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| import os | |
| import json | |
| import gradio as gr | |
| import numpy as np | |
| from gradio import processing_utils | |
| from packaging import version | |
| from PIL import Image, ImageDraw | |
| import functools | |
| from caption_anything.model import CaptionAnything | |
| from caption_anything.utils.image_editing_utils import create_bubble_frame | |
| from caption_anything.utils.utils import mask_painter, seg_model_map, prepare_segmenter, image_resize | |
| from caption_anything.utils.parser import parse_augment | |
| from caption_anything.captioner import build_captioner | |
| from caption_anything.text_refiner import build_text_refiner | |
| from caption_anything.segmenter import build_segmenter | |
| from caption_anything.utils.chatbot import ConversationBot, build_chatbot_tools, get_new_image_name | |
| from segment_anything import sam_model_registry | |
| import easyocr | |
| args = parse_augment() | |
| args.segmenter = "huge" | |
| args.segmenter_checkpoint = "sam_vit_h_4b8939.pth" | |
| args.clip_filter = True | |
| if args.segmenter_checkpoint is None: | |
| _, segmenter_checkpoint = prepare_segmenter(args.segmenter) | |
| else: | |
| segmenter_checkpoint = args.segmenter_checkpoint | |
| shared_captioner = build_captioner(args.captioner, args.device, args) | |
| shared_sam_model = sam_model_registry[seg_model_map[args.segmenter]](checkpoint=segmenter_checkpoint).to(args.device) | |
| ocr_lang = ["ch_tra", "en"] | |
| shared_ocr_reader = easyocr.Reader(ocr_lang) | |
| tools_dict = {e.split('_')[0].strip(): e.split('_')[1].strip() for e in args.chat_tools_dict.split(',')} | |
| shared_chatbot_tools = build_chatbot_tools(tools_dict) | |
| class ImageSketcher(gr.Image): | |
| """ | |
| Fix the bug of gradio.Image that cannot upload with tool == 'sketch'. | |
| """ | |
| is_template = True # Magic to make this work with gradio.Block, don't remove unless you know what you're doing. | |
| def __init__(self, **kwargs): | |
| super().__init__(tool="sketch", **kwargs) | |
| def preprocess(self, x): | |
| if self.tool == 'sketch' and self.source in ["upload", "webcam"]: | |
| assert isinstance(x, dict) | |
| if x['mask'] is None: | |
| decode_image = processing_utils.decode_base64_to_image(x['image']) | |
| width, height = decode_image.size | |
| mask = np.zeros((height, width, 4), dtype=np.uint8) | |
| mask[..., -1] = 255 | |
| mask = self.postprocess(mask) | |
| x['mask'] = mask | |
| return super().preprocess(x) | |
| def build_caption_anything_with_models(args, api_key="", captioner=None, sam_model=None, ocr_reader=None, text_refiner=None, | |
| session_id=None): | |
| segmenter = build_segmenter(args.segmenter, args.device, args, model=sam_model) | |
| captioner = captioner | |
| if session_id is not None: | |
| print('Init caption anything for session {}'.format(session_id)) | |
| return CaptionAnything(args, api_key, captioner=captioner, segmenter=segmenter, ocr_reader=ocr_reader, text_refiner=text_refiner) | |
| def init_openai_api_key(api_key=""): | |
| text_refiner = None | |
| visual_chatgpt = None | |
| if api_key and len(api_key) > 30: | |
| try: | |
| text_refiner = build_text_refiner(args.text_refiner, args.device, args, api_key) | |
| assert len(text_refiner.llm('hi')) > 0 # test | |
| visual_chatgpt = ConversationBot(shared_chatbot_tools, api_key) | |
| except: | |
| text_refiner = None | |
| visual_chatgpt = None | |
| openai_available = text_refiner is not None | |
| if openai_available: | |
| return [gr.update(visible=True)]*6 + [gr.update(visible=False)]*2 + [text_refiner, visual_chatgpt, None] | |
| else: | |
| return [gr.update(visible=False)]*6 + [gr.update(visible=True)]*2 + [text_refiner, visual_chatgpt, 'Your OpenAI API Key is not available'] | |
| def init_wo_openai_api_key(): | |
| return [gr.update(visible=False)]*4 + [gr.update(visible=True)]*2 + [gr.update(visible=False)]*2 + [None, None, None] | |
| def get_click_prompt(chat_input, click_state, click_mode): | |
| inputs = json.loads(chat_input) | |
| if click_mode == 'Continuous': | |
| points = click_state[0] | |
| labels = click_state[1] | |
| for input in inputs: | |
| points.append(input[:2]) | |
| labels.append(input[2]) | |
| elif click_mode == 'Single': | |
| points = [] | |
| labels = [] | |
| for input in inputs: | |
| points.append(input[:2]) | |
| labels.append(input[2]) | |
| click_state[0] = points | |
| click_state[1] = labels | |
| else: | |
| raise NotImplementedError | |
| prompt = { | |
| "prompt_type": ["click"], | |
| "input_point": click_state[0], | |
| "input_label": click_state[1], | |
| "multimask_output": "True", | |
| } | |
| return prompt | |
| def update_click_state(click_state, caption, click_mode): | |
| if click_mode == 'Continuous': | |
| click_state[2].append(caption) | |
| elif click_mode == 'Single': | |
| click_state[2] = [caption] | |
| else: | |
| raise NotImplementedError | |
| def chat_input_callback(*args): | |
| visual_chatgpt, chat_input, click_state, state, aux_state = args | |
| if visual_chatgpt is not None: | |
| return visual_chatgpt.run_text(chat_input, state, aux_state) | |
| else: | |
| response = "Text refiner is not initilzed, please input openai api key." | |
| state = state + [(chat_input, response)] | |
| return state, state | |
| def upload_callback(image_input, state, visual_chatgpt=None): | |
| if isinstance(image_input, dict): # if upload from sketcher_input, input contains image and mask | |
| image_input, mask = image_input['image'], image_input['mask'] | |
| click_state = [[], [], []] | |
| image_input = image_resize(image_input, res=1024) | |
| model = build_caption_anything_with_models( | |
| args, | |
| api_key="", | |
| captioner=shared_captioner, | |
| sam_model=shared_sam_model, | |
| ocr_reader=shared_ocr_reader, | |
| session_id=iface.app_id | |
| ) | |
| model.segmenter.set_image(image_input) | |
| image_embedding = model.image_embedding | |
| original_size = model.original_size | |
| input_size = model.input_size | |
| if visual_chatgpt is not None: | |
| print('upload_callback: add caption to chatGPT memory') | |
| new_image_path = get_new_image_name('chat_image', func_name='upload') | |
| image_input.save(new_image_path) | |
| visual_chatgpt.current_image = new_image_path | |
| img_caption = model.captioner.inference(image_input, filter=False, args={'text_prompt':''})['caption'] | |
| Human_prompt = f'\nHuman: The description of the image with path {new_image_path} is: {img_caption}. This information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n' | |
| AI_prompt = "Received." | |
| visual_chatgpt.global_prompt = Human_prompt + 'AI: ' + AI_prompt | |
| visual_chatgpt.agent.memory.buffer = visual_chatgpt.agent.memory.buffer + visual_chatgpt.global_prompt | |
| state = [(None, 'Received new image, resize it to width {} and height {}: '.format(image_input.size[0], image_input.size[1]))] | |
| return state, state, image_input, click_state, image_input, image_input, image_embedding, \ | |
| original_size, input_size | |
| def inference_click(image_input, point_prompt, click_mode, enable_wiki, language, sentiment, factuality, | |
| length, image_embedding, state, click_state, original_size, input_size, text_refiner, visual_chatgpt, | |
| evt: gr.SelectData): | |
| click_index = evt.index | |
| if point_prompt == 'Positive': | |
| coordinate = "[[{}, {}, 1]]".format(str(click_index[0]), str(click_index[1])) | |
| else: | |
| coordinate = "[[{}, {}, 0]]".format(str(click_index[0]), str(click_index[1])) | |
| prompt = get_click_prompt(coordinate, click_state, click_mode) | |
| input_points = prompt['input_point'] | |
| input_labels = prompt['input_label'] | |
| controls = {'length': length, | |
| 'sentiment': sentiment, | |
| 'factuality': factuality, | |
| 'language': language} | |
| model = build_caption_anything_with_models( | |
| args, | |
| api_key="", | |
| captioner=shared_captioner, | |
| sam_model=shared_sam_model, | |
| ocr_reader=shared_ocr_reader, | |
| text_refiner=text_refiner, | |
| session_id=iface.app_id | |
| ) | |
| model.setup(image_embedding, original_size, input_size, is_image_set=True) | |
| enable_wiki = True if enable_wiki in ['True', 'TRUE', 'true', True, 'Yes', 'YES', 'yes'] else False | |
| out = model.inference(image_input, prompt, controls, disable_gpt=True, enable_wiki=enable_wiki, verbose=True, args={'clip_filter': False})[0] | |
| state = state + [("Image point: {}, Input label: {}".format(prompt["input_point"], prompt["input_label"]), None)] | |
| state = state + [(None, "raw_caption: {}".format(out['generated_captions']['raw_caption']))] | |
| update_click_state(click_state, out['generated_captions']['raw_caption'], click_mode) | |
| text = out['generated_captions']['raw_caption'] | |
| input_mask = np.array(out['mask'].convert('P')) | |
| image_input = mask_painter(np.array(image_input), input_mask) | |
| origin_image_input = image_input | |
| image_input = create_bubble_frame(image_input, text, (click_index[0], click_index[1]), input_mask, | |
| input_points=input_points, input_labels=input_labels) | |
| x, y = input_points[-1] | |
| if visual_chatgpt is not None: | |
| print('inference_click: add caption to chatGPT memory') | |
| new_crop_save_path = get_new_image_name('chat_image', func_name='crop') | |
| Image.open(out["crop_save_path"]).save(new_crop_save_path) | |
| point_prompt = f'You should primarly use tools on the selected regional image (description: {text}, path: {new_crop_save_path}), which is a part of the whole image (path: {visual_chatgpt.current_image}). If human mentioned some objects not in the selected region, you can use tools on the whole image.' | |
| visual_chatgpt.point_prompt = point_prompt | |
| yield state, state, click_state, image_input | |
| if not args.disable_gpt and model.text_refiner: | |
| refined_caption = model.text_refiner.inference(query=text, controls=controls, context=out['context_captions'], | |
| enable_wiki=enable_wiki) | |
| # new_cap = 'Original: ' + text + '. Refined: ' + refined_caption['caption'] | |
| new_cap = refined_caption['caption'] | |
| if refined_caption['wiki']: | |
| state = state + [(None, "Wiki: {}".format(refined_caption['wiki']))] | |
| state = state + [(None, f"caption: {new_cap}")] | |
| refined_image_input = create_bubble_frame(origin_image_input, new_cap, (click_index[0], click_index[1]), | |
| input_mask, | |
| input_points=input_points, input_labels=input_labels) | |
| yield state, state, click_state, refined_image_input | |
| def get_sketch_prompt(mask: Image.Image): | |
| """ | |
| Get the prompt for the sketcher. | |
| TODO: This is a temporary solution. We should cluster the sketch and get the bounding box of each cluster. | |
| """ | |
| mask = np.asarray(mask)[..., 0] | |
| # Get the bounding box of the sketch | |
| y, x = np.where(mask != 0) | |
| x1, y1 = np.min(x), np.min(y) | |
| x2, y2 = np.max(x), np.max(y) | |
| prompt = { | |
| 'prompt_type': ['box'], | |
| 'input_boxes': [ | |
| [x1, y1, x2, y2] | |
| ] | |
| } | |
| return prompt | |
| def inference_traject(sketcher_image, enable_wiki, language, sentiment, factuality, length, image_embedding, state, | |
| original_size, input_size, text_refiner): | |
| image_input, mask = sketcher_image['image'], sketcher_image['mask'] | |
| prompt = get_sketch_prompt(mask) | |
| boxes = prompt['input_boxes'] | |
| controls = {'length': length, | |
| 'sentiment': sentiment, | |
| 'factuality': factuality, | |
| 'language': language} | |
| model = build_caption_anything_with_models( | |
| args, | |
| api_key="", | |
| captioner=shared_captioner, | |
| sam_model=shared_sam_model, | |
| ocr_reader=shared_ocr_reader, | |
| text_refiner=text_refiner, | |
| session_id=iface.app_id | |
| ) | |
| model.setup(image_embedding, original_size, input_size, is_image_set=True) | |
| enable_wiki = True if enable_wiki in ['True', 'TRUE', 'true', True, 'Yes', 'YES', 'yes'] else False | |
| out = model.inference(image_input, prompt, controls, disable_gpt=True, enable_wiki=enable_wiki)[0] | |
| # Update components and states | |
| state.append((f'Box: {boxes}', None)) | |
| state.append((None, f'raw_caption: {out["generated_captions"]["raw_caption"]}')) | |
| text = out['generated_captions']['raw_caption'] | |
| input_mask = np.array(out['mask'].convert('P')) | |
| image_input = mask_painter(np.array(image_input), input_mask) | |
| origin_image_input = image_input | |
| fake_click_index = (int((boxes[0][0] + boxes[0][2]) / 2), int((boxes[0][1] + boxes[0][3]) / 2)) | |
| image_input = create_bubble_frame(image_input, text, fake_click_index, input_mask) | |
| yield state, state, image_input | |
| if not args.disable_gpt and model.text_refiner: | |
| refined_caption = model.text_refiner.inference(query=text, controls=controls, context=out['context_captions'], | |
| enable_wiki=enable_wiki) | |
| new_cap = refined_caption['caption'] | |
| if refined_caption['wiki']: | |
| state = state + [(None, "Wiki: {}".format(refined_caption['wiki']))] | |
| state = state + [(None, f"caption: {new_cap}")] | |
| refined_image_input = create_bubble_frame(origin_image_input, new_cap, fake_click_index, input_mask) | |
| yield state, state, refined_image_input | |
| def clear_chat_memory(visual_chatgpt, keep_global=False): | |
| if visual_chatgpt is not None: | |
| visual_chatgpt.memory.clear() | |
| visual_chatgpt.point_prompt = "" | |
| if keep_global: | |
| visual_chatgpt.agent.memory.buffer = visual_chatgpt.global_prompt | |
| else: | |
| visual_chatgpt.current_image = None | |
| visual_chatgpt.global_prompt = "" | |
| def cap_everything(image_input, visual_chatgpt, text_refiner): | |
| model = build_caption_anything_with_models( | |
| args, | |
| api_key="", | |
| captioner=shared_captioner, | |
| sam_model=shared_sam_model, | |
| ocr_reader=shared_ocr_reader, | |
| text_refiner=text_refiner, | |
| session_id=iface.app_id | |
| ) | |
| paragraph = model.inference_cap_everything(image_input, verbose=True) | |
| # state = state + [(None, f"Caption Everything: {paragraph}")] | |
| Human_prompt = f'\nThe description of the image with path {visual_chatgpt.current_image} is:\n{paragraph}\nThis information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n' | |
| AI_prompt = "Received." | |
| visual_chatgpt.global_prompt = Human_prompt + 'AI: ' + AI_prompt | |
| visual_chatgpt.agent.memory.buffer = visual_chatgpt.agent.memory.buffer + visual_chatgpt.global_prompt | |
| return paragraph | |
| def get_style(): | |
| current_version = version.parse(gr.__version__) | |
| if current_version <= version.parse('3.24.1'): | |
| style = ''' | |
| #image_sketcher{min-height:500px} | |
| #image_sketcher [data-testid="image"], #image_sketcher [data-testid="image"] > div{min-height: 500px} | |
| #image_upload{min-height:500px} | |
| #image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 500px} | |
| ''' | |
| elif current_version <= version.parse('3.27'): | |
| style = ''' | |
| #image_sketcher{min-height:500px} | |
| #image_upload{min-height:500px} | |
| ''' | |
| else: | |
| style = None | |
| return style | |
| def create_ui(): | |
| title = """<p><h1 align="center">Caption-Anything</h1></p> | |
| """ | |
| description = """<p>Gradio demo for Caption Anything, image to dense captioning generation with various language styles. To use it, simply upload your image, or click one of the examples to load them. Code: <a href="https://github.com/ttengwang/Caption-Anything">https://github.com/ttengwang/Caption-Anything</a> <a href="https://huggingface.co/spaces/TencentARC/Caption-Anything?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>""" | |
| examples = [ | |
| ["test_images/img35.webp"], | |
| ["test_images/img2.jpg"], | |
| ["test_images/img5.jpg"], | |
| ["test_images/img12.jpg"], | |
| ["test_images/img14.jpg"], | |
| ["test_images/qingming3.jpeg"], | |
| ["test_images/img1.jpg"], | |
| ] | |
| with gr.Blocks( | |
| css=get_style() | |
| ) as iface: | |
| state = gr.State([]) | |
| click_state = gr.State([[], [], []]) | |
| # chat_state = gr.State([]) | |
| origin_image = gr.State(None) | |
| image_embedding = gr.State(None) | |
| text_refiner = gr.State(None) | |
| visual_chatgpt = gr.State(None) | |
| original_size = gr.State(None) | |
| input_size = gr.State(None) | |
| # img_caption = gr.State(None) | |
| aux_state = gr.State([]) | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| with gr.Row(): | |
| with gr.Column(scale=1.0): | |
| with gr.Column(visible=False) as modules_not_need_gpt: | |
| with gr.Tab("Click"): | |
| image_input = gr.Image(type="pil", interactive=True, elem_id="image_upload") | |
| example_image = gr.Image(type="pil", interactive=False, visible=False) | |
| with gr.Row(scale=1.0): | |
| with gr.Row(scale=0.4): | |
| point_prompt = gr.Radio( | |
| choices=["Positive", "Negative"], | |
| value="Positive", | |
| label="Point Prompt", | |
| interactive=True) | |
| click_mode = gr.Radio( | |
| choices=["Continuous", "Single"], | |
| value="Continuous", | |
| label="Clicking Mode", | |
| interactive=True) | |
| with gr.Row(scale=0.4): | |
| clear_button_click = gr.Button(value="Clear Clicks", interactive=True) | |
| clear_button_image = gr.Button(value="Clear Image", interactive=True) | |
| with gr.Tab("Trajectory (beta)"): | |
| sketcher_input = ImageSketcher(type="pil", interactive=True, brush_radius=20, | |
| elem_id="image_sketcher") | |
| with gr.Row(): | |
| submit_button_sketcher = gr.Button(value="Submit", interactive=True) | |
| with gr.Column(visible=False) as modules_need_gpt1: | |
| with gr.Row(scale=1.0): | |
| language = gr.Dropdown( | |
| ['English', 'Chinese', 'French', "Spanish", "Arabic", "Portuguese", "Cantonese"], | |
| value="English", label="Language", interactive=True) | |
| sentiment = gr.Radio( | |
| choices=["Positive", "Natural", "Negative"], | |
| value="Natural", | |
| label="Sentiment", | |
| interactive=True, | |
| ) | |
| with gr.Row(scale=1.0): | |
| factuality = gr.Radio( | |
| choices=["Factual", "Imagination"], | |
| value="Factual", | |
| label="Factuality", | |
| interactive=True, | |
| ) | |
| length = gr.Slider( | |
| minimum=10, | |
| maximum=80, | |
| value=10, | |
| step=1, | |
| interactive=True, | |
| label="Generated Caption Length", | |
| ) | |
| enable_wiki = gr.Radio( | |
| choices=["Yes", "No"], | |
| value="No", | |
| label="Enable Wiki", | |
| interactive=True) | |
| # with gr.Column(visible=True) as modules_not_need_gpt3: | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[example_image], | |
| ) | |
| with gr.Column(scale=0.5): | |
| with gr.Column(visible=True) as module_key_input: | |
| openai_api_key = gr.Textbox( | |
| placeholder="Input openAI API key", | |
| show_label=False, | |
| label="OpenAI API Key", | |
| lines=1, | |
| type="password") | |
| with gr.Row(scale=0.5): | |
| enable_chatGPT_button = gr.Button(value="Run with ChatGPT", interactive=True, variant='primary') | |
| disable_chatGPT_button = gr.Button(value="Run without ChatGPT (Faster)", interactive=True, | |
| variant='primary') | |
| with gr.Column(visible=False) as module_notification_box: | |
| notification_box = gr.Textbox(lines=1, label="Notification", max_lines=5, show_label=False) | |
| with gr.Column(visible=False) as modules_need_gpt2: | |
| paragraph_output = gr.Textbox(lines=7, label="Describe Everything", max_lines=7) | |
| with gr.Column(visible=False) as modules_need_gpt0: | |
| cap_everything_button = gr.Button(value="Caption Everything in a Paragraph", interactive=True) | |
| with gr.Column(visible=False) as modules_not_need_gpt2: | |
| chatbot = gr.Chatbot(label="Chatbox", ).style(height=550, scale=0.5) | |
| with gr.Column(visible=False) as modules_need_gpt3: | |
| chat_input = gr.Textbox(show_label=False, placeholder="Enter text and press Enter").style( | |
| container=False) | |
| with gr.Row(): | |
| clear_button_text = gr.Button(value="Clear Text", interactive=True) | |
| submit_button_text = gr.Button(value="Submit", interactive=True, variant="primary") | |
| openai_api_key.submit(init_openai_api_key, inputs=[openai_api_key], | |
| outputs=[modules_need_gpt0, modules_need_gpt1, modules_need_gpt2, modules_need_gpt3, modules_not_need_gpt, | |
| modules_not_need_gpt2, module_key_input, module_notification_box, text_refiner, visual_chatgpt, notification_box]) | |
| enable_chatGPT_button.click(init_openai_api_key, inputs=[openai_api_key], | |
| outputs=[modules_need_gpt0, modules_need_gpt1, modules_need_gpt2, modules_need_gpt3, | |
| modules_not_need_gpt, | |
| modules_not_need_gpt2, module_key_input, module_notification_box, text_refiner, visual_chatgpt, notification_box]) | |
| disable_chatGPT_button.click(init_wo_openai_api_key, | |
| outputs=[modules_need_gpt0, modules_need_gpt1, modules_need_gpt2, modules_need_gpt3, | |
| modules_not_need_gpt, | |
| modules_not_need_gpt2, module_key_input, module_notification_box, text_refiner, visual_chatgpt, notification_box]) | |
| enable_chatGPT_button.click( | |
| lambda: (None, [], [], [[], [], []], "", "", ""), | |
| [], | |
| [image_input, chatbot, state, click_state, paragraph_output, origin_image], | |
| queue=False, | |
| show_progress=False | |
| ) | |
| openai_api_key.submit( | |
| lambda: (None, [], [], [[], [], []], "", "", ""), | |
| [], | |
| [image_input, chatbot, state, click_state, paragraph_output, origin_image], | |
| queue=False, | |
| show_progress=False | |
| ) | |
| cap_everything_button.click(cap_everything, [origin_image, visual_chatgpt, text_refiner], [paragraph_output]) | |
| clear_button_click.click( | |
| lambda x: ([[], [], []], x), | |
| [origin_image], | |
| [click_state, image_input], | |
| queue=False, | |
| show_progress=False | |
| ) | |
| clear_button_click.click(functools.partial(clear_chat_memory, keep_global=True), inputs=[visual_chatgpt]) | |
| clear_button_image.click( | |
| lambda: (None, [], [], [[], [], []], "", "", ""), | |
| [], | |
| [image_input, chatbot, state, click_state, paragraph_output, origin_image], | |
| queue=False, | |
| show_progress=False | |
| ) | |
| clear_button_image.click(clear_chat_memory, inputs=[visual_chatgpt]) | |
| clear_button_text.click( | |
| lambda: ([], [], [[], [], [], []]), | |
| [], | |
| [chatbot, state, click_state], | |
| queue=False, | |
| show_progress=False | |
| ) | |
| clear_button_text.click(clear_chat_memory, inputs=[visual_chatgpt]) | |
| image_input.clear( | |
| lambda: (None, [], [], [[], [], []], "", "", ""), | |
| [], | |
| [image_input, chatbot, state, click_state, paragraph_output, origin_image], | |
| queue=False, | |
| show_progress=False | |
| ) | |
| image_input.clear(clear_chat_memory, inputs=[visual_chatgpt]) | |
| image_input.upload(upload_callback, [image_input, state, visual_chatgpt], | |
| [chatbot, state, origin_image, click_state, image_input, sketcher_input, | |
| image_embedding, original_size, input_size]) | |
| sketcher_input.upload(upload_callback, [sketcher_input, state, visual_chatgpt], | |
| [chatbot, state, origin_image, click_state, image_input, sketcher_input, | |
| image_embedding, original_size, input_size]) | |
| chat_input.submit(chat_input_callback, [visual_chatgpt, chat_input, click_state, state, aux_state], | |
| [chatbot, state, aux_state]) | |
| chat_input.submit(lambda: "", None, chat_input) | |
| submit_button_text.click(chat_input_callback, [visual_chatgpt, chat_input, click_state, state, aux_state], | |
| [chatbot, state, aux_state]) | |
| submit_button_text.click(lambda: "", None, chat_input) | |
| example_image.change(upload_callback, [example_image, state, visual_chatgpt], | |
| [chatbot, state, origin_image, click_state, image_input, sketcher_input, | |
| image_embedding, original_size, input_size]) | |
| example_image.change(clear_chat_memory, inputs=[visual_chatgpt]) | |
| # select coordinate | |
| image_input.select( | |
| inference_click, | |
| inputs=[ | |
| origin_image, point_prompt, click_mode, enable_wiki, language, sentiment, factuality, length, | |
| image_embedding, state, click_state, original_size, input_size, text_refiner, visual_chatgpt | |
| ], | |
| outputs=[chatbot, state, click_state, image_input], | |
| show_progress=False, queue=True | |
| ) | |
| submit_button_sketcher.click( | |
| inference_traject, | |
| inputs=[ | |
| sketcher_input, enable_wiki, language, sentiment, factuality, length, image_embedding, state, | |
| original_size, input_size, text_refiner | |
| ], | |
| outputs=[chatbot, state, sketcher_input], | |
| show_progress=False, queue=True | |
| ) | |
| return iface | |
| if __name__ == '__main__': | |
| iface = create_ui() | |
| iface.queue(concurrency_count=5, api_open=False, max_size=10) | |
| iface.launch(server_name="0.0.0.0", enable_queue=True) | |