const tf = require('@tensorflow/tfjs-node');
const nsfw = require('nsfwjs');
const faceDetection = require('@tensorflow-models/face-detection');
const faceModel = faceDetection.SupportedModels.MediaPipeFaceDetector;
const path = require('path');
const url = require('url');

module.exports = class Tensorflow {
	constructor(client) {
		Object.defineProperty(this, 'client', { value: client });

		this.nsfwjs = null;
		this.faceDetector = null;
		this.styleModel = null;
		this.transformerModel = null;
	}

	async loadNSFWJS() {
		const nsfwjs = await nsfw.load();
		this.nsfwjs = nsfwjs;
		return this.nsfwjs;
	}

	async loadFaceDetector() {
		const faceDetector = await faceDetection.createDetector(faceModel, { runtime: 'tfjs', maxFaces: 10 });
		this.faceDetector = faceDetector;
		return this.faceDetector;
	}

	async loadStyleModel() {
		const model = await tf.loadGraphModel(
			url.pathToFileURL(path.join(__dirname, '..', 'tf_models', 'style_js', 'model.json')).href
		);
		this.styleModel = model;
		return this.styleModel;
	}

	async loadTransformerModel() {
		const model = await tf.loadGraphModel(
			url.pathToFileURL(path.join(__dirname, '..', 'tf_models', 'transformer_js', 'model.json')).href
		);
		this.transformerModel = model;
		return this.transformerModel;
	}

	async detectFaces(imgData) {
		if (Buffer.byteLength(imgData) >= 8e+6) return 'size';
		tf.setBackend('tensorflow');
		const image = tf.node.decodeImage(imgData, 3);
		tf.setBackend('cpu');
		const faces = await this.faceDetector.estimateFaces(image);
		tf.setBackend('tensorflow');
		image.dispose();
		if (!faces || !faces.length) return null;
		return faces;
	}

	async isImageNSFW(image, bool = true) {
		const img = await tf.node.decodeImage(image, 3);
		const predictions = await this.nsfwjs.classify(img);
		img.dispose();
		if (bool) {
			const results = [];
			results.push(predictions[0]);
			for (const result of predictions) {
				if (result.className === predictions[0].className) continue;
				if (result.probability >= predictions[0].probability - 0.1) results.push(result);
			}
			return results.some(result => result.className !== 'Drawing' && result.className !== 'Neutral');
		}
		return predictions;
	}

	async stylizeImage(image, styleImg) {
		const imageTensor = await tf.node.decodeImage(image, 3);
		const loadedImage = imageTensor.toFloat().div(tf.scalar(255)).expandDims();
		imageTensor.dispose();
		const styleTensor = tf.node.decodeImage(styleImg, 3);
		const loadedStyle = styleTensor.toFloat().div(tf.scalar(255)).expandDims();
		styleTensor.dispose();
		const stylePrediction = await this.styleModel.predict(loadedStyle);
		loadedStyle.dispose();
		const stylizedImage = (await this.transformerModel.predict([loadedImage, stylePrediction])).squeeze();
		loadedImage.dispose();
		stylePrediction.dispose();
		const encodedImg = stylizedImage.mul(255).toInt();
		stylizedImage.dispose();
		const buffer = await tf.node.encodeJpeg(encodedImg);
		encodedImg.dispose();
		return Buffer.from(buffer);
	}
};