Create demo.py

demo.py

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+import argparse
+import os
+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 *
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Demo")
+    parser.add_argument("--cfg-path", default='eval_configs/Florence.yaml',
+                        help="path to configuration file.")
+    parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.")
+    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:{}'.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 = 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 visualize_all_bbox_together(image, generation):
+    if image is None:
+        return None, ''
+
+    generation = html.unescape(generation)
+    print('gen begin', 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):
+    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)
+    output = ''
+    for new_output in streamer:
+        escapped = escape_markdown(new_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 = [
+        '',
+        '[grounding] describe this image in detail',
+        '[refer] ',
+        '[detection] ',
+        '[identify] what is this ',
+        '[vqa] '
+    ]
+    instruct_list = [
+        '**Hint:** Type in whatever you want',
+        '**Hint:** Send the command to generate a grounded image description',
+        '**Hint:** Type in a phrase about an object in the image and send the command',
+        '**Hint:** Type in a caption or phrase, and see object locations in the image',
+        '**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',
+        '**Hint:** Send a question to get a short answer',
+    ]
+    return prompt_list[idx], instruct_list[idx]
+
+
+
+
+chat = Chat(model, vis_processor, device=device)
+
+title = """<h1 align="center">FlorenceMedVQA: A Versatile Vision-Language
+Model for Comprehensive Medical Application    </h1>"""
+#description = 'Welcome to your AI-Powered Medical Chatbot!'
+
+About_Chatbot= '''
+This chatbot is designed to provide accurate and reliable answers to your medical questions. 
+Whether you're seeking general health advice or have specific inquiries about symptoms, treatments, or medical conditions, our AI is here to assist you 24/7.
+While this tool offers valuable information, it’s important to consult with a healthcare professional for personalized medical advice.
+
+'''
+
+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()
+    gr.Markdown()
+
+    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(About_Chatbot)
+
+        with gr.Column():
+            chat_state = gr.State(value=None)
+            img_list = gr.State(value=[])
+            chatbot = gr.Chatbot()
+
+            dataset = gr.Dataset(
+                components=[gr.Textbox(visible=False)],
+                samples=[['VQA']],
+                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])
+
+    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()
+