badalnabizade

badalnabizade

badalnabizade

badalnabizade

badalnabizade

badalnabizade

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.

badalnabizade

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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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 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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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']