Python

# chatbot/app.py

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr

# Load pretrained DialoGPT model and tokenizer
checkpoint = "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)

# Chat history for session
chat_history_ids = None

def respond(user_input, history=[]):
    global chat_history_ids
    
    # Encode user input and append to chat history
    new_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
    
    if chat_history_ids is not None:
        bot_input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1)
    else:
        bot_input_ids = new_input_ids

    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)

    output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)

    history.append((user_input, output))
    return history, history

# Gradio UI
with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox(label="Type your message here")
    clear = gr.Button("Clear Chat")

    state = gr.State([])

    msg.submit(respond, [msg, state], [chatbot, state])
    clear.click(lambda: ([], []), None, [chatbot, state])

if __name__ == "__main__":
    demo.launch()