videollama2/serve/gradio_web_server.py

import os
import json
import time
import hashlib
import requests
import argparse
import datetime

import numpy as np
import gradio as gr
from decord import VideoReader, cpu

from videollama2.constants import LOGDIR, NUM_FRAMES
from videollama2.conversation import (default_conversation, conv_templates,SeparatorStyle)
from videollama2.utils import (build_logger, server_error_msg, violates_moderation, moderation_msg)


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

headers = {"User-Agent": "Videollama2 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
    # (state, chatbot, textbox, imagebox, videobox, upvote, downvote, flag, generate, clear)
    return (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5


def clear_history(request: gr.Request):
    logger.info(f"clear_history. ip: {request.client.host}")
    state = default_conversation.copy()
    # (state, chatbot, textbox, imagebox, videobox, upvote, downvote, flag, generate, clear)
    return (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5


def add_text_ori(state, text, image, video, image_process_mode, request: gr.Request):
    # note: imagebox itself is PIL object while videobox is filepath
    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
    assert image is None or video is None, "Please don't feed image and video inputs at the same time!!!"
    text = text[:1536]  # Hard cut-off
    if image is not None:
        # here image is the PIL object itself
        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.modality = "image"
    if video is not None:
        print("Video box:", video)
        # here video is the file path of video
        text = text[:1200]  # Hard cut-off for images
        if '<video>' not in text:
            # text = '<Image><image></Image>' + text
            text = text + '\n<video>'
        text = (text, video, image_process_mode)
        if len(state.get_videos(return_pil=True)) > 0:
            state = default_conversation.copy()
        state.modality = "video"
        print("Set modality as video...")
    state.append_message(state.roles[0], text)
    state.append_message(state.roles[1], None)
    state.skip_next = False
    # (state, chatbot, textbox, imagebox, videobox, upvote, downvote, flag, generate, clear)
    return (state, state.to_gradio_chatbot(), "", None, None) + (disable_btn,) * 5


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

    # if input is new video or image ,reset the state
    if image is not None or video is not None:
        state = default_conversation.copy()

    if len(text) <= 0 and image is None and video is None:
        state.skip_next = True
        return (state, state.to_gradio_chatbot(), "", None, 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

    # process the input video
    if video is not None:
        text = text[:1200]  # 
        if '<video>' not in text:
            text = text + '\n<video>'
        text = (text, video, image_process_mode)
        state.modality = "video"
    # process the input image
    elif image is not None:
        text = text[:1200]  # 
        if '<image>' not in text:
            text = text + '\n<image>'
        text = (text, image, image_process_mode)
        state.modality = "image"
    elif state.modality == "image" and len(text)>0:
        state.modality = "image_text"  
        text = text[:1536]  # Hard cut-off
    elif state.modality == "video" and len(text)>0:
        state.modality = "video_text"  
        text = text[:1536]  # Hard cut-off

    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, 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

    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 "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)
        new_state.modality = state.modality
        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()
    if state.modality == "image" or state.modality == "image_text":
        all_images = state.get_images(return_pil=True) # return PIL.Image object
    elif state.modality == "video" or state.modality == "video_text":
        all_images = state.get_videos(return_pil=True) # return video frames where each frame is a PIL.Image object
    all_image_hash = [hashlib.md5(image.tobytes()).hexdigest() for image in all_images]
    for idx, (image, hash) in enumerate(zip(all_images, all_image_hash)):
        t = datetime.datetime.now()
        if state.modality == "image" or state.modality == "image_text":
            filename = os.path.join(LOGDIR, "serve_images", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{hash}.jpg")
        elif state.modality == "video" or state.modality == "video_text":
            filename = os.path.join(LOGDIR, "serve_videos", f"{t.year}-{t.month:02d}-{t.day:02d}", f"{hash}_{idx}.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}',
        "images": f'List of {len(all_image_hash)} images: {all_image_hash}',
    }
    logger.info(f"==== request ====\n{pload}")

    if state.modality == "image" or state.modality == "image_text":
        pload['images'] = state.get_images()
    elif state.modality == "video" or state.modality == "video_text":
        pload['images'] = state.get_videos()

    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=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(start_tstamp, 4),
            #"state": state.dict(),
            "images": all_image_hash,
            "ip": request.client.host,
        }
        fout.write(json.dumps(data) + "\n")

title_markdown = ("""
# The publicl release of VideoLLaMA2
""")

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)
    with gr.Blocks(title="Video-Llama", 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")
                videobox = gr.Video()
                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", "What is unusual about this image?"],
                    [f"{cur_dir}/examples/waterview.jpg", "What are the things I should be cautious about when I visit here?"],
                    [f"{cur_dir}/examples/desert.jpg", "If there are factual errors in the questions, point it out; if not, proceed answering the question. What’s happening in the desert?"],
                ], inputs=[imagebox, textbox], label="Image examples")

                # video example inputs
                gr.Examples(examples=[
                [f"{cur_dir}/examples/sample_demo_1.mp4", "Why is this video funny?"],
                [f"{cur_dir}/examples/sample_demo_3.mp4", "Can you identify any safety hazards in this video?"],
                [f"{cur_dir}/examples/1034346401.mp4", "What is this young woman doing?"]
                ], inputs=[videobox, textbox], label="Video examples")
                #[f"{cur_dir}/examples/sample_demo_9.mp4", "Describe the video in detail and please do not generate repetitive content."]

                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="Videollama2 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)
                    #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, videobox] + btn_list).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, videobox] + btn_list)

        textbox.submit(add_text, [state, textbox, imagebox, videobox, image_process_mode], [state, chatbot, textbox, imagebox, videobox] + btn_list
            ).then(http_bot, [state, model_selector, temperature, top_p, max_output_tokens],
                   [state, chatbot] + btn_list)
        submit_btn.click(add_text, [state, textbox, imagebox, videobox, image_process_mode], [state, chatbot, textbox, imagebox, videobox] + btn_list
            ).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)
        elif args.model_list_mode == "reload":
            demo.load(load_demo_refresh_model_list, None, [state, model_selector])
        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=10)
    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)
    demo.queue(
        concurrency_count=args.concurrency_count,
        api_open=False
    ).launch(
        server_name=args.host,
        server_port=args.port,
        share=args.share
    )