Update app.py

app.py

@@ -52,11 +52,6 @@ with gr.Blocks() as demo:
             fn = get_tutor_reply, inputs = [study_tutor], outputs = [user_chat_input, chatbot, study_tutor], queue=True
         )
 
-        # Testing the chat history storage, can be deleted at deployment
-        test_btn = gr.Button("View your chat history")
-        chat_history = gr.JSON(label = "conversation history")
-        test_btn.click(get_conversation_history, inputs=[study_tutor], outputs=[chat_history, study_tutor])
-
         # Download conversation history file
         with gr.Blocks():
             gr.Markdown("""
@@ -74,6 +69,11 @@ with gr.Blocks() as demo:
         export_dialogue_button_txt.click(save_txt, study_tutor, file_download, show_progress=True)
         export_dialogue_button_csv.click(save_csv, study_tutor, file_download, show_progress=True)
 
+        # Testing the chat history storage, can be deleted at deployment
+        test_btn = gr.Button("View your chat history")
+        chat_history = gr.JSON(label = "conversation history")
+        test_btn.click(get_conversation_history, inputs=[study_tutor], outputs=[chat_history, study_tutor])
+
     # Instructor interface
     with gr.Tab("Instructor Only"):
         """
@@ -119,7 +119,7 @@ with gr.Blocks() as demo:
             # and load it here to chain with the student interface
             
             # TODO: Currently, the instructor prompt is handled as text input and stored in the vector store (and in the learning objective),
-            # which means the tutor now is still a question-answering tutor who viewed the prompt as context input. 
+            # which means the tutor now is still a question-answering tutor who viewed the prompt as context (but not really acting based on it). 
             # We need to find a way to provide the prompt directly to the model and set its status. 
     
 demo.queue().launch(server_name='0.0.0.0', server_port=7860)