dot(axis2, axis1) * axis1
            axis2 /= np.linalg.norm(axis2)
            axes = np.vstack([axis1, axis2])
        else:
            # 3+ likes: PCA plane
            center = likes.mean(0)
            likes_centered = likes - center
            u, s, vt = np.linalg.svd(likes_centered, full_matrices=False)
            axes = vt[:2]  # First 2 principal components
    
    return axes, center

def assign_spiral_coords(n_movies: int):
    """
    Assign 2D grid coordinates in outward spiral pattern
    Returns array of shape (n_movies, 2) with integer coordinates
    """
    coords = np.zeros((n_movies, 2), dtype=int)
    if n_movies == 0:
        return coords
    
    coords[0] = [0, 0]  # Start at origin
    
    if n_movies == 1:
        return coords
    
    # Spiral pattern: right, up, left, down, repeat with increasing distances
    dx, dy = [1, 0, -1, 0], [0, 1, 0, -1]
    direction = 0
    steps = 1
    x, y = 0, 0
    idx = 1
    
    while idx < n_movies:
        for _ in range(2):  # Each step count is used twice (except the first)
            for _ in range(steps):
                if idx >= n_movies:
                    break
                x += dx[direction]
                y += dy[direction]
                coords[idx] = [x, y]
                idx += 1
            direction = (direction + 1) % 4
            if idx >= n_movies:
                break
        steps += 1
    
    return coords

def compute_barycenter(liked_indices: List[int], coords: np.ndarray):
    """Compute barycenter of liked movies in 2D grid"""
    if not liked_indices:
        return [0.0, 0.0]
    
    liked_coords = coords[liked_indices]
    bary = liked_coords.mean(0)
    return bary.tolist()

# FastAPI app setup
app = FastAPI(title="Karl-Movie Vector Backend", version="1.0.0")

# Ajouter l'import du router admin
try:
    from .routers import admin
except ImportError:
    from app.routers import admin

# Ajouter le router admin
app.include_router(admin.router)

# CORS configuration
DEV_ORIGINS = [
    "http://localhost:5173",
    "http://127.0.0.1:5173",
    "http://localhost:8888",
    "https://*.bolt.run",
    "https://*.stackblitz.io",
]

PROD_ORIGINS = ["https://karl.movie"]

origins = DEV_ORIGINS if os.getenv("ENV") != "prod" else PROD_ORIGINS

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_methods=["POST", "GET"],
    allow_headers=["*"],
)

@app.on_event("startup")
async def startup_event():
    """Load data on startup"""
    global vectors, id_map, faiss_index, movie_metadata
    vectors, id_map, faiss_index, movie_metadata = load_data()
    
    # Vérifier et mettre à jour les vecteurs si nécessaire au démarrage
    if os.getenv('AUTO_UPDATE_VECTORS', 'false').lower() == 'true':
        # Lancer en arrière-plan sans attendre
        import asyncio
        try:
            from .services.vector_updater import VectorUpdater
        except ImportError:
            from app.services.vector_updater import VectorUpdater
        
        vector_updater = VectorUpdater()
        asyncio.create_task(vector_updater.update_vectors_if_needed())

@app.get("/health")
async def health_check():
    """Health check endpoint"""
    return {"status": "healthy", "vectors_loaded": vectors is not None}

async def get_movie_from_tmdb(tmdb_id: int):
    """Fetch a single movie from TMDB API if not in local index"""
    try:
        settings = get_settings()
        import requests
        
        url = f"https://api.themoviedb.org/3/movie/{tmdb_id}"
        params = {"api_key": settings.tmdb_api_key}
        
        response = requests.get(url, params=params, timeout=10)
        if response.status_code == 200:
            return response.json()
        else:
            logger.warning(f"TMDB API returned {response.status_code} for movie {tmdb_id}")
            return None
    except Exception as e:
        logger.error(f"Error fetching movie {tmdb_id} from TMDB: {e}")
        return None

@app.post("/explore", response_model=ExploreResponse)
async def explore(
    request: ExploreRequest,
    token: str = Depends(verify_token)
):
    """
    Main endpoint: find movies closest to user's preference subspace
    """
    start_time = time.time()
    
    try:
        # Ensure top_k doesn't exceed available movies
        total_movies = len(vectors) if vectors is not None else 0
        actual_top_k = min(request.top_k, total_movies)
        
        if actual_top_k <= 0:
            raise HTTPException(status_code=400, detail="No movies available")
        
        # Convert TMDB IDs to internal indices
        liked_indices = []
        disliked_indices = []
        missing_movies = []
        
        for tmdb_id in request.liked_ids:
            if str(tmdb_id) in id_map:
                liked_indices.append(id_map[str(tmdb_id)])
            else:
                logger.warning(f"TMDB ID {tmdb_id} not found in index")
                # Optionally fetch movie info for debugging
                movie_info = await get_movie_from_tmdb(tmdb_id)
                if movie_info:
                    missing_movies.append({
                        "id": tmdb_id,
                        "title": movie_info.get("title", "Unknown"),
                        "release_date": movie_info.get("release_date", "Unknown")
                    })
                    logger.info(f"Missing movie: {movie_info.get('title')} ({movie_info.get('release_date', 'Unknown')})")
        
        for tmdb_id in request.disliked_ids:
            if str(tmdb_id) in id_map:
                disliked_indices.append(id_map[str(tmdb_id)])
            else:
                logger.warning(f"TMDB ID {tmdb_id} not found in index")
        
        # Log missing movies for debugging
        if missing_movies:
            logger.info(f"Missing {len(missing_movies)} movies from index: {[m['title'] for m in missing_movies]}")
        
        # Get embedding vectors
        liked_vectors = vectors[liked_indices] if liked_indices else None
        disliked_vectors = vectors[disliked_indices] if disliked_indices else None
        
        # Build user subspace
        axes, center = build_plane(liked_vectors, disliked_vectors)
        
        # Project all vectors onto the 2D subspace
        projections = np.dot(vectors - center, axes.T)  # Shape: (N, 2)
        
        # Reconstruct vectors in original space
        reconstructed = np.dot(projections, axes) + center
        
        # Compute distances to subspace (residuals)
        residuals = np.linalg.norm(vectors - reconstructed, axis=1)
        
        # Get top-k closest movies - use proper bounds checking
        if actual_top_k >= len(residuals):
            # If we want all movies, just sort them
            top_k_indices = np.argsort(residuals)
        else:
            # Use argpartition for efficiency when we want a subset
            top_k_indices = np.argpartition(residuals, actual_top_k-1)[:actual_top_k]
            top_k_indices = top_k_indices[np.argsort(residuals[top_k_indices])]
        
        # Assign spiral coordinates
        spiral_coords = assign_spiral_coords(len(top_k_indices))
        
        # Compute barycenter of liked movies
        liked_positions = [i for i, idx in enumerate(top_k_indices) if idx in liked_indices]
        bary = compute_barycenter(liked_positions, spiral_coords)
        
        # Translate grid so barycenter is at origin
        spiral_coords = spiral_coords - np.array(bary)
        
        # Build response
        movies = []
        reverse_id_map = {v: k for k, v in id_map.items()}
        
        for i, movie_idx in enumerate(top_k_indices):
            tmdb_id = int(reverse_id_map[movie_idx])
            metadata = movie_metadata.get(str(tmdb_id), {})
            
            movie = MovieResult(
                id=tmdb_id,
                title=metadata.get("title", f"Movie {tmdb_id}"),
                year=metadata.get("year", 0),
                poster_path=metadata.get("poster_path"),
                genres=metadata.get("genres", []),
                coords=spiral_coords[i].tolist()
            )
            movies.append(movie)
        
        response = ExploreResponse(
            movies=movies,
            bary=[0.0, 0.0],  # Always [0,0] since we translated
            center=center.tolist()
        )
        
        elapsed = time.time() - start_time
        logger.info(f"Explore request processed in {elapsed:.3f}s - {len(request.liked_ids)} likes ({len(liked_indices)} found), {len(request.disliked_ids)} dislikes ({len(disliked_indices)} found), {len(movies)} results")
        
        return response
        
    except Exception as e:
        logger.error(f"Error processing explore request: {e}")
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)