import os import numpy as np import pandas as pd import requests import spotipy import streamlit as st from PIL import Image from spotipy.oauth2 import SpotifyClientCredentials st.set_page_config( page_title="Find Songs Similar to Yours🎤", page_icon="🎤", layout="wide" ) # Spotify API SPOTIPY_CLIENT_ID = os.environ["CLIENT_ID"] SPOTIPY_CLIENT_SECRET = os.environ["CLIENT_SECRET"] sp = spotipy.Spotify( auth_manager=SpotifyClientCredentials( client_id=SPOTIPY_CLIENT_ID, client_secret=SPOTIPY_CLIENT_SECRET ) ) """ # Analyze Song and Get Recommendations🎤 Input a song title and the app will return recommendations as well as the features of the song. Data is obtained using the Python library [Spotipy](https://spotipy.readthedocs.io/en/2.18.0/) that uses [Spotify Web API.](https://developer.spotify.com/documentation/web-api/) """ song = st.text_input("Enter a song title", value="Somebody Else") search = sp.search(q="track:" + song, type="track") class GetSongInfo: def __init__(self, search): self.search = search def song_id(self): song_id = search["tracks"]["items"][0]["id"] # -gets song id return song_id def song_album(self): song_album = search["tracks"]["items"][0]["album"][ "name" ] # -gets song album name return song_album def song_image(self): song_image = search["tracks"]["items"][0]["album"]["images"][0][ "url" ] # -gets song image URL return song_image def song_artist_name(self): song_artist_name = search["tracks"]["items"][0]["artists"][0][ "name" ] # -gets artist for song return song_artist_name def song_name(self): song_name = search["tracks"]["items"][0]["name"] # -gets song name return song_name def song_preview(self): song_preview = search["tracks"]["items"][0]["preview_url"] return song_preview songs = GetSongInfo(song) ### def url(song): url_to_song = "https://open.spotify.com/track/" + songs.song_id() st.write( f"Link to stream '{songs.song_name()}' by {songs.song_artist_name()} on Spotify: {url_to_song}" ) # Set up two-column layout for Streamlit app image, stats = st.columns(2) with image: try: url(song) r = requests.get(songs.song_image()) open("img/" + songs.song_id() + ".jpg", "w+b").write(r.content) image_album = Image.open("img/" + songs.song_id() + ".jpg") st.image( image_album, caption=f"{songs.song_artist_name()} - {songs.song_album()}", use_column_width="auto", ) feat = sp.audio_features(tracks=[songs.song_id()]) features = feat[0] p = pd.Series(features).to_frame() data_feat = p.loc[ [ "acousticness", "danceability", "energy", "liveness", "speechiness", "valence", ] ] bpm = p.loc[["tempo"]] values = bpm.values[0] bpms = values.item() ticks = np.linspace(0, 1, 11) plot = data_feat.plot.barh( xticks=ticks, legend=False, color="limegreen" ) # Use Pandas plot plot.set_xlabel("Value") plot.set_ylabel("Parameters") plot.set_title(f"Analysing '{songs.song_name()}' by {songs.song_artist_name()}") plot.invert_yaxis() st.pyplot(plot.figure) st.subheader(f"BPM (Beats Per Minute): {bpms}") st.warning( "Note: Audio previews may have very high default volume and will reset after page refresh" ) st.audio(songs.song_preview(), format="audio/wav") except IndexError or NameError: st.error( "This error is possibly due to the API being unable to find the song. Maybe try to retype it using the song title followed by artist without any hyphens (e.g. In my Blood Shawn Mendes)" ) # Recommendations with stats: st.subheader("You might also like") reco = sp.recommendations( seed_artists=None, seed_tracks=[songs.song_id()], seed_genres=[], limit=10 ) for i in reco["tracks"]: st.write(f"\"{i['name']}\" - {i['artists'][0]['name']}") image_reco = requests.get(i["album"]["images"][2]["url"]) open("img/" + i["id"] + ".jpg", "w+b").write(image_reco.content) st.image(Image.open("img/" + i["id"] + ".jpg"))