Include chatbot methods within the Blocks flow

app.py

@@ -1,7 +1,8 @@
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr
 import torch
-
+import random
+import time
 
 # tokenizer = AutoTokenizer.from_pretrained("chavinlo/gpt4-x-alpaca")
 # model = AutoModelForCausalLM.from_pretrained("chavinlo/gpt4-x-alpaca")
@@ -9,33 +10,38 @@ import torch
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
-def user(message, history):
-    return "", history + [[message, None]]
-
-def bot(history):
-    user_message = history[-1][0]
-    new_user_input_ids = tokenizer.encode(user_message + tokenizer.eos_token, return_tensors='pt')
-
-    # append the new user input tokens to the chat history
-    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-
-    # generate a response
-    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+# def bot(history):
+#     user_message = history[-1][0]
+#     new_user_input_ids = tokenizer.encode(user_message + tokenizer.eos_token, return_tensors='pt')
+#
+#     # append the new user input tokens to the chat history
+#     bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+#
+#     # generate a response
+#     history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
+#
+#     # convert the tokens to text, and then split the responses into lines
+#     response = tokenizer.decode(history[0]).split("<|endoftext|>")
+#     response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
+#     return history
 
-    # convert the tokens to text, and then split the responses into lines
-    response = tokenizer.decode(history[0]).split("<|endoftext|>")
-    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
-    return history
-
-## Use the above methods to create the Chatbot interface
 with gr.Blocks() as demo:
     chatbot = gr.Chatbot()
     msg = gr.Textbox()
     clear = gr.Button("Clear")
 
+    def user(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot(history):
+        bot_message = random.choice(["Yes", "No"])
+        history[-1][1] = bot_message
+        time.sleep(1)
+        return history
+
     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
         bot, chatbot, chatbot
     )
     clear.click(lambda: None, None, chatbot, queue=False)
 
-demo.launch()
+demo.launch()