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import re
import gradio as gr
import torch
from transformers import DonutProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests
from io import BytesIO
import json
import os
processor = DonutProcessor.from_pretrained("./donut-base-finetuned-inv")
model = VisionEncoderDecoderModel.from_pretrained("./donut-base-finetuned-inv")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
def process_document(image):
#can't save uploaded file locally, but needs to be converted from nparray to PIL
im1 = Image.fromarray(image)
#send notification through telegram
TOKEN = os.getenv('TELEGRAM_BOT_TOKEN')
CHAT_ID = os.getenv('TELEGRAM_CHANNEL_ID')
url = f'https://api.telegram.org/bot{TOKEN}/sendPhoto?chat_id={CHAT_ID}'
bio = BytesIO()
bio.name = 'image.jpeg'
im1.save(bio, 'JPEG')
bio.seek(0)
media = {"type": "photo", "media": "attach://photo", "caption": "New doc is being tried out:"}
data = {"media": json.dumps(media)}
response = requests.post(url, files={'photo': bio}, data=data)
# prepare encoder inputs
pixel_values = processor(image, return_tensors="pt").pixel_values
# prepare decoder inputs
task_prompt = "<s_cord-v2>"
decoder_input_ids = processor.tokenizer(task_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, "")
sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token
return processor.token2json(sequence)
description = '<p>Using Donut model finetuned on Invoices for retrieval of following information:</p><ul><li><span style="color:black">DocType</span></span></li><li><span style="color:black">Currency</span></span></li><li><span style="color:black">DocumentDate</span></span></li><li><span style="color:black">GrossAmount</span></span></li><li><span style="color:black">InvoiceNumber</span></span></li><li><span style="color:black">NetAmount</span></span></li><li><span style="color:black">TaxAmount</span></span></li><li><span style="color:black">OrderNumber</span></span></li><li><span style="color:black">CreditorCountry</span></span></li></ul><p>To use it, simply upload your image and click 'submit', or click one of the examples to load them. Read more at the links below.</p><p> </p><p>(because this is running on the free cpu tier, it will take about 40 secs before you see a result)</p><p>Have fun 😎</p><p>Toon Beerten</p>'
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>"
demo = gr.Interface(
fn=process_document,
inputs="image",
outputs="json",
title="Demo: Donut 🍩 for invoice header retrieval",
description=description,
article=article,
enable_queue=True,
examples=[["example.png"], ["example_2.png"], ["example_3.jpg"]],
cache_examples=False)
demo.launch() |