Update app.py

app.py

@@ -6,67 +6,57 @@ import pandas as pd
 import numpy as np
 from sklearn.metrics.pairwise import cosine_similarity
 
-# Check if scikit-learn is installed
-try:
-    import sklearn
-    st.write("scikit-learn is installed.")
-except ImportError:
-    st.error("scikit-learn is not installed.")
-
-# Load your emotion prediction model
+# 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}")
+# 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')  # Correct the path
+tokenizer = joblib.load('tokenizer.pkl')
 
 # Load the dataset and preprocess
-df = pd.read_csv('df1.csv')  
+df = pd.read_csv('df1.csv')
 df = df.drop(['Unnamed: 0', 'lyrics_filename', 'analysis_url', 'track_href', "type", "id", "uri", 'mood'], axis=1)
 
-# Set up the title of the app
-st.title('Emotion and Audio Feature-based Song Recommendation System')
+# Load the similarity matrix
+similarity_matrix = np.load('similarity_matrix.npy')
+
+# Load the content-based recommendation function
+recommend_cont = joblib.load('recommendation_function.joblib')
 
-# Input field for lyrics
-st.header('Enter Song Lyrics')
-lyrics = st.text_area("Input the lyrics of the song here:")
+# Load the hybrid recommendation function
+hybrid_recommendation = joblib.load('hybrid_recommendation_function.joblib')
 
-# Input fields for audio features
-st.header('Enter Audio Features')
-audio_features = []
+# Load the content-based recommendation function
+recommend_cont = joblib.load('recommendation_cont_function.joblib')
 
-# Display only relevant columns for audio features
-audio_feature_columns = ['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness',
-                         'instrumentalness', 'liveness', 'valence', 'tempo']
+# Load the KNN model
+knn = joblib.load('knn_model.joblib')
 
-for feature_name in audio_feature_columns:
-    feature = st.number_input(f"Enter value for {feature_name}:", step=0.01)
-    audio_features.append(feature)
+# Load the KNN recommendation function
+recommend_knn = joblib.load('recommendation_knn_function.joblib')
+
+# Set up the title of the app
+st.title('Emotion and Audio Feature-based Song Recommendation System')
 
-# Predict and Recommend button
-if st.button('Predict Emotion and Recommend Songs'):
-    if lyrics and all(audio_features):
-        # 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()
+# Get data from index 0
+query_data = df.iloc[0]
 
-        # Combine emotion and audio features for recommendation
-        combined_features = np.concatenate([emotion, audio_features])
+# Process the lyrics
+sequence = tokenizer.texts_to_sequences([query_data['lyrics']])
+padded_sequence = pad_sequences(sequence, maxlen=50)  # Adjust the maxlen to match the expected input size
+emotion = emotion_model.predict(padded_sequence).flatten()
 
-        # Generate recommendations using the KNN model
-        knn_distances, knn_indices = recommender_model.kneighbors([combined_features], n_neighbors=5)
-        knn_recommended_songs = df.iloc[knn_indices.flatten()]
+# Combine emotion and audio features for recommendation
+combined_features = np.concatenate([emotion, query_data[audio_feature_columns].values])
 
-        st.write("Emotion Detected:", emotion[0])
-        st.header('Recommended Songs (KNN)')
-        for _, song in knn_recommended_songs.iterrows():
-            st.write(song)
+# Generate recommendations using the hybrid model
+hybrid_recs = hybrid_recommendation(song_index=0)
 
-    else:
-        st.error("Please fill in all the fields.")
+st.write("Emotion Detected:", emotion[0])
+st.header('Recommended Songs (Hybrid)')
+st.write(hybrid_recs)