"""
Small demo application to explore Gradio.
"""

import argparse
import os
from functools import partial

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
):
    """
    Function to run the DIE model.
    :param image_raw: raw image
    :param num_of_die_iterations: number of DIE iterations
    :param die_model: DIE model
    :param device: device
    :return: cleaned 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)
    image_raw_resized_square_tensor = image_raw_resized_square_tensor.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
    image_die_resized = remove_square_padding(
        original_image=image_raw,
        square_image=image_die,
        resize_back_to_original=True
    )


    return image_die_resized


def main():
    """
    Main function to run the Gradio demo.
    :return:
    """

    args = parse_arguments()

    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                  "by removing various types of degradation, thereby making historical documents more legible " \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\n"

    # TODO: Add a description for the Number of DIE iterations parameter!

    num_of_die_iterations_list = [1, 2, 3]

    die_token = os.getenv("DIE_TOKEN")

    # Load example images from the private repository
    example_image_list = []
    example_images = [
        "_002.jpg",
        "_004.jpg",
        "_005.jpg",
        "_006.jpg",
        "_007.jpg",
        "_010.jpg",
        "_097.jpg",
    ]

    for image_filename in example_images:
        image_path = hf_hub_download(
            repo_id="gabar92/DIE",
            filename=f"{args.example_image_path}/{image_filename}",
            use_auth_token=die_token
        )
        example_image_list.append([Image.open(image_path)])

    # Load DIE model
    # model_path = hf_hub_download(
    #    repo_id="gabar92/DIE",
    #    filename=args.die_model_path,
    #    use_auth_token=die_token
    #)
    model_path = "./DIE/2024_08_09_model_epoch_89.pt"
    die_model = UNetDIEModel(args=args, model_path=model_path)

    # Partially apply the model and device arguments to die_inference
    partial_die_inference = partial(die_inference, device=args.device, die_model=die_model)

    demo = gr.Interface(
        fn=partial_die_inference,
        inputs=[
            gr.Image(type="pil", label="Degraded Document Image"),
            gr.Dropdown(num_of_die_iterations_list, label="Number of DIE iterations", value=1),
        ],
        outputs=gr.Image(type="pil", label="Clean Document Image"),
        title="Document Image Enhancement (DIE) model",
        description=description,
        examples=example_image_list
    )

    demo.launch(server_name="0.0.0.0", server_port=7860)


def parse_arguments():
    """
    Parse arguments.
    :return: argument namespace
    """

    parser = argparse.ArgumentParser()

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

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

    return parser.parse_args()


if __name__ == "__main__":

    main()