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| 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 | |
| def playlist_model(url, model, max_gen=3, same_art=5): | |
| log = [] | |
| Fresult = [] | |
| try: | |
| log.append('Start logging') | |
| uri = url.split('/')[-1].split('?')[0] | |
| stream = open("Spotify/Spotify5.yaml") | |
| spotify_details = yaml.safe_load(stream) | |
| auth_manager = SpotifyClientCredentials( | |
| client_id=spotify_details['Client_id'], client_secret=spotify_details['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) >= 50: | |
| 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[:50] | |
| 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/50)) | |
| 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(50) | |
| 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(50) | |
| 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(50) | |
| 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(50) | |
| 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(50) | |
| 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(50) | |
| 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): | |
| uri = url.split('/')[-1].split('?')[0] | |
| stream= open("Spotify/Spotify.yaml") | |
| spotify_details = yaml.safe_load(stream) | |
| auth_manager = SpotifyClientCredentials(client_id=spotify_details['Client_id'],client_secret=spotify_details['client_secret']) | |
| sp = spotipy.client.Spotify(auth_manager=auth_manager) | |
| log = [] | |
| Fresult = [] | |
| try: | |
| log.append('Starting Spotify Model') | |
| top=sp.artist_top_tracks(uri,country=region) | |
| for i in range(10) : | |
| 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] | |
| stream = open("Spotify/Spotify.yaml") | |
| spotify_details = yaml.safe_load(stream) | |
| auth_manager = SpotifyClientCredentials( | |
| client_id=spotify_details['Client_id'], client_secret=spotify_details['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/50)) | |
| 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(50) | |
| 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(50) | |
| 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(50) | |
| 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(50) | |
| 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(50) | |
| 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(50) | |
| 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) | |