app.py

import re
import gradio as gr
import torch
from transformers import UdopProcessor, UdopForConditionalGeneration

repo_id = "microsoft/udop-large"

processor = UdopProcessor.from_pretrained(repo_id)
model = UdopForConditionalGeneration.from_pretrained(repo_id)

def process_document(image, question):
    
    pixel_values = processor(image, return_tensors="pt").pixel_values
    encoding = processor(images=image, text=question, return_tensors="pt")
    outputs = model.generate(**encoding, max_new_tokens=20)
    generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]
    
    return generated_text

description = "Unofficial Gradio Demo for UDOP for DocVQA (document visual question answering). 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/pdf/2212.02623.pdf' target='_blank'>Unifying Vision, Text, and Layout for Universal Document Processing</a> | <a href='https://github.com/microsoft/UDOP' target='_blank'>Github Repo</a></p>"

demo = gr.Interface(
    fn=process_document,
    inputs=["image", "text"],
    outputs="text",
    title="Demo: UDOP for DocVQA",
    description=description,
    article=article,
    enable_queue=True,
    # examples=[["example_1.png", "When is the coffee break?"], ["example_2.jpeg", "What's the population of Stoddard?"]],
    cache_examples=False)

demo.launch()