Code viewer [Ancient Brain]

Code viewer for World: Tensflow Doodle
const CLASSES = 3;
const IMAGE_SIZE = 784;

const CAT = 0;
const RAINBOW = 1;
const TRAIN = 2;

let catsData;
let trainsData;
let rainbowsData;

let data;
let model;

function preload() {
  catsData = loadBytes('/uploads/cindy15/cats1000.bin');
  trainsData = loadBytes('/uploads/cindy15/trains1000.bin');
  rainbowsData = loadBytes('/uploads/cindy15/rainbows1000.bin');
}

function setup() {
  createCanvas(280, 280);
    
 $.getScript ( "https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.6.0/p5.min.js", function()
 {
   $.getScript ( "https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.6.0/addons/p5.dom.min.js", function()
   {
        $.getScript ( "https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.8.0", function()
        {
                $.getScript ( "/uploads/cindy15/loadbinary.js", function()
        {
            $.getScript ( "/uploads/cindy15/data.js", function()
        {
            $.getScript ( "/uploads/cindy15/classifier.js", function()
        {
            console.log ("All JS loaded");
        preload();
          let total = (catsData.bytes.length + rainbowsData.bytes.length + trainsData.bytes.length) / IMAGE_SIZE;
  data = new DoodleData(total);
  data.load(catsData.bytes, CAT);
  data.load(rainbowsData.bytes, RAINBOW);
  data.load(trainsData.bytes, TRAIN);
  data.shuffle();

  model = new Classifier();
        });
        });});});});
   });

  // model.train(data);

  background(255);

  let trainButton = createButton('#train');
  trainButton.position(50,50)
  // let epochCounter = 0;
  trainButton.mousePressed(function() {
    model.train(data);
  });

   let testButton = select('#test');
   testButton.mousePressed(function() {});

   let guessButton = select('#guess');
   guessButton.mousePressed(function() {
     let inputs = [];
     let img = get();
     img.resize(28, 28);
     img.loadPixels();
     for (let i = 0; i < len; i++) {
       let bright = img.pixels[i * 4];
       inputs[i] = (255 - bright) / 255.0;
     }
  
     let guess = nn.predict(inputs);
     // console.log(guess);
     let m = max(guess);
     let classification = guess.indexOf(m);
     if (classification === CAT) {
       console.log("cat");
     } else if (classification === RAINBOW) {
       console.log("rainbow");
     } else if (classification === TRAIN) {
       console.log("train");
     }
  
     //image(img, 0, 0);
   });

  let clearButton = select('#clear');
  clearButton.mousePressed(function() {
    background(255);
  });
  


var html =" <body> <button id='train'>train</button> <button id='test'>test</button>  <button id='guess'>guess</button>  <button id='clear'>clear</button></body>";
AB.msg(html)
}
function draw() {
  strokeWeight(8);
  stroke(0);
  if (mouseIsPressed) {
    line(pmouseX, pmouseY, mouseX, mouseY);
  }
}