llava/serve/new_gws.py

import argparse
import datetime
import json
import os
import time

import gradio as gr
import requests

from llava.conversation import (default_conversation, conv_templates,
                                   SeparatorStyle)
from llava.constants import LOGDIR
from llava.utils import (build_logger, server_error_msg,
    violates_moderation, moderation_msg)
import hashlib

from PIL import Image, ImageDraw,ImageFont
def get_scale_factor(original_size):
    original_width, original_height = original_size
    new_width = min(nearest_multiple_of_224_at_least_224(original_width, ceiling=False), 1344)
    scale_factor = new_width / original_width
    return scale_factor


def nearest_multiple_of_224_at_least_224(num, ceiling=False):
    if num <= 224:
        return 224
    division, remainder = divmod(num, 224)
    if ceiling and remainder > 0:
        return (division + 1) * 224
    if remainder < 112:
        return division * 224
    else:
        return (division + 1) * 224

logger = build_logger("gradio_web_server", "gradio_web_server.log")

headers = {"User-Agent": "UGround Client"}

no_change_btn = gr.Button()
enable_btn = gr.Button(interactive=True)
disable_btn = gr.Button(interactive=False)

priority = {
    "vicuna-13b": "aaaaaaa",
    "koala-13b": "aaaaaab",
}


def get_conv_log_filename():
    t = datetime.datetime.now()
    name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
    return name


def get_model_list():
    ret = requests.post(args.controller_url + "/refresh_all_workers")
    assert ret.status_code == 200
    ret = requests.post(args.controller_url + "/list_models")
    models = ret.json()["models"]
    models.sort(key=lambda x: priority.get(x, x))
    logger.info(f"Models: {models}")
    return models


get_window_url_params = """
function() {
    const params = new URLSearchParams(window.location.search);
    url_params = Object.fromEntries(params);
    console.log(url_params);
    return url_params;
    }
"""


def load_demo(url_params, request: gr.Request):
    logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}")

    dropdown_update = gr.Dropdown(visible=True)
    if "model" in url_params:
        model = url_params["model"]
        if model in models:
            dropdown_update = gr.Dropdown(value=model, visible=True)

    state = default_conversation.copy()
    return state, dropdown_update


def load_demo_refresh_model_list(request: gr.Request):
    logger.info(f"load_demo. ip: {request.client.host}")
    models = get_model_list()
    state = default_conversation.copy()
    dropdown_update = gr.Dropdown(
        choices=models,
        value=models[0] if len(models) > 0 else ""
    )
    return state, dropdown_update


def vote_last_response(state, vote_type, model_selector, request: gr.Request):
    with open(get_conv_log_filename(), "a") as fout:
        data = {
            "tstamp": round(time.time(), 4),
            "type": vote_type,
            "model": model_selector,
            "state": state.dict(),
            "ip": request.client.host,
        }
        fout.write(json.dumps(data) + "\n")


def upvote_last_response(state, model_selector, request: gr.Request):
    logger.info(f"upvote. ip: {request.client.host}")
    vote_last_response(state, "upvote", model_selector, request)
    return ("",) + (disable_btn,) * 3


def downvote_last_response(state, model_selector, request: gr.Request):
    logger.info(f"downvote. ip: {request.client.host}")
    vote_last_response(state, "downvote", model_selector, request)
    return ("",) + (disable_btn,) * 3


def flag_last_response(state, model_selector, request: gr.Request):
    logger.info(f"flag. ip: {request.client.host}")
    vote_last_response(state, "flag", model_selector, request)
    return ("",) + (disable_btn,) * 3


def regenerate(state, image_process_mode, request: gr.Request):
    logger.info(f"regenerate. ip: {request.client.host}")
    state.messages[-1][-1] = None
    prev_human_msg = state.messages[-2]
    if type(prev_human_msg[1]) in (tuple, list):
        prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode)
    state.skip_next = False
    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5


def clear_history(request: gr.Request):
    logger.info(f"clear_history. ip: {request.client.host}")
    state = default_conversation.copy()
    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5


def add_text(state, text, image, image_process_mode, request: gr.Request):
    logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}")


    if image is not None:
    # 默认的宽度和高度
        default_width = (1344, 896)
        # 读取图像
        original_width, original_height = image.size

        # Resize逻辑基于宽度和高度比较
        if original_width >= original_height:
            # 基于宽度调整大小
            new_width = default_width[0]
            resize_scale = new_width / original_width
            new_height = round(original_height * resize_scale)
        else:
            # 基于高度调整大小
            new_width = default_width[1]
            resize_scale = new_width / original_width
            new_height = round(original_height * resize_scale)

        # 调整图像大小
        image = image.resize((new_width, new_height))



    if len(text) <= 0 and image is None:
        state.skip_next = True
        return (state, state.to_gradio_chatbot(), "", None) + (no_change_btn,) * 5
    if args.moderate:
        flagged = violates_moderation(text)
        if flagged:
            state.skip_next = True
            return (state, state.to_gradio_chatbot(), moderation_msg, None) + (
                no_change_btn,) * 5

    text = text[:16384]  # Hard cut-off
    if image is not None:
        text = text[:16384]  # Hard cut-off for images
        if '<image>' not in text:
            # text = '<Image><image></Image>' + text
            text ="In the screenshot, where are the pixel coordinates (x, y) of the element corresponding to "+ f"\"{text}\"?"  + '\n<image>'



        text = (text, image, image_process_mode)
        state = default_conversation.copy()
    state.append_message(state.roles[0], text)
    state.append_message(state.roles[1], None)
    state.skip_next = False
    return (state, state.to_gradio_chatbot(), "", None) + (disable_btn,) * 5


def http_bot(state, model_selector, temperature, top_p, max_new_tokens, request: gr.Request):
    logger.info(f"http_bot. ip: {request.client.host}")
    start_tstamp = time.time()
    # model_name = model_selector
    model_name="osunlp/UGround"
    if state.skip_next:
        # This generate call is skipped due to invalid inputs
        yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
        return

    if len(state.messages) == state.offset + 2:
        # First round of conversation
        # if "llava" in model_name.lower():
        #     if 'llama-2' in model_name.lower():
        #         template_name = "llava_llama_2"
        #     elif "mistral" in model_name.lower() or "mixtral" in model_name.lower():
        #         if 'orca' in model_name.lower():
        #             template_name = "mistral_orca"
        #         elif 'hermes' in model_name.lower():
        #             template_name = "chatml_direct"
        #         else:
        #             template_name = "mistral_instruct"
        #     elif 'llava-v1.6-34b' in model_name.lower():
        #         template_name = "chatml_direct"
        #     elif "v1" in model_name.lower():
        #         if 'mmtag' in model_name.lower():
        #             template_name = "v1_mmtag"
        #         elif 'plain' in model_name.lower() and 'finetune' not in model_name.lower():
        #             template_name = "v1_mmtag"
        #         else:
        #             template_name = "llava_v1"
        #     elif "mpt" in model_name.lower():
        #         template_name = "mpt"
        #     else:
        #         if 'mmtag' in model_name.lower():
        #             template_name = "v0_mmtag"
        #         elif 'plain' in model_name.lower() and 'finetune' not in model_name.lower():
        #             template_name = "v0_mmtag"
        #         else:
        #             template_name = "llava_v0"
        # elif "mpt" in model_name:
        #     template_name = "mpt_text"
        # elif "llama-2" in model_name:
        #     template_name = "llama_2"
        # else:
        #     template_name = "vicuna_v1"
        template_name='llava_v1'
        new_state = conv_templates[template_name].copy()
        new_state.append_message(new_state.roles[0], state.messages[-2][1])
        new_state.append_message(new_state.roles[1], None)
        state = new_state

    # Query worker address
    controller_url = args.controller_url
    ret = requests.post(controller_url + "/get_worker_address",
            json={"model": model_name})
    worker_addr = ret.json()["address"]
    logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}")

    # No available worker
    if worker_addr == "":
        state.messages[-1][-1] = server_error_msg
        yield (state, state.to_gradio_chatbot(), disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
        return

    # Construct prompt
    prompt = state.get_prompt()

    all_images = state.get_images(return_pil=True)
    all_image_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in all_images]
    for image, hash in zip(all_images, all_image_hash):
        t = datetime.datetime.now()
        filename = os.path.join(LOGDIR, "serve_images", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{hash}.jpg")
        if not os.path.isfile(filename):
            os.makedirs(os.path.dirname(filename), exist_ok=True)
            image.save(filename)

    # Make requests
    pload = {
        "model": model_name,
        "prompt": prompt,
        "temperature": float(temperature),
        "top_p": float(top_p),
        "max_new_tokens": min(int(max_new_tokens), 16384),
        "stop": state.sep if state.sep_style in [SeparatorStyle.SINGLE, SeparatorStyle.MPT] else state.sep2,
        "images": f'List of {len(state.get_images())} images: {all_image_hash}',
    }
    logger.info(f"==== request ====\n{pload}")

    pload['images'] = state.get_images()

    state.messages[-1][-1] = "▌"
    yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5

    try:
        # Stream output
        response = requests.post(worker_addr + "/worker_generate_stream",
            headers=headers, json=pload, stream=True, timeout=20)
        for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
            if chunk:
                data = json.loads(chunk.decode())
                if data["error_code"] == 0:
                    output = data["text"][len(prompt):].strip()
                    state.messages[-1][-1] = output + "▌"
                    yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 5
                else:
                    output = data["text"] + f" (error_code: {data['error_code']})"
                    state.messages[-1][-1] = output
                    yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
                    return
                time.sleep(0.03)
    except requests.exceptions.RequestException as e:
        state.messages[-1][-1] = server_error_msg
        yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
        return

    state.messages[-1][-1] = state.messages[-1][-1][:-1]
    yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5

    finish_tstamp = time.time()
    logger.info(f"{output}")

    with open(get_conv_log_filename(), "a") as fout:
        data = {
            "tstamp": round(finish_tstamp, 4),
            "type": "chat",
            "model": model_name,
            "start": round(start_tstamp, 4),
            "finish": round(finish_tstamp, 4),
            "state": state.dict(),
            "images": all_image_hash,
            "ip": request.client.host,
        }
        fout.write(json.dumps(data) + "\n")

title_markdown = ("""
# UGround: Universal Visual Grounding For GUI Agents
 [[Project Page]](https://osu-nlp-group.github.io/UGround/) [[Paper]](https://github.com/OSU-NLP-Group/UGround/blob/gh-pages/static/papers/UGround_paper.pdf) [[Code]](https://github.com/OSU-NLP-Group/UGround) [[Model]](https://huggingface.co/osunlp/UGround)
""")

tos_markdown = ("""
### Terms of use
By using this service, users are required to agree to the following terms:
The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
""")


learn_more_markdown = ("""
### License
The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
""")

block_css = """

#buttons button {
    min-width: min(120px,100%);
}

"""

def build_demo(embed_mode, cur_dir=None, concurrency_count=10):
    textbox = gr.Textbox(show_label=False, placeholder="Enter an element description (referring expression) and press ENTER", container=False)
    with gr.Blocks(title="UGround", theme=gr.themes.Default(), css=block_css) as demo:
        state = gr.State()

        if not embed_mode:
            gr.Markdown(title_markdown)

        with gr.Row():
            with gr.Column(scale=3):
                with gr.Row(elem_id="model_selector_row"):
                    model_selector = gr.Dropdown(
                        choices=models,
                        value=models[0] if len(models) > 0 else "",
                        interactive=True,
                        show_label=False,
                        container=False)

                imagebox = gr.Image(type="pil")
                image_process_mode = gr.Radio(
                    ["Crop", "Resize", "Pad", "Default"],
                    value="Default",
                    label="Preprocess for non-square image", visible=False)

                if cur_dir is None:
                    cur_dir = os.path.dirname(os.path.abspath(__file__))
                gr.Examples(examples=[
                    [f"{cur_dir}/amazon.jpg", "Where is the search bar at the top of the page?"],
                    # [f"{cur_dir}/examples/waterview.jpg", "What are the things I should be cautious about when I visit here?"],
                ], inputs=[imagebox, textbox])

                with gr.Accordion("Parameters", open=False) as parameter_row:
                    temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.1, interactive=True, label="Temperature",)
                    top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.7, step=0.1, interactive=True, label="Top P",)
                    max_output_tokens = gr.Slider(minimum=0, maximum=16384, value=16384, step=64, interactive=True, label="Max output tokens",)

            with gr.Column(scale=8):
                chatbot = gr.Chatbot(
                    elem_id="chatbot",
                    label="UGround Chatbot",
                    height=650,
                    layout="panel",
                )
                with gr.Row():
                    with gr.Column(scale=8):
                        textbox.render()
                    with gr.Column(scale=1, min_width=50):
                        submit_btn = gr.Button(value="Send", variant="primary")
                with gr.Row(elem_id="buttons") as button_row:
                    upvote_btn = gr.Button(value="👍  Upvote", interactive=False)
                    downvote_btn = gr.Button(value="👎  Downvote", interactive=False)
                    flag_btn = gr.Button(value="⚠️  Flag", interactive=False)
                    #stop_btn = gr.Button(value="⏹️  Stop Generation", interactive=False)
                    regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=False)
                    clear_btn = gr.Button(value="🗑️  Clear", interactive=False)

        if not embed_mode:
            gr.Markdown(tos_markdown)
            gr.Markdown(learn_more_markdown)
        url_params = gr.JSON(visible=False)

        # Register listeners
        btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn]
        upvote_btn.click(
            upvote_last_response,
            [state, model_selector],
            [textbox, upvote_btn, downvote_btn, flag_btn]
        )
        downvote_btn.click(
            downvote_last_response,
            [state, model_selector],
            [textbox, upvote_btn, downvote_btn, flag_btn]
        )
        flag_btn.click(
            flag_last_response,
            [state, model_selector],
            [textbox, upvote_btn, downvote_btn, flag_btn]
        )

        regenerate_btn.click(
            regenerate,
            [state, image_process_mode],
            [state, chatbot, textbox, imagebox] + btn_list
        ).then(
            http_bot,
            [state, model_selector, temperature, top_p, max_output_tokens],
            [state, chatbot] + btn_list,
            concurrency_limit=concurrency_count
        )

        clear_btn.click(
            clear_history,
            None,
            [state, chatbot, textbox, imagebox] + btn_list,
            queue=False
        )

        textbox.submit(
            add_text,
            [state, textbox, imagebox, image_process_mode],
            [state, chatbot, textbox, imagebox] + btn_list,
            queue=False
        ).then(
            http_bot,
            [state, model_selector, temperature, top_p, max_output_tokens],
            [state, chatbot] + btn_list,
            concurrency_limit=concurrency_count
        )

        submit_btn.click(
            add_text,
            [state, textbox, imagebox, image_process_mode],
            [state, chatbot, textbox, imagebox] + btn_list
        ).then(
            http_bot,
            [state, model_selector, temperature, top_p, max_output_tokens],
            [state, chatbot] + btn_list,
            concurrency_limit=concurrency_count
        )

        if args.model_list_mode == "once":
            demo.load(
                load_demo,
                [url_params],
                [state, model_selector],
                js=get_window_url_params
            )
        elif args.model_list_mode == "reload":
            demo.load(
                load_demo_refresh_model_list,
                None,
                [state, model_selector],
                queue=False
            )
        else:
            raise ValueError(f"Unknown model list mode: {args.model_list_mode}")

    return demo


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default="0.0.0.0")
    parser.add_argument("--port", type=int)
    parser.add_argument("--controller-url", type=str, default="http://localhost:21001")
    parser.add_argument("--concurrency-count", type=int, default=2)
    parser.add_argument("--model-list-mode", type=str, default="once",
        choices=["once", "reload"])
    parser.add_argument("--share", action="store_true")
    parser.add_argument("--moderate", action="store_true")
    parser.add_argument("--embed", action="store_true")
    args = parser.parse_args()
    logger.info(f"args: {args}")

    models = get_model_list()

    logger.info(args)
    demo = build_demo(args.embed, concurrency_count=args.concurrency_count)
    demo.queue(
        api_open=False
    ).launch(
        server_name=args.host,
        server_port=args.port,
        share=args.share
    )