let classifier;

// Model URL

let imageModelURL = 'https://teachablemachine.withgoogle.com/models/J2eE_yCVZ/';

​

// Video

let video;

let flippedVideo;

// To store the classification

let label = "";

​

let question;

let questionFade = 0;

​

let yes;

let yesFade = 0;

​

let no;

let noFade = 0;

​

let love;

let loveFade = 0;

​

let laugh;

let laughFade = 0;

​

​

// Load the model first

function preload() {

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

  question = loadImage("question.png");

  yes = loadImage("yes.png");

  no = loadImage("no.png");

  laugh = loadImage("laugh.png");

  love = createVideo("love.mp4");

}

​

function mousePressed() {

    love.hide();

  love.loop();

}

​

function setup() {

  createCanvas(800, 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);

      imageMode(CORNER);

​

  // Draw the video

  // tint(255);

  // image(flippedVideo, 0, 0);

  if (label == 'question') {

    questionFade = 255;

  } else if (label == 'yes') {

    yesFade = 255;

  } else if (label == 'no') {

    noFade = 255;

  } else if (label == 'love') {

    loveFade = 255;

  } else if (label == 'laugh') {

    laughFade = 255;

  }

​

  if (questionFade > 0) {

    tint(255, questionFade);

    image(question, 0, 0);

    questionFade -= 10;

  }

​

  if (yesFade > 0) {

    tint(255, yesFade);

    image(yes, 0, 0);

    yesFade -= 10;

  }

​

​

  if (loveFade > 0) {

    tint(255, loveFade);

    image(love, 0, 0, width, height);

    loveFade -= 10;

  }

​

  if (noFade > 0) {

    tint(255, noFade);

    image(no, 0, 0);

    noFade -= 10;

  }

​

  if (laughFade > 0) {

    tint(255, laughFade);

    imageMode(CENTER);

    image(laugh, width/2, height/2, 720, 720);

    laughFade -= 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);

}

​

// 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();

}