Spotify Recommender API Call

One of my favorite features in Spotify are the recommendations. The app’s recommendations includes the Discover Weekly,  Daily Mix, Release Radar and the Artist Radio features. I could go through hours of recommendations substituting white noise while working on projects and usually encounter a song or an artist that appeals to my guitar/keyboard driven sensibilities in a session. While Discover Weekly, Daily Mix yield gems once in a while, the song specific ones usually based on Artist / Song radio yield a lot more matches to my sensibilities.

The recommendations endpoints that generates reccs based on a seed is a favorite. I’ve usually had a good match rate with songs that “stick” based on the API. There are plenty of other endpoints (artists, songs  etc) that could be easily plugged in to generate relevant predictions.

The API documentation of Spotify has always been stellar and its usability is enhanced by being able to test all the API calls easily within their developer console.

This API also has a bunch of parameters that can be configured for fine-tuning the recommendation: key, genre, loudness, energy, instrumentalness, popularity, speechiness, danceability etc.

Per the official docs – “Recommendations are generated based on the available information for a given seed entity and matched against similar artists and tracks. If there is sufficient information about the provided seeds, a list of tracks will be returned together with pool size details. For artists and tracks that are very new or obscure there might not be enough data to generate a list of tracks.

One of the key things here is to generate seeds for the recommendations, this can be done by using endpoints like Get a User’s Top Artists and Tracks to obtain artists and tracks based on my listening history and use these artists and tracks as seeds for the Get Recommendations Based on Seeds endpoint. This endpoint will only return tracks. 
The Web API Authorization Guide is a must to read before querying these endpoints and the developer console makes it super easy to try out different endpoints. 

I wanted a quick way to query the recommendations API for new recommendations and the combination of the streamlit + Spotify API was quick simple solve to get that working. At a high level I wanted to be able to query a song or artist and generate recommendations based on it. A secondary need is also to collect data for a reccomender I am training to customize ML-driven reccomendations but more on that in a different post.

A lot of the code is boilerplate and pretty self explanatory but at a high level it consists of the class to interact with the Spotify API ( ,  a UI wrapper using Streamlit to render the app (  Given a client id and client secret, gets client credentials from the Spotify API to invoke the search. Sample code inline with comments. The code can obviously be much more modular and pythonic but for investing a quick hour of hacking, this got the job done.

class SpotifyAPI(object):
    access_token = None
    access_token_expires =
    access_token_did_expire = True
    client_id = None
    client_secret = None
    token_url = ''

    def __init__(self, client_id, client_secret, *args, **kwargs):
        self.client_id = client_id
        self.client_secret = client_secret

    # Given a client id and client secret, gets client credentials from the Spotify API.
    def get_client_credentials(self):
        ''' Returns a base64 encoded string '''
        client_id = self.client_id
        client_secret = self.client_secret
        if client_secret == None or client_id == None:
            raise Exception("check client IDs")
        client_creds = f"{client_id}:{client_secret}"
        client_creds_b64 = base64.b64encode(client_creds.encode())
        return client_creds_b64.decode()

    def get_token_header(self):  # Get header
        client_creds_b64 = self.get_client_credentials()
        return {"Authorization": f"Basic {client_creds_b64}"}

    def get_token_data(self):  # Get token
        return {
            "grant_type": "client_credentials"

    def perform_auth(self):  # perform auth only if access token has expired
        token_url = self.token_url
        token_data = self.get_token_data()
        token_headers = self.get_token_header()

        r =, data=token_data, headers=token_headers)

        if r.status_code not in range(200, 299):
            print("Could not authenticate client")
        data = r.json()
        now =
        access_token = data["access_token"]
        expires_in = data['expires_in']
        expires = now + datetime.timedelta(seconds=expires_in)
        self.access_token = access_token
        self.access_token_expires = expires
        self.access_token_did_expire = expires < now
        return True

    def get_access_token(self):

        token = self.access_token
        expires = self.access_token_expires
        now =
        if expires < now:
            return self.get_access_token()
        elif token == None:
            return self.get_access_token()
        return token

    # search for an artist/track based on a search type provided
    def search(self, query, search_type="artist"):
        access_token = self.get_access_token()
        headers = {"Content-Type": "application/json",
                   "Authorization": f"Bearer { access_token}"}
        # using the  search API at
        search_url = ""
        data = {"q": query, "type": search_type.lower()}
        from urllib.parse import urlencode
        search_url_formatted = urlencode(data)
        search_r = requests.get(
            search_url+search_url_formatted, headers=headers)
        if search_r.status_code not in range(200, 299):
            print("Encountered isse=ue")
            return search_r.json()
        return search_r.json()

    def get_meta(self, query, search_type="track"):  # meta data of a track
        resp =, search_type)
        all = []
        for i in range(len(resp['tracks']['items'])):
            track_name = resp['tracks']['items'][i]['name']
            track_id = resp['tracks']['items'][i]['id']
            artist_name = resp['tracks']['items'][i]['artists'][0]['name']
            artist_id = resp['tracks']['items'][i]['artists'][0]['id']
            album_name = resp['tracks']['items'][i]['album']['name']
            images = resp['tracks']['items'][i]['album']['images'][0]['url']

            raw = [track_name, track_id, artist_name, artist_id, images]

        return all


The get_recommended_songs function is the core of the app querying the API for results based on the query passed in. The more the parameters the better the results. Customizing the call to any API call is fairly trivial.

   def get_reccomended_songs(self, limit=5, seed_artists='', seed_tracks='', market="US",
                              seed_genres="rock", target_danceability=0.1):  # reccomendations API
        access_token = self.get_access_token()
        endpoint_url = ""
        all_recs = []
        self.limit = limit
        self.seed_artists = seed_artists
        self.seed_tracks = seed_tracks = market
        self.seed_genres = seed_genres
        self.target_danceability = target_danceability

        # API query plus some additions
        query = f'{endpoint_url}limit={limit}&market={market}&seed_genres={seed_genres}&target_danceability={target_danceability}'
        query += f'&seed_artists={seed_artists}'
        query += f'&seed_tracks={seed_tracks}'
        response = requests.get(query, headers={
                                "Content-type": "application/json", "Authorization": f"Bearer {access_token}"})
        json_response = response.json()

        # print(json_response)
        if response:
            print("Reccomended songs")
            for i, j in enumerate(json_response['tracks']):
                track_name = j['name']
                artist_name = j['artists'][0]['name']
                link = j['artists'][0]['external_urls']['spotify']

                print(f"{i+1}) \"{j['name']}\" by {j['artists'][0]['name']}")
                reccs = [track_name, artist_name, link]
            return all_recs

Wrapping both the calls in a Streamlist app is refreshingly simple and dockerizing and pushing to Azure container registry was trivial.



To run the app, run:
streamlit run

Deployed at

Part 2 to follow at some point as I continue building out a custom recommender that compares the current personalizer with a custom personalizer that takes in Audio features and more personalized inputs and tuneable parameters.