rename application file

app.py

@@ -0,0 +1,43 @@
+import streamlit as st
+import pandas as pd
+
+
+st.title("Movie Recommender")
+ 
+st.write("""
+### Project description
+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. 
+The recommendation process will take ca. 15 seconds. 
+""")
+
+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)