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| import streamlit as st | |
| import streamlit.components.v1 as components | |
| from PIL import Image | |
| import spotipy.util as util | |
| import pickle | |
| import spotipy | |
| from utils import * | |
| st.set_page_config( | |
| page_title="EmotionPlaylist", | |
| page_icon="🎧", | |
| ) | |
| debug = False | |
| dir_path = os.path.dirname(os.path.realpath(__file__)) | |
| st.title('Customize Emotional Playlists') | |
| def centered_button(func, text, n_columns=7, args=None): | |
| columns = st.columns(np.ones(n_columns)) | |
| with columns[n_columns//2]: | |
| return func(text) | |
| # get credentials | |
| def setup_credentials(): | |
| if 'client_id' in os.environ.keys() and 'client_secret' in os.environ.keys(): | |
| client_info = dict(client_id=os.environ['client_id'], | |
| client_secret=os.environ['client_secret']) | |
| else: | |
| with open(dir_path + "/ids.pk", 'rb') as f: | |
| client_info = pickle.load(f) | |
| os.environ['SPOTIPY_CLIENT_ID'] = client_info['client_id'] | |
| os.environ['SPOTIPY_CLIENT_SECRET'] = client_info['client_secret'] | |
| os.environ['SPOTIPY_REDIRECT_URI'] = 'https://huggingface.co/spaces/ccolas/EmotionPlaylist/' | |
| relevant_audio_features = ["danceability", "energy", "loudness", "mode", "valence", "tempo"] | |
| def get_client(): | |
| scope = "playlist-modify-public" | |
| token = util.prompt_for_user_token(scope=scope) | |
| sp = spotipy.Spotify(auth=token) | |
| user_id = sp.me()['id'] | |
| return sp, user_id | |
| def extract_uris_from_links(links, url_type): | |
| assert url_type in ['playlist', 'artist', 'user'] | |
| urls = links.split('\n') | |
| uris = [] | |
| for url in urls: | |
| if 'playlist' in url: | |
| uri = url.split(f'{url_type}/')[-1].split('?')[0] | |
| else: | |
| uri = url.split('?')[0] | |
| uris.append(uri) | |
| return uris | |
| def wall_of_checkboxes(labels, max_width=10): | |
| n_labels = len(labels) | |
| n_rows = int(np.ceil(n_labels/max_width)) | |
| checkboxes = [] | |
| for i in range(n_rows): | |
| columns = st.columns(np.ones(max_width)) | |
| row_length = n_labels % max_width if i == n_rows - 1 else max_width | |
| for j in range(row_length): | |
| with columns[j]: | |
| checkboxes.append(st.empty()) | |
| return checkboxes | |
| def aggregate_genres(genres, legit_genres, verbose=False): | |
| genres_output = dict() | |
| legit_genres_formatted = [lg.replace('-', '').replace(' ', '') for lg in legit_genres] | |
| for glabel in genres.keys(): | |
| if verbose: print('\n', glabel) | |
| glabel_formatted = glabel.replace(' ', '').replace('-', '') | |
| best_match = None | |
| best_match_score = 0 | |
| for legit_glabel, legit_glabel_formatted in zip(legit_genres, legit_genres_formatted): | |
| if 'jazz' in glabel_formatted: | |
| best_match = 'jazz' | |
| if verbose: print('\t', 'pop') | |
| break | |
| if 'ukpop' in glabel_formatted: | |
| best_match = 'pop' | |
| if verbose: print('\t', 'pop') | |
| break | |
| if legit_glabel_formatted == glabel_formatted: | |
| if verbose: print('\t', legit_glabel_formatted) | |
| best_match = legit_glabel | |
| break | |
| elif glabel_formatted in legit_glabel_formatted: | |
| if verbose: print('\t', legit_glabel_formatted) | |
| if len(glabel_formatted) > best_match_score: | |
| best_match = legit_glabel | |
| best_match_score = len(glabel_formatted) | |
| elif legit_glabel_formatted in glabel_formatted: | |
| if verbose: print('\t', legit_glabel_formatted) | |
| if len(legit_glabel_formatted) > best_match_score: | |
| best_match = legit_glabel | |
| best_match_score = len(legit_glabel_formatted) | |
| if best_match is not None: | |
| if verbose: print('\t', '-->', best_match) | |
| if best_match in genres_output.keys(): | |
| genres_output[best_match] += genres[glabel] | |
| else: | |
| genres_output[best_match] = genres[glabel] | |
| return genres_output | |
| def setup_streamlite(): | |
| setup_credentials() | |
| image = Image.open(dir_path + '/image.png') | |
| st.image(image) | |
| st.markdown("This app let's you quickly build playlists in a customized way: ") | |
| st.markdown("* **It's easy**: you won't have to add songs one by one,\n" | |
| "* **You're in control**: you provide a source of candidate songs, select a list of genres and choose the mood for the playlist.") | |
| st.subheader("Step 1: Connect to your Spotify app") | |
| st.markdown("Log into your Spotify account to let the app create the custom playlist.") | |
| if 'login' not in st.session_state: | |
| login = centered_button(st.button, 'Log in', n_columns=7) | |
| if login or debug: | |
| sp, user_id = get_client() | |
| legit_genres = sp.recommendation_genre_seeds()['genres'] | |
| st.session_state['login'] = (sp, user_id, legit_genres) | |
| if 'login' in st.session_state or debug: | |
| if not debug: sp, user_id, legit_genres = st.session_state['login'] | |
| st.success('You are logged in.') | |
| st.subheader("Step 2: Select candidate songs") | |
| st.markdown("This can be done in three ways: \n" | |
| "1. Get songs from a list of artists\n" | |
| "2. Get songs from a list of users (and their playlists)\n" | |
| "3. Get songs from a list of playlists.\n" | |
| "For this you'll need to collect the urls of artists, users and/or playlists by clicking on 'Share' and copying the urls." | |
| "You need to provide at least one source of music.") | |
| label_artist = "Add a list of artist urls, one per line (optional)" | |
| artists_links = st.text_area(label_artist, value="") | |
| users_playlists = "Add a list of users urls, one per line (optional)" | |
| users_links = st.text_area(users_playlists, value="") | |
| label_playlists = "Add a list of playlists urls, one per line (optional)" | |
| playlist_links = st.text_area(label_playlists, value="https://open.spotify.com/playlist/1H7a4q8JZArMQiidRy6qon?si=529184bbe93c4f73") | |
| button = centered_button(st.button, 'Extract music', n_columns=5) | |
| if button or debug: | |
| if playlist_links != "": | |
| playlist_uris = extract_uris_from_links(playlist_links, url_type='playlist') | |
| else: | |
| raise ValueError('Please enter a list of playlists') | |
| # Scanning playlists | |
| st.spinner(text="Scanning music sources..") | |
| data_tracks = get_all_tracks_from_playlists(sp, playlist_uris, verbose=True) | |
| st.success(f'{len(data_tracks.keys())} tracks found!') | |
| # Extract audio features | |
| st.spinner(text="Extracting audio features..") | |
| all_tracks_uris = np.array(list(data_tracks.keys())) | |
| all_audio_features = [data_tracks[uri]['track']['audio_features'] for uri in all_tracks_uris] | |
| 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])) | |
| genres = dict() | |
| for index, uri in enumerate(all_tracks_uris): | |
| track = data_tracks[uri] | |
| track_genres = track['track']['genres'] | |
| for g in track_genres: | |
| if g not in genres.keys(): | |
| genres[g] = [index] | |
| else: | |
| genres[g].append(index) | |
| genres = aggregate_genres(genres, legit_genres) | |
| genres_labels = sorted(genres.keys()) | |
| st.success(f'Audio features extracted!') | |
| st.session_state['music_extracted'] = dict(all_tracks_uris=all_tracks_uris, | |
| all_tracks_audio_features=all_tracks_audio_features, | |
| genres=genres, | |
| genres_labels=genres_labels) | |
| if 'music_extracted' in st.session_state.keys(): | |
| all_tracks_uris = st.session_state['music_extracted']['all_tracks_uris'] | |
| all_tracks_audio_features = st.session_state['music_extracted']['all_tracks_audio_features'] | |
| genres = st.session_state['music_extracted']['genres'] | |
| genres_labels = st.session_state['music_extracted']['genres_labels'] | |
| st.subheader("Step 3: Customize it!") | |
| st.markdown("##### Which genres?") | |
| st.markdown("Check boxes to select genres, see how many tracks were selected below. Note: to check all, first uncheck all (bug).") | |
| columns = st.columns(np.ones(5)) | |
| with columns[1]: | |
| check_all = st.button('Check all') | |
| with columns[3]: | |
| uncheck_all = st.button('Uncheck all') | |
| if 'checkboxes' not in st.session_state.keys(): | |
| st.session_state['checkboxes'] = [True] * len(genres_labels) | |
| empty_checkboxes = wall_of_checkboxes(genres_labels, max_width=5) | |
| if check_all: | |
| st.session_state['checkboxes'] = [True] * len(genres_labels) | |
| if uncheck_all: | |
| st.session_state['checkboxes'] = [False] * len(genres_labels) | |
| for i_emc, emc in enumerate(empty_checkboxes): | |
| st.session_state['checkboxes'][i_emc] = emc.checkbox(genres_labels[i_emc], value=st.session_state['checkboxes'][i_emc]) | |
| # filter songs by genres | |
| selected_labels = [genres_labels[i] for i in range(len(genres_labels)) if st.session_state['checkboxes'][i]] | |
| genre_selected_indexes = [] | |
| for label in selected_labels: | |
| genre_selected_indexes += genres[label] | |
| genre_selected_indexes = np.array(sorted(set(genre_selected_indexes))) | |
| if len(genre_selected_indexes) < 10: | |
| st.warning('Please select more genres or add more music sources.') | |
| else: | |
| st.success(f'{len(genre_selected_indexes)} candidate tracks selected.') | |
| st.markdown("##### What's the mood?") | |
| valence = st.slider('Valence (0 negative, 100 positive)', min_value=0, max_value=100, value=100, step=1) / 100 | |
| energy = st.slider('Energy (0 low, 100 high)', min_value=0, max_value=100, value=100, step=1) / 100 | |
| danceability = st.slider('Danceability (0 low, 100 high)', min_value=0, max_value=100, value=100, step=1) / 100 | |
| target_mood = np.array([valence, energy, danceability]).reshape(1, 3) | |
| candidate_moods = np.array([np.array(all_tracks_audio_features[feature])[genre_selected_indexes] for feature in ['valence', 'energy', 'danceability']]).T | |
| distances = np.sqrt(((candidate_moods - target_mood) ** 2).sum(axis=1)) | |
| min_dist_indexes = np.argsort(distances) | |
| n_candidates = distances.shape[0] | |
| if n_candidates < 25: | |
| st.warning('Please add more music sources or select more genres.') | |
| else: | |
| playlist_length = st.number_input(f'Pick a playlist length, given {n_candidates} candidates.', min_value=5, | |
| value=min(10, n_candidates//5), max_value=n_candidates//5) | |
| selected_tracks_indexes = genre_selected_indexes[min_dist_indexes[:playlist_length]] | |
| selected_tracks_uris = all_tracks_uris[selected_tracks_indexes] | |
| np.random.shuffle(selected_tracks_uris) | |
| playlist_name = st.text_input('Playlist name', value='Mood Playlist') | |
| if playlist_name == '': | |
| st.warning('Please enter a playlist name.') | |
| else: | |
| generation_button = centered_button(st.button, 'Generate playlist', n_columns=5) | |
| if generation_button: | |
| description = f'Emotion Playlist for Valence: {valence}, Energy: {energy}, Danceability: {danceability}). ' \ | |
| f'Playlist generated by the EmotionPlaylist app: https://huggingface.co/spaces/ccolas/EmotionPlaylist.' | |
| playlist_info = sp.user_playlist_create(user_id, playlist_name, public=True, collaborative=False, description=description) | |
| playlist_uri = playlist_info['uri'].split(':')[-1] | |
| sp.playlist_add_items(playlist_uri, selected_tracks_uris) | |
| st.write( | |
| f""" | |
| <html> | |
| <body> | |
| <center> | |
| <iframe style = "border-radius:12px" src="https://open.spotify.com/embed/playlist/{playlist_uri}" allowtransparency="true" | |
| allow="encrypted-media" width="80%" height="580" frameborder="0"></iframe></center></body></html> | |
| """, unsafe_allow_html=True) | |
| st.success(f'The playlist has been generated, find it [here](https://open.spotify.com/playlist/{playlist_uri}).') | |
| stop = 1 | |
| if __name__ == '__main__': | |
| setup_streamlite() |