let video;

​

var speech;

var recog;

​

let started = false;

let stop = false;

​

function setup() {

  noCanvas();

​

  speech = new p5.Speech(); // speech synthesis object

  speech.speak('hi there, what do you want to do?'); // say something 

​

  recog = new p5.SpeechRec('en-US'); // speech recognition object (will prompt for mic access)

  recog.onResult = showResult;

  recog.continuous = true; // do continuous recognition

    recog.interimResults = true; // allow partial recognition (faster, less accurate)  

    recog.start(); // start listening

}

​

function showResult()

{

   console.log(recog.resultString); // log the result

   if (!started && recog.resultString.indexOf("start") > -1) {

     started = true;

     stop = false;

    // Create a camera input

    video = createCapture(VIDEO);

    // Initialize the Image Classifier method with MobileNet and the video as the second argument

    classifier = ml5.imageClassifier('MobileNet', video, modelReady);       

   } else if (started && recog.resultString.indexOf("stop")) {

            stop = true;  

            //  started = true;

​

   }

}

​

​

function modelReady() {

  // Change the status of the model once its ready

  //select('#status').html('Model Loaded');

  console.log("Model Loaded");

  // Call the classifyVideo function to start classifying the video

  classifyVideo();

}

​

// Get a prediction for the current video frame

function classifyVideo() {

  classifier.predict(gotResult);

}

​

// When we get a result

function gotResult(err, results) {

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

  speech.speak(results[0].className);

  console.log(results[0].className);

​

  //select('#result').html(results[0].className);

  //select('#probability').html(nf(results[0].probability, 0, 2));

  if (!stop) {

      classifyVideo();

  }

}