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Update app.py
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app.py
CHANGED
@@ -1,158 +1,190 @@
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import
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import pickle
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import spotipy
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import spotipy.util as sp_util
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import requests
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dir_path = os.path.dirname(os.path.realpath(__file__))
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#
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#
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print('
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sp =
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uris.append(uri)
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return uris
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def wall_of_checkboxes(labels, max_width=10):
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n_labels = len(labels)
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n_rows = int(np.ceil(n_labels/max_width))
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checkboxes = []
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for i in range(n_rows):
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columns = st.columns(np.ones(max_width))
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row_length = n_labels % max_width if i == n_rows - 1 else max_width
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for j in range(row_length):
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with columns[j]:
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checkboxes.append(st.empty())
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return checkboxes
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def aggregate_genres(genres, legit_genres, verbose=False):
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genres_output = dict()
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legit_genres_formatted = [lg.replace('-', '').replace(' ', '') for lg in legit_genres]
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for glabel in genres.keys():
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if verbose: print('\n', glabel)
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glabel_formatted = glabel.replace(' ', '').replace('-', '')
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best_match = None
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best_match_score = 0
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for legit_glabel, legit_glabel_formatted in zip(legit_genres, legit_genres_formatted):
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if 'jazz' in glabel_formatted:
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best_match = 'jazz'
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if verbose: print('\t', 'pop')
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break
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if 'ukpop' in glabel_formatted:
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best_match = 'pop'
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if verbose: print('\t', 'pop')
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break
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if legit_glabel_formatted == glabel_formatted:
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if verbose: print('\t', legit_glabel_formatted)
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best_match = legit_glabel
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break
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elif glabel_formatted in legit_glabel_formatted:
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if verbose: print('\t', legit_glabel_formatted)
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if len(glabel_formatted) > best_match_score:
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best_match = legit_glabel
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best_match_score = len(glabel_formatted)
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elif legit_glabel_formatted in glabel_formatted:
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if verbose: print('\t', legit_glabel_formatted)
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if len(legit_glabel_formatted) > best_match_score:
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best_match = legit_glabel
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best_match_score = len(legit_glabel_formatted)
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if best_match is not None:
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if verbose: print('\t', '-->', best_match)
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if best_match in genres_output.keys():
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genres_output[best_match] += genres[glabel]
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else:
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from PIL import Image
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from utils import *
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from app_utils import *
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debug = False
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dir_path = os.path.dirname(os.path.realpath(__file__))
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# os.environ['FLASK_APP'] = dir_path + 'app2.py'
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# if debug: os.environ['FLASK_ENV'] = 'development'
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#
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#
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# app = Flask(__name__)
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# app.config['SECRET_KEY'] = os.urandom(64)
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# app.config['SESSION_TYPE'] = 'filesystem'
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# app.config['SESSION_FILE_DIR'] = './.flask_session/'
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# Session(app)
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st.set_page_config(
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page_title="EmotionPlaylist",
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page_icon="🎧",
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)
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st.title('Customize Emotional Playlists')
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def setup_streamlite():
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setup_credentials()
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print('Here1')
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image = Image.open(dir_path + '/image.png')
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st.image(image)
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st.markdown("This app let's you quickly build playlists in a customized way: ")
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st.markdown("* **It's easy**: you won't have to add songs one by one,\n"
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"* **You're in control**: you provide a source of candidate songs, select a list of genres and choose the mood for the playlist.")
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st.subheader("Step 1: Connect to your Spotify app")
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st.markdown("Log into your Spotify account to let the app create the custom playlist.")
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print('Here2')
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if 'login' not in st.session_state:
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sp, user_id = new_get_client(session=st.session_state)
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if sp != None:
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print("USER", user_id)
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legit_genres = sp.recommendation_genre_seeds()['genres']
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st.session_state['login'] = (sp, user_id, legit_genres)
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# if 'login' not in st.session_state:
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# login = centered_button(st.button, 'Log in', n_columns=7)
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# if login or debug:
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# sp, user_id = get_client(session=st.session_state)
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# user_id = sp.me()['id']
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# legit_genres = sp.recommendation_genre_seeds()['genres']
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# st.session_state['login'] = (sp, user_id, legit_genres)
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if 'login' in st.session_state or debug:
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print('Here8')
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if not debug: sp, user_id, legit_genres = st.session_state['login']
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st.success('You are logged in.')
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st.subheader("Step 2: Select candidate songs")
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st.markdown("This can be done in three ways: \n"
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"1. Get songs from a list of artists\n"
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"2. Get songs from a list of users (and their playlists)\n"
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"3. Get songs from a list of playlists.\n"
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"For this you'll need to collect the urls of artists, users and/or playlists by clicking on 'Share' and copying the urls."
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"You need to provide at least one source of music.")
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label_artist = "Add a list of artist urls, one per line (optional)"
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artists_links = st.text_area(label_artist, value="")
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users_playlists = "Add a list of users urls, one per line (optional)"
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users_links = st.text_area(users_playlists, value="")
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label_playlists = "Add a list of playlists urls, one per line (optional)"
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playlist_links = st.text_area(label_playlists, value="https://open.spotify.com/playlist/1H7a4q8JZArMQiidRy6qon?si=529184bbe93c4f73")
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button = centered_button(st.button, 'Extract music', n_columns=5)
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if button or debug:
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if playlist_links != "":
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playlist_uris = extract_uris_from_links(playlist_links, url_type='playlist')
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else:
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raise ValueError('Please enter a list of playlists')
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# Scanning playlists
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st.spinner(text="Scanning music sources..")
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data_tracks = get_all_tracks_from_playlists(sp, playlist_uris, verbose=True)
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st.success(f'{len(data_tracks.keys())} tracks found!')
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# Extract audio features
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st.spinner(text="Extracting audio features..")
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all_tracks_uris = np.array(list(data_tracks.keys()))
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all_audio_features = [data_tracks[uri]['track']['audio_features'] for uri in all_tracks_uris]
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all_tracks_audio_features = dict(zip(relevant_audio_features, [[audio_f[k] for audio_f in all_audio_features] for k in relevant_audio_features]))
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genres = dict()
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for index, uri in enumerate(all_tracks_uris):
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track = data_tracks[uri]
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track_genres = track['track']['genres']
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for g in track_genres:
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if g not in genres.keys():
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genres[g] = [index]
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else:
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genres[g].append(index)
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genres = aggregate_genres(genres, legit_genres)
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genres_labels = sorted(genres.keys())
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st.success(f'Audio features extracted!')
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st.session_state['music_extracted'] = dict(all_tracks_uris=all_tracks_uris,
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all_tracks_audio_features=all_tracks_audio_features,
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genres=genres,
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genres_labels=genres_labels)
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if 'music_extracted' in st.session_state.keys():
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all_tracks_uris = st.session_state['music_extracted']['all_tracks_uris']
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all_tracks_audio_features = st.session_state['music_extracted']['all_tracks_audio_features']
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genres = st.session_state['music_extracted']['genres']
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genres_labels = st.session_state['music_extracted']['genres_labels']
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st.subheader("Step 3: Customize it!")
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st.markdown("##### Which genres?")
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st.markdown("Check boxes to select genres, see how many tracks were selected below. Note: to check all, first uncheck all (bug).")
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columns = st.columns(np.ones(5))
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with columns[1]:
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check_all = st.button('Check all')
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with columns[3]:
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uncheck_all = st.button('Uncheck all')
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if 'checkboxes' not in st.session_state.keys():
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st.session_state['checkboxes'] = [True] * len(genres_labels)
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empty_checkboxes = wall_of_checkboxes(genres_labels, max_width=5)
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if check_all:
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st.session_state['checkboxes'] = [True] * len(genres_labels)
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if uncheck_all:
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st.session_state['checkboxes'] = [False] * len(genres_labels)
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for i_emc, emc in enumerate(empty_checkboxes):
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st.session_state['checkboxes'][i_emc] = emc.checkbox(genres_labels[i_emc], value=st.session_state['checkboxes'][i_emc])
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# filter songs by genres
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selected_labels = [genres_labels[i] for i in range(len(genres_labels)) if st.session_state['checkboxes'][i]]
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genre_selected_indexes = []
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for label in selected_labels:
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genre_selected_indexes += genres[label]
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genre_selected_indexes = np.array(sorted(set(genre_selected_indexes)))
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if len(genre_selected_indexes) < 10:
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st.warning('Please select more genres or add more music sources.')
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else:
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st.success(f'{len(genre_selected_indexes)} candidate tracks selected.')
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st.markdown("##### What's the mood?")
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valence = st.slider('Valence (0 negative, 100 positive)', min_value=0, max_value=100, value=100, step=1) / 100
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energy = st.slider('Energy (0 low, 100 high)', min_value=0, max_value=100, value=100, step=1) / 100
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danceability = st.slider('Danceability (0 low, 100 high)', min_value=0, max_value=100, value=100, step=1) / 100
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target_mood = np.array([valence, energy, danceability]).reshape(1, 3)
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candidate_moods = np.array([np.array(all_tracks_audio_features[feature])[genre_selected_indexes] for feature in ['valence', 'energy', 'danceability']]).T
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distances = np.sqrt(((candidate_moods - target_mood) ** 2).sum(axis=1))
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min_dist_indexes = np.argsort(distances)
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n_candidates = distances.shape[0]
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if n_candidates < 25:
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st.warning('Please add more music sources or select more genres.')
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else:
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playlist_length = st.number_input(f'Pick a playlist length, given {n_candidates} candidates.', min_value=5,
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value=min(10, n_candidates//5), max_value=n_candidates//5)
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selected_tracks_indexes = genre_selected_indexes[min_dist_indexes[:playlist_length]]
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selected_tracks_uris = all_tracks_uris[selected_tracks_indexes]
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np.random.shuffle(selected_tracks_uris)
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playlist_name = st.text_input('Playlist name', value='Mood Playlist')
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if playlist_name == '':
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st.warning('Please enter a playlist name.')
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else:
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generation_button = centered_button(st.button, 'Generate playlist', n_columns=5)
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if generation_button:
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description = f'Emotion Playlist for Valence: {valence}, Energy: {energy}, Danceability: {danceability}). ' \
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f'Playlist generated by the EmotionPlaylist app: https://huggingface.co/spaces/ccolas/EmotionPlaylist.'
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playlist_info = sp.user_playlist_create(user_id, playlist_name, public=True, collaborative=False, description=description)
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playlist_uri = playlist_info['uri'].split(':')[-1]
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sp.playlist_add_items(playlist_uri, selected_tracks_uris)
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st.write(
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f"""
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<html>
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<body>
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<center>
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<iframe style = "border-radius:12px" src="https://open.spotify.com/embed/playlist/{playlist_uri}" allowtransparency="true"
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allow="encrypted-media" width="80%" height="580" frameborder="0"></iframe></center></body></html>
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""", unsafe_allow_html=True)
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st.success(f'The playlist has been generated, find it [here](https://open.spotify.com/playlist/{playlist_uri}).')
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stop = 1
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if __name__ == '__main__':
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setup_streamlite()
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