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import argparse
import gradio as gr
import torch
from PIL import Image
from donut import DonutModel
def demo_process_vqa(input_img, question):
global pretrained_model, task_prompt, task_name
# input_img = Image.fromarray(input_img)
user_prompt = task_prompt.replace("{user_input}", question)
output = pretrained_model.inference(input_img, prompt=user_prompt)["predictions"][0]
return output
def demo_process(input_img):
global pretrained_model, task_prompt, task_name,security_layer
input_img = Image.fromarray(input_img)
sec = security_layer.inference(image=input_img,prompt="<s_rvlcdip>")['predictions'][0]
print(sec)
if sec['class']=="invoice":
output = pretrained_model.inference(image=input_img, prompt="<s_cord-v2>")["predictions"][0]
return output
return sec
task_name="cord-v2"
if "docvqa" == task_name:
task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
else: # rvlcdip, cord, ...
task_prompt = f"<s_{task_name}>"
security_layer = DonutModel.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")
pretrained_model = DonutModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
if torch.cuda.is_available():
pretrained_model.half()
security_layer.half()
device = torch.device("cuda")
pretrained_model.to(device)
security_layer.to(device)
else:
pretrained_model.encoder.to(torch.bfloat16)
security_layer.encoder.to(torch.bfloat16)
pretrained_model.eval()
security_layer.eval()
demo = gr.Interface(
fn=demo_process_vqa if task_name == "docvqa" else demo_process,
inputs=["image", "text"] if task_name == "docvqa" else "image",
outputs="json",
title=f"Donut 🍩 demonstration for `{task_name}` task",
concurrency_limit=10,
description="Get invoice details if invoice"
)
demo.queue(default_concurrency_limit=2,max_size=5)
demo.launch(debug=True,share=True, inline=False)
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