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import gradio as gr
from transformers import AutoModel, AutoTokenizer
from PIL import Image
import torch
# Load model and tokenizer
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' if torch.cuda.is_available() else 'cpu',
use_safetensors=True,
pad_token_id=tokenizer.eos_token_id
)
model = model.eval()
if torch.cuda.is_available():
model = model.cuda()
# OCR function
def ocr_from_image(image_file, ocr_type):
if image_file is None:
return "Please upload an image."
# Ouvrir le fichier image avec PIL
image = Image.open(image_file).convert("RGB")
image_path = "uploaded_image.jpg"
image.save(image_path)
# Passer le chemin au modele
res = model.chat(tokenizer, image_path, ocr_type=ocr_type)
return res
# OCR types to choose from
ocr_types = ["ocr", "format"]
# Gradio interface
iface = gr.Interface(
fn=ocr_from_image,
inputs=[
gr.File(label="Upload Image", file_types=[".jpg", ".jpeg", ".png"]),
gr.Radio(ocr_types, label="OCR Type", value="ocr")
],
outputs="text",
title="?? GOT-OCR2.0 Transformer OCR",
description="Upload an image file and select the OCR type: plain text (`ocr`) or formatted (`format`)."
)
if __name__ == "__main__":
iface.launch(share=True)
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