#!/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


DESCRIPTION = """
# Age and Gender Estimation with Transformers from Face and Body Images in the Wild

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

def clear():
    return None, 0.4, 0.7, "Use persons and faces", None

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)

with gr.Blocks(css='style.css') as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Row():
        with gr.Column():
            image = gr.Image(label='Input', type='numpy')
            score_threshold = gr.Slider(0, 1, value=0.4, step=0.05, label='Detector Score Threshold')
            iou_threshold = gr.Slider(0, 1, value=0.7, step=0.05, label='NMS Iou Threshold')
            mode = 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")

            with gr.Row():
                clear_button = gr.Button("Clear")
                with gr.Column():
                    run_button = gr.Button("Submit")
        with gr.Column():
            result = gr.Image(label='Output', type='numpy')

    inputs = [image, score_threshold, iou_threshold, mode]
    gr.Examples(examples=examples,
                inputs=inputs,
                outputs=result,
                fn=func,
                cache_examples=False)
    run_button.click(fn=func, inputs=inputs, outputs=result, api_name='predict')
    clear_button.click(fn=clear, inputs=None, outputs=[image, score_threshold, iou_threshold, mode, result])

demo.queue(max_size=15).launch()