let classifier;

// Model URL

let imageModelURL = 'https://teachablemachine.withgoogle.com/models/QcSZp1lft/model.json';

​

// Video

let video;

let flippedVideo;

// To store the classification

let label = "";

​

let question;

let questionFade = 255;

​

// Load the model first

function preload() {

  classifier = ml5.imageClassifier(imageModelURL);

  question = loadImage('question.png');

}

​

function setup() {

  createCanvas(640, 480);

  // Create the video

  video = createCapture(VIDEO);

  video.size(640, 480);

  video.hide();

​

  flippedVideo = ml5.flipImage(video)

  // Start classifying

  classifyVideo();

}

​

function draw() {

  background(0);

  // Draw the video

  image(flippedVideo, 0, 0);

​

  if (label == 'Question') {

    questionFade = 255;

​

  }

  questionFade -= 10;

  if (questionFade > 0) {

    //tint(255,questionFade);

    image(question, 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);

}

​

// 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.

  // console.log(results[0]);

  label = results[0].label;

  // Classifiy again!

  classifyVideo();

}