Update app.py

app.py

@@ -13,7 +13,6 @@ with open(os.path.join(MODEL_DIR, "recommender_svd_mf.pkl"), "rb") as f:
 
 # Load movie metadata
 movies_df = pd.read_csv(MOVIE_DATA_PATH, low_memory=False)
-movies_df["movieId"] = movies_df["movieId"].astype(str)
 
 def recommend(user_id, top_k=5):
     """Generate top-k recommendations using SVD model."""
@@ -33,13 +32,9 @@ def recommend(user_id, top_k=5):
     # Build output
     results = []
     for mid, score in top_movies:
-        movie_rows = movies_df[movies_df["movieId"] == str(mid)]
-        if not movie_rows.empty:
-            row = movie_rows.iloc[0]
-            explanation = f"Because you liked movies with {row.get('actors', 'similar style')}."
-            results.append((row.get("title", "Unknown"), row.get("poster_url", None), explanation))
-        else:
-            results.append(("Unknown", None, "No explanation available."))
+        row = movies_df[movies_df["movieId"] == mid].iloc[0]
+        explanation = f"Because you liked movies with {row.get('actors', 'similar style')}."
+        results.append((row["title"], row.get("poster_url", None), explanation))
     
     return results