add api-integration

pages/3 - Similarity-Based Recommender.py

@@ -2,10 +2,20 @@ 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/recommender-systems/main/movie_data/movies.csv')
-ratings = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/recommender-systems/main/movie_data/ratings.csv')
+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 = (
@@ -24,6 +34,8 @@ st.write("""
 ### Instructions
 Type in the title of a movie for which you would like to receive similar recommendations.  
 Move the slider to the desired number of recommendations you wish to receive.  
+If you want to receive links for popular streaming services for the recommendations, type in your countrycode (popular values are "us" for the United States, and "de" for Germany)
+Leave this field empty if you don't want to get links. 
 Afterwards, simply click the "Get Recommendations" button to receive recommendations that are most similar to the given movie.  
   
 __Optional__: You can narrow down the recommendations by picking one or several genre(s).  
@@ -50,6 +62,44 @@ def get_similar_recommendations(movie_title, n, genres):
 
     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" 
+
+        return recommendations_ids[['title', 'genres', 'Streaming Availability']]
+
+
 def transform_genre_to_regex(genres):
     regex = ""
     for genre in genres:
@@ -79,6 +129,20 @@ genre_list = set([inner for outer in movies.genres.str.split('|') for inner in o
 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)
 
+streaming_country = st.text_input('Country for streaming information (e.g. "de" for Germany)')
+
+# API INFORMATION:
+url = "https://streaming-availability.p.rapidapi.com/get/basic"
+headers = {
+	"X-RapidAPI-Key": api_key,
+	"X-RapidAPI-Host": "streaming-availability.p.rapidapi.com"
+}
+
+
 # EXECUTION:
 if st.button("Get Recommendations"):
-    st.write(get_similar_recommendations(movie_title, number_of_recommendations, genres_regex))
+    if streaming_country == '':
+        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))