change app.py

app.py

@@ -2,42 +2,15 @@ import streamlit as st
 import pandas as pd
 
 
-st.title("Movie Recommender")
+st.title("Movie Recommenders")
  
 st.write("""
 ### Instructions
-Type in a movie title with the release year in brackets (e.g. "The Matrix (1999)"), choose the number of recommendations you wish, and the app will recommend movies based on your chosen movie.\n\n 
-The recommendation process will take ca. 15 seconds. 
+Check the Sidebar and choose a recommender that suits your purpose.  
 """)
 
-chosen_movie = st.text_input("Movie title and release year")
-number_of_recommendations = st.slider("Number of recommendations", 1, 10, 5)
-
 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')
 
 all_ratings = ratings.merge(movies, on='movieId')[['title', 'rating', 'userId']]
 all_ratings_pivoted = all_ratings.pivot_table(index='userId', columns='title', values='rating')
-
-def get_recommendations_for_movie(movie_name, n):
-    
-    eligible_movies = []
-
-    for movie in all_ratings_pivoted.columns:
-        nr_shared_ratings = all_ratings_pivoted.loc[all_ratings_pivoted[movie_name].notnull() & all_ratings_pivoted[movie].notnull(), [movie_name, movie]].count()[0]
-        if nr_shared_ratings >= 10:
-            eligible_movies.append(movie)
-
-    return (
-        all_ratings_pivoted
-            [eligible_movies]
-            .corrwith(all_ratings_pivoted[movie_name]).sort_values(ascending=False)[1:n+1]
-            .index
-    )
-
-if st.button("Recommend"):
-    
-    recommendations = get_recommendations_for_movie(chosen_movie, number_of_recommendations)
-
-    st.write("Recommendations for", chosen_movie)
-    st.write(recommendations)