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import gradio as gr
import pandas as pd
import pickle
import numpy as np
from collections import defaultdict
with open('best_svd.pkl', 'rb') as f:
best_svd = pickle.load(f)
with open('model_metadata.pkl', 'rb') as f:
metadata = pickle.load(f)
movies = metadata['movies_df']
ratings_filtered = metadata['ratings_filtered_df']
popular_movies = metadata['popular_movies']
def recommend_movies_gradio(user_id, n_recommendations):
try:
user_id = int(user_id)
n_recommendations = int(n_recommendations)
except:
return "Error: Please enter valid numbers"
if user_id not in ratings_filtered['userId'].values:
popular_recs = popular_movies.head(n_recommendations).merge(
movies[['movieId', 'title_clean', 'year']],
on='movieId'
)
output = f"User {user_id} not found. Showing popular movies:\n\n"
for i, row in enumerate(popular_recs.itertuples(), 1):
output += f"{i}. {row.title_clean} ({row.year})\n"
return output
user_ratings = ratings_filtered[ratings_filtered['userId'] == user_id]['movieId'].values
all_movies = ratings_filtered['movieId'].unique()
unseen_movies = [m for m in all_movies if m not in user_ratings]
predictions = []
for movie_id in unseen_movies:
pred = best_svd.predict(user_id, movie_id)
predictions.append({
'movieId': movie_id,
'score': pred.est
})
predictions_df = pd.DataFrame(predictions)
top_n = predictions_df.nlargest(n_recommendations, 'score')
top_n = top_n.merge(movies[['movieId', 'title_clean', 'year']], on='movieId')
output = f"Recommendations for User {user_id}:\n\n"
for i, row in enumerate(top_n.itertuples(), 1):
output += f"{i}. {row.title_clean} ({row.year}) - Rating: {row.score:.2f}\n"
return output
iface = gr.Interface(
fn=recommend_movies_gradio,
inputs=[
gr.Textbox(label="User ID", placeholder="Enter user ID (e.g., 1, 100, 500)"),
gr.Slider(minimum=5, maximum=50, value=10, step=5, label="Number of Recommendations")
],
outputs=gr.Textbox(label="Recommendations", lines=20),
title="🎬 Movie Recommendation System - MovieLens",
description="""
Get personalized movie recommendations using SVD (Singular Value Decomposition).
**Model Performance:**
- **RMSE**: 0.9338 (best prediction accuracy)
- **Precision@10**: 0.7968 (79.68% relevant recommendations)
- **NDCG@10**: 0.8514 (85.14% ranking quality)
- **Recall@10**: 0.2245 (22.46% of ALL relevant items in just 10 recommendations)
""",
examples=[
["1", 10],
["100", 15],
["500", 20]
]
)
if __name__ == "__main__":
iface.launch()
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