nodejs/generate_model.js

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

async function loadImages(folder) {
    const files = fs.readdirSync(folder);
    const images = [];
    const labels = [];
    for (const file of files) {
        const label = path.basename(folder);
        const imageBuffer = fs.readFileSync(path.join(folder, file));
        const imageTensor = tf.node.decodeImage(imageBuffer, 3)
            .resizeNearestNeighbor([128, 128])
            .toFloat()
            .div(tf.scalar(255.0));
        images.push(imageTensor);
        labels.push(label);
    }
    return { images: tf.stack(images), labels };
}

async function loadDataset(basePath) {
    const folders = fs.readdirSync(basePath);
    const data = [];
    const labelMap = {};
    folders.forEach((folder, index) => labelMap[folder] = index);

    for (const folder of folders) {
        const { images, labels } = await loadImages(path.join(basePath, folder));
        data.push({ images, labels: labels.map(label => labelMap[label]) });
    }

    return {
        images: tf.concat(data.map(d => d.images)),
        labels: tf.oneHot(tf.tensor1d(data.flatMap(d => d.labels), 'int32'), Object.keys(labelMap).length),
        labelMap
    };
}

async function trainModel() {
    const basePath = './training_images';  // Folder with labeled subfolders
    const dataset = await loadDataset(basePath);
    const { images, labels } = dataset;

    const model = tf.sequential();
    model.add(tf.layers.conv2d({
        inputShape: [128, 128, 3],
        filters: 32,
        kernelSize: 3,
        activation: 'relu'
    }));
    model.add(tf.layers.flatten());
    model.add(tf.layers.dense({ units: Object.keys(dataset.labelMap).length, activation: 'softmax' }));
    model.compile({ optimizer: 'adam', loss: 'categoricalCrossentropy', metrics: ['accuracy'] });

    await model.fit(images, labels, { epochs: 5 });
    await model.save('file://./saved-model');  // Save in TensorFlow.js format
    console.log('Model saved as TensorFlow.js format');
}

(async () => {
    await trainModel();
})();