Update app.py

app.py

@@ -79,12 +79,10 @@ def get_similar_and_recommend(input_text):
         ]
         dot_products[given_video_index] = -float("inf")
 
-        top_10_indices = np.argsort(dot_products)[[::-1][:10]
-        return [df.iloc[idx].to_dict() for idx in top_10_indices]
+        top 10_indices = np.argsort(dot_products)[::-1][:10]
+        return [df.iloc[idx].to_dict() for idx in top 10_indices]
 
-    top_10_recommended_videos_features = recommend_next_10_videos(
-        most_similar_index, all_video_embeddings
-    )
+    top_10_recommended_videos_features = recommend_next_10_videos(most_similar_index, all_video_embeddings)
 
     # Exclude unwanted features for recommended videos
     for recommended_video in top_10_recommended_videos_features:
@@ -92,7 +90,7 @@ def get_similar_and_recommend(input_text):
             if key in recommended_video:
                 del recommended_video[key]
 
-    # Create the output JSON with all features except the unwanted ones
+    # Create the output JSON with all features except unwanted ones
     output = {
         "most_similar_video": most_similar_video_features,
         "top_10_recommended_videos": top_10_recommended_videos_features,
@@ -100,13 +98,13 @@ def get_similar_and_recommend(input_text):
 
     return output
 
-# Update the Gradio interface to output a JSON object without unwanted features
+# Update the Gradio interface to output JSON without unwanted features
 interface = gr.Interface(
     fn=get_similar_and_recommend,
     inputs=gr.components.Textbox(label="Enter Text to Find Most Similar Video"),
     outputs=gr.JSON(),
     title="Video Recommendation System with GNN-based Recommendations",
-    description="Enter text to find the most similar video and get the top 10 recommended videos with all features except embeddings-related fields in a JSON object.",
+    description="Enter text to find the most similar video and get top 10 recommended videos with all features except embeddings-related fields in a JSON object.",
 )
 
 interface.launch()