app.py

import pandas as pd
import numpy as np
from surprise import SVD, Dataset, Reader, accuracy
from surprise.model_selection import train_test_split, cross_validate
from collections import defaultdict

class MovieRecommender:
    def __init__(self, ratings_path, movies_path):
        # Load data
        self.ratings = pd.read_csv(ratings_path)
        self.movies = pd.read_csv(movies_path)
        
        # Build Surprise dataset
        reader = Reader(rating_scale=(0.5, 5.0))
        self.data = Dataset.load_from_df(
            self.ratings[['userId', 'movieId', 'rating']], 
            reader
        )
        
        # Train model
        self.trainset = self.data.build_full_trainset()
        self.algo = SVD(n_factors=100, n_epochs=20, lr_all=0.005, reg_all=0.02)
        self.algo.fit(self.trainset)
        
    def recommend_movies(self, user_id, N):
        # Get all movie IDs
        all_movie_ids = self.movies['movieId'].unique()
        
        # Get movies user has already rated
        rated_movies = self.ratings[self.ratings['userId'] == user_id]['movieId'].values
        
        # Get unrated movies
        unrated_movies = [m for m in all_movie_ids if m not in rated_movies]
        
        # Predict ratings
        predictions = []
        for movie_id in unrated_movies:
            pred = self.algo.predict(user_id, movie_id)
            predictions.append((movie_id, pred.est))
        
        # Sort by predicted rating
        predictions.sort(key=lambda x: x[1], reverse=True)
        
        # Get top N
        top_n = predictions[:N]
        
        # Merge with movie titles
        results = []
        for movie_id, score in top_n:
            title = self.movies[self.movies['movieId'] == movie_id]['title'].values[0]
            results.append({
                'movieId': movie_id,
                'title': title,
                'predicted_rating': round(score, 2)
            })
        
        return results
    
    def evaluate(self):
        # Cross-validation
        results = cross_validate(
            self.algo, 
            self.data, 
            measures=['RMSE', 'MAE'],
            cv=5,
            verbose=False
        )
        
        # Custom metrics: Precision@K, Recall@K, NDCG@K
        trainset, testset = train_test_split(self.data, test_size=0.2)
        self.algo.fit(trainset)
        predictions = self.algo.test(testset)
        
        # Calculate Precision@K and Recall@K
        k = 10
        threshold = 4.0
        
        user_est_true = defaultdict(list)
        for uid, _, true_r, est, _ in predictions:
            user_est_true[uid].append((est, true_r))
        
        precisions = []
        recalls = []
        
        for uid, user_ratings in user_est_true.items():
            user_ratings.sort(key=lambda x: x[0], reverse=True)
            top_k = user_ratings[:k]
            
            n_rel = sum(1 for (_, true_r) in user_ratings if true_r >= threshold)
            n_rec_k = sum(1 for (est, _) in top_k if est >= threshold)
            n_rel_and_rec_k = sum(1 for (est, true_r) in top_k 
                                  if true_r >= threshold and est >= threshold)
            
            precisions.append(n_rel_and_rec_k / n_rec_k if n_rec_k > 0 else 0)
            recalls.append(n_rel_and_rec_k / n_rel if n_rel > 0 else 0)
        
        return {
            'rmse': np.mean(results['test_rmse']),
            'mae': np.mean(results['test_mae']),
            f'precision@{k}': np.mean(precisions),
            f'recall@{k}': np.mean(recalls)
        }


import gradio as gr

# Initialize recommender
recommender = MovieRecommender('ratings.csv', 'movies.csv')

def recommend_interface(user_id, n_recommendations):
    try:
        user_id = int(user_id)
        n_recommendations = int(n_recommendations)
        
        recommendations = recommender.recommend_movies(user_id, n_recommendations)
        
        output = []
        for i, rec in enumerate(recommendations, 1):
            output.append(f"{i}. {rec['title']} (Predicted: {rec['predicted_rating']})")
        
        return "\n".join(output)
    except Exception as e:
        return f"Error: {str(e)}"

# Create interface
demo = gr.Interface(
    fn=recommend_interface,
    inputs=[
        gr.Textbox(label="User ID", placeholder="Enter user ID"),
        gr.Slider(minimum=1, maximum=20, value=10, step=1, label="Number of Recommendations")
    ],
    outputs=gr.Textbox(label="Recommendations", lines=15),
    title="MovieLens Recommendation System",
    description="Enter a user ID to get personalized movie recommendations"
)

demo.launch()