let classifier;

// Model URL

let imageModelURL = 'https://teachablemachine.withgoogle.com/models/m9EZq5JlJ/';

​

// Video

let video;

let flippedVideo;

// To store the classification

let label = "";

​

let helloImage;

let helloFade = 0;

​

// Load the model first

function preload() {

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

  helloImage = loadImage("hello.png");

}

​

function setup() {

  createCanvas(1280, 720);

  // Create the video

  video = createCapture(VIDEO);

  video.size(160, 120);

  // video.hide();

​

  flippedVideo = ml5.flipImage(video);

  // Start classifying

  classifyVideo();

}

​

function draw() {

  background(0, 255, 0);

  // Draw the video

  // tint(255);

  // imageMode(CORNER)

  // image(flippedVideo, 0, 0);

  if (label == "Hello") {

    helloFade = 255;

  }

  if (helloFade > 0) {

    tint(255, helloFade);

    imageMode(CENTER);

    image(helloImage, width / 2, 60, 285, 120);

    helloFade -= 10;

  }

​

  // 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);

  flippedVideo.remove();

​

}

​

// 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();

}