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")

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()
    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()