#!/usr/bin/env python

from __future__ import annotations

import os
import pathlib
import shlex
import subprocess

if os.getenv("SYSTEM") == "spaces":
    subprocess.run(shlex.split("pip install click==7.1.2"))
    subprocess.run(shlex.split("pip install typer==0.9.4"))

    import mim

    mim.uninstall("mmcv-full", confirm_yes=True)
    mim.install("mmcv-full==1.5.0", is_yes=True)

    subprocess.run(shlex.split("pip uninstall -y opencv-python"))
    subprocess.run(shlex.split("pip uninstall -y opencv-python-headless"))
    subprocess.run(shlex.split("pip install opencv-python-headless==4.8.0.74"))

    with open("patch") as f:
        subprocess.run(shlex.split("patch -p1"), cwd="CBNetV2", stdin=f)
    subprocess.run("mv palette.py CBNetV2/mmdet/core/visualization/".split())


import gradio as gr

from model import Model

DESCRIPTION = "# [CBNetV2](https://github.com/VDIGPKU/CBNetV2)"

model = Model()

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

    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(model.models.keys()), value=model.model_name
                )
            with gr.Row():
                detect_button = gr.Button("Detect")
                detection_results = gr.State()
        with gr.Column():
            with gr.Row():
                detection_visualization = gr.Image(label="Detection Result", type="numpy")
            with gr.Row():
                visualization_score_threshold = gr.Slider(
                    label="Visualization Score Threshold", minimum=0, maximum=1, step=0.05, value=0.3
                )
            with gr.Row():
                redraw_button = gr.Button("Redraw")

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

    detector_name.change(fn=model.set_model_name, inputs=detector_name)
    detect_button.click(
        fn=model.detect_and_visualize,
        inputs=[
            input_image,
            visualization_score_threshold,
        ],
        outputs=[
            detection_results,
            detection_visualization,
        ],
    )
    redraw_button.click(
        fn=model.visualize_detection_results,
        inputs=[
            input_image,
            detection_results,
            visualization_score_threshold,
        ],
        outputs=detection_visualization,
    )

if __name__ == "__main__":
    demo.queue(max_size=10).launch()