Upload app.py

app.py

@@ -19,23 +19,22 @@ def recommend_movies_gradio(user_id, n_recommendations):
         user_id = int(user_id)
         n_recommendations = int(n_recommendations)
     except:
-        return "Error: Please enter valid numbers for User ID and N"
+        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', 'genres']],
+            movies[['movieId', 'title_clean', 'year']],
             on='movieId'
         )
-        result = popular_recs[['title_clean', 'year', 'genres', 'weighted_rating']].rename(
-            columns={'title_clean': 'Title', 'year': 'Year', 'genres': 'Genres', 'weighted_rating': 'Score'}
-        )
-        return f"User {user_id} not found. Showing popular movies:\n\n" + result.to_string(index=False)
+        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]
 
-    # Use SVD model (best performing model - RMSE: 0.9338, Precision@10: 0.7968)
     predictions = []
     for movie_id in unseen_movies:
         pred = best_svd.predict(user_id, movie_id)
@@ -46,13 +45,13 @@ def recommend_movies_gradio(user_id, n_recommendations):
 
     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')
 
-    top_n = top_n.merge(movies[['movieId', 'title_clean', 'year', 'genres']], on='movieId')
-    result = top_n[['title_clean', 'year', 'genres', 'score']].rename(
-        columns={'title_clean': 'Title', 'year': 'Year', 'genres': 'Genres', 'score': 'Predicted Rating'}
-    )
+    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 result.to_string(index=False)
+    return output
 
 iface = gr.Interface(
     fn=recommend_movies_gradio,