let classifier;

​

// Two variable to hold the label and confidence of the result

let clearButton;

let resultsP;

let canvas;

​

function preload() {

  classifier = ml5.imageClassifier("DoodleNet");

}

​

function setup() {

  canvas = createCanvas(280, 280);

  background(255);

  classifier.classifyStart(canvas, gotResult);

​

  // Create 'label' and 'confidence' div to hold results

  resultsP = createP("label");

  // Create a clear canvas button

  clearButton = createButton("clear canvas");

  clearButton.mousePressed(clearCanvas);

}

​

function clearCanvas() {

  background(255);

}

​

function draw() {

  strokeWeight(16);

  stroke(0);

  if (mouseIsPressed) {

    line(pmouseX, pmouseY, mouseX, mouseY);

  }

}

​

// A function to run when we get any errors and the results

function gotResult(results) {

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

  // console.log(results);

  // Show the first label and confidence

  let label = results[0].label;

  let confidence = results[0].confidence;

  resultsP.html(label + " " + nf(confidence, 0, 2));

}

​