Update app.py

app.py

@@ -35,8 +35,8 @@ def hybrid_recommendation(song_index):
 
     # Preprocess for KNN
     audio_features_knn = df[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',
-                                      'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',
-                                      'duration_ms', 'time_signature']].values.reshape(1, -1)
+                              'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',
+                              'duration_ms', 'time_signature']].values.reshape(1, -1)
     mood_cats = df[['mood_cats']]
     mood_cats_df = pd.DataFrame(mood_cats)
     audio_features_scaled_knn = scaler_knn.fit_transform(audio_features_knn)
@@ -44,14 +44,13 @@ def hybrid_recommendation(song_index):
     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]
 
     # Mapping emotion predictions to encoded categories
     emotion_mapping = {0: 'happy', 1: 'sad', 2: 'calm', 3: 'anger'}
-    encoded_emotion = np.argmax(predicted_emotion)
+    encoded_emotion = pd.Series(predicted_emotion).idxmax()
     emotion_category = emotion_mapping[encoded_emotion]
 
     # Compute cosine similarity for content-based recommendation