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"""
Donut
Copyright (c) 2022-present NAVER Corp.
MIT License
https://github.com/clovaai/donut
"""
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
input_img = Image.fromarray(input_img)
output = pretrained_model.inference(image=input_img, prompt=task_prompt)["predictions"][0]
return output
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--task", type=str, default="cord-v2")
parser.add_argument("--pretrained_path", type=str, default="naver-clova-ix/donut-base-finetuned-cord-v2")
parser.add_argument("--port", type=int, default=None)
parser.add_argument("--url", type=str, default=None)
parser.add_argument("--sample_img_path", type=str)
args, left_argv = parser.parse_known_args()
task_name = args.task
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}>"
example_sample = []
if args.sample_img_path:
example_sample.append(args.sample_img_path)
pretrained_model = DonutModel.from_pretrained(args.pretrained_path)
pretrained_model.encoder.to(torch.bfloat16)
pretrained_model.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",
examples=[example_sample] if example_sample else None,
)
demo.launch(server_name=args.url, server_port=args.port)
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