#!/usr/bin/env python

import os
import shlex
import subprocess

if os.getenv('SYSTEM') == 'spaces':
    GITHUB_TOKEN = os.getenv('GITHUB_TOKEN')
    GITHUB_USER = os.getenv('GITHUB_USER')
    git_repo = f"https://{GITHUB_TOKEN}@github.com/{GITHUB_USER}/xnet_demo.git"
    subprocess.call(shlex.split(f'pip install git+{git_repo}'))

import pathlib
import os
import gradio as gr
import huggingface_hub
import numpy as np
import functools
from dataclasses import dataclass

from xnet.predictor import Predictor


@dataclass
class Cfg:
    detector_weights: str
    checkpoint: str
    device: str = "cpu"
    with_persons: bool = True
    disable_faces: bool = False
    draw: bool = True


TITLE = 'Age and Gender Estimation with Transformers from Face and Body Images in the Wild'
DESCRIPTION = 'This is an official demo for https://github.com/...'

HF_TOKEN = os.getenv('HF_TOKEN')

def load_models():
    detector_path = huggingface_hub.hf_hub_download('iitolstykh/demo_yolov8_detector',
                                           'yolov8x_person_face.pt',
                                           use_auth_token=HF_TOKEN)

    age_gender_path = huggingface_hub.hf_hub_download('iitolstykh/demo_xnet_volo_cross',
                                           'checkpoint-377.pth.tar',
                                           use_auth_token=HF_TOKEN)

    predictor_cfg = Cfg(detector_path, age_gender_path)
    predictor = Predictor(predictor_cfg)

    return predictor

def detect(
        image: np.ndarray,
        score_threshold: float,
        iou_threshold: float,
        mode: str,
        predictor: Predictor
) -> np.ndarray:
    # input is rgb image, output must be rgb too

    predictor.detector.detector_kwargs['conf'] = score_threshold
    predictor.detector.detector_kwargs['iou'] = iou_threshold

    if mode == "Use persons and faces":
        use_persons = True
        disable_faces = False
    elif mode == "Use persons only":
        use_persons = True
        disable_faces = True
    elif mode == "Use faces only":
        use_persons = False
        disable_faces = False

    predictor.age_gender_model.meta.use_persons = use_persons
    predictor.age_gender_model.meta.disable_faces = disable_faces

    image = image[:, :, ::-1]  # RGB -> BGR
    detected_objects, out_im = predictor.recognize(image)
    return out_im[:, :, ::-1]  # BGR -> RGB


predictor = load_models()

image_dir = pathlib.Path('images')
examples = [[path.as_posix(), 0.4, 0.7, "Use persons and faces"] for path in sorted(image_dir.glob('*.jpg'))]

func = functools.partial(detect, predictor=predictor)

gr.Interface(
    fn=func,
    inputs=[
        gr.Image(label='Input', type='numpy'),
        gr.Slider(0, 1, value=0.4, step=0.05, label='Detector Score Threshold'),
        gr.Slider(0, 1, value=0.7, step=0.05, label='NMS Iou Threshold'),
        gr.Radio(["Use persons and faces", "Use persons only", "Use faces only"],
                 value="Use persons and faces",
                 label="Inference mode",
                 info="What to use for gender and age recognition"),
    ],
    outputs=gr.Image(label='Output', type='numpy'),
    examples=examples,
    examples_per_page=30,
    title=TITLE,
    description=DESCRIPTION,
).launch(show_api=False)