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54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
# Import the extra necessary libraries for this example
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# These libraries are not included in the default packages
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import pandas as pd
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from sklearn.cluster import KMeans
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import spotipy
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from spotipy.oauth2 import SpotifyOAuth
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# Set up Spotify credentials
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sp = spotipy.Spotify(auth_manager=SpotifyOAuth(
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client_id="YOUR_APP_CLIENT_ID",
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client_secret="YOUR_APP_CLIENT_SECRET",
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redirect_uri="YOUR_APP_REDIRECT_URI",
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scope="playlist-modify-private,user-library-read"))
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# get the user's username
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username = sp.me()['id']
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# Get the user's liked tracks
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saved_tracks = sp.current_user_saved_tracks(limit=50)['items']
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# Extract audio features for liked tracks
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track_ids = []
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audio_features = []
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for track in saved_tracks:
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track_ids.append(track['track']['id'])
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audio_features.append(sp.audio_features(track['track']['id'])[0])
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# Create a DataFrame from the audio features
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df = pd.DataFrame(audio_features)
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# Perform clustering on some audio features
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features = df[['danceability', 'energy', 'valence', 'acousticness']]
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kmeans = KMeans(n_clusters=5, random_state=42, n_init=10)
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df['cluster'] = kmeans.fit_predict(features)
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# Select a representative track from each cluster
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representative_tracks = []
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for cluster in range(5):
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cluster_tracks = df[df['cluster'] == cluster]
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representative_track = cluster_tracks.iloc[0]['id']
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representative_tracks.append(representative_track)
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# Create a playlist with the representative tracks
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playlist = sp.user_playlist_create(
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user=username, name='Personalized Playlist', public=False)
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sp.playlist_add_items(
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playlist_id=playlist['id'], items=representative_tracks)
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# Print the URL of the created playlist
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print("Playlist created successfully. You can access it at:",
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playlist['external_urls']['spotify'])
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