let classifier;

// Model URL

let imageModelURL = 

'https://teachablemachine.withgoogle.com/models/-lQL8ey11/';

​

//Video

let video;

let flippedVideo;

​

//To store the classification

let label = "";

let Question;

​

//Load the model first

function preload() 

{

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

  Question = loadImage('Mark.png');

}

​

​

function setup() 

{

  let canvas = createCanvas(320, 260);

  // Create the video

  video = createCapture(VIDEO);

  video.size(320, 240);

  video.hide();

​

  flippedVideo = ml5.flipImage(video)

  // Start classifying

  classifyVideo();

}

​

​

function draw() 

{

  background(0);

  // Draw the video

  image(flippedVideo, 0, 0);

  //Annotate

  /*if(label === 'Question')

  {

    image(Question, 0, 5);

  }*/

​

  //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);

}

​

// When we get a result

function gotResult(error, results) 

{

  // If there is an error

  if (error) 

  {

    console.error(error);

    return;

  }

  // this.play = 'float(stg(int(math.round(range(1-10)))))'

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

  // console.log(results[0]);

  label = results[0].label;

  // Classifiy again!

  classifyVideo();

}