Update app.py

app.py

@@ -7,15 +7,9 @@ import pandas as pd
 from sklearn.neighbors import NearestNeighbors
 from sklearn.preprocessing import StandardScaler
 
-# Load the LSTM model for emotion prediction
-emotion_model = load_model('lstm_model.h5')
-
 # Load the KNN model
 knn_model = joblib.load('knn_model.joblib')
 
-# Load the tokenizer
-tokenizer = joblib.load('tokenizer.pkl')
-
 # Load the dataset
 df = pd.read_csv('df1.csv')
 
@@ -48,19 +42,8 @@ st.title('KNN Recommender App')
 # Get song index from user input
 song_index_to_recommend = st.number_input('Enter song index:', min_value=0, max_value=len(df)-1, value=0)
 
-# Get lyrics for emotion prediction
-lyrics = df.iloc[song_index_to_recommend]['lyrics']
-
-# Process the lyrics
-sequence = tokenizer.texts_to_sequences([lyrics])
-padded_sequence = pad_sequences(sequence, maxlen=50)  # Adjust the maxlen to match the expected input size
-emotion = emotion_model.predict(padded_sequence).flatten()
-
 # Combine emotion and audio features for recommendation
-combined_features = np.concatenate([emotion, audio_features_scaled_knn[song_index_to_recommend]])
-
-# Display the predicted emotion and recommendations
-st.write(f"Predicted Emotion: {emotion}")
+#combined_features = np.concatenate([emotion, audio_features_scaled_knn[song_index_to_recommend]])
 
 if not knn_recs.empty:
     st.write("KNN Recommendations:")