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| import gradio as gr | |
| import spaces | |
| from transformers import AutoModel, AutoTokenizer | |
| from PIL import Image | |
| import numpy as np | |
| import os | |
| 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() | |
| html_file = './demo.html' | |
| def run_GOT(image_array, got_mode, fine_grained_mode="", ocr_color="", ocr_box=""): | |
| image = image_array | |
| if got_mode == "plain texts OCR": | |
| res = model.chat(tokenizer, image, ocr_type='ocr') | |
| elif got_mode == "format texts OCR": | |
| res = model.chat(tokenizer, image, ocr_type='format', render=True, save_render_file=html_file) | |
| elif got_mode == "plain multi-crop OCR": | |
| res = model.chat_crop(tokenizer, image, ocr_type='ocr') | |
| elif got_mode == "format multi-crop OCR": | |
| res = model.chat_crop(tokenizer, image, ocr_type='format', render=True, save_render_file=html_file) | |
| elif got_mode == "plain fine-grained OCR": | |
| res = model.chat(tokenizer, image, ocr_type='ocr', ocr_box=ocr_box, ocr_color=ocr_color) | |
| 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=html_file) | |
| print("res:\n", res) | |
| if "format" in got_mode: | |
| with open(html_file, 'r') as f: | |
| demo_html = f.read() | |
| print("demo_html: \n", demo_html) | |
| print(os.path.abspath(html_file)) | |
| 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") | |
| gr.Markdown( | |
| """ | |
| \\[ | |
| \\begin{array}{l} | |
| d_{L}\\left( C_{L},\\left\\{ v^{\\prime }\\right\\} \\right) =\\left\\vert C_{L}\\right\\vert | |
| +\\left\\vert \\left\\{ v^{\\prime }\\right\\} \\right\\vert +2\\left( d_{T}\\left( | |
| C_{L},v^{\\prime }\\right) -1\\right) \\\\\\ | |
| \\quad \\quad \\quad \\quad =\\left\\vert C_{v}\\right\\vert -1+\\left\\vert | |
| S_{v}^{*}\\right\\vert +2\\left( \\mathrm{rad}\\,T-1\\right) \\\\\\ | |
| \\quad \\quad \\quad \\quad =\\left\\vert C{ }_{v}\\right\\vert +\\left\\vert | |
| S_{v}^{*}\\right\\vert +2\\left( d_{T}\\left( C_{v},S_{v}^{*}\\right) -1\\right) \\\\\\ | |
| \\quad \\quad \\quad \\quad \\quad -1+2\\left( \\mathrm{rad}\\,T-d_{T}\\left( C_{v},S_{v}^{*}\\right) | |
| \\right) \\\\\\ | |
| \\quad \\quad \\quad \\quad =d_{L}\\left( C_{v},S_{v}^{*}\\right) +1+2\\left( \\mathrm{rad}% | |
| \\text{\\,}T-1-d_{T}\\left( C_{v},S_{v}^{*}\\right) \\right) \\\\\\ | |
| \\quad \\quad \\quad \\quad \\quad =e_{L}\\left( C_{v}\\right) +1+2\\left( \\mathrm{rad}% | |
| \\text{\\,}T -1-d_{T}\\left( C_{v},S_{v}^{*}\\right)\\right) . | |
| \\end{array} | |
| \\] | |
| """ | |
| ) | |
| with gr.Column(): | |
| html_show = gr.HTML(f'<a href="{html_file}" target="_blank">Open Demo HTML</a>') | |
| with gr.Column(): | |
| html_result = gr.HTML( | |
| value=""" | |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <title>MathJax Example</title> | |
| <script src="https://cdn.jsdelivr.net/npm/[email protected]/es5/bundle.js"></script> | |
| <style> | |
| #content { | |
| max-width: 800px; | |
| margin: auto; | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div id="content"> | |
| <p>Here is an example of a LaTeX formula:</p> | |
| <div id="math-content"> | |
| \\[ | |
| \\begin{array}{l} | |
| d_{L}\\left( C_{L},\\left\\{ v^{\\prime }\\right\\} \\right) =\\left\\vert C_{L}\\right\\vert | |
| +\\left\\vert \\left\\{ v^{\\prime }\\right\\} \\right\\vert +2\\left( d_{T}\\left( | |
| C_{L},v^{\\prime }\\right) -1\\right) \\\\\\ | |
| \\quad \\quad \\quad \\quad =\\left\\vert C_{v}\\right\\vert -1+\\left\\vert | |
| S_{v}^{*}\\right\\vert +2\\left( \\mathrm{rad}\\,T-1\\right) \\\\\\ | |
| \\quad \\quad \\quad \\quad =\\left\\vert C{ }_{v}\\right\\vert +\\left\\vert | |
| S_{v}^{*}\\right\\vert +2\\left( d_{T}\\left( C_{v},S_{v}^{*}\\right) -1\\right) \\\\\\ | |
| \\quad \\quad \\quad \\quad \\quad -1+2\\left( \\mathrm{rad}\\,T-d_{T}\\left( C_{v},S_{v}^{*}\\right) | |
| \\right) \\\\\\ | |
| \\quad \\quad \\quad \\quad =d_{L}\\left( C_{v},S_{v}^{*}\\right) +1+2\\left( \\mathrm{rad}% | |
| \\text{\\,}T-1-d_{T}\\left( C_{v},S_{v}^{*}\\right) \\right) \\\\\\ | |
| \\quad \\quad \\quad \\quad \\quad =e_{L}\\left( C_{v}\\right) +1+2\\left( \\mathrm{rad}% | |
| \\text{\\,}T -1-d_{T}\\left( C_{v},S_{v}^{*}\\right)\\right) . | |
| \\end{array} | |
| \\] | |
| </div> | |
| </div> | |
| </body> | |
| </html> | |
| """, | |
| label="rendered html", show_label=True) | |
| 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/giga.jpg", "format multi-crop OCR", "", "", ""], | |
| ["assets/aff2.png", "plain fine-grained OCR", "box", "", "[409,763,756,891]"], | |
| ["assets/color.png", "plain fine-grained OCR", "color", "red", ""], | |
| ], | |
| inputs=[image_input, task_dropdown, fine_grained_dropdown, color_dropdown, box_input], | |
| outputs=[ocr_result, html_result], | |
| fn = run_GOT, | |
| 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, fine_grained_dropdown, color_dropdown, box_input], | |
| outputs=[ocr_result, html_result] | |
| ) | |
| demo.launch() |