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Data/new_tracks.csv

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Data/streamlit.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:825f2815356f51fc0cf80bc7a3000612fab48daa925a32c0c3fe989555b2000b
+size 186112931

LICENSE

@@ -0,0 +1,201 @@
+Apache License
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README.md

@@ -0,0 +1,10 @@
+---
+title: Music Helix
+emoji: 🎶
+colorFrom: red
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.20.0
+app_file: main.py
+pinned: false
+---

gitignore.txt

@@ -0,0 +1,4 @@
+.cache
+__pycache__
+Spotify/*
+.streamlit/

main.py

@@ -0,0 +1,512 @@
+import streamlit as st
+from streamlit_option_menu import option_menu
+import streamlit.components.v1 as components
+import os
+import time
+import spotipy
+from spotipy.oauth2 import SpotifyOAuth, SpotifyClientCredentials
+import requests
+from spotifysearch import *
+from model import *
+
+
+#Authentication - without user
+#client_credentials_manager = SpotifyClientCredentials(client_id=Client_id, client_secret=client_secret)
+#sp = spotipy.Spotify(client_credentials_manager = client_credentials_manager)
+
+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' not in st.session_state:
+    st.session_state.model = 'Similarity Model'
+def update_radio2():
+    st.session_state.model=st.session_state.radio2
+if 'genre' not in st.session_state:
+    st.session_state.genre=3
+def update_num_genre():
+    st.session_state.genre=st.session_state.num_genre
+if 'artist' not in st.session_state:
+    st.session_state.artist=5
+def update_same_art():
+    st.session_state.artist=st.session_state.same_art
+if 'model2' not in st.session_state:
+    st.session_state.model2= 'Similarity Model'
+def update_radio1():
+    st.session_state.model2 =st.session_state.radio1
+
+if 'Region' not in st.session_state:
+    st.session_state.rg="US"
+def update_Region():
+    st.session_state.rg=st.session_state.Region
+if 'radio' not in st.session_state:
+    st.session_state.feature="Song"
+def update_radio0():
+    st.session_state.feature=st.session_state.radio
+
+if 'p_url' not in st.session_state:
+    st.session_state.p_url = 'Example: https://open.spotify.com/playlist/37i9dQZF1DX8FwnYE6PRvL?si=06ff6b38d4124af0'
+def update_playlist_url():
+    st.session_state.p_url = st.session_state.playlist_url
+
+if 's_url' not in st.session_state:
+    st.session_state.s_url = 'Example: https://open.spotify.com/track/5CQ30WqJwcep0pYcV4AMNc?si=ed4b04f153a24531'
+def update_song_url():
+    st.session_state.s_url = st.session_state.song_url
+
+if 'sn_url' not in st.session_state:
+    st.session_state.sn_url = 'In The End'
+def update_song_name_url():
+    st.session_state.sn_url = st.session_state.songname_url
+
+if 'a_url' not in st.session_state:
+    st.session_state.a_url = 'Example: https://open.spotify.com/artist/3RNrq3jvMZxD9ZyoOZbQOD?si=UNAsX20kRpG89bxOO8o7ew'
+def update_artist_url():
+    st.session_state.a_url = st.session_state.artist_url
+
+
+def play_recomm():
+    if 'rs' in st.session_state:
+        del st.session_state.rs,st.session_state.err
+    try:
+        if len(pd.read_csv('Data/new_tracks.csv')) >= 200:
+            with st.spinner('Updating the dataset...'):
+                x=update_dataset()
+                st.success('{} New tracks were added to the dataset.'.format(x))
+    except:
+        st.error("The dataset update failed. ")
+    with st.spinner('Getting Recommendations...'):
+        res,err = playlist_model(st.session_state.p_url,st.session_state.model,st.session_state.genre,st.session_state.artist)
+        st.session_state.rs=res
+        st.session_state.err=err
+    if len(st.session_state.rs)>=1:
+        if st.session_state.model == 'ReModel' or st.session_state.model == 'Experimental Model':
+            st.success('Go to the Result page to view the top {} recommendations, Thanks for taking the time to use this :D'.format(len(st.session_state.rs)))
+            st.success('- RedValis'.format(len(st.session_state.rs)))
+
+        else:
+            st.success('Go to the Result page to view the  Spotify recommendations')
+            st.success('- RedValis')
+    else:
+        st.error('Model failed. Check the log for more information.')   
+
+def art_recomm():
+    if 'rs' in st.session_state:
+        del st.session_state.rs,st.session_state.err
+    with st.spinner('Getting Recommendations...'):
+        res,err = top_tracks(st.session_state.a_url,st.session_state.rg)
+        st.session_state.rs=res
+        st.session_state.err=err
+    if len(st.session_state.rs)>=1:
+        st.success("Go to the Result page to view the Artist's top tracks, Thank you for taking the time to use this :D")
+        st.success("- RedValis")
+    else:
+        st.error('Model failed. Check the log for more information.')
+
+def song_recomm():
+    if 'rs' in st.session_state:
+        del st.session_state.rs,st.session_state.err
+    with st.spinner('Getting Recommendations...'):
+        res,err = song_model(st.session_state.s_url,st.session_state.model,st.session_state.genre,st.session_state.artist)
+        st.session_state.rs=res
+        st.session_state.err=err
+    if len(st.session_state.rs)>=1:
+        if st.session_state.model == 'ReModel' or st.session_state.model == 'Experimental Model':
+            st.success('Go to the Result page to view the top {} recommendations, Thank you for taking the time to use this :D'.format(len(st.session_state.rs)))
+            st.success('- RedValis'.format(len(st.session_state.rs)))
+        else:
+            st.success('Go to the Result page to view the  Spotify recommendations')
+            st.success('- RedValis')
+    else:
+        st.error('Model failed. Check the log for more information.')
+
+def playlist_page():
+    st.subheader("User Playlist")
+    st.markdown('---')
+    playlist_uri = (st.session_state.playlist_url).split('/')[-1].split('?')[0]
+    uri_link = 'https://open.spotify.com/embed/playlist/' + playlist_uri
+    components.iframe(uri_link, height=300)
+    return
+
+def song_page():
+    st.subheader("User Song")
+    st.markdown('---')
+    song_uri = (st.session_state.song_url).split('/')[-1].split('?')[0]
+    uri_link = 'https://open.spotify.com/embed/track/' + song_uri
+    components.iframe(uri_link, height=100)
+
+def artist_page():
+    st.subheader("User Artist")
+    st.markdown('---')
+    artist_uri = (st.session_state.artist_url).split('/')[-1].split('?')[0]
+    uri_link = 'https://open.spotify.com/embed/artist/' + artist_uri
+    components.iframe(uri_link, height=80)
+
+
+def spr_sidebar():
+    menu=option_menu(
+        menu_title=None,
+        options=['Home','Result','About','Log'],
+        icons=['house','book','info-square','terminal'],
+        menu_icon='cast',
+        default_index=0,
+        orientation='horizontal'
+    )
+    if menu=='Home':
+        st.session_state.app_mode = 'Home'
+    elif menu=='Result':
+        st.session_state.app_mode = 'Result'
+    elif menu=='About':
+        st.session_state.app_mode = 'About'
+    elif menu=='Log':
+        st.session_state.app_mode = 'Log'
+    
+def home_page():
+    st.session_state.radio=st.session_state.feature
+    st.session_state.radio2=st.session_state.model
+    st.session_state.num_genre=st.session_state.genre
+    st.session_state.same_art=st.session_state.artist
+    st.session_state.Region=st.session_state.rg
+
+    
+    st.title('Helical Recommendation System')
+    col,col2,col3=st.columns([2,2,3])
+    radio=col.radio("Feature",options=("Playlist","Song","Artist Top Tracks"),key='radio',on_change=update_radio0)
+    if radio =="Artist Top Tracks":
+        radio1=col2.radio("Model",options=["Similarity Model"],key='radio1',on_change=update_radio1)
+        Region=col3.selectbox("Please Choose Region",index=58,key='Region',on_change=update_Region,options=('AD', 'AR', 'AU', 'AT', 'BE', 'BO', 'BR', 'BG', 'CA', 'CL', 'CO', 'CR', 'CY', 'CZ', 'DK', 'DO', 'EC', 'SV', 'EE', 'FI', 'FR', 'DE', 'GR', 'GT', 'HN', 'HK', 'HU', 'IS', 'ID', 'IE', 'IT', 'JP', 'LV', 'LI', 'LT', 'LU', 'MY', 'MT', 'MX', 'MC', 'NL', 'NZ', 'NI', 'NO', 'PA', 'PY', 'PE', 'PH', 'PL', 'PT', 'SG', 'ES', 'SK', 'SE', 'CH', 'TW', 'TR', 'GB', 'US', 'UY'))
+    elif radio =="Playlist" or radio =="Song" :
+        radio2=col2.radio("Model",options=("ReModel","Experimental Model","Similarity Model"),key='radio2',on_change=update_radio2)
+        if st.session_state.radio2=="ReModel" or st.session_state.radio2=="Experimental Model":
+            num_genre=col3.selectbox("choose a number of genres to focus on",options=(1,2,3,4,5,6,7),index=2,key='num_genre',on_change=update_num_genre)
+            same_art=col3.selectbox("How many recommendations by the same artist",options=(1,2,3,4,5,7,10,15),index=3,key='same_art',on_change=update_same_art)
+
+
+    st.markdown("<br>", unsafe_allow_html=True)
+    
+    if radio == "Playlist" :
+        st.session_state.playlist_url = st.session_state.p_url
+        Url = st.text_input(label="Playlist Url",key='playlist_url',on_change=update_playlist_url)
+        playlist_page()
+        state =st.button('Get Recommendations')
+        with st.expander("Here's how to find any Playlist URL in Spotify"):
+            st.write(""" 
+                - Search for Playlist on the Spotify app
+                - Right Click on the Playlist you like
+                - Click "Share"
+                - Choose "Copy link to playlist"
+            """)
+            st.markdown("<br>", unsafe_allow_html=True)
+            st.image('spotify_get_playlist_url.png')
+        if state:
+            play_recomm()
+    elif radio == "Song" :
+        st.session_state.songname_url = st.session_state.sn_url
+        Url_a = st.text_input(label="Type a songs name to get the url for",key='get_url',on_change=update_song_name_url)
+
+        from spotifysearch.client import Client
+        myclient = Client(Client_id, client_secret)
+        results = myclient.search(Url_a)
+        tracks = results.get_tracks()
+
+        track = tracks
+        
+        result_length = len(tracks)
+
+        track_song_list = []
+        track_url_list = []
+        track_actual_url_list = []
+        anotherlist=[]
+
+
+        genrec = st.radio(
+            "Choose an option to search in:",
+            ["5x4"])
+
+
+        if genrec == "Full":
+        
+            for leupz in range(result_length):
+                thetrack = tracks[leupz]
+                st.write(thetrack.name)
+                name_desu = thetrack.name
+                track_song_list.append(name_desu)
+            
+                st.write(thetrack.url)            
+                urlname_desu = thetrack.name           
+                track_url_list.append(urlname_desu)      
+            
+                song_uri = (thetrack.url).split('/')[-1].split('?')[0]
+                uri_link = 'https://open.spotify.com/embed/track/' + song_uri
+                components.iframe(uri_link, height=100)
+
+
+            st.write(result_length)
+
+        elif genrec == "5x4":
+            for leupz in range(result_length):
+                thetrack = tracks[leupz]
+
+                name_desu = thetrack.name
+                track_song_list.append(name_desu)
+            
+       
+                urlname_desu = thetrack.url          
+                track_url_list.append(urlname_desu)      
+            
+                song_uri = (thetrack.url).split('/')[-1].split('?')[0]
+                uri_link = 'https://open.spotify.com/embed/track/' + song_uri
+                dem = uri_link
+
+                track_actual_url_list.append(dem)
+            notneeded_chkbox = st.checkbox('Click on this to see the results')
+
+            if notneeded_chkbox:
+########### WARNING: MAY CAUSE SOME TO FAINT :/
+                
+                st.write('Feature activated!')
+
+                pages = st.radio("Page",["1","2","3","4",], horizontal=True)
+
+                if pages == "1":
+                    Le_songs = st.radio(
+                        "Its time to choose...",
+                        [track_song_list[0],track_song_list[1],track_song_list[2],track_song_list[3],track_song_list[4]])
+                    
+                    if Le_songs == track_song_list[0]:
+                        st.session_state.s_url = track_actual_url_list[0]
+                        
+                    elif Le_songs == track_song_list[1]:
+                        st.session_state.s_url = track_actual_url_list[1]
+                        
+                    elif Le_songs == track_song_list[2]:
+                        st.session_state.s_url = track_actual_url_list[2]
+                    
+                    elif Le_songs == track_song_list[3]:
+                        st.session_state.s_url = track_actual_url_list[3]
+                        
+                    elif Le_songs == track_song_list[4]:
+                        st.session_state.s_url = track_actual_url_list[4]
+                        
+                        
+                    st.write('Previews')
+                
+                    components.iframe(track_actual_url_list[0], height=100)
+                    components.iframe(track_actual_url_list[1], height=100)
+                    components.iframe(track_actual_url_list[2], height=100)
+                    components.iframe(track_actual_url_list[3], height=100)
+                    components.iframe(track_actual_url_list[4], height=100)
+                    
+
+                if pages == "2":
+                    Le_songs = st.radio(
+                        "Its time to choose...",
+                        [track_song_list[5],track_song_list[6],track_song_list[7],track_song_list[8],track_song_list[9]])
+                    if Le_songs == track_song_list[5]:
+                        st.session_state.s_url = track_actual_url_list[5]
+                        
+                    elif Le_songs == track_song_list[6]:
+                        st.session_state.s_url = track_actual_url_list[6]
+                        
+                    elif Le_songs == track_song_list[7]:
+                        st.session_state.s_url = track_actual_url_list[7]
+                    
+                    elif Le_songs == track_song_list[8]:
+                        st.session_state.s_url = track_actual_url_list[8]
+                        
+                    elif Le_songs == track_song_list[9]:
+                        st.session_state.s_url = track_actual_url_list[9]
+                        
+                    st.write('Previews')
+                
+                    components.iframe(track_actual_url_list[5], height=100)
+                    components.iframe(track_actual_url_list[6], height=100)
+                    components.iframe(track_actual_url_list[7], height=100)
+                    components.iframe(track_actual_url_list[8], height=100)
+                    components.iframe(track_actual_url_list[9], height=100)
+                if pages == "3":
+                    Le_songs = st.radio(
+                        "Its time to choose...",
+                        [track_song_list[10],track_song_list[11],track_song_list[12],track_song_list[13],track_song_list[14]])
+
+                    if Le_songs == track_song_list[10]:
+                        st.session_state.s_url = track_actual_url_list[10]
+                        
+                    elif Le_songs == track_song_list[11]:
+                        st.session_state.s_url = track_actual_url_list[11]
+                        
+                    elif Le_songs == track_song_list[12]:
+                        st.session_state.s_url = track_actual_url_list[12]
+                    
+                    elif Le_songs == track_song_list[13]:
+                        st.session_state.s_url = track_actual_url_list[13]
+                        
+                    elif Le_songs == track_song_list[14]:
+                        st.session_state.s_url = track_actual_url_list[14]
+                    
+                    st.write('Previews')
+                
+                    components.iframe(track_actual_url_list[10], height=100)
+                    components.iframe(track_actual_url_list[11], height=100)
+                    components.iframe(track_actual_url_list[12], height=100)
+                    components.iframe(track_actual_url_list[13], height=100)
+                    components.iframe(track_actual_url_list[14], height=100)
+                if pages == "4":
+                    Le_songs = st.radio(
+                        "Its time to choose...",
+                        [track_song_list[15],track_song_list[16],track_song_list[17],track_song_list[18],track_song_list[19]])
+
+                    if Le_songs == track_song_list[15]:
+                        st.session_state.s_url = track_actual_url_list[15]
+                        
+                    elif Le_songs == track_song_list[16]:
+                        st.session_state.s_url = track_actual_url_list[16]
+                        
+                    elif Le_songs == track_song_list[17]:
+                        st.session_state.s_url = track_actual_url_list[17]
+                    
+                    elif Le_songs == track_song_list[18]:
+                        st.session_state.s_url = track_actual_url_list[18]
+                        
+                    elif Le_songs == track_song_list[19]:
+                        st.session_state.s_url = track_actual_url_list[19]
+                    
+                    st.write('Previews')
+                
+                    components.iframe(track_actual_url_list[15], height=100)
+                    components.iframe(track_actual_url_list[16], height=100)
+                    components.iframe(track_actual_url_list[17], height=100)
+                    components.iframe(track_actual_url_list[18], height=100)
+                    components.iframe(track_actual_url_list[19], height=100)
+                    
+                else:
+                    st.write("""If you have clicked on an option, click on "Get recommendations" button to get recommendations""")
+
+     
+                st.write(result_length)
+        
+        st.session_state.song_url = st.session_state.s_url
+        Url = st.text_input(label="Song Url ",key='song_url',on_change=update_song_url)
+    
+        song_page()
+        state =st.button('Get Recommendations')
+        with st.expander("Here's how to find any Song URL in Spotify"):
+            st.write(""" 
+                - Search for Song on the Spotify app
+                - Right Click on the Song you like
+                - Click "Share"
+                - Choose "Copy link to Song"
+            """)
+            st.markdown("<br>", unsafe_allow_html=True)
+            st.image('spotify_get_song_url.png')
+
+            
+        if state:
+            song_recomm()
+    elif radio == "Artist Top Tracks" :
+        st.session_state.artist_url = st.session_state.a_url
+        Url = st.text_input(label="Artist Url",key='artist_url',on_change=update_artist_url)
+        artist_page()
+        state =st.button('Get Recommendations')
+        with st.expander("Here's how to find any Artist URL in Spotify"):
+            st.write(""" 
+                - Search for Artist on the Spotify app
+                - Right Click on the Artist you like
+                - Click "Share"
+                - Choose "Copy link to Artist"
+            """)
+            st.markdown("<br>", unsafe_allow_html=True)
+            st.image('spotify_get_artist_url.png')
+        if state:
+            art_recomm()
+    
+def result_page():
+    if 'rs' not in st.session_state:
+        st.error('Please select a model on the Home page and run Get Recommendations')
+    else:
+        st.success('Top {} recommendations'.format(len(st.session_state.rs)))
+        i=0
+        for uri in st.session_state.rs:
+         uri_link = "https://open.spotify.com/embed/track/" + uri + "?utm_source=generator&theme=0"
+         components.iframe(uri_link, height=80)
+         i+=1
+         if i%5==0:
+            time.sleep(1)
+def Log_page():
+    log=st.checkbox('Display Output', True, key='display_output')
+    if log == True:
+     if 'err' in st.session_state:
+        st.write(st.session_state.err)
+    with open('Data/streamlit.csv') as f:
+        st.download_button('Download Dataset', f,file_name='streamlit.csv')
+def About_page():
+    st.header('Development')
+    """
+    Made by RedValis
+    Massive thanks to ruby  "AbdelRahman" skies from github for code snippets that helped in the creation of our project
+    """
+    st.subheader('Spotify Million Playlist Dataset')
+    """
+    We're using the Million Playlist Dataset, which, as its name implies, consists of one million playlists.
+    contains a number of songs, and some metadata is included as well, such as the name of the playlist, duration, number of songs, number of artists, etc.
+    This allows for a great accuracy score and a larger number of songs scored according to how people organize their public playlists
+    """
+
+    """
+    It is created by sampling playlists from the billions of playlists that Spotify users have created over the years. 
+    Playlists that meet the following criteria were selected at random:
+    - Created by a user that resides in the United States and is at least 13 years old
+    - Was a public playlist at the time the MPD was generated
+    - Contains at least 10 tracks
+    - Contains no more than 300 tracks
+    - Contains at least 3 unique artists
+    - Contains at least 2 unique albums
+    - Has no local tracks (local tracks are non-Spotify tracks that a user has on their local device
+    - Has at least one follower (not including the creator
+    - Was created after January 1, 2010 and before December 1, 2020
+    - Does not have an offensive title
+    - Does not have an adult-oriented title if the playlist was created by a user under 18 years of age
+    Information about the Dataset [here](https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge)
+    """
+    st.subheader('Audio Features Explanation')
+    """
+    This is the list of audio features that we use to determine the similarity between songs.
+    For anyone who would like to inspect the source code 
+    
+    | Variable | Description |
+    | :----: | :---: |
+    | Acousticness | A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic. |
+    | Danceability | Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable. |
+    | Energy | Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy. |
+    | Instrumentalness | Predicts whether a track contains no vocals. "Ooh" and "aah" sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly "vocal". The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0. |
+    | Key | The key the track is in. Integers map to pitches using standard Pitch Class notation. E.g. 0 = C, 1 = C♯/D♭, 2 = D, and so on. If no key was detected, the value is -1. |
+    | Liveness | Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live. |
+    | Loudness | The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track and are useful for comparing relative loudness of tracks. Loudness is the quality of a sound that is the primary psychological correlate of physical strength (amplitude). Values typically range between -60 and 0 db. |
+    | Mode | Mode indicates the modality (major or minor) of a track, the type of scale from which its melodic content is derived. Major is represented by 1 and minor is 0. |
+    | Speechiness | Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks. |
+    | Tempo | The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration. |
+    | Time Signature | An estimated time signature. The time signature (meter) is a notational convention to specify how many beats are in each bar (or measure). The time signature ranges from 3 to 7 indicating time signatures of "3/4", to "7/4". |
+    | Valence | A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry). |
+    
+    Information about features: [here](https://developer.spotify.com/documentation/web-api/reference/#/operations/get-audio-features)
+    """
+
+def main():
+    spr_sidebar()        
+    if st.session_state.app_mode == 'Home':
+        home_page()
+    if st.session_state.app_mode == 'Result':
+        result_page()
+    if st.session_state.app_mode == 'About' :
+        About_page()
+    if st.session_state.app_mode == 'Log':
+        Log_page()
+# Run main()
+#if __name__ == '__main__': this doesnt allow reletive imports >:()
+main()

model.py

@@ -0,0 +1,520 @@
+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
+
+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/Spotify.yaml")
+        spotify_details = yaml.safe_load(stream)
+        auth_manager = SpotifyClientCredentials(client_id=spotify_details['Client_id'], client_secret=spotify_details['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 == 'Similarity 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 Similarity 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 == 'ReModel':
+             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 == 'Experimental Model':
+             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 == 'ReModel':
+         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 == 'Experimental Model':
+         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):
+    log = []
+    Fresult = []
+    uri = url.split('/')[-1].split('?')[0]
+    try:
+        log.append('spotify local method')
+        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'])
+    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 Similarity 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]
+     try:
+        log.append('spotify local method')
+        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'])
+     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 == 'Similarity Model':
+        log.append('Starting Similarity 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 == 'ReModel':
+             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 == 'Experimental Model':
+             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 == 'ReModel':
+         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 == 'Experimental Model':
+         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)
+

requirements.txt

@@ -0,0 +1,7 @@
+pandas==1.4.4
+PyYAML==6.0
+scikit_learn==1.1.3
+spotipy==2.20.0
+streamlit==1.14.0
+streamlit_option_menu==0.3.2
+altair<5

spotifysearch/calls.py

@@ -0,0 +1,24 @@
+
+# THIS FILE IS RESPONSABLE FOR API CALLS
+
+from . import urlbuilder
+from requests import get, post
+
+
+def call_acess_token(credentials):
+    endpoint = 'https://accounts.spotify.com/api/token'
+    data = {
+        'grant_type':'client_credentials'
+    }
+    headers = {
+        'Authorization':f'Basic {credentials}'
+    }
+    return post(url=endpoint, data=data, headers=headers)
+
+
+def call_search(acess_token, args):
+    endpoint = urlbuilder.search_endpoint(*args)
+    headers = {
+        'Authorization':f'Bearer {acess_token}'
+    }
+    return get(url=endpoint, headers=headers)

spotifysearch/classes.py

@@ -0,0 +1,219 @@
+
+# THIS FILE IS RESPONSABLE FOR MANY CLASS DECLARATIONS
+
+import json
+import math
+from base64 import b64encode
+from urllib.request import urlretrieve
+from . import constructor
+from . import calls
+
+
+# AUTHENTICATION AND TOKENS
+class Authenticator:
+
+    def __init__(self, client_id:str, client_secret:str):
+        self.credentials = self.encode_credentials(client_id, client_secret)
+    
+
+    def encode_credentials(self, client_id, client_secret):
+        credentials = f'{client_id}:{client_secret}'
+        encoded_credentials = b64encode(credentials.encode('utf-8'))
+        return str(encoded_credentials, 'utf-8')
+
+
+    def get_acess_token(self):
+        response = calls.call_acess_token(self.credentials)
+        return response.json()['access_token']
+
+
+# OBJECTS
+
+# Base class for all classes
+class Base: 
+    
+    def __init__(self, data, type, name, url, id):
+        self.data = data
+        self.type = type
+        self.name = name
+        self.url = url
+        self.id = id
+    
+
+    def export_json(self, path:str):
+        file = open(path, 'w')
+        json.dump(self.data, file, indent=4)
+        file.close()
+
+
+# Base class for track-like objects
+class TrackBase(Base): 
+    
+    def __init__(self, data, type, name, url, id, explicit, duration_ms):
+        super().__init__(data, type, name, url, id)
+        self.explicit = explicit
+        self.duration_ms = duration_ms
+    
+
+    def get_formatted_duration(self) -> dict:
+        
+        duration_in_seconds = self.duration_ms / 1000
+        hours = 0
+        mins = math.floor(duration_in_seconds // 60)
+
+        if mins >= 60:
+            hours = math.floor(mins // 60)
+            mins = math.floor(mins % 60)
+        
+        secs = math.floor(duration_in_seconds % 60)
+
+        return {'hours':hours, 'minutes':mins, 'seconds':secs}
+    
+
+    def get_string_duration(self) -> str:
+     
+        duration = self.get_formatted_duration()
+        format = self.__format_duration
+        
+        hours = format(str(duration['hours']))
+        mins = format(str(duration['minutes']))
+        secs = format(str(duration['seconds']))       
+        
+        if int(hours):
+            return f'{hours}:{mins}:{secs}'
+        else:
+            return f'{mins}:{secs}'
+
+
+    def __format_duration(self, value:str):
+        if len(value) < 2:
+            return ('0' + value)
+        else: return value
+
+
+class Artist(Base):
+
+    def __init__(self, data:dict, type:str, name:str, url:str, id:str):
+        super().__init__(data, type, name, url, id)
+
+
+class AlbumCover:
+
+    def __init__(self, width, height, url):
+        self.width = width
+        self.height = height
+        self.url = url
+    
+
+    def export_image(self, path):
+        urlretrieve(self.url, path)
+
+
+class Album(Base):
+
+    def __init__(self, data:dict, type:str, name:str, url:str, id:str, 
+    images:list, artists:list, available_markets:list, release_date:str, total_tracks:int):
+        
+        super().__init__(data, type, name, url, id)
+        self.images = images
+        self.artists = artists
+        self.available_markets = available_markets
+        self.release_date = release_date
+        self.total_tracks = total_tracks
+        
+
+class TrackPreview:
+
+    def __init__(self, url):
+        self.url = url
+    
+
+    def export_audio(self, path):
+        urlretrieve(self.url, path)
+
+
+class Track(TrackBase):
+
+    def __init__(self, data:dict, type:str, name:str, url:str, id:str, explicit:bool,
+    duration_ms:int, preview:TrackPreview, artists:list, album:Album, available_markets:list, 
+    disc_number:int, popularity:int):
+        
+        super().__init__(data, type, name, url, id, explicit, duration_ms)
+        self.preview = preview
+        self.artists = artists
+        self.album = album
+        self.available_markets = available_markets
+        self.disc_number = disc_number
+        self.popularity = popularity
+
+
+class Episode(TrackBase):
+
+    def __init__(self, data:dict, type:str, name:str, url:str, id:str, 
+    explicit:bool,  duration_ms:int, preview:str, description:str, html_description:str,
+    images:list, language:str, languages:list, release_date:str):
+        
+        super().__init__(data, type, name, url, id, explicit, duration_ms)
+        self.preview = preview
+        self.description = description
+        self.html_description = html_description
+        self.images = images
+        self.language = language
+        self.languages = languages
+        self.release_date = release_date
+
+
+# CLIENT OBJECTS
+class Results(Base):
+
+    def __init__(self, data):
+        self.data = data
+    
+
+    def __get_items(self, type):     
+        
+        if type == 'artist':
+            try:
+                data = self.data['artists']['items']
+                func = constructor.artist
+            except KeyError:
+                return []
+        
+        elif type == 'track':
+            try:
+                data = self.data['tracks']['items']
+                func = constructor.track
+            except KeyError:
+                return []
+        
+        elif type == 'album':
+            try:
+                data = self.data['albums']['items']
+                func = constructor.album
+            except KeyError:
+                return []
+        
+        elif type == 'episode':
+            try:
+                data = self.data['episodes']['items']
+                func = constructor.episode
+            except KeyError:
+                return [] 
+        
+        return [func(item) for item in data]
+
+
+    def get_tracks(self) -> list:
+        return self.__get_items('track')
+    
+
+    def get_artists(self) -> list:
+        return self.__get_items('artist')
+    
+
+    def get_albums(self) -> list:
+        return self.__get_items('album')
+
+
+    def get_episodes(self) -> list:
+        return self.__get_items('episode')

spotifysearch/client.py

@@ -0,0 +1,19 @@
+
+# THIS FILE IS RESPONSABLE FOR DEALING WITH THE CLIENT
+
+from .classes import Authenticator, Results
+from . import calls
+
+
+class Client:
+
+    def __init__(self, client_id, client_secret):
+        self.auth = Authenticator(client_id, client_secret)
+
+
+    def search(self, keywords:str, *, types:list = ['track'], filters:dict = {}, 
+    market:str = None, limit:int = None, offset:int = None) -> Results:        
+        access_token = self.auth.get_acess_token()
+        args = (keywords, types, filters, market, limit, offset)
+        response = calls.call_search(access_token, args)
+        return Results(response.json())

spotifysearch/constructor.py

@@ -0,0 +1,80 @@
+
+# THIS FILE IS RESPONSABLE FOR THE CONSTRUCTION OF MANY OBJECTS
+
+from . import classes
+
+
+def get_available_markets(data):
+    try:
+        return data['available_markets']
+    except KeyError:
+        return None
+
+
+# BASE ARGUMENTS FOR ALL CLASSES
+def base_arguments(data):
+    arguments = dict(
+        data = data,
+        type = data['type'],
+        name = data['name'],
+        url = data['external_urls']['spotify'],
+        id  = data['id']
+    )
+    return arguments
+
+
+# BASE ARGUMENTS FOR TRACK-LIKE CLASSES
+def track_base_arguments(data):
+    arguments = dict(
+        explicit = data['explicit'],
+        duration_ms = data['duration_ms']
+    )
+    return arguments
+
+
+def artist(data):
+    return classes.Artist(**base_arguments(data))
+
+
+def track(data):
+    base = base_arguments(data)
+    track_base = track_base_arguments(data)
+
+    arguments = dict(
+        preview = data['preview_url'],
+        artists = [artist(artist_data) for artist_data in data['artists']],
+        album = album(data['album']),
+        available_markets = get_available_markets(data),
+        disc_number = data['disc_number'],
+        popularity = data['popularity']
+    )
+    return classes.Track(**{**base, **track_base, **arguments})
+
+
+def album(data):
+    base = base_arguments(data)
+
+    arguments = dict(
+        images = [classes.AlbumCover(image['width'], image['height'], image['url']) for image in data['images']],
+        artists = [artist(artist_data) for artist_data in data['artists']],
+        available_markets = get_available_markets(data),
+        release_date = data['release_date'],
+        total_tracks = data['total_tracks']
+    )
+    return classes.Album(**{**base, **arguments})
+
+
+def episode(data):
+    base = base_arguments(data)
+    track_base = track_base_arguments(data)
+
+    arguments = dict(
+        preview = data['audio_preview_url'],
+        description = data['description'],
+        html_description = data['html_description'],
+        images = data['images'],
+        language = data['language'],
+        languages = data['languages'],
+        release_date = data['release_date']
+    )
+    return classes.Episode(**{**base, **track_base, **arguments})

spotifysearch/urlbuilder.py

@@ -0,0 +1,28 @@
+
+# THIS FILE IS RESPONSABLE FOR BUILDING DYNAMIC URLS
+
+def search_endpoint(keywords:str, allowed_types:list, 
+filters:dict, market:str, limit:int, offset:int):
+    endpoint = 'https://api.spotify.com/v1/search?'
+    
+    # FORMAT QUERRY ITEMS AND FILTERS
+    querry_items = keywords.split(' ')   
+    for filter, value in filters.items():
+        value = value.replace(' ', '%20')
+        item = f'{filter}:{value}'
+        querry_items.append(item)
+
+    # REQUIRED ARGUMENTS
+    querry = 'q=' + '%20'.join(querry_items)
+    types = 'type=' + ','.join(allowed_types)
+    arguments = [querry, types]
+    
+    # OPTIONAL ARGUMENTS
+    if market: 
+        arguments.append(f'market={market}')
+    if limit: 
+        arguments.append(f'limit={limit}')  
+    if offset: 
+        arguments.append(f'offset={offset}')
+    
+    return endpoint + '&'.join(arguments)