app.py

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from collections import defaultdict
import gradio as gr
from optimum.onnxruntime import ORTModelForCausalLM


import itertools
import re


user_token = "<User>"
eos_token = "<EOS>"
bos_token = "<BOS>"
bot_token = "<Assistant>"

max_context_length = 750

def is_english_word(tested_string):
    pattern = re.compile(r"^[a-zA-Z]+$")
    return pattern.match(tested_string) is not None



def format(history):
    prompt = bos_token

    for idx, txt in enumerate(history):
        if idx % 2 == 0:
            prompt += f"{user_token}{txt}{eos_token}"
        else:
            prompt += f"{bot_token}{txt}"
    prompt += bot_token
    print(prompt)
    return prompt

def gradio(model, tokenizer):
    def response(
        user_input,
        chat_history,
        top_k,
        top_p,
        temperature,
        repetition_penalty,
        no_repeat_ngram_size,
    ):
        history = list(itertools.chain(*chat_history))
        history.append(user_input)

        prompt = format(history)

        input_ids = tokenizer.encode(
            prompt,
            return_tensors="pt",
            add_special_tokens=False,
        )[:, -max_context_length:]

        prompt_length = input_ids.shape[1]

        beam_output = model.generate(
            input_ids,
            pad_token_id=tokenizer.pad_token_id,
            max_new_tokens=250,
            num_beams=1, # with cpu
            top_k=top_k,
            top_p=top_p,
            no_repeat_ngram_size=no_repeat_ngram_size,
            temperature=temperature,
            repetition_penalty=repetition_penalty,
            early_stopping=True,
            do_sample=True
        )
        output = beam_output[0][prompt_length:]

        tokens = tokenizer.convert_ids_to_tokens(output)
        for i, token in enumerate(tokens[:-1]):
            if is_english_word(token) and is_english_word(tokens[i + 1]):
                tokens[i] = token + " "
        text = "".join(tokens).replace("##", "").replace("[UNK]", "").strip()

        return text

    bot = gr.Chatbot(show_copy_button=True, show_share_button=True)

    with gr.Blocks() as demo:
        gr.Markdown("GPT2 chatbot | Powered by nlp-greyfoss")

        with gr.Accordion("Parameters in generation", open=False):
            with gr.Row():
                top_k = gr.Slider(
                    2.0,
                    100.0,
                    label="top_k",
                    step=1,
                    value=50,
                    info="Limit the number of candidate tokens considered during decoding.",
                )
                top_p = gr.Slider(
                    0.1,
                    1.0,
                    label="top_p",
                    value=0.9,
                    info="Control the diversity of the output by selecting tokens with cumulative probabilities up to the Top-P threshold.",
                )
                temperature = gr.Slider(
                    0.1,
                    2.0,
                    label="temperature",
                    value=0.9,
                    info="Control the randomness of the generated text. A higher temperature results in more diverse and unpredictable outputs, while a lower temperature produces more conservative and coherent text.",
                )
                repetition_penalty = gr.Slider(
                    0.1,
                    2.0,
                    label="repetition_penalty",
                    value=1.2,
                    info="Discourage the model from generating repetitive tokens in a sequence.",
                )
                no_repeat_ngram_size = gr.Slider(
                    0,
                    100,
                    label="no_repeat_ngram_size",
                    step=1,
                    value=5,
                    info="Prevent the model from generating sequences of n consecutive tokens that have already been generated in the context. ",
                )

        gr.ChatInterface(
            response,
            chatbot=bot,
            additional_inputs=[
                top_k,
                top_p,
                temperature,
                repetition_penalty,
                no_repeat_ngram_size,
            ],
        )

    demo.queue().launch()




tokenizer = AutoTokenizer.from_pretrained("greyfoss/gpt2-chatbot-chinese")

model = ORTModelForCausalLM.from_pretrained("greyfoss/gpt2-chatbot-chinese", export=True)

gradio(model, tokenizer)