import gradio as gr
import pickle
import pandas as pd
import numpy as np
from surprise import SVD
import warnings
warnings.filterwarnings('ignore')

# Load models and data
print("Loading models...")
with open('svd_model.pkl', 'rb') as f:
    svd_model = pickle.load(f)

with open('movies.pkl', 'rb') as f:
    movies = pickle.load(f)

with open('ratings.pkl', 'rb') as f:
    ratings = pickle.load(f)

print("Models loaded successfully!")

def recommend_movies(user_id, num_recommendations, min_rating):
    """
    Generate movie recommendations for a user
    """
    try:
        user_id = int(user_id)
        num_recommendations = int(num_recommendations)
        min_rating = float(min_rating)
        
        # Check if user exists
        if user_id not in ratings['userId'].values:
            return f"⚠️ User ID {user_id} not found in database. Please try a different user ID (1-{ratings['userId'].max()})."
        
        # Get all movies
        all_movie_ids = movies['movieId'].unique()
        
        # Get movies the user has already rated
        rated_movies = ratings[ratings['userId'] == user_id]['movieId'].values
        
        # Get movies the user hasn't rated
        movies_to_predict = [mid for mid in all_movie_ids if mid not in rated_movies]
        
        # Predict ratings
        predictions = []
        for movie_id in movies_to_predict:
            pred = svd_model.predict(user_id, movie_id)
            if pred.est >= min_rating:
                predictions.append({
                    'movieId': movie_id,
                    'predicted_rating': pred.est
                })
        
        if not predictions:
            return f"No movies found with predicted rating >= {min_rating}. Try lowering the minimum rating."
        
        # Sort and get top N
        predictions_df = pd.DataFrame(predictions)
        predictions_df = predictions_df.sort_values('predicted_rating', ascending=False)
        top_recommendations = predictions_df.head(num_recommendations)
        
        # Merge with movie details
        recommendations = top_recommendations.merge(movies, on='movieId')
        recommendations['predicted_rating'] = recommendations['predicted_rating'].round(2)
        
        # Format output
        output = f"🎬 Top {len(recommendations)} Movie Recommendations for User {user_id}\n\n"
        
        for idx, row in recommendations.iterrows():
            output += f"{idx + 1}. **{row['title']}**\n"
            output += f"   ⭐ Predicted Rating: {row['predicted_rating']}/5.0\n"
            output += f"   🎭 Genres: {row['genres']}\n\n"
        
        return output
    
    except Exception as e:
        return f"❌ Error: {str(e)}"

def get_user_history(user_id):
    """
    Get user's rating history
    """
    try:
        user_id = int(user_id)
        
        if user_id not in ratings['userId'].values:
            return f"⚠️ User ID {user_id} not found."
        
        user_ratings = ratings[ratings['userId'] == user_id].merge(movies, on='movieId')
        user_ratings = user_ratings.sort_values('rating', ascending=False).head(10)
        
        output = f"📊 User {user_id}'s Top Rated Movies:\n\n"
        
        for idx, row in user_ratings.iterrows():
            output += f"• **{row['title']}** - ⭐ {row['rating']}/5.0\n"
            output += f"  Genres: {row['genres']}\n\n"
        
        return output
    
    except Exception as e:
        return f"❌ Error: {str(e)}"

# Create Gradio interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🎬 MovieLens Recommendation System
        ### Powered by SVD Matrix Factorization
        
        Get personalized movie recommendations based on collaborative filtering!
        """
    )
    
    with gr.Tab("🎯 Get Recommendations"):
        with gr.Row():
            with gr.Column():
                user_id_input = gr.Number(
                    label="User ID",
                    value=1,
                    info=f"Enter a user ID (1 to {ratings['userId'].max()})"
                )
                num_rec_input = gr.Slider(
                    minimum=5,
                    maximum=20,
                    value=10,
                    step=1,
                    label="Number of Recommendations"
                )
                min_rating_input = gr.Slider(
                    minimum=1.0,
                    maximum=5.0,
                    value=3.5,
                    step=0.5,
                    label="Minimum Predicted Rating"
                )
                recommend_btn = gr.Button("🎬 Get Recommendations", variant="primary")
            
            with gr.Column():
                recommendations_output = gr.Markdown(label="Recommendations")
        
        recommend_btn.click(
            fn=recommend_movies,
            inputs=[user_id_input, num_rec_input, min_rating_input],
            outputs=recommendations_output
        )
    
    with gr.Tab("📊 User History"):
        with gr.Row():
            with gr.Column():
                history_user_id = gr.Number(
                    label="User ID",
                    value=1,
                    info="Enter a user ID to see their rating history"
                )
                history_btn = gr.Button("📊 View History", variant="primary")
            
            with gr.Column():
                history_output = gr.Markdown(label="User History")
        
        history_btn.click(
            fn=get_user_history,
            inputs=history_user_id,
            outputs=history_output
        )
    
    gr.Markdown(
        """
        ---
        ### 📈 Model Information
        - **Algorithm**: SVD (Singular Value Decomposition)
        - **Dataset**: MovieLens Small (100K ratings)
        - **Evaluation Metrics**: RMSE, Precision@K, Recall@K, NDCG@K
        - **Best Performance**: Lowest RMSE and Highest NDCG among tested models
        """
    )

if __name__ == "__main__":
    demo.launch()