app.py

from transformers import TFAutoModelForCausalLM, AutoTokenizer
import tensorflow as tf
import gradio as gr

TITLE = "DialoGPT -- Chatbot"
DESCRIPTION = "<center>Have funny/existencial dialogs with non-human entities</center>"
EXAMPLES = [
    ["How will the world end?"],
    ["Does the universe have a purpose?"],
    ["Is the universe infinite?"],
    ["Was Einstein right about time being relative?"],
    ["What is Pythagoras theorem?"],
    ["What is the meaning of life?"],
]
ARTICLE = r"""<center>
              This application allowsa you to talk with a machine.
              In the back-end I'm using the DialoGPT model from microsoft.<br>
              This model extends GPT2 towards the conversational neural response generetion domain.<br>
              ArXiv paper: https://arxiv.org/abs/1911.00536<br>
              Done by dr. Gabriel Lopez<br> 
              For more please visit: <a href='https://sites.google.com/view/dr-gabriel-lopez/home'>My Page</a><br>
              </center>"""

tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = TFAutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")


def chat_with_bot(user_input, chat_history_and_input=[]):
    emb_user_input = tokenizer.encode(
        user_input + tokenizer.eos_token, return_tensors="tf"
    )
    print("chat_history:", chat_history_and_input)
    print("emb_user_input:", emb_user_input)
    if chat_history_and_input == []:
        bot_input_ids = emb_user_input  # first iteration
    else:
        bot_input_ids = tf.concat(
            [chat_history_and_input, emb_user_input], axis=-1
        )  # other iterations
    chat_history_and_input = model.generate(
        bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id
    ).numpy()
    # print
    bot_response = tokenizer.decode(
        chat_history_and_input[:, bot_input_ids.shape[-1] :][0],
        skip_special_tokens=True,
    )
    print(f"{bot_response=}")
    print(f"{chat_history_and_input=}")
    return bot_response, chat_history_and_input

gr.Interface(
    inputs=["text", "state"],
    outputs=["text", "state"],
    examples=EXAMPLES,
    title=TITLE,
    description=DESCRIPTION,
    article=ARTICLE,
    fn=chat_with_bot,
    allow_flagging=False,
).launch()