Update app.py

app.py

@@ -34,13 +34,17 @@ def hybrid_recommendation(song_index):
     predicted_emotion = emotion_model.predict(padded_sequence).flatten()
 
     # Preprocess for KNN
-    audio_features_knn = query_data[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',
+    audio_features_knn = df[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',
                                       'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',
                                       'duration_ms', 'time_signature']].values.reshape(1, -1)
-    mood_cats = query_data[['mood_cats']]
+    mood_cats = df[['mood_cats']]
     mood_cats_df = pd.DataFrame(mood_cats)
     audio_features_scaled_knn = scaler_knn.fit_transform(audio_features_knn)
-    combined_features = pd.concat([mood_cats_df, pd.DataFrame(audio_features_scaled_knn, columns=audio_features_knn.columns)], axis=1)
+
+    audio_features_df = pd.DataFrame(audio_features_scaled_knn, columns=audio_features_knn.columns)
+    # Combine mood and audio features
+    combined_features = pd.concat([mood_cats_df, audio_features_df], axis=1)
+    #combined_features = pd.concat([mood_cats_df, pd.DataFrame(audio_features_scaled_knn, columns=audio_features_knn.columns)], axis=1)
     
     # Predict using the KNN model
     knn_recommendations = knn_model.kneighbors(combined_features, n_neighbors=5, return_distance=False)[0]