#!/usr/bin/env python

from __future__ import annotations

import pathlib

import gradio as gr
import mediapipe as mp
import numpy as np

mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils

TITLE = "MediaPipe Face Detection"
DESCRIPTION = "https://google.github.io/mediapipe/"


def run(image: np.ndarray, model_selection: int, min_detection_confidence: float) -> np.ndarray:
    with mp_face_detection.FaceDetection(
        model_selection=model_selection, min_detection_confidence=min_detection_confidence
    ) as face_detection:
        results = face_detection.process(image)

    res = image[:, :, ::-1].copy()
    if results.detections is not None:
        for detection in results.detections:
            mp_drawing.draw_detection(res, detection)
    return res[:, :, ::-1]


model_types = [
    "Short-range model (best for faces within 2 meters)",
    "Full-range model (best for faces within 5 meters)",
]

image_paths = sorted(pathlib.Path("images").rglob("*.jpg"))
examples = [[path, model_types[0], 0.5] for path in image_paths]

demo = gr.Interface(
    fn=run,
    inputs=[
        gr.Image(label="Input", type="numpy"),
        gr.Radio(label="Model", choices=model_types, type="index", value=model_types[0]),
        gr.Slider(label="Minimum Detection Confidence", minimum=0, maximum=1, step=0.05, value=0.5),
    ],
    outputs=gr.Image(label="Output"),
    examples=examples,
    title=TITLE,
    description=DESCRIPTION,
)

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