import gradio as gr import spaces from transformers import AutoModel, AutoTokenizer 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 process_image(image, task, ocr_type=None, ocr_box=None, ocr_color=None, render=True): if task == "Plain Text OCR": res = model.chat(tokenizer, image, ocr_type='ocr') elif task == "Format Text OCR": res = model.chat(tokenizer, image, ocr_type='format') elif task == "Fine-grained OCR (Box)": res = model.chat(tokenizer, image, ocr_type=ocr_type, ocr_box=ocr_box) elif task == "Fine-grained OCR (Color)": res = model.chat(tokenizer, image, ocr_type=ocr_type, ocr_color=ocr_color) elif task == "Multi-crop OCR": res = model.chat_crop(tokenizer, image_file=image) elif task == "Render Formatted OCR": res = model.chat(tokenizer, image, ocr_type='format', render=True, save_render_file='./demo.html') with open('./demo.html', 'r') as f: html_content = f.read() return res, html_content return res, None def update_inputs(task): if task == "Plain Text OCR" or task == "Format Text OCR" or task == "Multi-crop OCR": return [gr.update(visible=False)] * 4 elif task == "Fine-grained OCR (Box)": return [ gr.update(visible=True, choices=["ocr", "format"]), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False) ] elif task == "Fine-grained OCR (Color)": return [ gr.update(visible=True, choices=["ocr", "format"]), gr.update(visible=False), gr.update(visible=True, choices=["red", "green", "blue"]), gr.update(visible=False) ] elif task == "Render Formatted OCR": return [gr.update(visible=False)] * 3 + [gr.update(visible=True)] def ocr_demo(image, task, ocr_type, ocr_box, ocr_color, render): res, html_content = process_image(image, task, ocr_type, ocr_box, ocr_color, render) if html_content: return res, html_content return res, None with gr.Blocks() as demo: gr.Markdown() gr.Markdown() 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**: [ucaslcl/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 text OCR", "format text OCR", "fine-grained OCR (Box)", "fine-grained OCR (Color)", "multi-crop OCR", "render Formatted OCR" ], label="Select Task", value="Plain Text OCR" ) ocr_type_dropdown = gr.Dropdown( choices=["ocr", "format"], label="OCR Type", visible=False ) ocr_box_input = gr.Textbox( label="OCR Box (x1,y1,x2,y2)", placeholder="e.g., 100,100,200,200", visible=False ) ocr_color_dropdown = gr.Dropdown( choices=["red", "green", "blue"], label="OCR Color", visible=False ) render_checkbox = gr.Checkbox( label="Render Result", visible=False ) submit_button = gr.Button("Process") with gr.Column(): output_text = gr.Textbox(label="OCR Result") output_html = gr.HTML(label="Rendered HTML Output") task_dropdown.change( update_inputs, inputs=[task_dropdown], outputs=[ocr_type_dropdown, ocr_box_input, ocr_color_dropdown, render_checkbox] ) submit_button.click( ocr_demo, inputs=[image_input, task_dropdown, ocr_type_dropdown, ocr_box_input, ocr_color_dropdown], outputs=[output_text, output_html] ) demo.launch()