Update app.py

app.py

@@ -6,85 +6,72 @@ import pandas as pd
 import numpy as np
 from sklearn.preprocessing import StandardScaler
 
-def load_models():
-    # Load the tokenizer (ensure it's the one used during training)
-    tokenizer = joblib.load('tokenizer.pkl')
+# Load the dataset
+df = pd.read_csv('df1.csv')
+df = df.drop(['Unnamed: 0', 'lyrics_filename', 'analysis_url', 'track_href', "type", "id", "uri"], axis=1)
 
-    # Load the emotion prediction model
-    emotion_model = load_model('lstm_model.h5')
+# Load the tokenizer (ensure it's the one used during training)
+tokenizer = joblib.load('tokenizer.pkl')
 
-    # Load the dataset
-    df = pd.read_csv('df1.csv')
-    df = df.drop(['Unnamed: 0', 'lyrics_filename', 'analysis_url', 'track_href', "type", "id", "uri"], axis=1)
+# Load the emotion prediction model
+emotion_model = load_model('lstm_model.h5')
 
-    # Preprocess for content-based
-    audio_feature_columns = ['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',
+# Preprocess for content-based
+audio_feature_columns = ['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',
            'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',
            'duration_ms', 'time_signature']
 
-    audio_features = df[audio_feature_columns]
-    mood_cats = df[['mood_cats']]
-    mood_cats_df = pd.DataFrame(mood_cats)
+audio_features = df[audio_feature_columns]
+mood_cats = df[['mood_cats']]
+mood_cats_df = pd.DataFrame(mood_cats)
 
-    # Normalize audio features for content-based
-    scaler_cb = StandardScaler()
-    audio_features_scaled_cb = scaler_cb.fit_transform(audio_features)
-    audio_features_df_cb = pd.DataFrame(audio_features_scaled_cb, columns=audio_feature_columns)
-    combined_features = pd.concat([mood_cats, audio_features_df_cb], axis=1)
+# Normalize audio features for content-based
+scaler_cb = StandardScaler()
+audio_features_scaled_cb = scaler_cb.fit_transform(audio_features)
+audio_features_df_cb = pd.DataFrame(audio_features_scaled_cb, columns=audio_feature_columns)
+combined_features = pd.concat([mood_cats, audio_features_df_cb], axis=1)
 
-    # Load the similarity matrix for content-based
-    similarity_matrix = np.load('similarity_matrix.npy')
+# Load the similarity matrix for content-based
+similarity_matrix = np.load('similarity_matrix.npy')
 
-    # Load the content-based recommendation function
-    recommend_cont = joblib.load('recommendation_cont_function.joblib')
+# Load the content-based recommendation function
+recommend_cont = joblib.load('recommendation_cont_function.joblib')
 
-    # Preprocessing for KNN
-    scaler_knn = StandardScaler()
-    audio_features_scaled_knn = scaler_knn.fit_transform(audio_features)
-    audio_features_df_knn = pd.DataFrame(audio_features_scaled_knn, columns=audio_feature_columns)
-    combined_features = pd.concat([mood_cats_df, audio_features_df_knn], axis=1)
+# Preprocessing for KNN
+scaler_knn = StandardScaler()
+audio_features_scaled_knn = scaler_knn.fit_transform(audio_features)
+audio_features_df_knn = pd.DataFrame(audio_features_scaled_knn, columns=audio_feature_columns)
+combined_features = pd.concat([mood_cats_df, audio_features_df_knn], axis=1)
 
-    # Load the KNN model
-    knn = joblib.load('knn_model.joblib')
+# Load the KNN model
+knn = joblib.load('knn_model.joblib')
 
-    # Load the KNN recommendation function
-    recommend_knn = joblib.load('recommendation_knn_function.joblib')
+# Load the KNN recommendation function
+recommend_knn = joblib.load('recommendation_knn_function.joblib')
 
-    # Load the hybrid recommendation function
-    hybrid_recommendation = joblib.load('hybrid_recommendation_function.joblib')
+# Load the hybrid recommendation function
+hybrid_recommendation = joblib.load('hybrid_recommendation_function.joblib')
 
-    return tokenizer, emotion_model, df, audio_feature_columns, combined_features_cb, similarity_matrix, recommend_cont, mood_cats_df, audio_features_df_knn, combined_features_knn, knn, recommend_knn, hybrid_recommendation
+# Call the function from the module
+hybrid_recs = hybrid_recommendation(song_index=0)
 
-def main():
-    # Set up the title of the app
-    st.title('Emotion and Audio Feature-based Song Recommendation System')
+# Set up the title of the app
+st.title('Emotion and Audio Feature-based Song Recommendation System')
 
-    # Load models and data
-    tokenizer, emotion_model, df, audio_feature_columns, combined_features_cb, similarity_matrix, recommend_cont, mood_cats_df, audio_features_df_knn, combined_features_knn, knn, recommend_knn, hybrid_recommendation = load_models()
+# Get data from index 0
+query_data = df.iloc[0]
 
-    # Get data from index 0
-    query_data = df.iloc[0]
+# Process the lyrics
+sequence = tokenizer.texts_to_sequences([query_data['lyrics']])
+padded_sequence = pad_sequences(sequence, maxlen=50)
+emotion = emotion_model.predict(padded_sequence).flatten()
 
-    # Process the lyrics
-    sequence = tokenizer.texts_to_sequences([query_data['lyrics']])
-    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_features.columns].values])
 
-    # Mapping emotion predictions to encoded categories
-    emotion_mapping = {0: 'happy', 1: 'sad', 2: 'calm', 3: 'anger'}
-    encoded_emotion = np.argmax(emotion)  
-    emotion_category = emotion_mapping[encoded_emotion]
-
-    # Combine emotion and audio features for recommendation
-    combined_features_hybrid = np.concatenate([encoded_emotion, query_data[audio_feature_columns].values])
-
-    # Generate recommendations using the hybrid model
-    hybrid_recs = hybrid_recommendation(song_index=0)
-
-    st.write("Emotion Detected:", emotion[0])
-    st.header('Recommended Songs (Hybrid)')
-    st.write(hybrid_recs)
-
-if __name__ == '__main__':
-    main()
+# Generate recommendations using the hybrid model
+hybrid_recs = hybrid_recommendation(song_index=0)
 
+st.write("Emotion Detected:", emotion[0])
+st.header('Recommended Songs (Hybrid)')
+st.write(hybrid_recs)