app.py

app.py

@@ -375,16 +375,19 @@ def recommend(movie, similarity_df, movies_df, ratings_df, links_df, k=5):
     try:
         index = movies_df[movies_df['title'] == movie].index[0]
 
-
-
         distances = sorted(list(enumerate(similarity_df.iloc[index])), reverse=True, key=lambda x: x[1])
 
         recommended_movies = []
-        for i in distances[1:k+1]:
+        for i in distances[1:]:
             movie_id = movies_df.iloc[i[0]]['movieId']
-            movie_details = get_movie_details(movie_id, movies_df, ratings_df, links_df)
-            if movie_details:
-                recommended_movies.append(movie_details)
+            num_ratings = ratings_df[ratings_df['movieId'] == movie_id].shape[0]
+            
+            if num_ratings > 100:
+                movie_details = get_movie_details(movie_id, movies_df, ratings_df, links_df)
+                if movie_details:
+                    recommended_movies.append(movie_details)
+                if len(recommended_movies) == k:
+                    break
         return recommended_movies
     except Exception as e:
         st.error(f"Error generating recommendations: {e}")
@@ -462,8 +465,8 @@ def main():
         if button:
             st.write("The rating bar here is token from our dataset and it's between 0 and 5.")
             if selected_movie:
-                recommendations = get_recommendation_item(DB_df, similarity_df, selected_movie , k)
-                # recommendations = recommend(selected_movie, similarity_df, movies_df, ratings_df, links_df, k)
+                # recommendations = get_recommendation_item(DB_df, similarity_df, selected_movie , k)
+                recommendations = recommend(selected_movie, similarity_df, movies_df, ratings_df, links_df, k)
                 if recommendations:
                     st.write(f"Similar movies to '{selected_movie}':")
                     num_cols = 2