app.py

import gradio as gr
import torch
from PIL import Image

#from donut import DonutModel

def demo_process(input_img):
    global pretrained_model, task_prompt, task_name
    # input_img = Image.fromarray(input_img)
    output = pretrained_model.inference(image=input_img, prompt=task_prompt)["predictions"][0]
    return output

task_prompt = f"<s_cord-v2>"

image = Image.open("/content/SKMBT_75122072616550_Page_37_Image_0001.png")
image.save("cord_sample_receipt1.png")
image = Image.open("/content/SKMBT_75122072616550_Page_50_Image_0001.png")
image.save("cord_sample_receipt2.png")

#pretrained_model = DonutModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
#pretrained_model.encoder.to(torch.bfloat16)

model = torch.load("/content/drive/MyDrive/fast_job/DONUT_model/donut/model.pt")
# Move model to GPU
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

demo = gr.Interface(
    fn=demo_process,
    inputs= gr.inputs.Image(type="pil"),
    outputs="json",
    title=f"Donut 🍩 demonstration for `Medical Prescription Dataset` task",
    description="""This model is trained with 200 medical prescription handwritten document images. <br>""",
    examples=[["cord_sample_receipt1.png"], ["cord_sample_receipt2.png"]],
    cache_examples=False,
)

demo.launch()