app.py

import argparse
import os
os.system("pip uninstall -y gradio")
os.system("pip install gradio==3.47.1")

import random
from collections import defaultdict

import cv2
import re

import numpy as np
from PIL import Image
import torch
import html
import gradio as gr

import torchvision.transforms as T
import torch.backends.cudnn as cudnn

from minigpt4.common.config import Config

from minigpt4.common.registry import registry
from minigpt4.conversation.conversation import Conversation, SeparatorStyle, Chat

# imports modules for registration
from minigpt4.datasets.builders import *
from minigpt4.models import *
from minigpt4.processors import *
from minigpt4.runners import *
from minigpt4.tasks import *

import socket
import os

def find_free_port(start_port, end_port):
    for port in range(start_port, end_port + 1):
        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
            if sock.connect_ex(('localhost', port)) != 0:  # Port is not open
                return port
    raise OSError(f"Cannot find empty port in range: {start_port}-{end_port}")

def set_gradio_server_port():
    start_port = 7870
    end_port = 9999
    free_port = find_free_port(start_port, end_port)
    os.environ["GRADIO_SERVER_PORT"] = str(free_port)
    print(f"Set GRADIO_SERVER_PORT to {free_port}")

# Set GRADIO_SERVER_PORT
set_gradio_server_port()

def parse_args():
    parser = argparse.ArgumentParser(description="Demo")
    parser.add_argument("--cfg-path", default='eval_configs/demo.yaml',
                        help="path to configuration file.")
    parser.add_argument(
        "--options",
        nargs="+",
        help="override some settings in the used config, the key-value pair "
             "in xxx=yyy format will be merged into config file (deprecate), "
             "change to --cfg-options instead.",
    )
    args = parser.parse_args()
    return args


random.seed(42)
np.random.seed(42)
torch.manual_seed(42)

cudnn.benchmark = False
cudnn.deterministic = True

print('Initializing Chat')
args = parse_args()
cfg = Config(args)

device = 'cuda'

model_config = cfg.model_cfg

print("model_config:", model_config)
model_cls = registry.get_model_class(model_config.arch)
model = model_cls.from_config(model_config).to(device)
bounding_box_size = 100

vis_processor_cfg = cfg.datasets_cfg.feature_face_caption.vis_processor.train
vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)

model = model.eval()

CONV_VISION = Conversation(
    system="",
    roles=(r"<s>[INST] ", r" [/INST]"),
    messages=[],
    offset=2,
    sep_style=SeparatorStyle.SINGLE,
    sep="",
)


def extract_substrings(string):
    # first check if there is no-finished bracket
    index = string.rfind('}')
    if index != -1:
        string = string[:index + 1]

    pattern = r'<p>(.*?)\}(?!<)'
    matches = re.findall(pattern, string)
    substrings = [match for match in matches]

    return substrings


def is_overlapping(rect1, rect2):
    x1, y1, x2, y2 = rect1
    x3, y3, x4, y4 = rect2
    return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4)


def computeIoU(bbox1, bbox2):
    x1, y1, x2, y2 = bbox1
    x3, y3, x4, y4 = bbox2
    intersection_x1 = max(x1, x3)
    intersection_y1 = max(y1, y3)
    intersection_x2 = min(x2, x4)
    intersection_y2 = min(y2, y4)
    intersection_area = max(0, intersection_x2 - intersection_x1 + 1) * max(0, intersection_y2 - intersection_y1 + 1)
    bbox1_area = (x2 - x1 + 1) * (y2 - y1 + 1)
    bbox2_area = (x4 - x3 + 1) * (y4 - y3 + 1)
    union_area = bbox1_area + bbox2_area - intersection_area
    iou = intersection_area / union_area
    return iou


def save_tmp_img(visual_img):
    file_name = "".join([str(random.randint(0, 9)) for _ in range(5)]) + ".jpg"
    file_path = "/tmp/gradio" + file_name
    visual_img.save(file_path)
    return file_path


def mask2bbox(mask):
    if mask is None:
        return ''
    mask = mask.resize([100, 100], resample=Image.NEAREST)
    mask = np.array(mask)[:, :, 0]

    rows = np.any(mask, axis=1)
    cols = np.any(mask, axis=0)

    if rows.sum():
        # Get the top, bottom, left, and right boundaries
        rmin, rmax = np.where(rows)[0][[0, -1]]
        cmin, cmax = np.where(cols)[0][[0, -1]]
        bbox = '{{<{}><{}><{}><{}>}}'.format(cmin, rmin, cmax, rmax)
    else:
        bbox = ''

    return bbox


def escape_markdown(text):
    # List of Markdown special characters that need to be escaped
    md_chars = ['<', '>']

    # Escape each special character
    for char in md_chars:
        text = text.replace(char, '\\' + char)

    return text


def reverse_escape(text):
    md_chars = ['\\<', '\\>']

    for char in md_chars:
        text = text.replace(char, char[1:])

    return text


colors = [
    (255, 0, 0),
    (0, 255, 0),
    (0, 0, 255),
    (210, 210, 0),
    (255, 0, 255),
    (0, 255, 255),
    (114, 128, 250),
    (0, 165, 255),
    (0, 128, 0),
    (144, 238, 144),
    (238, 238, 175),
    (255, 191, 0),
    (0, 128, 0),
    (226, 43, 138),
    (255, 0, 255),
    (0, 215, 255),
]

color_map = {
    f"{color_id}": f"#{hex(color[2])[2:].zfill(2)}{hex(color[1])[2:].zfill(2)}{hex(color[0])[2:].zfill(2)}" for
    color_id, color in enumerate(colors)
}

used_colors = colors

def get_first_frame(video_path):
    cap = cv2.VideoCapture(video_path)
    
    if not cap.isOpened():
        print("Error: Cannot open video.")
        return None

    ret, frame = cap.read()
    cap.release()

    if ret:
        return frame
    else:
        print("Error: Cannot read frame from video.")
        return None

def visualize_all_bbox_together(image, generation):
    if image is None:
        return None, ''
    
    if isinstance(image, str):  # is a image path
        raw_image = get_first_frame(image)
        frame_rgb = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB)
        image = Image.fromarray(frame_rgb)

    generation = html.unescape(generation)

    image_width, image_height = image.size
    image = image.resize([500, int(500 / image_width * image_height)])
    image_width, image_height = image.size

    string_list = extract_substrings(generation)
    if string_list:  # it is grounding or detection
        mode = 'all'
        entities = defaultdict(list)
        i = 0
        j = 0
        for string in string_list:
            try:
                obj, string = string.split('</p>')
            except ValueError:
                print('wrong string: ', string)
                continue
            bbox_list = string.split('<delim>')
            flag = False
            for bbox_string in bbox_list:
                integers = re.findall(r'-?\d+', bbox_string)
                if len(integers) == 4:
                    x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3])
                    left = x0 / bounding_box_size * image_width
                    bottom = y0 / bounding_box_size * image_height
                    right = x1 / bounding_box_size * image_width
                    top = y1 / bounding_box_size * image_height

                    entities[obj].append([left, bottom, right, top])

                    j += 1
                    flag = True
            if flag:
                i += 1
    else:
        integers = re.findall(r'-?\d+', generation)

        if len(integers) == 4:  # it is refer
            mode = 'single'

            entities = list()
            x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3])
            left = x0 / bounding_box_size * image_width
            bottom = y0 / bounding_box_size * image_height
            right = x1 / bounding_box_size * image_width
            top = y1 / bounding_box_size * image_height
            entities.append([left, bottom, right, top])
        else:
            # don't detect any valid bbox to visualize
            return None, ''

    if len(entities) == 0:
        return None, ''

    if isinstance(image, Image.Image):
        image_h = image.height
        image_w = image.width
        image = np.array(image)

    elif isinstance(image, str):
        if os.path.exists(image):
            pil_img = Image.open(image).convert("RGB")
            image = np.array(pil_img)[:, :, [2, 1, 0]]
            image_h = pil_img.height
            image_w = pil_img.width
        else:
            raise ValueError(f"invaild image path, {image}")
    elif isinstance(image, torch.Tensor):

        image_tensor = image.cpu()
        reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None]
        reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None]
        image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean
        pil_img = T.ToPILImage()(image_tensor)
        image_h = pil_img.height
        image_w = pil_img.width
        image = np.array(pil_img)[:, :, [2, 1, 0]]
    else:
        raise ValueError(f"invaild image format, {type(image)} for {image}")

    indices = list(range(len(entities)))

    new_image = image.copy()

    previous_bboxes = []
    # size of text
    text_size = 0.5
    # thickness of text
    text_line = 1  # int(max(1 * min(image_h, image_w) / 512, 1))
    box_line = 2
    (c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
    base_height = int(text_height * 0.675)
    text_offset_original = text_height - base_height
    text_spaces = 2

    # num_bboxes = sum(len(x[-1]) for x in entities)
    used_colors = colors  # random.sample(colors, k=num_bboxes)

    color_id = -1
    for entity_idx, entity_name in enumerate(entities):
        if mode == 'single' or mode == 'identify':
            bboxes = entity_name
            bboxes = [bboxes]
        else:
            bboxes = entities[entity_name]
        color_id += 1
        for bbox_id, (x1_norm, y1_norm, x2_norm, y2_norm) in enumerate(bboxes):
            skip_flag = False
            orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm), int(y1_norm), int(x2_norm), int(y2_norm)

            color = used_colors[entity_idx % len(used_colors)]  # tuple(np.random.randint(0, 255, size=3).tolist())
            new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line)

            if mode == 'all':
                l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1

                x1 = orig_x1 - l_o
                y1 = orig_y1 - l_o

                if y1 < text_height + text_offset_original + 2 * text_spaces:
                    y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces
                    x1 = orig_x1 + r_o

                # add text background
                (text_width, text_height), _ = cv2.getTextSize(f"  {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size,
                                                               text_line)
                text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (
                            text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1

                for prev_bbox in previous_bboxes:
                    if computeIoU((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']) > 0.95 and \
                            prev_bbox['phrase'] == entity_name:
                        skip_flag = True
                        break
                    while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']):
                        text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces)
                        text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces)
                        y1 += (text_height + text_offset_original + 2 * text_spaces)

                        if text_bg_y2 >= image_h:
                            text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces))
                            text_bg_y2 = image_h
                            y1 = image_h
                            break
                if not skip_flag:
                    alpha = 0.5
                    for i in range(text_bg_y1, text_bg_y2):
                        for j in range(text_bg_x1, text_bg_x2):
                            if i < image_h and j < image_w:
                                if j < text_bg_x1 + 1.35 * c_width:
                                    # original color
                                    bg_color = color
                                else:
                                    # white
                                    bg_color = [255, 255, 255]
                                new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(
                                    np.uint8)

                    cv2.putText(
                        new_image, f"  {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces),
                        cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA
                    )

                    previous_bboxes.append(
                        {'bbox': (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), 'phrase': entity_name})

    if mode == 'all':
        def color_iterator(colors):
            while True:
                for color in colors:
                    yield color

        color_gen = color_iterator(colors)

        # Add colors to phrases and remove <p></p>
        def colored_phrases(match):
            phrase = match.group(1)
            color = next(color_gen)
            return f'<span style="color:rgb{color}">{phrase}</span>'

        generation = re.sub(r'{<\d+><\d+><\d+><\d+>}|<delim>', '', generation)
        generation_colored = re.sub(r'<p>(.*?)</p>', colored_phrases, generation)
    else:
        generation_colored = ''

    pil_image = Image.fromarray(new_image)
    return pil_image, generation_colored


def gradio_reset(chat_state, img_list):
    if chat_state is not None:
        chat_state.messages = []
    if img_list is not None:
        img_list = []
    return None, gr.update(value=None, interactive=True), gr.update(placeholder='Upload your image and chat',
                                                                    interactive=True), chat_state, img_list


def image_upload_trigger(upload_flag, replace_flag, img_list):
    # set the upload flag to true when receive a new image.
    # if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
    upload_flag = 1
    if img_list:
        replace_flag = 1
    return upload_flag, replace_flag


def example_trigger(text_input, image, upload_flag, replace_flag, img_list):
    # set the upload flag to true when receive a new image.
    # if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
    upload_flag = 1
    if img_list or replace_flag == 1:
        replace_flag = 1

    return upload_flag, replace_flag


def gradio_ask(user_message, chatbot, chat_state, gr_img, img_list, upload_flag, replace_flag):    
    print("+++gradio_ask+++")
    
    if len(user_message) == 0:
        text_box_show = 'Input should not be empty!'
    else:
        text_box_show = ''

    print('user_message:', user_message)
    print('chatbot:', chatbot)
    print('chat_state:', chat_state)


    if isinstance(gr_img, dict):
        gr_img, mask = gr_img['image'], gr_img['mask']
    else:
        mask = None

    if '[identify]' in user_message:
        # check if user provide bbox in the text input
        integers = re.findall(r'-?\d+', user_message)
        if len(integers) != 4:  # no bbox in text
            bbox = mask2bbox(mask)
            user_message = user_message + bbox

    if chat_state is None:
        chat_state = CONV_VISION.copy()

    if upload_flag:
        if replace_flag:
            chat_state = CONV_VISION.copy()  # new image, reset everything
            replace_flag = 0
            chatbot = []
        img_list = []
        llm_message = chat.upload_img(gr_img, chat_state, img_list)
        upload_flag = 0

    chat.ask(user_message, chat_state)
    print('user_message: ', user_message)
    print('chat_state: ', chat_state)

    chatbot = chatbot + [[user_message, None]]

    if '[identify]' in user_message:
        visual_img, _ = visualize_all_bbox_together(gr_img, user_message)
        if visual_img is not None:
            file_path = save_tmp_img(visual_img)
            chatbot = chatbot + [[(file_path,), None]]

    return text_box_show, chatbot, chat_state, img_list, upload_flag, replace_flag


def gradio_answer(chatbot, chat_state, img_list, temperature):
    print("--gradio_answer--")
    # print('img_list: ', img_list)
    llm_message = chat.answer(conv=chat_state,
                              img_list=img_list,
                              temperature=temperature,
                              max_new_tokens=500,
                              max_length=2000)[0]
    chatbot[-1][1] = llm_message
    print('gradio_answer: ', llm_message)

    return chatbot, chat_state

def process_english_text(text):
    if len(text) < 2:
        return text
    text = text[0].upper() + text[1:]

    sentences = text.split('. ')
    corrected_sentences = [s.capitalize() for s in sentences]
    text = '. '.join(corrected_sentences)

    if text.endswith(','):
        text = text[:-1]
    if not text.endswith('.'):
        text += '.'

    return text


def gradio_stream_answer(chatbot, chat_state, img_list, temperature):
    print('---gradio_stream_answer---')
    if len(img_list) > 0:
        if not isinstance(img_list[0], torch.Tensor):
            chat.encode_img(img_list)
    print(chat)
    streamer = chat.stream_answer(conv=chat_state,
                                  img_list=img_list,
                                  temperature=temperature,
                                  max_new_tokens=500,
                                  max_length=2000)
    output = ''
    print('streamer:', streamer)
    for new_output in streamer:
        escapped = escape_markdown(new_output)
        output += escapped
        chatbot[-1][1] = output
        chatbot[-1][1] = process_english_text(chatbot[-1][1])
        yield chatbot, chat_state
    chat_state.messages[-1][1] = '</s>'
    print('output:', output)
    return chatbot, chat_state


def gradio_visualize(chatbot, gr_img):
    if isinstance(gr_img, dict):
        gr_img, mask = gr_img['image'], gr_img['mask']

    unescaped = reverse_escape(chatbot[-1][1])
    visual_img, generation_color = visualize_all_bbox_together(gr_img, unescaped)
    if visual_img is not None:
        if len(generation_color):
            chatbot[-1][1] = generation_color
        file_path = save_tmp_img(visual_img)
        chatbot = chatbot + [[None, (file_path,)]]

    return chatbot


def gradio_taskselect(idx):
    prompt_list = [
        '',
        '[reason] ',
        '[emotion] ',
        '[visual] ',
        '[audio] '
    ]
    instruct_list = [
        '**Hint:** Type in whatever you want',
        '**Hint:** Send the command to multimodal emotion reasoning',
        '**Hint:** Send the command to multimodal emotion recognition',
        '**Hint:** Send the command to generate visual description',
        '**Hint:** Send the command to generate audio description'
    ]
    return prompt_list[idx], instruct_list[idx]




chat = Chat(model, vis_processor, device=device)

title = """<h1 align="center">Emotion-LLaMA Demo</h1>"""
description = 'Welcome to Our Emotion-LLaMA Chatbot Demo!'
article = """<p><a href='https://anonymous.4open.science/r/Emotion-LLaMA'><img src='https://img.shields.io/badge/Project-Page-Green'></a></p>"""

introduction = '''
For Abilities Involging Multimodal Emotion Understanding:
1. Reason: Click **Send** to generate a multimodal emotion description.
2. Emotion: Click **Send** to generate an emotion label.
3. Visual: Click **Send** to generate a visual description.
4. Audio: Click **Send** to generate an audio description.
5. No Tag: Input whatever you want and click **Send** without any tagging.

You can also simply chat in free form!
'''

text_input = gr.Textbox(placeholder='Upload your image and chat', interactive=True, show_label=False, container=False, scale=8)
with gr.Blocks() as demo:
    gr.Markdown(title)
    # gr.Markdown(description)
    gr.Markdown(article)

    with gr.Row():
        with gr.Column(scale=0.5):
            # image = gr.Image(type="pil", tool='sketch', brush_radius=20)
            image = gr.Video(sources=["upload", "webcam"])

            temperature = gr.Slider(
                minimum=0.1,
                maximum=1.5,
                value=0.2,
                step=0.1,
                interactive=True,
                label="Temperature",
            )

            clear = gr.Button("Restart")

            gr.Markdown(introduction)

        with gr.Column():
            chat_state = gr.State(value=None)
            img_list = gr.State(value=[])
            chatbot = gr.Chatbot(label='Emotion-LLaMA')

            dataset = gr.Dataset(
                components=[gr.Textbox(visible=False)],
                samples=[['No Tag'], ['reason'], ['emotion'], ['visual'], ['audio']],
                type="index",
                label='Task Shortcuts',
            )
            task_inst = gr.Markdown('**Hint:** Upload your video and chat')
            with gr.Row():
                text_input.render()
                send = gr.Button("Send", variant='primary', size='sm', scale=1)

    upload_flag = gr.State(value=0)
    replace_flag = gr.State(value=0)
    image.upload(image_upload_trigger, [upload_flag, replace_flag, img_list], [upload_flag, replace_flag])

    with gr.Row():
        with gr.Column():
            gr.Examples(examples=[
                ["examples/samplenew_00004251.mp4", "[detection] face", upload_flag, replace_flag, img_list],
                ["examples/sample_00000338.mp4", "The person in video says: Oh no, my phone and wallet are all in my bag. [emotion] Please determine which emotion label in the video represents: happy, sad, neutral, angry, worried, surprise.", upload_flag, replace_flag, img_list],
                ["examples/sample_00000669.mp4", "The person in video says: Why are you looking at me like this? It's just a woman, so you have to have something to do with me.  [emotion] Determine the emotional state shown in the video, choosing from happy, sad, neutral, angry, worried, or surprise.", upload_flag, replace_flag, img_list],
                ["examples/sample_00003462.mp4", "The person in video says: Do you believe that you push me around?  [emotion] Assess and label the emotion evident in the video: could it be happy, sad, neutral, angry, worried, surprise?", upload_flag, replace_flag, img_list],
                ["examples/sample_00000727.mp4", "The person in video says: No, this, I have to get up! You, I'm sorry, everyone. I'm sorry, it's from the German side.  [emotion] Identify the displayed emotion in the video: is it happy, sad, neutral, angry, worried, or surprise?", upload_flag, replace_flag, img_list],
                ["examples/samplenew_00061200.mp4", "The person in video says: Me: I'm not going in anymore, scared.  [emotion] Identify the displayed emotion in the video: is it happy, sad, neutral, angry, fear, contempt, doubt, worried, or surprise?", upload_flag, replace_flag, img_list],
            ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger,
                outputs=[upload_flag, replace_flag])
        with gr.Column():
            gr.Examples(examples=[
                ["examples/samplenew_00051251.mp4", "In what state is the person in the video, say the following: \"Do you really think so?\"", upload_flag, replace_flag, img_list],
                ["examples/sample_00004735.mp4", "[visual] What are the emotions of the woman in the video?", upload_flag, replace_flag, img_list],
                ["examples/sample_00002422.mp4", "[audio] Analyze the speaker's voice in the video.", upload_flag, replace_flag, img_list],
                ["examples/sample_00001073.mp4", "The person in video says: Make him different from before. I like the way you are now.  [reason] Please analyze all the clues in the video and reason out the emotional label of the person in the video.", upload_flag, replace_flag, img_list],
                ["examples/sample_00004671.mp4", "The person in video says: Won't you? Impossible! Fan Xiaomei is not such a person.  [reason] What are the facial expressions and vocal tone used in the video? What is the intended meaning behind his words? Which emotion does this reflect?", upload_flag, replace_flag, img_list],
                ["examples/sample_00005854.mp4", "The person in video says: Bastard! Boss, you don't choose, you prefer.  [reason] Please integrate information from various modalities to infer the emotional category of the person in the video.", upload_flag, replace_flag, img_list],
            ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger,
                outputs=[upload_flag, replace_flag])

    dataset.click(
        gradio_taskselect,
        inputs=[dataset],
        outputs=[text_input, task_inst],
        show_progress="hidden",
        postprocess=False,
        queue=False,
    )

    text_input.submit(
        gradio_ask,
        [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag],
        [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False
    ).success(
        gradio_stream_answer,
        [chatbot, chat_state, img_list, temperature],
        [chatbot, chat_state]
    ).success(
        gradio_visualize,
        [chatbot, image],
        [chatbot],
        queue=False,
    )

    send.click(
        gradio_ask,
        [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag],
        [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False
    ).success(
        gradio_stream_answer,
        [chatbot, chat_state, img_list, temperature],
        [chatbot, chat_state]
    ).success(
        gradio_visualize,
        [chatbot, image],
        [chatbot],
        queue=False,
    )

    clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, chat_state, img_list], queue=False)

demo.launch(share=True, enable_queue=True)
# demo.launch(share=True)