gradio_app.py

from threading import Thread
from typing import Iterator
from transformers import AutoModel, AutoTokenizer, AutoImageProcessor, TextIteratorStreamer
from PIL import Image as PILImage
import tempfile
import torch
import gradio as gr


def get_gradio_demo(model, tokenizer, image_processor) -> gr.Interface:

    def get_prompt(message: str, chat_history: list[tuple[str, str]],
                system_prompt: str) -> str:
        texts = [f'#instruction: {system_prompt}\n', '#context:\n']
        texts += [f"human: {user_input.strip()}\nagent: {response.strip()}\n" for user_input, response in chat_history if isinstance(user_input, str)]
        texts += [f'human: {message.strip()}']
        return ''.join(texts)


    def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int:
        prompt = get_prompt(message, chat_history, system_prompt)
        input_ids = tokenizer([prompt], return_tensors='np', add_special_tokens=False)['input_ids']
        return input_ids.shape[-1]


    def run(image: PILImage.Image,
            message: str,
            chat_history: list[tuple[str, str]],
            system_prompt: str,
            max_new_tokens: int = 192,
            temperature: float = 0.1,
            top_p: float = 0.9,
            top_k: int = 50) -> Iterator[str]:
        prompt = get_prompt(message, chat_history, system_prompt)
        patch_images = image_processor([image], return_tensors="pt").pixel_values.to(torch.float16).to('cuda')
        inputs = tokenizer([prompt], return_tensors='pt').to('cuda')

        streamer = TextIteratorStreamer(tokenizer, timeout=10.)  # 
        generate_kwargs = dict(
            inputs,
            patch_images=patch_images,
            streamer=streamer,
            max_length=max_new_tokens,
            do_sample=True,
            top_p=top_p,
            top_k=top_k,
            temperature=temperature,
            num_beams=1,
        )
        t = Thread(target=model.generate, kwargs=generate_kwargs)
        t.start()

        outputs = []
        for text in streamer:
            outputs.append(text)
            yield ''.join(outputs).replace("not yet.", "").replace("<s>", "").replace("</s>", "").strip()

    # -------

    DEFAULT_SYSTEM_PROMPT = """can you specify which region the context describes?"""
    MAX_MAX_NEW_TOKENS = 512
    DEFAULT_MAX_NEW_TOKENS = 128
    MAX_INPUT_TOKEN_LENGTH = 512

    DESCRIPTION = """<h1 align="center">TiO Demo</h1>
    <div align="center">https://huggingface.co/jxu124/TiO</div>
    """

    LICENSE = """
    <p/>

    ---
    """

    if not torch.cuda.is_available():
        DESCRIPTION += '\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>'


    def upload_image(file_obj):
        chatbot = [[(file_obj.name,), None]]
        return (gr.update(visible=False), gr.update(interactive=True, placeholder='Type a message...',), chatbot)


    def clear_and_save_textbox(message: str) -> tuple[str, str]:
        return '', message


    def display_input(message: str,
                    history: list[tuple[str, str]]) -> list[tuple[str, str]]:
        if len(history) == 0:
            raise gr.Error(f'Upload an image first and try again.')
        history.append((message, ''))
        return history


    def delete_prev_fn(
            history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]:
        try:
            message, _ = history.pop()
        except IndexError:
            message = ''
        return history, message or ''


    def generate(
        message: str,
        history_with_input: list[tuple[str, str]],
        system_prompt: str,
        max_new_tokens: int,
        temperature: float,
        top_p: float,
        top_k: int,
    ) -> Iterator[list[tuple[str, str]]]:
        if max_new_tokens > MAX_MAX_NEW_TOKENS:
            raise ValueError

        image = PILImage.open(history_with_input[0][0][0])
        history = history_with_input[:-1]
        generator = run(image, message, history, system_prompt, max_new_tokens, temperature, top_p, top_k)
        try:
            first_response = next(generator)
            yield history + [(message, first_response)]
        except StopIteration:
            yield history + [(message, '')]
        for response in generator:
            if "region:" in response:
                bboxes = model.utils.sbbox_to_bbox(response)
                if len(bboxes):
                    with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as f:
                        model.utils.show_mask(image, bboxes).save(f)
                    chatbot = history + [(message, "OK, I see."), (None, (f.name,))]
            else:
                chatbot = history + [(message, response)]
            yield chatbot


    def process_example(message: str) -> tuple[str, list[tuple[str, str]]]:
        generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 192, 1, 0.95, 50)
        for x in generator:
            pass
        return '', x


    def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None:
        input_token_length = get_input_token_length(message, chat_history[:-1], system_prompt)
        if input_token_length > MAX_INPUT_TOKEN_LENGTH:
            raise gr.Error(f'The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.')


    with gr.Blocks() as demo:
        gr.Markdown(DESCRIPTION)

        with gr.Group():
            chatbot = gr.Chatbot(label='Chatbot')
            imagebox = gr.File(
                file_types=["image"],
                show_label=False,
            )
            with gr.Row():
                textbox = gr.Textbox(
                    container=False,
                    show_label=False,
                    interactive=False,
                    placeholder='Upload an image...',
                    scale=10,
                )
                submit_button = gr.Button('Submit',
                                        variant='primary',
                                        scale=1,
                                        min_width=0)
        with gr.Row():
            retry_button = gr.Button('🔄  Retry', variant='secondary')
            undo_button = gr.Button('↩️ Undo', variant='secondary')
            clear_button = gr.Button('🗑️  Clear', variant='secondary')

        saved_input = gr.State()

        with gr.Accordion(label='Advanced options', open=False):
            system_prompt = gr.Textbox(label='System prompt',
                                    value=DEFAULT_SYSTEM_PROMPT,
                                    lines=6)
            max_new_tokens = gr.Slider(
                label='Max new tokens',
                minimum=1,
                maximum=MAX_MAX_NEW_TOKENS,
                step=1,
                value=DEFAULT_MAX_NEW_TOKENS,
            )
            temperature = gr.Slider(
                label='Temperature',
                minimum=0.1,
                maximum=4.0,
                step=0.1,
                value=0.5,
            )
            top_p = gr.Slider(
                label='Top-p (nucleus sampling)',
                minimum=0.05,
                maximum=1.0,
                step=0.05,
                value=0.9,
            )
            top_k = gr.Slider(
                label='Top-k',
                minimum=1,
                maximum=1000,
                step=1,
                value=20,
            )

        gr.Markdown(LICENSE)
        imagebox.upload(
            fn=upload_image,
            inputs=imagebox,
            outputs=[imagebox, textbox, chatbot],
            api_name=None,
            queue=False,
        )

        textbox.submit(
            fn=clear_and_save_textbox,
            inputs=textbox,
            outputs=[textbox, saved_input],
            api_name=None,
            queue=False,
        ).then(
            fn=display_input,
            inputs=[saved_input, chatbot],
            outputs=chatbot,
            api_name=None,
            queue=False,
        ).then(
            fn=check_input_token_length,
            inputs=[saved_input, chatbot, system_prompt],
            api_name=None,
            queue=False,
        ).success(
            fn=generate,
            inputs=[
                saved_input,
                chatbot,
                system_prompt,
                max_new_tokens,
                temperature,
                top_p,
                top_k,
            ],
            outputs=chatbot,
            api_name="generate",
        )

        button_event_preprocess = submit_button.click(
            fn=clear_and_save_textbox,
            inputs=textbox,
            outputs=[textbox, saved_input],
            api_name=None,
            queue=False,
        ).then(
            fn=display_input,
            inputs=[saved_input, chatbot],
            outputs=chatbot,
            api_name=None,
            queue=False,
        ).then(
            fn=check_input_token_length,
            inputs=[saved_input, chatbot, system_prompt],
            api_name=None,
            queue=False,
        ).success(
            fn=generate,
            inputs=[
                saved_input,
                chatbot,
                system_prompt,
                max_new_tokens,
                temperature,
                top_p,
                top_k,
            ],
            outputs=chatbot,
            api_name=None,
        )

        retry_button.click(
            fn=delete_prev_fn,
            inputs=chatbot,
            outputs=[chatbot, saved_input],
            api_name=None,
            queue=False,
        ).then(
            fn=display_input,
            inputs=[saved_input, chatbot],
            outputs=chatbot,
            api_name=None,
            queue=False,
        ).then(
            fn=generate,
            inputs=[
                saved_input,
                chatbot,
                system_prompt,
                max_new_tokens,
                temperature,
                top_p,
                top_k,
            ],
            outputs=chatbot,
            api_name=None,
        )

        undo_button.click(
            fn=delete_prev_fn,
            inputs=chatbot,
            outputs=[chatbot, saved_input],
            api_name=None,
            queue=False,
        ).then(
            fn=lambda x: x,
            inputs=[saved_input],
            outputs=textbox,
            api_name=None,
            queue=False,
        )

        clear_button.click(
            fn=lambda: ([], '', gr.update(value=None, visible=True), gr.update(interactive=False, placeholder='Upload an image...',)),
            outputs=[chatbot, saved_input, imagebox, textbox],
            queue=False,
            api_name=None,
        )

    return demo


def main(model_id: str = 'jxu124/TiO', host: str = "0.0.0.0", port: int = None):
    assert torch.cuda.is_available()
    model = AutoModel.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16).cuda()
    tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
    image_processor = AutoImageProcessor.from_pretrained(model_id)

    # ---- gradio demo ----
    model.get_gradio_demo(tokenizer, image_processor).queue(max_size=20).launch(server_name=host, server_port=port)


if __name__ == "__main__":
    import fire
    fire.Fire(main)