let classifier; // teachable machine classifier variable

let imageModelURL = "https://teachablemachine.withgoogle.com/models/Rx9iVhlk9/"; // Model URL

​

// To store the classification

let label = "";

​

function preload() {

  classifier = ml5.imageClassifier(imageModelURL + "model.json"); //load TM model

}

​

function setup() {

  createCanvas(400, 400);

}

​

function draw() {}

​

// Get a prediction for the current video frame

function classify() {

  /*******************************************************/

  //really weird way of passing the canvas as an image to Teachable machine... I am looking for a better way to do this but for now here it is:

  canvasImage = loadImage(

    document.querySelector("canvas").toDataURL("image/jpeg", 0.1)

  );

  setTimeout(function () {

    classifier.classify(canvasImage, gotResult);

  }, 100);

  /*******************************************************/

}

​

// When we get a result

function gotResult(error, results) {

  // If there is an error

  if (error) {

    console.error(error);

    return;

  }

  // The results are in an array ordered by confidence.

  label = results[0].label;

  state = "result";

}

​