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import argparse
import gradio as gr
import os
import torch
from donut import DonutModel
from PIL import Image
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)
return pretrained_model.inference(input_img, prompt=user_prompt)["predictions"][0]
def demo_process(input_img):
global pretrained_model, task_prompt, task_name
input_img = Image.fromarray(input_img)
best_output = pretrained_model.inference(image=input_img, prompt=task_prompt)["predictions"][0]
return best_output["text_sequence"].split(" </s_MachineReadableZone>")[0]
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--task", type=str, default="s_passport")
parser.add_argument("--pretrained_path", type=str, default=os.getcwd())
parser.add_argument("--port", type=int, default=12345)
parser.add_argument("--url", type=str, default="0.0.0.0")
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 = [os.path.join("images", image) for image in os.listdir("images")]
if args.sample_img_path:
example_sample.append(args.sample_img_path)
pretrained_model = DonutModel.from_pretrained(args.pretrained_path)
if torch.cuda.is_available():
pretrained_model.half()
device = torch.device("cuda")
pretrained_model.to(device)
pretrained_model.eval()
gr.Interface(
fn=demo_process_vqa if task_name == "docvqa" else demo_process,
inputs=["image", "text"] if task_name == "docvqa" else "image",
outputs="text",
title="Demo of MRZ Extraction model based on 🍩 architecture",
examples=example_sample if example_sample else None
).launch()
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