Update app.py

app.py

@@ -1,80 +1,81 @@
-
-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)
-    """,
-    examples=[
-        ["1", 10],
-        ["100", 15],
-        ["500", 20]
-    ]
-)
-
-if __name__ == "__main__":
-    iface.launch()
+
+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 is solid performance)
+    """,
+    examples=[
+        ["1", 10],
+        ["100", 15],
+        ["500", 20]
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()