Update app.py

app.py

@@ -4,18 +4,11 @@ from tensorflow.keras.preprocessing.sequence import pad_sequences
 import joblib
 import pandas as pd
 import numpy as np
-from sklearn.metrics.pairwise import cosine_similarity
 from sklearn.preprocessing import StandardScaler
 
 # Load the emotion prediction model
 emotion_model = load_model('lstm_model.h5')
 
-# Load the KNN recommender model
-# try:
-#    recommender_model = joblib.load('knn_model.pkl')
-# except Exception as e:
-#    st.error(f"Error loading KNN model: {e}")
-
 # Load the tokenizer (ensure it's the one used during training)
 tokenizer = joblib.load('tokenizer.pkl')
 
@@ -23,21 +16,15 @@ tokenizer = joblib.load('tokenizer.pkl')
 df = pd.read_csv('df1.csv')
 df = df.drop(['Unnamed: 0', 'lyrics_filename', 'analysis_url', 'track_href', "type", "id", "uri"], axis=1)
 
-# Load the similarity matrix
-similarity_matrix = np.load('similarity_matrix.npy')
+# Load the content-based recommendation module
+recommend_cont_module = joblib.load('recommendation_cont_function.joblib')
 
-# Load the content-based recommendation function
-recommend_cont_func = joblib.load('recommendation_cont_function.joblib')
+# Call the function from the module
+hybrid_recs = recommend_cont_module.recommend_cont(song_index=0)
 
 # Load the hybrid recommendation function
 hybrid_recommendation = joblib.load('hybrid_recommendation_function.joblib')
 
-# Load the KNN model
-knn = joblib.load('knn_model.joblib')
-
-# Load the KNN recommendation function
-recommend_knn = joblib.load('recommendation_knn_function.joblib')
-
 # Preprocess for content-based
 audio_features = df[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',
                      'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',
@@ -50,18 +37,6 @@ audio_features_df = pd.DataFrame(audio_features_scaled, columns=audio_features.c
 mood_cats_df = pd.DataFrame(mood_cats)
 combined_features_content = pd.concat([mood_cats_df, audio_features_df], axis=1)
 
-# Preprocess for KNN
-audio_features_knn = df[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',
-                          'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',
-                          'duration_ms', 'time_signature']]
-mood_cats_knn = df[['mood_cats']]
-
-scaler_knn = StandardScaler()
-audio_features_scaled_knn = scaler_knn.fit_transform(audio_features_knn)
-audio_features_df_knn = pd.DataFrame(audio_features_scaled_knn, columns=audio_features_knn.columns)
-mood_cats_df_knn = pd.DataFrame(mood_cats_knn)
-combined_features_knn = pd.concat([mood_cats_df_knn, audio_features_df_knn], axis=1)
-
 # Set up the title of the app
 st.title('Emotion and Audio Feature-based Song Recommendation System')