app.py

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

tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-medium')
model = AutoModelWithLMHead.from_pretrained('output-medium')

chat_history_ids = None
step = 0


def predict(input, chat_history_ids=chat_history_ids, step=step):
    # encode the new user input, add the eos_token and return a tensor in Pytorch
    new_user_input_ids = tokenizer.encode(
        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 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=1000,
        pad_token_id=tokenizer.eos_token_id,
        no_repeat_ngram_size=3,
        do_sample=True,
        top_k=100,
        top_p=0.7,
        temperature=0.8
    )
    step = step + 1
    output = tokenizer.decode(
        chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
    return output


gr.Interface(fn=predict, inputs="text", outputs="text").launch(debug=True)