gradio_web_server.py

import argparse
import datetime
import json
import os
import time
from PIL import Image
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


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

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

no_change_btn = gr.Button.update()
enable_btn = gr.Button.update(interactive=True)
disable_btn = gr.Button.update(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.update(visible=True)
    if "model" in url_params:
        model = url_params["model"]
        if model in models:
            dropdown_update = gr.Dropdown.update(
                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.update(
        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 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[:1536]  # Hard cut-off
    if image is not None:
        text = text[:1200]  # Hard cut-off for images
        if '<image>' not in text:
            # text = '<Image><image></Image>' + text
            text = text + '\n<image>'
        text = (text, image, image_process_mode)
        if len(state.get_images(return_pil=True)) > 0:
            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 batch_process_images(folder_path, textbox, model_selector, temperature, top_p, max_output_tokens, request: gr.Request):
    print("Starting batch processing of images")

    # Initialize counters and timer
    image_files = [f for f in os.listdir(folder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif'))]
    total_images = len(image_files)
    processed_images = 0
    total_processing_time = 0

    # Process each image file
    for filename in image_files:
        image_path = os.path.join(folder_path, filename)
        start_time = time.time()
        
        with Image.open(image_path) as image:
            state = default_conversation.copy()
            state, _, _, _, _, _, _, _, _ = add_text(state, textbox, image, "Default", request)

            # Call http_bot and iterate over the generator
            response_text = ""
            for state_update in http_bot(state, model_selector, temperature, top_p, max_output_tokens, request):
                # Update state and extract response text
                state, chatbot_output, *_ = state_update
                response_text = chatbot_output

            # Save the final response to a file
            try:
                with open(os.path.splitext(image_path)[0] + '.txt', 'w') as f:
                    f.write(response_text[0][1])
            except Exception as e:
                print(f"An error occurred: {e}")

        # Update processing information
        processed_images += 1
        processing_time = time.time() - start_time
        total_processing_time += processing_time
        average_processing_time = total_processing_time / processed_images
        images_left = total_images - processed_images
        eta_seconds = average_processing_time * images_left
        eta = datetime.timedelta(seconds=int(eta_seconds))

        # Display progress information
        print(f"{processed_images}/{total_images} images processed, {images_left} left, average process time {average_processing_time :.2f} seconds, ETA: {str(eta)}")

    return "Batch processing completed."

	


def http_bot(state, model_selector, temperature, top_p, max_new_tokens, request: gr.Request):
    logger.info(f"http_bot. ip: {request.client.host}")
    print(f"model_selector {model_selector}")
    start_tstamp = time.time()
    model_name = model_selector
    
    if state.skip_next:
        # This generate call is skipped due to invalid inputs
        print("invalid input state.skip_next")
        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 "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"
        print(f"template_name {template_name}")
        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
        print(f"error No available worker")
        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), 1536),
        "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
    print(f"entering Stream output")
    try:
        # Stream output
        response = requests.post(worker_addr + "/worker_generate_stream",
            headers=headers, json=pload, stream=True, timeout=10)
        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 = ("""
Most Up To Date Scripts On : https://www.patreon.com/posts/sota-very-best-90744385 \n
Original Project :  https://llava-vl.github.io
""")

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):
    textbox = gr.Textbox(show_label=False, placeholder="Enter text and press ENTER", container=False)
    
    # New components for batch processing
    folder_input = gr.Textbox(label="Enter Folder Path for Batch Processing")
    batch_btn = gr.Button("Batch Process")

    with gr.Blocks(title="LLaVA", 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)

                cur_dir = os.path.dirname(os.path.abspath(__file__))
                gr.Examples(examples=[
                    [f"{cur_dir}/examples/extreme_ironing.jpg", "just caption the image with details, colors, items, objects, emotions, art style, drawing style and objects but do not add any description or comment. do not miss any item in the given image"],
                ], inputs=[imagebox, textbox])

                with gr.Accordion("Parameters", open=False) as parameter_row:
                    temperature = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, 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=1024, value=512, step=64, interactive=True, label="Max output tokens",)

            with gr.Column(scale=8):
                chatbot = gr.Chatbot(elem_id="chatbot", label="LLaVA Chatbot", height=550)
                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)
                    regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=False)
                    clear_btn = gr.Button(value="🗑️  Clear", interactive=False)

        url_params = gr.JSON(visible=False)

        # Add new components for batch processing
        with gr.Row():
            folder_input.render()
            batch_btn.render()

        # Batch processing button event
        batch_btn.click(
            batch_process_images,  # This function needs to be defined to handle batch processing
            inputs=[folder_input, textbox, model_selector , temperature, top_p, max_output_tokens],
            outputs=[]
        )

        # 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],
            queue=False
        )
        downvote_btn.click(
            downvote_last_response,
            [state, model_selector],
            [textbox, upvote_btn, downvote_btn, flag_btn],
            queue=False
        )
        flag_btn.click(
            flag_last_response,
            [state, model_selector],
            [textbox, upvote_btn, downvote_btn, flag_btn],
            queue=False
        )

        regenerate_btn.click(
            regenerate,
            [state, 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
        )

        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
        )

        submit_btn.click(
            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
        )

        if args.model_list_mode == "once":
            demo.load(
                load_demo,
                [url_params],
                [state, model_selector],
                _js=get_window_url_params,
                queue=False
            )
        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:10000")
    parser.add_argument("--concurrency-count", type=int, default=10)
    parser.add_argument("--model-list-mode", type=str, default="reload",
        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)
    demo.queue(
        concurrency_count=args.concurrency_count,
        api_open=False
    ).launch(
        server_name=args.host,
        server_port=args.port,
        share=args.share
    )