'use strict';

const Service = require('egg').Service;
const tf = require('@tensorflow/tfjs-node');
// const jpeg = require('jpeg-js');
const { Image } = require('image-js');
const contentBoundary = require('../util/contentBoundary');

const convert = async img => {
  // const image = await jpeg.decode(img, true);
  const image = await Image.load(img.filepath);
  // console.log('jpeg.decode:', image);
  const numChannels = 3;
  const numPixels = image.width * image.height;
  const values = new Int32Array(numPixels * numChannels);

  for (let i = 0; i < numPixels; i++) {
    for (let c = 0; c < numChannels; ++c) { values[i * numChannels + c] = image.data[i * 4 + c]; }
  }

  return tf.tensor3d(values, [ image.height, image.width, numChannels ], 'int32');
};


class ContentCheckService extends Service {
  async contentCheck(img) {
    try {
      const image = await convert(img);
      const predictions = await this.app.nsfwModel.classify(image);
      console.log('Predictions: ', predictions);
      const isSafeContent = contentBoundary(predictions);
      return isSafeContent;
    } catch (err) {
      throw new Error(err);
    }
  }
}

module.exports = ContentCheckService;