Update app.py

app.py

@@ -44,9 +44,14 @@ def find_similar_texts(model_name, input_text):
         distances = euclidean_distances(embedding_matrix, input_embedding).flatten()
         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', 'title']]
-     #   formatted_output = '\n\n'.join([f"Patent No: {row['patent no']}\n {row['title']}\n {row['abstract']}\n" for index, row in top_five.iterrows()])
-        formatted_output = '\n\n'.join([f"<strong>Patent No: {row['patent no']}\n<strong>Title: {row['title']}\n<strong>Abstract: {row['abstract']}" for index, row in top_five.iterrows()])
+    #    top_five = sorted_embeddings.head(5)[['abstract', 'patent no', 'title']]
+    #    formatted_output = '\n\n'.join([f"<Patent No: {row['patent no']}\nTitle: {row['title']}\nAbstract: {row['abstract']}" for index, row in top_five.iterrows()])
+        formatted_output = '\n\n'.join([
+        f"<strong>Patent No:</strong> {row['patent no']}<br>"
+        f"<strong>Title:</strong> {row['title']}<br>"
+        f"<strong>Abstract:</strong> {row['abstract']}"
+    for index, row in top_five.iterrows()
+])
         return formatted_output
     else:
         return "It seems there is no patent abstract close to your description."