pages/1 - Popularity-Based Recommender.py

import streamlit as st
import pandas as pd

# DATA:
movies = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/recommender-systems/main/movie_data/movies.csv')
ratings = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/recommender-systems/main/movie_data/ratings.csv')

# INSTRUCTIONS:
st.title("Popularity-Based Recommender")
st.write("""
### Instructions
Move the slider to the desired number of recommendations you wish to receive. 
Afterwards, simply click the "Get Recommendations" button to receive recommendations of the most popular movies in our database. 
If you want, you can narrow it down by picking one or several genre(s).  
""")

# FUNCTIONS:

def get_popular_recommendations(n, genres):
    return (
        ratings
            .groupby('movieId')
            .agg(avg_rating = ('rating', 'mean'), num_ratings = ('rating', 'count'))
            .merge(movies, on='movieId')
            .assign(combined_rating = lambda x: x['avg_rating'] * x['num_ratings']**0.5)
            [lambda df: df["genres"].str.contains(genres, regex=True)]
            .sort_values('combined_rating', ascending=False)
            .head(n)
            [['title', 'avg_rating', 'genres']]
    )

def transform_genre_to_regex(genres):
    regex = ""
    for genre in genres:
        regex += f"(?=.*{genre})"
    return regex

# USER INPUT:
number_of_recommendations = st.slider("Number of recommendations", 1, 10, 5)

genre_list = set([inner for outer in movies.genres.str.split('|') for inner in outer])
genres = st.multiselect('Optional: Select one or more genres', genre_list, default=None, key=None, help=None, on_change=None, args=None, kwargs=None, disabled=False)
genres_regex = transform_genre_to_regex(genres)

# EXECUTION:
if st.button("Get Recommendations"):
    st.write(get_popular_recommendations(number_of_recommendations, genres_regex))