const tf = require('@tensorflow/tfjs-node');
const fs = require('fs');
const path = require('path');

// Path ke model
const modelPath = path.join(
  __dirname,
  '../resources/ResNet-50_tomato-leaf-disease-tfjs_nobg/model.json'
);

let model;

const loadModel = async () => {
  try {
    model = await tf.loadLayersModel(`file://${modelPath}`);
    console.log('Model loaded successfully.');
  } catch (err) {
    console.error('Error loading model:', err);
    throw new Error('Model loading failed.');
  }
};

loadModel();

const preprocessImage = async (imagePath) => {
  const imageBuffer = fs.readFileSync(imagePath);
  let tensor = tf.node.decodeImage(imageBuffer, 3);
  tensor = tf.image.resizeBilinear(tensor, [224, 224]);
  tensor = tensor.expandDims(0);
  //tensor = tensor.toFloat().div(tf.scalar(255.0));
  return tensor;
};

// Fungsi untuk mengklasifikasikan gambar
const classifyImage = async (imagePath) => {
  try {
    const classes = [
      'bacterial_spot',
      'healthy',
      'late_blight',
      'leaf_curl_virus',
      'leaf_mold',
      'mosaic_virus',
      'septoria_leaf_spot',
    ];

    const tensor = await preprocessImage(imagePath);
    const predictions = model.predict(tensor);
    const predictedClassIndex = predictions.argMax(-1).dataSync()[0];

    // Ambil nama kelas berdasarkan indeks
    const predictedClassName = classes[predictedClassIndex];

    return { index: predictedClassIndex, class: predictedClassName };
  } catch (err) {
    console.error('Error during classification:', err);
    throw new Error('Error during classification.');
  }
};

module.exports = { classifyImage };