Update app.py

app.py

@@ -6,7 +6,6 @@ import joblib
 import pandas as pd
 from sklearn.neighbors import NearestNeighbors
 from sklearn.preprocessing import StandardScaler
-from sklearn.metrics.pairwise import cosine_similarity
 
 # Load the LSTM model for emotion prediction
 emotion_model = load_model('lstm_model.h5')
@@ -20,7 +19,7 @@ tokenizer = joblib.load('tokenizer.pkl')
 # Load the dataset
 df = pd.read_csv('df1.csv')
 
-# Preprocess for content-based
+# Preprocess for KNN
 audio_feature_columns = ['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',
        'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',
        'duration_ms', 'time_signature']
@@ -29,28 +28,12 @@ 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_cb = pd.concat([mood_cats, audio_features_df_cb], axis=1)
-
-# Preprocessing for KNN
+# Normalize audio features 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_knn = pd.concat([mood_cats_df, audio_features_df_knn], axis=1)
 
-# Function for content-based recommendation
-def recommend_cont(song_index, num_recommendations=5):
-    song_similarity = similarity_matrix[song_index]
-    # Get indices and similarity scores of top similar songs
-    similar_songs = sorted(list(enumerate(song_similarity)), key=lambda x: x[1], reverse=True)[1:num_recommendations+1]
-    recommended_song_indices = [idx for idx, similarity in similar_songs]
-    recommended_songs = df.iloc[recommended_song_indices].copy()
-    recommended_songs['score'] = [similarity for idx, similarity in similar_songs]
-    return recommended_songs
-
 # Function for KNN-based recommendation
 def recommend_knn(query_index, n_recommendations=5):
     distances, indices = knn_model.kneighbors(combined_features_knn.iloc[query_index].values.reshape(1, -1), n_neighbors=n_recommendations)
@@ -59,22 +42,8 @@ def recommend_knn(query_index, n_recommendations=5):
     recommended_songs['score'] = 1 / (1 + distances.flatten())  # Inverse of distance
     return recommended_songs
 
-# Function for hybrid recommendation
-def hybrid_recommendation(song_index, top_n=10):
-    # Get recommendations from both models
-    content_based_recs = recommend_cont(song_index, top_n)
-    knn_based_recs = recommend_knn(song_index, top_n)
-
-    # Combine recommendations
-    combined_recs = pd.concat([content_based_recs, knn_based_recs])
-
-    # Group by song index (or identifier) and average scores
-    hybrid_recs = combined_recs.groupby(combined_recs.index).mean().sort_values(by='score', ascending=False).head(top_n)
-
-    return hybrid_recs
-
 # Set up the title of the app
-st.title('Hybrid Recommender App')
+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)
@@ -90,11 +59,11 @@ 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]])
 
-# Get hybrid recommendations
-hybrid_recs = hybrid_recommendation(song_index_to_recommend)
+# Get KNN-based recommendations
+knn_recs = recommend_knn(song_index_to_recommend)
 
 # Display the predicted emotion and recommendations
 st.write(f"Predicted Emotion: {emotion}")
-st.write("Hybrid Recommendations:")
-for index in hybrid_recs.index:
-    st.write(f"Song Index: {index}, Title: {df.iloc[index]['title']}, Artist: {df.iloc[index]['artist']}, Score: {hybrid_recs.loc[index, 'score']}")
+st.write("KNN Recommendations:")
+for index in knn_recs.index:
+    st.write(f"Song Index: {index}, Title: {df.iloc[index]['title']}, Artist: {df.iloc[index]['artist']}, Score: {knn_recs.loc[index, 'score']}")