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

title = 'ChatBot'
description = 'This is a test model i created to learn how to create one haha'
examples = [['What is life?']]

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

def predict(input, history=[]):
    new_user_input_ids = tokenizer.encode(
        input + tokenizer.eos_token, return_tensors='pt'
    )
    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)

    history = model.generate(
        bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
    ).tolist()

    response = tokenizer.decode(history[0]).split('<|endoftext|>')

    response = [
        (response[i], response[i+1]) for i in range(0, len(response) - 1, 2)
    ]
    return response, history

gr.Interface(
    fn=predict,
    title=title,
    description=description,
    examples=examples,
    inputs=['text', 'state'],
    outputs=['chatbot', 'state'],
).launch()