app.py

from PIL import Image
from utils import *
from app_utils import *

debug = False
dir_path = os.path.dirname(os.path.realpath(__file__))
# os.environ['FLASK_APP'] = dir_path + 'app2.py'
# if debug: os.environ['FLASK_ENV'] = 'development'
#
#
# app = Flask(__name__)
# app.config['SECRET_KEY'] = os.urandom(64)
# app.config['SESSION_TYPE'] = 'filesystem'
# app.config['SESSION_FILE_DIR'] = './.flask_session/'
# Session(app)
st.set_page_config(
    page_title="EmotionPlaylist",
    page_icon="🎧",
)
st.title('Customize Emotional Playlists')



def setup_streamlite():
    setup_credentials()

    print('Here1')
    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.")
    print('Here2')
    if 'login' not in st.session_state:
        sp, user_id = new_get_client(session=st.session_state)
        if sp != None:
            print("USER", user_id)
            legit_genres = sp.recommendation_genre_seeds()['genres']
            st.session_state['login'] = (sp, user_id, legit_genres)

    # 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(session=st.session_state)
    #         user_id = sp.me()['id']
    #         legit_genres = sp.recommendation_genre_seeds()['genres']
    #         st.session_state['login'] = (sp, user_id, legit_genres)

    if 'login' in st.session_state or debug:
        print('Here8')
        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()