Update app.py

app.py

@@ -36,7 +36,8 @@ if UseMemory:
       repo = Repository(
       local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
       )
-            
+
+       
 def store_message(name: str, message: str):
     if name and message:
         with open(DATA_FILE, "a") as csvfile:
@@ -72,6 +73,7 @@ title = "💬ChatBack🧠💾"
 description = """Chatbot With persistent memory dataset allowing multiagent system AI to access a shared dataset as memory pool with stored interactions. 
  Current Best SOTA Chatbot:  https://huggingface.co/facebook/blenderbot-400M-distill?text=Hey+my+name+is+ChatBack%21+Are+you+ready+to+rock%3F  """
 
+
 def chat(message, history):
     history = history or []
     if history: 
@@ -96,7 +98,6 @@ def chat(message, history):
     return history, history
 
 
-
 gr.Interface(
     fn=chat,
     theme="huggingface",
@@ -106,20 +107,7 @@ gr.Interface(
     title=title,
     allow_flagging="never",
 
-    
     description=f"Gradio chatbot backed by memory in a dataset repository.",
     article=f"The memory dataset for saves is [{DATASET_REPO_URL}]({DATASET_REPO_URL}) 🦃Thanks!🦃 Check out HF Datasets: https://huggingface.co/spaces/awacke1/FreddysDatasetViewer  SOTA papers code and datasets on chat are here: https://paperswithcode.com/datasets?q=chat&v=lst&o=newest"
 
     ).launch(debug=True)
-        
-#demo = gr.Blocks()
-#with demo:
-#    audio_file = gr.inputs.Audio(source="microphone", type="filepath")
-#    text = gr.Textbox(label="Speech to Text")
-#    TTSchoice = gr.inputs.Radio( label="Pick a Text to Speech Model", choices=MODEL_NAMES,   )
-#    audio = gr.Audio(label="Output", interactive=False)
-#    b1 = gr.Button("Recognize Speech")
-#    b5 = gr.Button("Read It Back Aloud")
-#    b1.click(speech_to_text, inputs=audio_file, outputs=text)
-#    b5.click(tts,  inputs=[text,TTSchoice], outputs=audio)    
-#demo.launch(share=True)