Upload app.py

app.py

@@ -70,19 +70,18 @@ train_data, test_data= train_test_split(df, test_size=0.15, random_state=42 )
 howto= """Welcome to the <b>Medical Chatbot</b>, powered by Gradio.
 Currently, the chatbot can WELCOME YOU, PREDICT DISEASE based on your symptoms and SUGGEST POSSIBLE SOLUTIONS AND RECOMENDATIONS, and BID YOU FAREWELL.
 <b>How to Start:</b> Simply type your messages in the textbox to chat with the Chatbot and press enter!<br><br>
-The bot will respond based on the best possible answers to your messages.
+The bot will respond based on the best possible answers to your messages."""
 
-"""
 # Create the gradio demo
 with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""") as demo:
-    gr.HTML('<h1 align="center">Medical Chatbot: ARIN 7102')
-    #gr.HTML('<h3 align="center">To know more about this project')
-    with gr.Accordion("Follow these Steps to use the Gradio WebUI", open=True):
-        gr.HTML(howto)
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox()
-    clear = gr.ClearButton([msg, chatbot])
-'''
+  gr.HTML('<h1 align="center">Medical Chatbot: ARIN 7102')
+  #gr.HTML('<h3 align="center">To know more about this project')
+  with gr.Accordion("Follow these Steps to use the Gradio WebUI", open=True):
+      gr.HTML(howto)
+  chatbot = gr.Chatbot()
+  msg = gr.Textbox()
+  clear = gr.ClearButton([msg, chatbot])
+  '''
   def respond(message, chat_history):
     # Create couple of if-else statements to capture/mimick peoples's Interaction
     embedder = SentenceTransformer(args.embedder, device=device)
@@ -92,8 +91,8 @@ with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
     chat_history.append((message, bot_message))
     time.sleep(2)
     return "", chat_history
-'''
-def respond(message, chat_history, base_model = "microsoft/phi-2", device=device): # "meta-llama/Meta-Llama-3-70B"
+  '''
+  def respond(message, chat_history, base_model = "/home/henry/Desktop/ARIN7102/phi-2", device=device): # "meta-llama/Meta-Llama-3-70B"
         # define the model and tokenizer.
         # model = PhiForCausalLM.from_pretrained(base_model)
         model = AutoModelForCausalLM.from_pretrained(base_model)
@@ -121,8 +120,9 @@ def respond(message, chat_history, base_model = "microsoft/phi-2", device=device
         time.sleep(2)
         return "", chat_history
     #return bot_message
-
-msg.submit(respond, [msg, chatbot], [msg, chatbot])
+       
+  msg.submit(respond, [msg, chatbot], [msg, chatbot])
+  
 
 # Launch the demo
 demo.launch()