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import gradio as gr
import torch
from PIL import Image
from transformers import DonutProcessor, VisionEncoderDecoderModel
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "selvakumarcts/sk_invoice_receipts"
# Load model and processor
processor = DonutProcessor.from_pretrained(model_repo_id)
model = VisionEncoderDecoderModel.from_pretrained(model_repo_id).to(device)
# Inference function
def infer(image):
image = image.convert("RGB")
pixel_values = processor(image, return_tensors="pt").pixel_values.to(device)
output = model.generate(pixel_values, max_length=512)
result = processor.batch_decode(output, skip_special_tokens=True)[0]
return result
# UI
with gr.Blocks() as demo:
gr.Markdown(" # Invoice/Receipt Reader (Donut Model)")
with gr.Column():
image_input = gr.Image(type="pil", label="Upload Image")
run_button = gr.Button("Run")
result = gr.Textbox(label="Extracted JSON", lines=10)
run_button.click(fn=infer, inputs=[image_input], outputs=[result])
if __name__ == "__main__":
demo.launch()
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