movie-recommender-deployed / pages /2 - Similarity-Based Recommender.py
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import streamlit as st
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
import re
import requests
# DATA:
movies = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/movie-recommender-streamlit/main/data/movies.csv')
ratings = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/movie-recommender-streamlit/main/data/ratings.csv')
links = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/movie-recommender-streamlit/main/data/links.csv')
# clean titles column by moving "The" and "A" to the beginning of the string
# this makes it more searchable for users
movies.loc[lambda df: df["title"].str.contains(", The", regex=True), 'title'] = 'The ' + movies['title']
movies.loc[lambda df: df["title"].str.contains(", The", regex=True), 'title'] = movies['title'].str.replace(", The", '', regex=True)
movies.loc[lambda df: df["title"].str.contains(", A", regex=True), 'title'] = 'A ' + movies['title']
movies.loc[lambda df: df["title"].str.contains(", A", regex=True), 'title'] = movies['title'].str.replace(", A", '', regex=True)
# create "database" to use for recommendations
movie_user_matrix = (
ratings
.merge(movies, on='movieId')[['title', 'rating', 'userId']]
.pivot_table(index='title', columns='userId', values='rating')
.fillna(0)
)
similarities_movies = pd.DataFrame(cosine_similarity(movie_user_matrix),
index=movie_user_matrix.index,
columns=movie_user_matrix.index)
# TITLE:
st.title("User-Based Recommender")
# FUNCTIONS:
def get_similar_recommendations(movie_title, n, genres):
# select similarity for chosen movie
similarities = pd.DataFrame(
(similarities_movies.query("index != @movie_title")[movie_title] / sum(similarities_movies.query("index != @movie_title")[movie_title]))
.sort_values(ascending= False))
# exclude genres if necessary and return the n movies with the highest similarity
recommendations = (
similarities
.merge(movies, how= 'left', left_index = True, right_on = 'title')
[lambda df: df["genres"].str.contains(genres, regex=True)]
.head(n)
[['title', 'genres']]
)
recommendations.rename(columns= {'title': 'Movie Title', 'genres': 'Genres'}, inplace = True)
return recommendations
def get_similar_recommendations_streaming(movie_title, n, genres, country, url, headers):
# select similarity for chosen movie
similarities = pd.DataFrame(
(similarities_movies.query("index != @movie_title")[movie_title] / sum(similarities_movies.query("index != @movie_title")[movie_title]))
.sort_values(ascending= False))
# exclude genres if necessary and return the n movies with the highest similarity
recommendations = (
similarities
.merge(movies, how= 'left', left_index = True, right_on = 'title')
[lambda df: df["genres"].str.contains(genres, regex=True)]
.head(n)
[['title', 'genres', 'movieId']]
)
# merge recommendations with links df to get imdbIds for the API calls
recommendations_ids = recommendations.merge(links, how = 'left', on = 'movieId')[['title', 'genres', 'imdbId']]
recommendations_ids['imdbId'] = 'tt0' + recommendations_ids['imdbId'].astype('str')
imdb_ids = list(recommendations_ids['imdbId'])
# create new column for streaming links
recommendations_ids['Streaming Availability'] = ""
# loop through imdb_ids to make one api call for each to get available streaming links
for id in imdb_ids:
# make api call
querystring = {"country":country,"imdb_id":id,"output_language":"en"}
response = requests.request("GET", url, headers=headers, params=querystring)
streaming_info = response.json()
for streaming_service in streaming_info['streamingInfo']:
recommendations_ids.loc[recommendations_ids['imdbId'] == id, 'Streaming Availability'] += f"{streaming_service}: {streaming_info['streamingInfo'][streaming_service][country]['link']} \n"
recommendations_ids.rename(columns= {'title': 'Movie Title', 'genres': 'Genres'}, inplace = True)
return recommendations_ids[['Movie Title', 'Genres', 'Streaming Availability']]
def transform_genre_to_regex(genres):
regex = ""
for genre in genres:
regex += f"(?=.*{genre})"
return regex
def find_movie_title(user_input):
title_list = movies.title.unique()
r = re.compile(f".*{user_input}.*")
result = []
for title in title_list:
match = r.findall(title)
if match:
result.append(match)
return result[0][0]
# USER INPUT:
st.write("""
Type in the name of your movie.
""")
movie_title_raw = st.text_input('Movie Title')
movie_title = find_movie_title(movie_title_raw)
st.write("""
Move the slider to the desired number of recommendations you wish to receive.
""")
number_of_recommendations = st.slider("Number of recommendations", 1, 10, 5)
st.write("""
__Optional__: You can narrow down the recommendations by picking one or several genre(s).
However, the more genres you choose, the fewer movies will be recommended.
""")
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)
st.write("""
__Optional__: You can receive links for popular streaming services for each recommendation (if available) by selecting your countrycode.
Select none if you don't want to get streaming links.
""")
streaming_country = st.selectbox('Optional: Country for streaming information', ('none', 'de', 'us'))
# API INFORMATION:
url = "https://streaming-availability.p.rapidapi.com/get/basic"
headers = {
"X-RapidAPI-Key": st.secrets["api_key"],
"X-RapidAPI-Host": "streaming-availability.p.rapidapi.com"
}
# EXECUTION:
if st.button("Get Recommendations"):
if streaming_country == 'none':
st.write(get_similar_recommendations(movie_title, number_of_recommendations, genres_regex))
else:
st.write("Double-click on the Streaming-Availability column to see all links.")
st.write(get_similar_recommendations_streaming(movie_title, number_of_recommendations, genres_regex, streaming_country, url, headers))