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import base64
import os
import gradio as gr
import spaces
import torch
from transformers import AutoModel, AutoTokenizer
model_name = "ucaslcl/GOT-OCR2_0"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, device_map=device)
model = model.eval().to(device)
@spaces.GPU()
def ocr_process(image, got_mode, ocr_color="", ocr_box="", progress=gr.Progress()):
if image is None:
return "错误:未提供图片"
try:
image_path = image
result_path = f"{os.path.splitext(image_path)[0]}_result.html"
progress(0, desc="开始处理...")
if "plain" in got_mode:
progress(0.3, desc="执行OCR识别...")
if "multi-crop" in got_mode:
res = model.chat_crop(tokenizer, image_path, ocr_type="ocr")
else:
res = model.chat(tokenizer, image_path, ocr_type="ocr", ocr_box=ocr_box, ocr_color=ocr_color)
progress(1, desc="处理完成")
return res
elif "format" in got_mode:
progress(0.3, desc="执行OCR识别...")
if "multi-crop" in got_mode:
res = model.chat_crop(tokenizer, image_path, ocr_type="format", render=True, save_render_file=result_path)
else:
res = model.chat(tokenizer, image_path, ocr_type="format", ocr_box=ocr_box, ocr_color=ocr_color, render=True, save_render_file=result_path)
progress(0.7, desc="生成结果...")
if os.path.exists(result_path):
with open(result_path, "r", encoding="utf-8") as f:
html_content = f.read()
encoded_html = base64.b64encode(html_content.encode("utf-8")).decode("utf-8")
data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}"
preview = f'<iframe src="{data_uri}" width="100%" height="600px"></iframe>'
download_link = f'<a href="{data_uri}" download="result.html">下载完整结果</a>'
progress(1, desc="处理完成")
return f"{download_link}\n\n{preview}"
return "错误: 未知的OCR模式"
except Exception as e:
return f"错误: {str(e)}"
with gr.Blocks() as demo:
gr.Markdown("# OCR 图像识别")
with gr.Row():
image_input = gr.Image(type="filepath", label="上传图片")
got_mode = 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="OCR模式",
value="plain texts OCR",
)
with gr.Row():
ocr_color = gr.Textbox(label="OCR颜色 (仅用于fine-grained模式)")
ocr_box = gr.Textbox(label="OCR边界框 (仅用于fine-grained模式)")
submit_button = gr.Button("开始OCR识别")
output = gr.HTML(label="识别结果")
submit_button.click(ocr_process, inputs=[image_input, got_mode, ocr_color, ocr_box], outputs=output)
if __name__ == "__main__":
demo.launch()
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