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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
@st.cache(allow_output_mutation=True)
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()
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