Update app.py

app.py

@@ -65,6 +65,10 @@ def get_similar_and_recommend(input_text):
     # Get all features of the most similar video
     most_similar_video_features = df.iloc[most_similar_index].to_dict()
 
+    if "text_for_embedding" in most_similar_video_features:
+        del most_similar_video_features["text_for_embedding"]
+    if "embeddings" in most_similar_video_features:
+        del most_similar_video_features["embeddings"]
     # Recommend the top 10 videos based on GNN embeddings
     def recommend_top_10(given_video_index, all_video_embeddings):
         dot_products = [
@@ -89,7 +93,12 @@ def get_similar_and_recommend(input_text):
 
     # Calculate the weight for each GNN output
     video_weights = [weight] * len(top_10_recommended_videos_features)
-
+    # Exclude unwanted features for recommended videos
+    for recommended_video in top_10_recommended_videos_features:
+        if "text_for_embedding" in recommended_video:
+            del recommended_video["text_for_embedding"]
+        if "embeddings" in recommended_video:
+            del recommended_video["embeddings"]
     # Create the output JSON with the most similar video, final recommendations, and weights
     output = {
         "search_context": {