#!/usr/bin/env python

from __future__ import annotations

import deepdanbooru as dd
import huggingface_hub
import numpy as np
import PIL.Image
import tensorflow as tf

def load_model() -> tf.keras.Model:
    path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru',
                                           'model-resnet_custom_v3.h5')
    model = tf.keras.models.load_model(path)
    return model


def load_labels() -> list[str]:
    path = huggingface_hub.hf_hub_download('public-data/DeepDanbooru',
                                           'tags.txt')
    with open(path) as f:
        labels = [line.strip() for line in f.readlines()]
    return labels


model = load_model()
labels = load_labels()


def genTag(image: PIL.Image.Image, score_threshold: float):
    _, height, width, _ = model.input_shape
    image = np.asarray(image)
    image = tf.image.resize(image,
                            size=(height, width),
                            method=tf.image.ResizeMethod.AREA,
                            preserve_aspect_ratio=True)
    image = image.numpy()
    image = dd.image.transform_and_pad_image(image, width, height)
    image = image / 255.
    probs = model.predict(image[None, ...])[0]
    probs = probs.astype(float)

    indices = np.argsort(probs)[::-1]
    result_all = dict()
    result_threshold = dict()
    result_html = ''
    for index in indices:
        label = labels[index]
        prob = probs[index]
        result_all[label] = prob
        if prob < score_threshold:
            break
        result_threshold[label] = prob

    return result_threshold