Delete geochat_demo.py

geochat_demo.py

@@ -1,706 +0,0 @@
-import argparse
-import os
-import random
-from collections import defaultdict
-
-import cv2
-import re
-import math
-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 geochat.conversation import conv_templates, Chat
-from geochat.model.builder import load_pretrained_model
-from geochat.mm_utils import  get_model_name_from_path
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Demo")
-    # parser = argparse.ArgumentParser()
-    parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
-    parser.add_argument("--model-base", type=str, default=None)
-    parser.add_argument("--gpu-id", type=str,default=0)
-    parser.add_argument("--device", type=str, default="cuda")
-    parser.add_argument("--conv-mode", type=str, default=None)
-    parser.add_argument("--max-new-tokens", type=int, default=300)
-    parser.add_argument("--load-8bit", action="store_true")
-    parser.add_argument("--load-4bit", action="store_true")
-    parser.add_argument("--debug", action="store_true")
-    parser.add_argument("--image-aspect-ratio", type=str, default='pad')
-    # args = parser.parse_args()
-    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)
-
-model_name = get_model_name_from_path(args.model_path)
-tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device)
-
-device = 'cuda:{}'.format(args.gpu_id)
-
-# model_config = cfg.model_cfg
-# model_config.device_8bit = args.gpu_id
-# 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.cc_sbu_align.vis_processor.train
-# vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
-
-model = model.eval()
-
-CONV_VISION = conv_templates['llava_v1'].copy()
-
-def bbox_and_angle_to_polygon(x1, y1, x2, y2, a):
-    # Calculate center coordinates
-    x_ctr = (x1 + x2) / 2
-    y_ctr = (y1 + y2) / 2
-    
-    # Calculate width and height
-    w = abs(x2 - x1)
-    h = abs(y2 - y1)
-    
-    # Calculate the angle in radians
-    angle_rad = math.radians(a)
-    
-    # Calculate coordinates of the four corners of the rotated bounding box
-    cos_a = math.cos(angle_rad)
-    sin_a = math.sin(angle_rad)
-    
-    x1_rot = cos_a * (-w / 2) - sin_a * (-h / 2) + x_ctr
-    y1_rot = sin_a * (-w / 2) + cos_a * (-h / 2) + y_ctr
-    
-    x2_rot = cos_a * (w / 2) - sin_a * (-h / 2) + x_ctr
-    y2_rot = sin_a * (w / 2) + cos_a * (-h / 2) + y_ctr
-    
-    x3_rot = cos_a * (w / 2) - sin_a * (h / 2) + x_ctr
-    y3_rot = sin_a * (w / 2) + cos_a * (h / 2) + y_ctr
-    
-    x4_rot = cos_a * (-w / 2) - sin_a * (h / 2) + x_ctr
-    y4_rot = sin_a * (-w / 2) + cos_a * (h / 2) + y_ctr
-    
-    # Return the polygon coordinates
-    polygon_coords = np.array((x1_rot, y1_rot, x2_rot, y2_rot, x3_rot, y3_rot, x4_rot, y4_rot))
-    
-    return polygon_coords
-
-def rotate_bbox(top_right, bottom_left, angle_degrees):
-    # Convert angle to radians
-    angle_radians = np.radians(angle_degrees)
-
-    # Calculate the center of the rectangle
-    center = ((top_right[0] + bottom_left[0]) / 2, (top_right[1] + bottom_left[1]) / 2)
-
-    # Calculate the width and height of the rectangle
-    width = top_right[0] - bottom_left[0]
-    height = top_right[1] - bottom_left[1]
-
-    # Create a rotation matrix
-    rotation_matrix = cv2.getRotationMatrix2D(center, angle_degrees, 1)
-
-    # Create an array of the rectangle corners
-    rectangle_points = np.array([[bottom_left[0], bottom_left[1]],
-                                 [top_right[0], bottom_left[1]],
-                                 [top_right[0], top_right[1]],
-                                 [bottom_left[0], top_right[1]]], dtype=np.float32)
-
-    # Rotate the rectangle points
-    rotated_rectangle = cv2.transform(np.array([rectangle_points]), rotation_matrix)[0]
-
-    return rotated_rectangle
-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 visualize_all_bbox_together(image, generation):
-    if image is None:
-        return None, ''
-
-    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
-            if "}{" in string:
-                string=string.replace("}{","}<delim>{")
-            bbox_list = string.split('<delim>')
-            flag = False
-            for bbox_string in bbox_list:
-                integers = re.findall(r'-?\d+', bbox_string)
-                if len(integers)==4:
-                    angle=0
-                else:
-                    angle=integers[4]
-                integers=integers[:-1]
-                
-                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,angle])
-
-                    j += 1
-                    flag = True
-            if flag:
-                i += 1
-    else:
-        integers = re.findall(r'-?\d+', generation)
-        # if len(integers)==4:
-        angle=0
-        # else:
-            # angle=integers[4]
-        integers=integers[:-1]
-        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,angle])
-        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"invalid image format, {type(image)} for {image}")
-
-    indices = list(range(len(entities)))
-
-    new_image = image.copy()
-
-    previous_bboxes = []
-    # size of text
-    text_size = 0.4
-    # 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,angle) in enumerate(bboxes):
-            skip_flag = False
-            orig_x1, orig_y1, orig_x2, orig_y2,angle = int(x1_norm), int(y1_norm), int(x2_norm), int(y2_norm), int(angle)
-
-            color = used_colors[entity_idx % len(used_colors)] # tuple(np.random.randint(0, 255, size=3).tolist())
-            top_right=(orig_x1,orig_y1)
-            bottom_left=(orig_x2,orig_y2)
-            angle=angle
-            rotated_bbox = rotate_bbox(top_right, bottom_left, angle)
-            new_image=cv2.polylines(new_image, [rotated_bbox.astype(np.int32)], isClosed=True,thickness=2, color=color)
-
-            # 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):
-    if len(user_message) == 0:
-        text_box_show = 'Input should not be empty!'
-    else:
-        text_box_show = ''
-
-    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)
-
-    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):
-#     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
-#     return chatbot, chat_state
-
-
-def gradio_stream_answer(chatbot, chat_state, img_list, temperature):
-    if len(img_list) > 0:
-        if not isinstance(img_list[0], torch.Tensor):
-            chat.encode_img(img_list)
-    streamer = chat.stream_answer(conv=chat_state,
-                                  img_list=img_list,
-                                  temperature=temperature,
-                                  max_new_tokens=500,
-                                  max_length=2000)
-    # chatbot[-1][1] = output
-    # chat_state.messages[-1][1] = '</s>'
-    
-    output = ''
-    for new_output in streamer:
-        # print(new_output)
-        output=output+new_output
-    print(output)
-    # if "{" in output:
-    #     chatbot[-1][1]="Grounding and referring expression is still under work."
-    # else:
-    output = escape_markdown(output)
-        # output += escapped
-    chatbot[-1][1] = output
-    yield chatbot, chat_state
-    chat_state.messages[-1][1] = '</s>'
-    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 = [
-        '',
-        'Classify the image in the following classes: ',
-        '[identify] what is this ',
-    ]
-    instruct_list = [
-        '**Hint:** Type in whatever you want',
-        '**Hint:** Type in the classes you want the model to classify in',
-        '**Hint:** Draw a bounding box on the uploaded image then send the command. Click the "clear" botton on the top right of the image before redraw',
-    ]
-    return prompt_list[idx], instruct_list[idx]
-
-
-
-
-chat = Chat(model, image_processor,tokenizer, device=device)
-
-
-title = """<h1 align="center">GeoChat Demo</h1>"""
-description = 'Welcome to Our GeoChat Chatbot Demo!'
-article = """<div style="display: flex;"><p style="display: inline-block;"><a href='https://mbzuai-oryx.github.io/GeoChat'><img src='https://img.shields.io/badge/Project-Page-Green'></a></p><p style="display: inline-block;"><a href='https://arxiv.org/abs/2311.15826'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></p><p style="display: inline-block;"><a href='https://github.com/mbzuai-oryx/GeoChat/tree/main'><img src='https://img.shields.io/badge/GitHub-Repo-blue'></a></p><p style="display: inline-block;"><a href='https://youtu.be/KOKtkkKpNDk?feature=shared'><img src='https://img.shields.io/badge/YouTube-Video-red'></a></p></div>"""
-# article = """<p><a href='https://minigpt-v2.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a></p>"""
-
-introduction = '''
-1. Identify: Draw the bounding box on the uploaded image window and CLICK **Send** to generate the bounding box. (CLICK "clear" button before re-drawing next time).
-2. 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=12)
-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)
-
-            temperature = gr.Slider(
-                minimum=0.1,
-                maximum=1.5,
-                value=0.6,
-                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='GeoChat')
-
-            dataset = gr.Dataset(
-                components=[gr.Textbox(visible=False)],
-                samples=[['No Tag'], ['Scene Classification'],['Identify']],
-                type="index",
-                label='Task Shortcuts',
-            )
-            task_inst = gr.Markdown('**Hint:** Upload your image 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=[
-                ["demo_images/train_2956_0001.png", "Where are the airplanes located and what is their type?", upload_flag, replace_flag,
-                 img_list],
-                ["demo_images/7292.JPG", "How many buildings are flooded?", 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=[
-                ["demo_images/church_183.png", "Classify the image in the following classes: Church, Beach, Dense Residential, Storage Tanks.",
-                 upload_flag, replace_flag, img_list],
-                ["demo_images/04444.png", "[identify] what is this {<8><26><22><37>}", 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,server_name='0.0.0.0')