import gradio as gr import torch from transformers import AutoModel, AutoTokenizer, AutoConfig import os import base64 import spaces import io from PIL import Image import numpy as np import yaml from pathlib import Path from globe import title, description, modelinfor, joinus model_name = 'ucaslcl/GOT-OCR2_0' tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True) config = AutoConfig.from_pretrained(model_name, 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, pad_token_id=tokenizer.eos_token_id) model = model.eval().cuda() model.config.pad_token_id = tokenizer.eos_token_id def image_to_base64(image): buffered = io.BytesIO() image.save(buffered, format="PNG") return base64.b64encode(buffered.getvalue()).decode() html_file = './demo.html' @spaces.GPU def process_image(image, task, ocr_type=None, ocr_box=None, ocr_color=None): if task == "Plain Text OCR": res = model.chat(tokenizer, image, ocr_type='ocr') return res, None elif task == "Format Text OCR": res = model.chat(tokenizer, image, ocr_type='format', render=True, save_render_file=html_file) elif task == "Fine-grained OCR (Box)": res = model.chat(tokenizer, image, ocr_type=ocr_type, ocr_box=ocr_box, render=True, save_render_file=html_file) elif task == "Fine-grained OCR (Color)": res = model.chat(tokenizer, image, ocr_type=ocr_type, ocr_color=ocr_color, render=True, save_render_file=html_file) elif task == "Multi-crop OCR": res = model.chat_crop(tokenizer, image, ocr_type='format', render=True, save_render_file=html_file) elif task == "Render Formatted OCR": res = model.chat(tokenizer, image, ocr_type='format', render=True, save_render_file=html_file) with open(html_file, 'r') as f: html_content = f.read() return res, html_content def update_inputs(task): if task in ["Plain Text OCR", "Format Text OCR", "Multi-crop OCR", "Render Formatted OCR"]: return [gr.update(visible=False)] * 3 elif task == "Fine-grained OCR (Box)": return [ gr.update(visible=True, choices=["ocr", "format"]), gr.update(visible=True), 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"]), ] def ocr_demo(image, task, ocr_type, ocr_box, ocr_color): res, html_content = process_image(image, task, ocr_type, ocr_box, ocr_color) res = f"${res}$" res = res.replace("$\\begin{tabular}", "\\begin{tabular}") res = res.replace("\\end{tabular}$", "\\end{tabular}") res = res.replace("\\(", "") res = res.replace("\\)", "") if html_content: html_string = f'' return res, html_string return res, None import gradio as gr with gr.Blocks() as demo: gr.Markdown(title) gr.Markdown(description) gr.Markdown(joinus) with gr.Column(): image_input = gr.Image(type="filepath", label="Input 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 ) submit_button = gr.Button("Process") output_markdown = gr.Markdown(label="🫴🏻📸GOT-OCR") output_html = gr.HTML(label="🫴🏻📸GOT-OCR") gr.Markdown(modelinfor) task_dropdown.change( update_inputs, inputs=[task_dropdown], outputs=[ocr_type_dropdown, ocr_box_input, ocr_color_dropdown] ) submit_button.click( ocr_demo, inputs=[image_input, task_dropdown, ocr_type_dropdown, ocr_box_input, ocr_color_dropdown], outputs=[output_markdown, output_html] ) if __name__ == "__main__": demo.launch()