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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)