Update app.py

app.py

@@ -45,7 +45,8 @@ def find_similar_texts(model_name, input_text):
         text_embeddings['euclidean_distance'] = distances
         sorted_embeddings = text_embeddings.sort_values(by='euclidean_distance', ascending=True)
         top_five = sorted_embeddings.head(5)[['abstract', 'patent no']]
-        formatted_output = '\n\n'.join([f"Patent No: {row['patent no']}\nAbstract: {row['abstract']}\n" for index, row in top_five.iterrows()])
+     #   formatted_output = '\n\n'.join([f"Patent No: {row['patent no']}\nAbstract: {row['abstract']}\n" for index, row in top_five.iterrows()])
+        formatted_output = '\n\n'.join([f"Patent No: {row['patent no']}\nTitle: {row['title']}\nAbstract: {row['abstract']}\n" for index, row in top_five.iterrows()])
         return formatted_output
     else:
         return "It seems there is no patent abstract close to your description."
@@ -56,7 +57,7 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             model_selector = gr.Dropdown(choices=list(model_options.keys()), label="Chose Sentence-Transformer")
-            text_input = gr.Textbox(lines=2, placeholder="machine learning for drug dosing", label="input_text (like <<machine learning for drug dosing>>. Remember, this is only a small selection of machine learning patents!)")
+            text_input = gr.Textbox(lines=2, placeholder="machine learning for drug dosing", label="input_text (example: machine learning for drug dosing. Remember, this is only a small selection of machine learning patents!)")
             submit_button = gr.Button("search")
             
         with gr.Column():