Update app.py

app.py

@@ -278,6 +278,7 @@ def extract_problem_domains(df,
         cluster_labels = kmeans.fit_predict(X)
     
     # # BERTopic approach (commented out)
+    console_messages.append("BERT is currently commented...")
     # topic_model = BERTopic()
     # topics, _ = topic_model.fit_transform(df[text_column].tolist())
     # topic_model.reduce_topics(df[text_column].tolist(), nr_topics=optimal_n_clusters)
@@ -298,9 +299,8 @@ def extract_problem_domains(df,
             # print(f"top_words: {top_words}, type: {type(top_words)}")
             # print(f"center.argsort(): {center.argsort()}, type: {type(center.argsort())}")
             
-            console_messages.append(f"top_words: {top_words}, type: {type(top_words)}",
-                                    f"center.argsort(): {center.argsort()}, type: {type(center.argsort())}"
-                                   )
+            console_messages.append(f"top_words: {top_words}, type: {type(top_words)}")
+            console_messages.append(f"center.argsort(): {center.argsort()}, type: {type(center.argsort())}")
 
             
             # top_word_indices = center.argsort()[-top_words:][::-1]