Update app.py

app.py

@@ -27,14 +27,11 @@ df = df.drop(['Unnamed: 0', 'lyrics_filename', 'analysis_url', 'track_href', "ty
 similarity_matrix = np.load('similarity_matrix.npy')
 
 # Load the content-based recommendation function
-recommend_cont = joblib.load('recommendation_cont_function.joblib')
+recommend_cont_func = joblib.load('recommendation_cont_function.joblib')
 
 # Load the hybrid recommendation function
 hybrid_recommendation = joblib.load('hybrid_recommendation_function.joblib')
 
-# Load the content-based recommendation function
-recommend_cont = joblib.load('recommendation_cont_function.joblib')
-
 # Load the KNN model
 knn = joblib.load('knn_model.joblib')
 
@@ -77,7 +74,7 @@ padded_sequence = pad_sequences(sequence, maxlen=50)
 emotion = emotion_model.predict(padded_sequence).flatten()
 
 # Combine emotion and audio features for recommendation
-combined_features_hybrid = np.concatenate([emotion, query_data[audio_feature_columns].values])
+combined_features_hybrid = np.concatenate([emotion, query_data[audio_features.columns].values])
 
 # Generate recommendations using the hybrid model
 hybrid_recs = hybrid_recommendation(song_index=0)