let imageModelURL = "https://teachablemachine.withgoogle.com/models/nZO5OW_tV/";

​

let classifier;

let video;

let flippedVideo;

let label = ""; // to store the classification

​

// load the model first

function preload() {

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

}

​

function setup() {

  createCanvas(640, 480);

  // create the video

  video = createCapture(VIDEO);

  video.size(640, 480);

  video.hide();

  // Teachable Machine recorded the camera as if looking into a mirror

  // (right and left swapped); because of this, we flip the camera image as well

  flippedVideo = ml5.flipImage(video);

  // start classifying

  classifyVideo();

}

​

function draw() {

  background(0);

  // draw the video

  image(flippedVideo, 0, 0);

​

  // draw the label

  fill(255);

  textSize(16);

  textAlign(CENTER);

  text(label, width / 2, height - 4);

}

​

// get a prediction for the current video frame

function classifyVideo() {

  flippedVideo = ml5.flipImage(video);

  classifier.classify(flippedVideo, gotResult);

}

​

// this function is executed whenever classification finishes

// it's thus a "callback function"

function gotResult(error, results) {

  // the results are in an array ordered by confidence

  // console.log(results[0]);

  label = results[0].label;

  // classifiy again!

  classifyVideo();

}

​