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

tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")

# Initialize chat history
chat_history_ids = None

def chat_cpu(user_input):
    global chat_history_ids
    # Encode the new user input, add the eos_token, and return a tensor in PyTorch
    new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')

    # Append the new user input tokens to the chat history
    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if chat_history_ids is not None else new_user_input_ids

    # Generate a response while limiting the total chat history to 1000 tokens
    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)

    # Pretty print last output tokens from bot
    response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    return "DialoGPT: {}".format(response)

iface = gr.Interface(fn=chat_cpu, inputs="text", outputs="text")
iface.launch(share=True)