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| import os | |
| os.environ['OPENBLAS_NUM_THREADS'] = '1' | |
| os.environ['MKL_NUM_THREADS'] = '1' | |
| os.environ['OMP_NUM_THREADS'] = '1' | |
| import streamlit as st | |
| import pandas as pd | |
| from sklearn.preprocessing import MinMaxScaler | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from sklearn.cluster import KMeans | |
| from sklearn.metrics import silhouette_score | |
| # Set the number of threads for OpenBLAS, MKL, and OpenMP | |
| # Load and preprocess the dataset | |
| def load_data(): | |
| data = pd.read_csv('C:/Users/19452/Downloads/tracks_features.csv/tracks_features.csv') | |
| # Normalize the features | |
| features_to_normalize = ['danceability', 'energy', 'valence', 'tempo', 'loudness'] | |
| scaler = MinMaxScaler() | |
| data[features_to_normalize] = scaler.fit_transform(data[features_to_normalize]) | |
| # Apply K-Means clustering | |
| kmeans = KMeans(n_clusters=10, n_init=10, random_state=42) | |
| data['cluster'] = kmeans.fit_predict(data[features_to_normalize]) | |
| # Calculate evaluation metrics | |
| inertia = kmeans.inertia_ | |
| silhouette = silhouette_score(data[features_to_normalize], data['cluster']) | |
| return data, kmeans, inertia, silhouette | |
| data, kmeans_model, inertia, silhouette = load_data() | |
| # Define mood criteria based on features | |
| def filter_songs_by_mood(data, selected_moods): | |
| mood_criteria = { | |
| "Happy/Upbeat": (data['valence'] > 0.75) & (data['energy'] > 0.75), | |
| "Calm/Relaxing": (data['energy'] < 0.25) & (data['tempo'] < 0.5), | |
| "Sad/Melancholic": (data['valence'] < 0.25) & (data['energy'] < 0.25), | |
| "Energetic/Exciting": (data['tempo'] > 0.75) & (data['energy'] > 0.75) | |
| } | |
| filtered_frames = [data[mood_criteria[mood]] for mood in selected_moods if mood in mood_criteria] | |
| filtered_data = pd.concat(filtered_frames).drop_duplicates() if filtered_frames else pd.DataFrame() | |
| return filtered_data.head(5) # Return only the top 5 results | |
| # Function to find similar songs | |
| def find_similar_songs(favorite_song_name, data, features, n_recommendations=5): | |
| favorite_song_index = data.index[data['name'].str.lower() == favorite_song_name.lower()].tolist() | |
| if not favorite_song_index: | |
| return None, "Song not found. Please check the spelling or try another song." | |
| favorite_song_index = favorite_song_index[0] # take the first match | |
| favorite_song_features = data.loc[favorite_song_index, features].values.reshape(1, -1) | |
| similarity = cosine_similarity(data[features], favorite_song_features).flatten() | |
| similar_indices = similarity.argsort()[-n_recommendations-1:-1][::-1] | |
| return data.loc[similar_indices].head(5), None # Return only the top 5 results | |
| # Function to recommend songs from the same cluster | |
| def recommend_from_cluster(favorite_song_name, data, kmeans, features, n_recommendations=5): | |
| song_data = data[data['name'].str.lower() == favorite_song_name.lower()] | |
| if song_data.empty: | |
| return None, "Song not found. Please check the spelling or try another song." | |
| song_cluster = song_data.iloc[0]['cluster'] | |
| cluster_songs = data[data['cluster'] == song_cluster] | |
| cluster_songs = cluster_songs[cluster_songs['name'].str.lower() != favorite_song_name.lower()] | |
| return cluster_songs.sample(min(len(cluster_songs), n_recommendations)), None | |
| # Streamlit app | |
| def main(): | |
| st.title('Music Recommender System') | |
| # Display the evaluation metrics | |
| st.write(f"Inertia: {inertia:.2f}") | |
| st.write(f"Silhouette Score: {silhouette:.2f}") | |
| # User selects moods | |
| moods = ["Happy/Upbeat", "Calm/Relaxing", "Sad/Melancholic", "Energetic/Exciting"] | |
| selected_moods = st.multiselect('Select your mood:', moods) | |
| # User inputs their favorite song | |
| favorite_song = st.text_input('Enter your favorite song:') | |
| if selected_moods: | |
| recommendations = filter_songs_by_mood(data, selected_moods) | |
| if not recommendations.empty: | |
| st.subheader('Recommended Songs based on your mood:') | |
| st.dataframe(recommendations[['name', 'artists']]) | |
| else: | |
| st.write("No songs match the selected moods. Try selecting different moods.") | |
| if favorite_song: | |
| cluster_songs, error_message = recommend_from_cluster(favorite_song, data, kmeans_model, features=['danceability', 'energy', 'valence', 'tempo', 'loudness']) | |
| if cluster_songs is not None: | |
| st.subheader('Songs from the same vibe cluster as your favorite:') | |
| st.dataframe(cluster_songs[['name', 'artists']]) | |
| else: | |
| st.error(error_message) | |
| if __name__ == "__main__": | |
| main() | |