app.py

import torch
import streamlit as st
import os

from PIL import Image
from io import BytesIO
from transformers import VisionEncoderDecoderModel, VisionEncoderDecoderConfig , DonutProcessor

task_prompt = "<s_unstructured-invoices>"

def run_prediction(sample):
    global pretrained_model, processor, task_prompt
    if isinstance(sample, dict):
        # prepare inputs
        pixel_values = torch.tensor(sample["pixel_values"]).unsqueeze(0)
    else:  # sample is an image
        # prepare encoder inputs
        pixel_values = processor(sample, return_tensors="pt").pixel_values
    
    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids

    outputs = pretrained_model.generate(
        pixel_values.to(device),
        decoder_input_ids=decoder_input_ids.to(device)
    )

    # process output
    prediction = processor.token2json(processor.batch_decode(outputs)[0])
    
    # load reference target
    if isinstance(sample, dict):
        target = processor.token2json(sample["target_sequence"])
    else:
        target = "<not_provided>"
    
    return prediction, target
    

logo = Image.open("./img/rsz_unstructured_logo.png")
st.image(logo)

st.markdown('''
### Invoice Parser
This is an OCR-free Document Understanding Transformer. It was fine-tuned with 1000 invoice images -> RVL-CDIP dataset.
The original implementation can be found on [here](https://github.com/clovaai/donut).

At [Unstructured.io](https://github.com/Unstructured-IO/unstructured) we are on a mission to build custom preprocessing pipelines for labeling, training, or production ML-ready pipelines. 
Come and join us in our public repos and contribute! Each of your contributions and feedback holds great value and is very significant to the community.
''')

image_upload = None
photo = None
with st.sidebar:
    # file upload
    uploaded_file = st.file_uploader("Upload an invoice")
    if uploaded_file is not None:
        # To read file as bytes:
        image_bytes_data = uploaded_file.getvalue()
        image_upload = Image.open(BytesIO(image_bytes_data))  #.frombytes('RGBA', (128,128), image_bytes_data, 'raw')
        # st.write(bytes_data)

col1, col2 = st.columns(2)

if image_upload:
    image = image_upload
else:
    image = Image.open(f"./img/4fabfaab-1299.png")

with col1:
    st.image(image, caption='Your target invoice')

with st.spinner(f'baking the invoice ...'):
        processor = DonutProcessor.from_pretrained("unstructuredio/donut-invoices", max_length=1200, use_auth_token=os.environ['TOKEN'])
        pretrained_model = VisionEncoderDecoderModel.from_pretrained("unstructuredio/donut-invoices", max_length=1200, use_auth_token=os.environ['TOKEN'])
        
        device = "cuda" if torch.cuda.is_available() else "cpu"
        pretrained_model.to(device)

with col2:
    st.info(f'Parsing invoice')
    parsed_info, _ = run_prediction(image.convert("RGB"))
    st.text(f'\nInvoice Summary:')
    st.json(parsed_info)