Update app.py

app.py

@@ -6,7 +6,7 @@ from sklearn.feature_extraction.text import TfidfVectorizer
 
 
 movies_df = pd.read_csv("movies.csv")
-
+links = pd.read_csv("imdbLinks.csv")
 genre_df = movies_df['genres'].str.get_dummies("|")
 st.title("Movie Recommendation System")
 @st.cache_data
@@ -53,6 +53,8 @@ if not movie:
                 movie_id = movies_df[movies_df['title'] == random_movies.iloc[id]].index
                 similar_movies_idx = similarity_matrix[movie_id[0]].argsort()[::-1][1:7]
                 similar_movies = movies_df['title'].iloc[similar_movies_idx]
+                movies_links = links.iloc[similar_movies_idx].to_numpy()
+                st.write(movie_links)
                 with col2:
                     st.subheader("Recommended Movies")
                     for i,j in enumerate(similar_movies[:5]):