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()