#!/usr/bin/env python

from __future__ import annotations

import argparse
import pathlib
import tarfile

import gradio as gr

from model import AppDetModel, AppPoseModel

DESCRIPTION = "# [ViTPose](https://github.com/ViTAE-Transformer/ViTPose)"


def set_example_image(example: list) -> dict:
    return gr.Image.update(value=example[0])


def extract_tar() -> None:
    if pathlib.Path("mmdet_configs/configs").exists():
        return
    with tarfile.open("mmdet_configs/configs.tar") as f:
        f.extractall("mmdet_configs")


extract_tar()

det_model = AppDetModel()
pose_model = AppPoseModel()

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)

    with gr.Box():
        gr.Markdown("## Step 1")
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    input_image = gr.Image(label="Input Image", type="numpy")
                with gr.Row():
                    detector_name = gr.Dropdown(
                        label="Detector",
                        choices=list(det_model.MODEL_DICT.keys()),
                        value=det_model.model_name,
                    )
                with gr.Row():
                    detect_button = gr.Button("Detect")
                    det_preds = gr.Variable()
            with gr.Column():
                with gr.Row():
                    detection_visualization = gr.Image(
                        label="Detection Result", type="numpy", elem_id="det-result"
                    )
                with gr.Row():
                    vis_det_score_threshold = gr.Slider(
                        label="Visualization Score Threshold",
                        minimum=0,
                        maximum=1,
                        step=0.05,
                        value=0.5,
                    )
                with gr.Row():
                    redraw_det_button = gr.Button(value="Redraw")

                with gr.Row():
                    with gr.Accordion("JSON", open=False):
                        json_detect = gr.JSON()

        with gr.Row():
            paths = sorted(pathlib.Path("images").rglob("*.jpg"))
            example_images = gr.Examples(
                examples=[[path.as_posix()] for path in paths], inputs=input_image
            )

    with gr.Box():
        gr.Markdown("## Step 2")
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    pose_model_name = gr.Dropdown(
                        label="Pose Model",
                        choices=list(pose_model.MODEL_DICT.keys()),
                        value=pose_model.model_name,
                    )
                det_score_threshold = gr.Slider(
                    label="Box Score Threshold",
                    minimum=0,
                    maximum=1,
                    step=0.05,
                    value=0.5,
                )
                with gr.Row():
                    predict_button = gr.Button("Predict")
                    pose_preds = gr.Variable()
            with gr.Column():
                with gr.Row():
                    pose_visualization = gr.Image(
                        label="Result", type="numpy", elem_id="pose-result"
                    )
                with gr.Row():
                    vis_kpt_score_threshold = gr.Slider(
                        label="Visualization Score Threshold",
                        minimum=0,
                        maximum=1,
                        step=0.05,
                        value=0.3,
                    )
                with gr.Row():
                    vis_dot_radius = gr.Slider(
                        label="Dot Radius", minimum=1, maximum=10, step=1, value=4
                    )
                with gr.Row():
                    vis_line_thickness = gr.Slider(
                        label="Line Thickness", minimum=1, maximum=10, step=1, value=2
                    )
                with gr.Row():
                    redraw_pose_button = gr.Button("Redraw")

                with gr.Row():
                    with gr.Accordion("JSON", open=False):
                        json_pose = gr.JSON()

    detect_button.click(
        fn=det_model.run,
        inputs=[
            detector_name,
            input_image,
            vis_det_score_threshold,
        ],
        outputs=[det_preds, detection_visualization, json_detect],
    )

    detector_name.change(fn=det_model.set_model, inputs=detector_name, outputs=None)
    detect_button.click(
        fn=det_model.run,
        inputs=[
            detector_name,
            input_image,
            vis_det_score_threshold,
        ],
        outputs=[
            det_preds,
            detection_visualization,
        ],
    )
    redraw_det_button.click(
        fn=det_model.visualize_detection_results,
        inputs=[
            input_image,
            det_preds,
            vis_det_score_threshold,
        ],
        outputs=detection_visualization,
    )

    pose_model_name.change(
        fn=pose_model.set_model, inputs=pose_model_name, outputs=None
    )
    predict_button.click(
        fn=pose_model.run,
        inputs=[
            pose_model_name,
            input_image,
            det_preds,
            det_score_threshold,
            vis_kpt_score_threshold,
            vis_dot_radius,
            vis_line_thickness,
        ],
        outputs=[pose_preds, pose_visualization, json_pose],
    )
    redraw_pose_button.click(
        fn=pose_model.visualize_pose_results,
        inputs=[
            input_image,
            pose_preds,
            vis_kpt_score_threshold,
            vis_dot_radius,
            vis_line_thickness,
        ],
        outputs=pose_visualization,
    )

demo.queue(api_open=False).launch()