Update app.py

app.py

@@ -64,7 +64,7 @@ def compare_text(transcript, categories, threshold):
         phrases = torch.stack(phrases_list)
         cosine_scores = util.cos_sim(embeddings, phrases).numpy()
         max_scores = np.max(cosine_scores, axis=1)
-        df_cosines[df_category.iloc[0,2]] = np.round(max_scores * 1000) / 1000.0
+        df_cosines[df_category.iloc[0,2]] = np.rint(max_scores * 1000) / 1000.0
         for num_sentence, scores in enumerate(cosine_scores):
             for num_phrase, score in enumerate(scores):
                 if score >= threshold:
@@ -73,14 +73,14 @@ def compare_text(transcript, categories, threshold):
                         'sentence': sentences[num_sentence],
                         'phrase': df_category.at[num_phrase,'example'],
                         'category': df_category.at[num_phrase,'label'],
-                        'similarity': np.round(score * 1000) / 1000.0
+                        'similarity': np.rint(score * 1000) / 1000.0
                     }
                     df_results = df_results.append(new_row, ignore_index=True)
 
     df_results = df_results.sort_values(['line','similarity'],ascending=[True,False])
 
     df_summary = pd.DataFrame(df_cosines.max(numeric_only=True),columns=['similarity'])
-    df_summary['ok'] = np.where(df_summary['similarity'] > threshold, True, False)
+    df_summary['ok'] = np.where(df_summary['similarity'] > threshold, True, False)    
 
     fig = px.bar(
         df_summary,