app.py

import argparse
import os
from functools import partial
import torch

import gradio as gr
from PIL import Image
from huggingface_hub import hf_hub_download

from die_model import UNetDIEModel
from utils import resize_image, make_image_square, cast_pil_image_to_torch_tensor_with_4_channel_dim, remove_square_padding


def die_inference(image_raw, num_of_die_iterations, die_model, device):
    """
    Applies the DIE model for document enhancement on a provided image.
    """
    # preprocess
    image_raw_resized = resize_image(image_raw, 1500)
    image_raw_resized_square = make_image_square(image_raw_resized)
    image_raw_resized_square_tensor = cast_pil_image_to_torch_tensor_with_4_channel_dim(image_raw_resized_square).to(device)
    
    # convert string to int
    num_of_die_iterations = int(num_of_die_iterations)
    
    # inference
    image_die = die_model.enhance_document_image(
        image_raw_list=[image_raw_resized_square_tensor],
        num_of_die_iterations=num_of_die_iterations
    )[0]
    
    # postprocess
    return remove_square_padding(
        original_image=image_raw,
        square_image=image_die,
        resize_back_to_original=True
    )


def main():
    """
    Main function to set up and run the Gradio demo.
    """
    
    args = parse_arguments()
    
    args.device = 'cuda' if torch.cuda.is_available() else 'cpu'

    # Set up model
    die_token = os.getenv("DIE_TOKEN")
    
    args.die_model_path = hf_hub_download(
        repo_id="gabar92/die",
        filename=args.die_model_path,
        use_auth_token=die_token
    )
    
    die_model = UNetDIEModel(args=args)

    # Prepare example images
    example_image_list = [
        [Image.open(os.path.join(args.example_image_path, image_path))]
        for image_path in os.listdir(args.example_image_path)
    ]
    
    description = "Welcome to the Document Image Enhancement (DIE) model demo on Hugging Face!\n\n" \
                  "" \
                  "This interactive application showcases a specialized AI model developed by " \
                  "the [Artificial Intelligence group](https://ai.renyi.hu) at the [Alfréd Rényi Institute of Mathematics](https://renyi.hu).\n\n" \
                  "" \
                  "Our DIE model is designed to enhance and restore archival and aged document images " \
                  "by removing various types of degradation, thereby making historical documents more legible " \
                  "and suitable for Optical Character Recognition (OCR) processing.\n\n" \
                  "" \
                  "The model effectively tackles 20-30 types of domain-specific noise found in historical records, " \
                  "such as scribbles, bleed-through text, faded or worn text, blurriness, textured noise, " \
                  "and unwanted background elements. " \
                  "By applying deep learning techniques, specifically a U-Net-based architecture, " \
                  "the model accurately cleans and clarifies text while preserving original details. " \
                  "This improved clarity dramatically boosts OCR accuracy, making it an ideal " \
                  "pre-processing tool in digitization workflows.\n\n" \
                  "" \
                  "If you’re interested in learning more about the model’s capabilities or potential applications, " \
                  "please contact us at: gabar92@renyi.hu.\n" 

    # Partial function for inference with model and device arguments
    partial_die_inference = partial(die_inference, die_model=die_model, device=args.device)

    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                gr.Markdown("## Document Image Enhancement (DIE) model")

        with gr.Row():
            with gr.Column():
                gr.Markdown(description)
            with gr.Column():
                # Display QR code as an image in Gradio
                gr.Image(value=Image.open("logo/qr-code.png").resize((400, 400)), label="QR Code")

        with gr.Row():
            with gr.Column():
                input_image = gr.Image(type="pil", label="Upload Degraded Document Image")
                num_iterations = gr.Dropdown([1, 2, 3], label="Number of DIE Iterations", value=1)
                run_button = gr.Button("Enhance Image")

            with gr.Column():
                output_image = gr.Image(type="pil", label="Enhanced Document Image")
        
        # Display example images
        gr.Examples(
            examples=example_image_list,
            inputs=[input_image],
            label="Example Images - Source: National Archives of Hungary and Budapest City Archives",
        )
        
        # Button trigger for inference
        run_button.click(partial_die_inference, [input_image, num_iterations], output_image)

    demo.launch()


def parse_arguments():
    """
    Parses command-line arguments.
    :return: argument namespace
    """
    parser = argparse.ArgumentParser()

    parser.add_argument("--die_model_path", default="2024_08_09_model_epoch_89.pt")

    parser.add_argument("--example_image_path", default="example_images")
    
    return parser.parse_args()
    

if __name__ == "__main__":
    main()