model.py

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):
    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 = []

    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')
        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')
        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')
        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')
        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)