"""
File: app.py
Author: Elena Ryumina and Dmitry Ryumin
Description: Description: Main application file for Facial_Expression_Recognition.
             The file defines the Gradio interface, sets up the main blocks,
             and includes event handlers for various components.
License: MIT License
"""

import gradio as gr

# Importing necessary components for the Gradio app
from app.description import DESCRIPTION
from app.app_utils import preprocess_and_predict


def clear():
    return (
        gr.Image(value=None, type="pil"),
        gr.Image(value=None, scale=1, elem_classes="dl2"),
        gr.Label(value=None, num_top_classes=3, scale=1, elem_classes="dl3"),
    )

md = """
    App developers: ``Elena Ryumina`` and ``Dmitry Ryumin``

    Methodology developers: ``Elena Ryumina``, ``Denis Dresvyanskiy`` and ``Alexey Karpov``

    Model developer: ``Elena Ryumina``

    TensorFlow to PyTorch model converter: ``Maxim Markitantov`` and ``Elena Ryumina``

    Citation

    If you are using EMO-AffectNetModel in your research, please consider to cite research [paper](https://www.sciencedirect.com/science/article/pii/S0925231222012656). Here is an example of BibTeX entry:

    <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto"><pre><span class="pl-k">@article</span>{<span class="pl-en">RYUMINA2022</span>,
    <span class="pl-s">title</span>        = <span class="pl-s"><span class="pl-pds">{</span>In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus Study<span class="pl-pds">}</span></span>,
    <span class="pl-s">author</span>       = <span class="pl-s"><span class="pl-pds">{</span>Elena Ryumina and Denis Dresvyanskiy and Alexey Karpov<span class="pl-pds">}</span></span>,
    <span class="pl-s">journal</span>      = <span class="pl-s"><span class="pl-pds">{</span>Neurocomputing<span class="pl-pds">}</span></span>,
    <span class="pl-s">year</span>         = <span class="pl-s"><span class="pl-pds">{</span>2022<span class="pl-pds">}</span></span>,
    <span class="pl-s">doi</span>          = <span class="pl-s"><span class="pl-pds">{</span>10.1016/j.neucom.2022.10.013<span class="pl-pds">}</span></span>,
    <span class="pl-s">url</span>          = <span class="pl-s"><span class="pl-pds">{</span>https://www.sciencedirect.com/science/article/pii/S0925231222012656<span class="pl-pds">}</span></span>,
    }</div>
"""


with gr.Blocks(css="app.css") as demo:
    with gr.Tab("App"):
        gr.Markdown(value=DESCRIPTION)
        with gr.Row():
            with gr.Column(scale=2, elem_classes="dl1"):
                input_image = gr.Image(type="pil")
                with gr.Row():
                    clear_btn = gr.Button(
                        value="Clear", interactive=True, scale=1, elem_classes="clear"
                    )
                    submit = gr.Button(
                        value="Submit", interactive=True, scale=1, elem_classes="submit"
                    )
            with gr.Column(scale=1, elem_classes="dl4"):
                output_image = gr.Image(scale=1, elem_classes="dl2")
                output_label = gr.Label(num_top_classes=3, scale=1, elem_classes="dl3")
        gr.Examples(
            [
                "images/fig7.jpg",
                "images/fig1.jpg",
                "images/fig2.jpg",
                "images/fig3.jpg",
                "images/fig4.jpg",
                "images/fig5.jpg",
                "images/fig6.jpg",
            ],
            [input_image],
        )
    with gr.Tab("Authors"):
        gr.Markdown(value=md)

    submit.click(
        fn=preprocess_and_predict,
        inputs=[input_image],
        outputs=[output_image, output_label],
        queue=True,
    )
    clear_btn.click(
        fn=clear,
        inputs=[],
        outputs=[input_image, output_image, output_label],
        queue=True,
    )

if __name__ == "__main__":
    demo.queue(api_open=False).launch(share=False)