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def get_album_tracks(album_id):
    """
    Returns: dictionary object.
        Track ids as keys, track names as values.
    """
    tracks = []
    results = sp.album_tracks(album_id)
    tracks.extend(results['items'])
    # By default album_tracks function can obtain up to 50 item from spotify API in one try.
    # If there is more than 50 items, this function will return 'next'.

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def get_artist_albums(artist):
    """
    ----------
    artist: artist's information that obtained by get_artist function.
    ----------
    Returns: dictionary object.
        Album names as keys, album ids as values.
    """
    albums = []
    results = sp.artist_albums(artist['id'], album_type='album')

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def get_artist(name):
    """
    ----------
    name: 'Name of Spotify artist'
    ----------
    Returns: dictionary object.
        Given Spotify Artist's information.
    """
    results = sp.search(q='artist:' + name, type='artist')
    items = results['artists']['items']

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import spotipy
from spotipy.oauth2 import SpotifyClientCredentials

with open('app_token.txt', 'r') as file:
    tokens = file.read()

token_dict = json.loads(tokens)

client_credentials_manager = \
            SpotifyClientCredentials(client_id=token_dict['client_id'],

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movie_names = movies.set_index('movieId')['title'].to_dict()
g=ratings.groupby('movieId')['rating'].count()

#TopMovies are top 3000 movies that got more user reviews than others. 
topMovies=g.sort_values(ascending=False).index.values[:3000]

uniq = ratings.movieId.unique()  # Ids of unique movies.
name2idx = {o:i for i,o in enumerate(uniq)}
topMovieIdx = np.array([name2idx[o] for o in topMovies])  # Indices of top rated movies in ratings.csv

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import pandas as pd 
import numpy as np
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
from tensorflow.keras.layers import Input, add, dot, Flatten, Embedding,Dropout, concatenate
from tensorflow.keras.regularizers import l2
from tensorflow.keras import optimizers

ratings = pd.read_csv('./data/ratings.csv')
ratings.drop('timestamp', 1, inplace=True)

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Serverless Movie Recommmendation System

Application is online in here

How it Works:

• 1 User signs up

• 1.1. main.py assigns a new id to registered user.

• 1.2. main.py writes user's id, name, mail and hashed password to USER table.

• 2 User signs in

• 2.1. main.py checks whether user has selected movies in order to get recommendations.

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