import gradio as gr import spaces from transformers import AutoModel, AutoTokenizer from PIL import Image import numpy as np tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True) model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True) model = model.eval().cuda() @spaces.GPU def run_GOT(image_array, got_mode, ocr_box="", ocr_color=""): image = Image.fromarray(np.uint8(image_array)) if got_mode == "plain texts OCR": res = model.chat(tokenizer, image, ocr_type='ocr', gradio_input=True) elif got_mode == "format texts OCR": res = model.chat(tokenizer, image, ocr_type='format', render=True, save_render_file='./demo.html', gradio_input=True) elif got_mode == "plain multi-crop OCR": res = model.chat_crop(tokenizer, image, ocr_type='ocr', gradio_input=True) elif got_mode == "format multi-crop OCR": res = model.chat_crop(tokenizer, image, ocr_type='format', render=True, save_render_file='./demo.html', gradio_input=True) elif got_mode == "plain fine-grained OCR": res = model.chat(tokenizer, image, ocr_type='ocr', ocr_box=ocr_box, ocr_color=ocr_color, gradio_input=True) elif got_mode == "format fine-grained OCR": res = model.chat(tokenizer, image, ocr_type='format', ocr_box=ocr_box, ocr_color=ocr_color, render=True, save_render_file='./demo.html', gradio_input=True) if "format" in got_mode: with open('./demo.html', 'r') as f: demo_html = f.read() return res, demo_html return res, None def task_update(task): if "fine-grained" in task: return [ gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), ] else: return [ gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), ] def fine_grained_update(task): if task == "box": return [ gr.update(visible=False, value = ""), gr.update(visible=True), ] elif task == 'color': return [ gr.update(visible=True), gr.update(visible=False, value = ""), ] with gr.Blocks() as demo: gr.Markdown(""" # "General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model" "🔥🔥🔥This is the official online demo of GOT-OCR-2.0 model!!!" ### Repo - **Hugging Face**: [ucaslcl/GOT-OCR2_0](https://huggingface.co/ucaslcl/GOT-OCR2_0) - **GitHub**: [Ucas-HaoranWei/GOT-OCR2_0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/) - **Paper**: [AriXiv](https://arxiv.org/abs/2409.01704) """) with gr.Row(): with gr.Column(): image_input = gr.Image(type="filepath", label="upload your image") task_dropdown = gr.Dropdown( choices=[ "plain texts OCR", "format texts OCR", "plain multi-crop OCR", "format multi-crop OCR", "plain fine-grained OCR", "format fine-grained OCR", ], label="Choose one mode of GOT", value="plain texts OCR" ) fine_grained_dropdown = gr.Dropdown( choices=["box", "color"], label="fine-grained type", visible=False ) color_dropdown = gr.Dropdown( choices=["red", "green", "blue"], label="color list", visible=False ) box_input = gr.Textbox( label="input box: [x1,y1,x2,y2]", placeholder="e.g., [0,0,100,100]", visible=False ) submit_button = gr.Button("Submit") with gr.Column(): ocr_result = gr.Textbox(label="GOT output") html_result = gr.HTML(label="rendered html") gr.Examples( examples=[ ["assets/coco.jpg", "plain texts OCR", "", ""], ["assets/en2.png", "plain texts OCR", "", ""], ["assets/eq.jpg", "format texts OCR", "", ""], ["assets/table.jpg", "format texts OCR", "", ""], ["assets/aff2.png", "plain fine-grained OCR", "[409,763,756,891]", ""], ], inputs=[image_input, task_dropdown], label="examples", ) task_dropdown.change( task_update, inputs=[task_dropdown], outputs=[fine_grained_dropdown, color_dropdown, box_input] ) fine_grained_dropdown.change( fine_grained_update, inputs=[fine_grained_dropdown], outputs=[color_dropdown, box_input] ) submit_button.click( run_GOT, inputs=[image_input, task_dropdown, box_input, color_dropdown], outputs=[ocr_result, html_result] ) demo.launch(share=True)