let featureExtractor;

let capture;

let penButton;

let micButton;

let trainButton;

let predictButton;

let numPenImages = 0;

let numMicImages = 0;

let paragraph;

​

function setup() {

  noCanvas();

  capture = createCapture(VIDEO);

  capture.size(400, 300);

  featureExtractor = ml5.featureExtractor('MobileNet', modelReady);

  classifier = featureExtractor.classification(capture, videoReady);

  setupButtons();

}

​

function modelReady() {

    print('Model is ready!');

}

​

function videoReady() {

    print('Video is ready!'); 

}

​

function setupButtons() {

    penButton = createButton('happy');

  penButton.mousePressed(function() {

    numPenImages++;

    print(numPenImages);

    classifier.addImage('happy');

  });

    micButton = createButton('sad'); 

  micButton.mousePressed(function() {

    numMicImages++;

    print(numMicImages);

    classifier.addImage('sad');

  });

    trainButton = createButton('train'); 

  trainButton.mousePressed(function() {

    classifier.train(function(lossValue) {

        if (lossValue) {

        print(lossValue);

      } else {

        print('Done Training!'); 

      }

    });

  });

  predictButton = createButton('predict');

  predictButton.mousePressed(classify);

  paragraph = createP('');

}

​

function classify() {

    classifier.classify(processResults); 

}

​

function processResults(err, result) {

  if (err) { 

    console.error(err)

  } else {

    paragraph.html(result);

    classify();

  }

}