`bot` method now replaced with responses from DialoGPT

app.py

@@ -10,20 +10,6 @@ import time
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
-# 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
 
 with gr.Blocks() as demo:
     chatbot = gr.Chatbot()
@@ -34,11 +20,25 @@ with gr.Blocks() as demo:
         return "", history + [[user_message, None]]
 
     def bot(history):
-        bot_message = random.choice(["Yes", "No"])
-        history[-1][1] = bot_message
-        time.sleep(1)
+        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
+
+        # Add the new response to the chat history
+        history = history + [tokenizer.encode(response[0][0] + tokenizer.eos_token, return_tensors='pt')]
+
         return history
 
+
     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
         bot, chatbot, chatbot
     )