import pandas as pd import spotipy from spotipy.oauth2 import SpotifyOAuth, SpotifyClientCredentials import yaml import re from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity from sklearn.preprocessing import MinMaxScaler import pickle import streamlit as st import os import dotenv dotenv.load_dotenv() spotify_client_id = os.getenv("CLIENT_ID") spotify_client_secret = os.getenv("CLIENT_SECRET") def get_track_info(track_uri): stream = open("Spotify.yaml") spotify_details = yaml.safe_load(stream) auth_manager = SpotifyClientCredentials(client_id=spotify_client_id, client_secret=spotify_client_secret) sp = spotipy.client.Spotify(auth_manager=auth_manager) # Get track information track_info = sp.track(track_uri) # Extract track name and artist name track_name = track_info['name'] artist_name = track_info['artists'][0]['name'] # Return the track name and artist name return track_name, artist_name def get_track_names(playlist_id): track_names = [] stream = open("Spotify.yaml") spotify_details = yaml.safe_load(stream) auth_manager = SpotifyClientCredentials(client_id=spotify_client_id, client_secret=spotify_client_secret) sp = spotipy.client.Spotify(auth_manager=auth_manager) # Get playlist playlist = sp.playlist(playlist_id) # Extract track names for item in playlist['tracks']['items']: track = item['track'] track_name = track['name'] artists = [artist['name'] for artist in track['artists']] track_names.append({'track_name': track_name, 'artist_name': artists[0]}) return track_names def parse_results(results): # Initialize lists to store results names = [] artists = [] uris = [] # Loop through each track in the results for idx, item in enumerate(results['tracks']['items']): names.append(item['name']) artists.append(item['artists'][0]['name']) uris.append(item['uri']) # Create a DataFrame df = pd.DataFrame({ 'Name': names, 'Artist': artists, 'URI': uris }) return df def search_spotify(query): log = [] try: log.append('spotify local method') stream = open("Spotify.yaml") spotify_details = yaml.safe_load(stream) auth_manager = SpotifyClientCredentials(client_id=spotify_client_id, client_secret=spotify_client_secret) except: log.append('spotify .streamlit method') try: Client_id=st.secrets["Client_ID"] client_secret=st.secrets["Client_secret"] auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret) except: log.append('spotify hug method') Client_id=os.environ['Client_ID'] client_secret=os.environ['Client_secret'] auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret) sp = spotipy.client.Spotify(auth_manager=auth_manager) results = sp.search(q=query, type='track,playlist') return results def playlist_model(url, model, max_gen=3, same_art=5): log = [] Fresult = [] try: log.append('Start logging') uri = url.split('/')[-1].split('?')[0] try: log.append('spotify local method') stream = open("Spotify.yaml") spotify_details = yaml.safe_load(stream) auth_manager = SpotifyClientCredentials(client_id=spotify_client_id, client_secret=spotify_client_secret) except: log.append('spotify .streamlit method') try: Client_id=st.secrets["Client_ID"] client_secret=st.secrets["Client_secret"] auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret) except: log.append('spotify hug method') Client_id=os.environ['Client_ID'] client_secret=os.environ['Client_secret'] auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret) sp = spotipy.client.Spotify(auth_manager=auth_manager) if model == 'Spotify Model': def get_IDs(user, playlist_id): try: log.append('start playlist extraction') track_ids = [] playlist = sp.user_playlist(user, playlist_id) for item in playlist['tracks']['items']: track = item['track'] track_ids.append(track['id']) return track_ids except Exception as e: log.append('Failed to load the playlist') log.append(e) track_ids = get_IDs('Ruby', uri) track_ids_uni = list(set(track_ids)) log.append('Starting Spotify Model') Spotifyresult = pd.DataFrame() for i in range(len(track_ids_uni)-5): if len(Spotifyresult) >= 5: break try: ff = sp.recommendations(seed_tracks=list(track_ids_uni[i:i+5]), limit=5) except Exception as e: log.append(e) continue for z in range(5): result = pd.DataFrame([z+(5*i)+1]) result['uri'] = ff['tracks'][z]['id'] Spotifyresult = pd.concat([Spotifyresult, result], axis=0) Spotifyresult.drop_duplicates(subset=['uri'], inplace=True,keep='first') Fresult = Spotifyresult.uri[:5] log.append('Model run successfully') return Fresult, log lendf=len(pd.read_csv('data/streamlit.csv',usecols=['track_uri'])) dtypes = {'track_uri': 'object', 'artist_uri': 'object', 'album_uri': 'object', 'danceability': 'float16', 'energy': 'float16', 'key': 'float16', 'loudness': 'float16', 'mode': 'float16', 'speechiness': 'float16', 'acousticness': 'float16', 'instrumentalness': 'float16', 'liveness': 'float16', 'valence': 'float16', 'tempo': 'float16', 'duration_ms': 'float32', 'time_signature': 'float16', 'Track_release_date': 'int8', 'Track_pop': 'int8', 'Artist_pop': 'int8', 'Artist_genres': 'object'} col_name= ['track_uri', 'artist_uri', 'album_uri', 'danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'duration_ms', 'time_signature', 'Track_release_date', 'Track_pop', 'Artist_pop', 'Artist_genres'] try: def get_IDs(user, playlist_id): log.append('start playlist extraction') track_ids = [] artist_id = [] playlist = sp.user_playlist(user, playlist_id) for item in playlist['tracks']['items']: track = item['track'] track_ids.append(track['id']) artist = item['track']['artists'] artist_id.append(artist[0]['id']) return track_ids, artist_id except Exception as e: log.append('Failed to load the playlist') log.append(e) track_ids, artist_id = get_IDs('Ruby', uri) log.append("Number of Track : {}".format(len(track_ids))) artist_id_uni = list(set(artist_id)) track_ids_uni = list(set(track_ids)) log.append("Number of unique Artists : {}".format(len(artist_id_uni))) log.append("Number of unique Tracks : {}".format(len(track_ids_uni))) def extract(track_ids_uni, artist_id_uni): err = [] err.append('Start audio features extraction') audio_features = pd.DataFrame() for i in range(0, len(track_ids_uni), 25): try: track_feature = sp.audio_features(track_ids_uni[i:i+25]) track_df = pd.DataFrame(track_feature) audio_features = pd.concat([audio_features, track_df], axis=0) except Exception as e: err.append(e) continue err.append('Start track features extraction') track_ = pd.DataFrame() for i in range(0, len(track_ids_uni), 25): try: track_features = sp.tracks(track_ids_uni[i:i+25]) for x in range(25): track_pop = pd.DataFrame([track_ids_uni[i+x]], columns=['Track_uri']) track_pop['Track_release_date'] = track_features['tracks'][x]['album']['release_date'] track_pop['Track_pop'] = track_features['tracks'][x]["popularity"] track_pop['Artist_uri'] = track_features['tracks'][x]['artists'][0]['id'] track_pop['Album_uri'] = track_features['tracks'][x]['album']['id'] track_ = pd.concat([track_, track_pop], axis=0) except Exception as e: err.append(e) continue err.append('Start artist features extraction') artist_ = pd.DataFrame() for i in range(0, len(artist_id_uni), 25): try: artist_features = sp.artists(artist_id_uni[i:i+25]) for x in range(25): artist_df = pd.DataFrame([artist_id_uni[i+x]], columns=['Artist_uri']) artist_pop = artist_features['artists'][x]["popularity"] artist_genres = artist_features['artists'][x]["genres"] artist_df["Artist_pop"] = artist_pop if artist_genres: artist_df["genres"] = " ".join([re.sub(' ', '_', i) for i in artist_genres]) else: artist_df["genres"] = "unknown" artist_ = pd.concat([artist_, artist_df], axis=0) except Exception as e: err.append(e) continue try: test = pd.DataFrame( track_, columns=['Track_uri', 'Artist_uri', 'Album_uri']) test.rename(columns={'Track_uri': 'track_uri', 'Artist_uri': 'artist_uri', 'Album_uri': 'album_uri'}, inplace=True) audio_features.drop( columns=['type', 'uri', 'track_href', 'analysis_url'], axis=1, inplace=True) test = pd.merge(test, audio_features, left_on="track_uri", right_on="id", how='outer') test = pd.merge(test, track_, left_on="track_uri", right_on="Track_uri", how='outer') test = pd.merge(test, artist_, left_on="artist_uri", right_on="Artist_uri", how='outer') test.rename(columns={'genres': 'Artist_genres'}, inplace=True) test.drop(columns=['Track_uri', 'Artist_uri_x', 'Artist_uri_y', 'Album_uri', 'id'], axis=1, inplace=True) test.dropna(axis=0, inplace=True) test['Track_pop'] = test['Track_pop'].apply(lambda x: int(x/5)) test['Artist_pop'] = test['Artist_pop'].apply(lambda x: int(x/5)) test['Track_release_date'] = test['Track_release_date'].apply(lambda x: x.split('-')[0]) test['Track_release_date'] = test['Track_release_date'].astype('int16') test['Track_release_date'] = test['Track_release_date'].apply(lambda x: int(x/5)) test[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'time_signature']] = test[[ 'danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'time_signature']].astype('float16') test[['duration_ms']] = test[['duration_ms']].astype('float32') test[['Track_release_date', 'Track_pop', 'Artist_pop']] = test[[ 'Track_release_date', 'Track_pop', 'Artist_pop']].astype('int8') except Exception as e: err.append(e) err.append('Finish extraction') return test, err test, err = extract(track_ids_uni, artist_id_uni) for i in err: log.append(i) del err grow = test.copy() test['Artist_genres'] = test['Artist_genres'].apply(lambda x: x.split(" ")) tfidf = TfidfVectorizer(max_features=max_gen) tfidf_matrix = tfidf.fit_transform(test['Artist_genres'].apply(lambda x: " ".join(x))) genre_df = pd.DataFrame(tfidf_matrix.toarray()) genre_df.columns = ['genre' + "|" +i for i in tfidf.get_feature_names_out()] genre_df = genre_df.astype('float16') test.drop(columns=['Artist_genres'], axis=1, inplace=True) test = pd.concat([test.reset_index(drop=True),genre_df.reset_index(drop=True)], axis=1) Fresult = pd.DataFrame() x = 1 for i in range(int(lendf/2), lendf+1, int(lendf/2)): try: df = pd.read_csv('data/streamlit.csv',names= col_name,dtype=dtypes,skiprows=x,nrows=i) log.append('reading data frame chunks from {} to {}'.format(x,i)) except Exception as e: log.append('Failed to load grow') log.append(e) grow = grow[~grow['track_uri'].isin(df['track_uri'].values)] df = df[~df['track_uri'].isin(test['track_uri'].values)] df['Artist_genres'] = df['Artist_genres'].apply(lambda x: x.split(" ")) tfidf_matrix = tfidf.transform(df['Artist_genres'].apply(lambda x: " ".join(x))) genre_df = pd.DataFrame(tfidf_matrix.toarray()) genre_df.columns = ['genre' + "|" +i for i in tfidf.get_feature_names_out()] genre_df = genre_df.astype('float16') df.drop(columns=['Artist_genres'], axis=1, inplace=True) df = pd.concat([df.reset_index(drop=True), genre_df.reset_index(drop=True)], axis=1) del genre_df try: df.drop(columns=['genre|unknown'], axis=1, inplace=True) test.drop(columns=['genre|unknown'], axis=1, inplace=True) except: log.append('genre|unknown not found') log.append('Scaling the data .....') if x == 1: sc = pickle.load(open('data/sc.sav','rb')) df.iloc[:, 3:19] = sc.transform(df.iloc[:, 3:19]) test.iloc[:, 3:19] = sc.transform(test.iloc[:, 3:19]) log.append("Creating playlist vector") playvec = pd.DataFrame(test.sum(axis=0)).T else: df.iloc[:, 3:19] = sc.transform(df.iloc[:, 3:19]) x = i if model == 'Model 1': df['sim']=cosine_similarity(df.drop(['track_uri', 'artist_uri', 'album_uri'], axis = 1),playvec.drop(['track_uri', 'artist_uri', 'album_uri'], axis = 1)) df['sim2']=cosine_similarity(df.iloc[:,16:-1],playvec.iloc[:,16:]) df['sim3']=cosine_similarity(df.iloc[:,19:-2],playvec.iloc[:,19:]) df = df.sort_values(['sim3','sim2','sim'],ascending = False,kind='stable').groupby('artist_uri').head(same_art).head(5) Fresult = pd.concat([Fresult, df], axis=0) Fresult = Fresult.sort_values(['sim3', 'sim2', 'sim'],ascending=False,kind='stable') Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first') Fresult = Fresult.groupby('artist_uri').head(same_art).head(5) elif model == 'Model 2': df['sim'] = cosine_similarity(df.iloc[:, 3:16], playvec.iloc[:, 3:16]) df['sim2'] = cosine_similarity(df.loc[:, df.columns.str.startswith('T') | df.columns.str.startswith('A')], playvec.loc[:, playvec.columns.str.startswith('T') | playvec.columns.str.startswith('A')]) df['sim3'] = cosine_similarity(df.loc[:, df.columns.str.startswith('genre')], playvec.loc[:, playvec.columns.str.startswith('genre')]) df['sim4'] = (df['sim']+df['sim2']+df['sim3'])/3 df = df.sort_values(['sim4'], ascending=False,kind='stable').groupby('artist_uri').head(same_art).head(5) Fresult = pd.concat([Fresult, df], axis=0) Fresult = Fresult.sort_values(['sim4'], ascending=False,kind='stable') Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first') Fresult = Fresult.groupby('artist_uri').head(same_art).head(5) del test try: del df log.append('Getting Result') except: log.append('Getting Result') if model == 'Model 1': Fresult = Fresult.sort_values(['sim3', 'sim2', 'sim'],ascending=False,kind='stable') Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first') Fresult = Fresult.groupby('artist_uri').head(same_art).track_uri.head(5) elif model == 'Model 2': Fresult = Fresult.sort_values(['sim4'], ascending=False,kind='stable') Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first') Fresult = Fresult.groupby('artist_uri').head(same_art).track_uri.head(5) log.append('{} New Tracks Found'.format(len(grow))) if(len(grow)>=1): try: new=pd.read_csv('data/new_tracks.csv',dtype=dtypes) new=pd.concat([new, grow], axis=0) new=new[new.Track_pop >0] new.drop_duplicates(subset=['track_uri'], inplace=True,keep='last') new.to_csv('data/new_tracks.csv',index=False) except: grow.to_csv('data/new_tracks.csv', index=False) log.append('Model run successfully') except Exception as e: log.append("Model Failed") log.append(e) return Fresult, log def top_tracks(url,region): log = [] Fresult = [] uri = url.split('/')[-1].split('?')[0] try: log.append('spotify local method') stream = open("Spotify.yaml") spotify_details = yaml.safe_load(stream) auth_manager = SpotifyClientCredentials(client_id=spotify_client_id, client_secret=spotify_client_secret) except: log.append('spotify .streamlit method') try: Client_id=st.secrets["Client_ID"] client_secret=st.secrets["Client_secret"] auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret) except: log.append('spotify hug method') Client_id=os.environ['Client_ID'] client_secret=os.environ['Client_secret'] auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret) sp = spotipy.client.Spotify(auth_manager=auth_manager) try: log.append('Starting Spotify Model') top=sp.artist_top_tracks(uri,country=region) for i in range(5) : Fresult.append(top['tracks'][i]['id']) log.append('Model run successfully') except Exception as e: log.append("Model Failed") log.append(e) return Fresult,log def song_model(url, model, max_gen=3, same_art=5): log = [] Fresult = [] try: log.append('Start logging') uri = url.split('/')[-1].split('?')[0] try: log.append('spotify local method') stream = open("Spotify.yaml") spotify_details = yaml.safe_load(stream) auth_manager = SpotifyClientCredentials(client_id=spotify_client_id, client_secret=spotify_client_secret) except: log.append('spotify .streamlit method') try: Client_id=st.secrets["Client_ID"] client_secret=st.secrets["Client_secret"] auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret) except: log.append('spotify hug method') Client_id=os.environ['Client_ID'] client_secret=os.environ['Client_secret'] auth_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret) sp = spotipy.client.Spotify(auth_manager=auth_manager) if model == 'Spotify Model': log.append('Starting Spotify Model') aa=sp.recommendations(seed_tracks=[uri], limit=25) for i in range(25): Fresult.append(aa['tracks'][i]['id']) log.append('Model run successfully') return Fresult, log lendf=len(pd.read_csv('data/streamlit.csv',usecols=['track_uri'])) dtypes = {'track_uri': 'object', 'artist_uri': 'object', 'album_uri': 'object', 'danceability': 'float16', 'energy': 'float16', 'key': 'float16', 'loudness': 'float16', 'mode': 'float16', 'speechiness': 'float16', 'acousticness': 'float16', 'instrumentalness': 'float16', 'liveness': 'float16', 'valence': 'float16', 'tempo': 'float16', 'duration_ms': 'float32', 'time_signature': 'float16', 'Track_release_date': 'int8', 'Track_pop': 'int8', 'Artist_pop': 'int8', 'Artist_genres': 'object'} col_name= ['track_uri', 'artist_uri', 'album_uri', 'danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'duration_ms', 'time_signature', 'Track_release_date', 'Track_pop', 'Artist_pop', 'Artist_genres'] log.append('Start audio features extraction') audio_features = pd.DataFrame(sp.audio_features([uri])) log.append('Start track features extraction') track_ = pd.DataFrame() track_features = sp.tracks([uri]) track_pop = pd.DataFrame([uri], columns=['Track_uri']) track_pop['Track_release_date'] = track_features['tracks'][0]['album']['release_date'] track_pop['Track_pop'] = track_features['tracks'][0]["popularity"] track_pop['Artist_uri'] = track_features['tracks'][0]['artists'][0]['id'] track_pop['Album_uri'] = track_features['tracks'][0]['album']['id'] track_ = pd.concat([track_, track_pop], axis=0) log.append('Start artist features extraction') artist_id_uni=list(track_['Artist_uri']) artist_ = pd.DataFrame() artist_features = sp.artists(artist_id_uni) artist_df = pd.DataFrame(artist_id_uni, columns=['Artist_uri']) artist_pop = artist_features['artists'][0]["popularity"] artist_genres = artist_features['artists'][0]["genres"] artist_df["Artist_pop"] = artist_pop if artist_genres: artist_df["genres"] = " ".join([re.sub(' ', '_', i) for i in artist_genres]) else: artist_df["genres"] = "unknown" artist_ = pd.concat([artist_, artist_df], axis=0) try: test = pd.DataFrame(track_, columns=['Track_uri', 'Artist_uri', 'Album_uri']) test.rename(columns={'Track_uri': 'track_uri','Artist_uri': 'artist_uri', 'Album_uri': 'album_uri'}, inplace=True) audio_features.drop(columns=['type', 'uri', 'track_href', 'analysis_url'], axis=1, inplace=True) test = pd.merge(test, audio_features,left_on="track_uri", right_on="id", how='outer') test = pd.merge(test, track_, left_on="track_uri",right_on="Track_uri", how='outer') test = pd.merge(test, artist_, left_on="artist_uri",right_on="Artist_uri", how='outer') test.rename(columns={'genres': 'Artist_genres'}, inplace=True) test.drop(columns=['Track_uri', 'Artist_uri_x','Artist_uri_y', 'Album_uri', 'id'], axis=1, inplace=True) test.dropna(axis=0, inplace=True) test['Track_pop'] = test['Track_pop'].apply(lambda x: int(x/5)) test['Artist_pop'] = test['Artist_pop'].apply(lambda x: int(x/5)) test['Track_release_date'] = test['Track_release_date'].apply(lambda x: x.split('-')[0]) test['Track_release_date'] = test['Track_release_date'].astype('int16') test['Track_release_date'] = test['Track_release_date'].apply(lambda x: int(x/5)) test[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'time_signature']] = test[['danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'time_signature']].astype('float16') test[['duration_ms']] = test[['duration_ms']].astype('float32') test[['Track_release_date', 'Track_pop', 'Artist_pop']] = test[['Track_release_date', 'Track_pop', 'Artist_pop']].astype('int8') except Exception as e: log.append(e) log.append('Finish extraction') grow = test.copy() test['Artist_genres'] = test['Artist_genres'].apply(lambda x: x.split(" ")) tfidf = TfidfVectorizer(max_features=max_gen) tfidf_matrix = tfidf.fit_transform(test['Artist_genres'].apply(lambda x: " ".join(x))) genre_df = pd.DataFrame(tfidf_matrix.toarray()) genre_df.columns = ['genre' + "|" +i for i in tfidf.get_feature_names_out()] genre_df = genre_df.astype('float16') test.drop(columns=['Artist_genres'], axis=1, inplace=True) test = pd.concat([test.reset_index(drop=True),genre_df.reset_index(drop=True)], axis=1) Fresult = pd.DataFrame() x = 1 for i in range(int(lendf/2), lendf+1, int(lendf/2)): try: df = pd.read_csv('data/streamlit.csv',names= col_name,dtype=dtypes,skiprows=x,nrows=i) log.append('reading data frame chunks from {} to {}'.format(x,i)) except Exception as e: log.append('Failed to load grow') log.append(e) grow = grow[~grow['track_uri'].isin(df['track_uri'].values)] df = df[~df['track_uri'].isin(test['track_uri'].values)] df['Artist_genres'] = df['Artist_genres'].apply(lambda x: x.split(" ")) tfidf_matrix = tfidf.transform(df['Artist_genres'].apply(lambda x: " ".join(x))) genre_df = pd.DataFrame(tfidf_matrix.toarray()) genre_df.columns = ['genre' + "|" +i for i in tfidf.get_feature_names_out()] genre_df = genre_df.astype('float16') df.drop(columns=['Artist_genres'], axis=1, inplace=True) df = pd.concat([df.reset_index(drop=True), genre_df.reset_index(drop=True)], axis=1) del genre_df try: df.drop(columns=['genre|unknown'], axis=1, inplace=True) test.drop(columns=['genre|unknown'], axis=1, inplace=True) except: log.append('genre|unknown not found') log.append('Scaling the data .....') if x == 1: sc = pickle.load(open('data/sc.sav','rb')) df.iloc[:, 3:19] = sc.transform(df.iloc[:, 3:19]) test.iloc[:, 3:19] = sc.transform(test.iloc[:, 3:19]) log.append("Creating playlist vector") playvec = pd.DataFrame(test.sum(axis=0)).T else: df.iloc[:, 3:19] = sc.transform(df.iloc[:, 3:19]) x = i if model == 'Model 1': df['sim']=cosine_similarity(df.drop(['track_uri', 'artist_uri', 'album_uri'], axis = 1),playvec.drop(['track_uri', 'artist_uri', 'album_uri'], axis = 1)) df['sim2']=cosine_similarity(df.iloc[:,16:-1],playvec.iloc[:,16:]) df['sim3']=cosine_similarity(df.iloc[:,19:-2],playvec.iloc[:,19:]) df = df.sort_values(['sim3','sim2','sim'],ascending = False,kind='stable').groupby('artist_uri').head(same_art).head(5) Fresult = pd.concat([Fresult, df], axis=0) Fresult = Fresult.sort_values(['sim3', 'sim2', 'sim'],ascending=False,kind='stable') Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first') Fresult = Fresult.groupby('artist_uri').head(same_art).head(5) elif model == 'Model 2': df['sim'] = cosine_similarity(df.iloc[:, 3:16], playvec.iloc[:, 3:16]) df['sim2'] = cosine_similarity(df.loc[:, df.columns.str.startswith('T') | df.columns.str.startswith('A')], playvec.loc[:, playvec.columns.str.startswith('T') | playvec.columns.str.startswith('A')]) df['sim3'] = cosine_similarity(df.loc[:, df.columns.str.startswith('genre')], playvec.loc[:, playvec.columns.str.startswith('genre')]) df['sim4'] = (df['sim']+df['sim2']+df['sim3'])/3 df = df.sort_values(['sim4'], ascending=False,kind='stable').groupby('artist_uri').head(same_art).head(5) Fresult = pd.concat([Fresult, df], axis=0) Fresult = Fresult.sort_values(['sim4'], ascending=False,kind='stable') Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first') Fresult = Fresult.groupby('artist_uri').head(same_art).head(5) del test try: del df log.append('Getting Result') except: log.append('Getting Result') if model == 'Model 1': Fresult = Fresult.sort_values(['sim3', 'sim2', 'sim'],ascending=False,kind='stable') Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first') Fresult = Fresult.groupby('artist_uri').head(same_art).track_uri.head(5) elif model == 'Model 2': Fresult = Fresult.sort_values(['sim4'], ascending=False,kind='stable') Fresult.drop_duplicates(subset=['track_uri'], inplace=True,keep='first') Fresult = Fresult.groupby('artist_uri').head(same_art).track_uri.head(5) log.append('{} New Tracks Found'.format(len(grow))) if(len(grow)>=1): try: new=pd.read_csv('data/new_tracks.csv',dtype=dtypes) new=pd.concat([new, grow], axis=0) new=new[new.Track_pop >0] new.drop_duplicates(subset=['track_uri'], inplace=True,keep='last') new.to_csv('data/new_tracks.csv',index=False) except: grow.to_csv('data/new_tracks.csv', index=False) log.append('Model run successfully') except Exception as e: log.append("Model Failed") log.append(e) return Fresult, log def update_dataset(): col_name= ['track_uri', 'artist_uri', 'album_uri', 'danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo', 'duration_ms', 'time_signature', 'Track_release_date', 'Track_pop', 'Artist_pop', 'Artist_genres'] dtypes = {'track_uri': 'object', 'artist_uri': 'object', 'album_uri': 'object', 'danceability': 'float16', 'energy': 'float16', 'key': 'float16', 'loudness': 'float16', 'mode': 'float16', 'speechiness': 'float16', 'acousticness': 'float16', 'instrumentalness': 'float16', 'liveness': 'float16', 'valence': 'float16', 'tempo': 'float16', 'duration_ms': 'float32', 'time_signature': 'float16', 'Track_release_date': 'int8', 'Track_pop': 'int8', 'Artist_pop': 'int8', 'Artist_genres': 'object'} df = pd.read_csv('data/streamlit.csv',dtype=dtypes) grow = pd.read_csv('data/new_tracks.csv',dtype=dtypes) cur = len(df) df=pd.concat([df,grow],axis=0) grow=pd.DataFrame(columns=col_name) grow.to_csv('data/new_tracks.csv',index=False) df=df[df.Track_pop >0] df.drop_duplicates(subset=['track_uri'],inplace=True,keep='last') df.dropna(axis=0,inplace=True) df.to_csv('data/streamlit.csv',index=False) return (len(df)-cur)