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import re
import gradio as gr
from PIL import Image
import torch
from transformers import DonutProcessor, VisionEncoderDecoderModel
pretrained_repo_name = "ivelin/donut-refexp-draft"
processor = DonutProcessor.from_pretrained(pretrained_repo_name)
model = VisionEncoderDecoderModel.from_pretrained(pretrained_repo_name)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
def process_refexp(image: Image, prompt: str):
print(f"(image, prompt): {image}, {prompt}")
# trim prompt to 80 characters and normalize to lowercase
prompt = prompt[:80].lower()
# prepare encoder inputs
pixel_values = processor(image, return_tensors="pt").pixel_values
# prepare decoder inputs
task_prompt = "<s_refexp><s_prompt>{user_input}</s_prompt><s_refexp>"
prompt = task_prompt.replace("{user_input}", prompt)
decoder_input_ids = processor.tokenizer(
prompt, add_special_tokens=False, return_tensors="pt").input_ids
# generate answer
outputs = model.generate(
pixel_values.to(device),
decoder_input_ids=decoder_input_ids.to(device),
max_length=model.decoder.config.max_position_embeddings,
early_stopping=True,
pad_token_id=processor.tokenizer.pad_token_id,
eos_token_id=processor.tokenizer.eos_token_id,
use_cache=True,
num_beams=1,
bad_words_ids=[[processor.tokenizer.unk_token_id]],
return_dict_in_generate=True,
)
# postprocess
sequence = processor.batch_decode(outputs.sequences)[0]
sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(
processor.tokenizer.pad_token, "")
# remove first task start token
sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()
bbox = processor.token2json(sequence)
print(f"predicted bounding box: {bbox}")
print(f"image object: {image}")
print(f"image size: {image.size}")
width, height = image.size
print(f"image width, height: {width, height}")
print(f"prompt: {sample['prompt']}")
xmin = math.floor(width*bbox["xmin"])
ymin = math.floor(height*bbox["ymin"])
xmax = math.floor(width*bbox["xmax"])
ymax = math.floor(height*bbox["ymax"])
print(
f"to image pixel values: xmin, ymin, xmax, ymax: {xmin, ymin, xmax, ymax}")
shape = [(xmin, ymin), (xmax, ymax)]
# create rectangle image
img1 = ImageDraw.Draw(image)
img1.rectangle(shape, outline="green", width=5)
return image, bbox
title = "Demo: Donut 🍩 for UI RefExp"
description = "Gradio Demo for Donut RefExp task, an instance of `VisionEncoderDecoderModel` fine-tuned on UIBert RefExp Dataset (UI Referring Expression). To use it, simply upload your image and type a question and click 'submit', or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.15664' target='_blank'>Donut: OCR-free Document Understanding Transformer</a> | <a href='https://github.com/clovaai/donut' target='_blank'>Github Repo</a></p>"
examples = [["example_1.jpg", "select the setting icon from top right corner"],
["example_2.jpg", "enter the text field next to the name"]]
demo = gr.Interface(fn=process_refexp,
inputs=["image", "text"],
outputs=["image", "json"],
title=title,
description=description,
article=article,
examples=examples)
demo.launch(cache_examples=True)
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