import os
os.system('pip install gradio==2.3.5b0')
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import gradio as gr

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



def dialogpt(text):
    history = gr.get_state() or []
    # encode the new user input, add the eos_token and return a tensor in Pytorch
    for step in range(50000):

        new_user_input_ids = tokenizer.encode(text + 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 step > 0 else new_user_input_ids

        # generated a response while limiting the total chat history to 1000 tokens, 
        chat_history_ids = model.generate(bot_input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id)
        response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
        history.append((text, response))
        gr.set_state(history)
        # pretty print last ouput tokens from bot
        html = "<div class='chatbot'>"
        for user_msg, resp_msg in history:
            html += f"<div class='user_msg'>{user_msg}</div>"
            html += f"<div class='resp_msg'>{resp_msg}</div>"
        html += "</div>"
        return html


inputs = gr.inputs.Textbox(lines=1, label="Input Text")
outputs =  gr.outputs.Textbox(label="DialoGPT")

title = "DialoGPT"
description = "Gradio demo for Microsoft DialoGPT: A State-of-the-Art Large-scale Pretrained Response Generation Model. To use it, simply input text or click one of the examples text to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1911.00536'>DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation</a> | <a href='https://github.com/microsoft/DialoGPT'>Github Repo</a></p>"
examples = [
            ["Hi, how are you?"],
            ["How far away is the moon?"],
]

gr.Interface(dialogpt, inputs, "html", title=title, description=description, article=article, examples=examples,css="""
    .chatbox {display:flex;flex-direction:column}
    .user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
    .user_msg {background-color:cornflowerblue;color:white;align-self:start}
    .resp_msg {background-color:lightgray;align-self:self-end}
""").launch(debug=True)