Update app.py

app.py

@@ -62,48 +62,39 @@ def get_similar_and_recommend(input_text):
     similarities = cosine_similarity([input_embedding], embeddings_matrix)[0]
     most_similar_index = np.argmax(similarities)
 
-    most_similar_video = {
-        "title": df["title"].iloc[most_similar_index],
-        "description": df["description"].iloc[most_similar_index],
-        "similarity_score": similarities[most_similar_index],
-    }
+    # Get all features of the most similar video
+    most_similar_video_features = df.iloc[most_similar_index].to_dict()
 
     # Recommend the top 10 videos based on GNN embeddings and dot product
     def recommend_next_10_videos(given_video_index, all_video_embeddings):
         dot_products = [
-            torch.dot(all_video_embeddings[given_video_index].cpu(), all_video_embeddings[i].cpu())
+            torch.dot(all_video_embeddings[given_video_index], all_video_embeddings[i])
             for i in range(all_video_embeddings.shape[0])
         ]
         dot_products[given_video_index] = -float("inf")
 
         top_10_indices = np.argsort(dot_products)[::-1][:10]
-        recommendations = [df["title"].iloc[idx] for idx in top_10_indices]
-        return recommendations
+        return [df.iloc[idx].to_dict() for idx in top_10_indices]
 
-    top_10_recommendations = recommend_next_10_videos(
+    top_10_recommended_videos_features = recommend_next_10_videos(
         most_similar_index, all_video_embeddings
     )
 
-    return (
-        most_similar_video["title"],
-        most_similar_video["description"],
-        most_similar_video["similarity_score"],
-        top_10_recommendations,
-    )
+    # Create the output JSON with all features
+    output = {
+        "most_similar_video": most_similar_video_features,
+        "top_10_recommended_videos": top_10_recommended_videos_features,
+    }
+
+    return output
 
-# Update the Gradio interface to fix the output type
+# Update the Gradio interface to output a JSON object containing all features
 interface = gr.Interface(
     fn=get_similar_and_recommend,
     inputs=gr.components.Textbox(label="Enter Text to Find Most Similar Video"),
-    outputs=[
-        gr.components.Textbox(label="Video Title"),
-        gr.components.Textbox(label="Video Description"),
-        gr.components.Textbox(label="Similarity Score"),
-        gr.components.Textbox(label="Top 10 Recommended Videos", lines=10),  # Handle a list
-    ],
+    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 based on dot product and GNN embeddings.",
+    description="Enter text to find the most similar video and get the top 10 recommended videos with all features in a JSON object.",
 )
 
-# Launch the Gradio interface
-interface.launch()
+interface.launch()