let video;

let label = "waiting...";

let confidence = 0.0;

let classifier;

let modelURL = 'https://teachablemachine.withgoogle.com/models/9ZhtUP8P/'; //you can go to this URL to test your model outside of P5JS if you wish. Mightt be a good troubleshooting method? 

​

let imgA;

let imgB;

​

// STEP 1: Load the model and your outputs.

function preload() {

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

    imgA = loadImage("0.png");

  imgB = loadImage("1.png");

}

​

function setup() {

  createCanvas(640, 520);

  video = createCapture(VIDEO);

  video.hide();

  classifyVideo();

}

​

function draw() {

  background(0);

  image(video, 0, 0);

​

    // STEP 4: Show image output if confidence is over a set value

  if (label == "Person" && confidence > 0.9) {

    image(imgA, 0, 0, width, video.height);

  } else if (label == "Phone" && confidence > 0.9) {

    image(imgB, 0, 0, width, video.height);

  }  else if (label == "nothing") {

    //don't display any image

  }

  // STEP 5: Show current label

  textSize(42);

  textAlign(CENTER, CENTER);

  //label = label.toUpperCase(); //capitalise

  fill(255, 0, 255);

  text(label + " " + confidence, width / 2, height - 16);

​

​

}

​

// STEP 2: Do the classifying

function classifyVideo() {

  classifier.classify(video, gotResults);

}

​

// STEP 3: Get the classification

function gotResults(error, results) {

  if (error) {

    console.error(error);

    return;

  }

  // Store the label and classify again

  //these are the variables useful to you!!!

  console.log(results);

  label = results[0].label; //classification label

  confidence = nf(results[0].confidence, 0, 2); //confidence %

  classifyVideo();

}

​

​

​

//IGNORE THIS 

/*more UI

​

  noStroke();

  fill(255);

  if (label === 'HAND') {

​

    rect(10, 10, confidence, 25, 5);

  } else {

    rect(10, 10, 10, 25, 5);

​

  }

​

  if (label === 'PERSON') {

    rect(10, 50, confidence, 25, 5);

  } else {

    rect(10, 50, 10, 25, 5);

​

  }

*/