​

let classifier;

​

// Teachable Machine model URL:

let soundModelURL = "https://teachablemachine.withgoogle.com/models/oj4f3Dfry/";

let options = { probabilityThreshold: 0.7 };

​

let label = "listening...";

let confidence = 0.0;

let outputs = [];

​

function setup() {

  createCanvas(600, 400);

  background(0);

​

  classifier = ml5.soundClassifier(soundModelURL + "model.json", options, modelReady);

}

​

function draw() {

  background(0);

  fill(255);

  // use of the result!

  noStroke();

  if (label == "Go") {                 // case-sensitive

    fill(0, 255, 0);

    circle(width / 2, height / 2, 200);

    fill(255);

    textSize(30);

    text(label, width / 2 - 20, height / 2 + 10);

  }

  else if (label == "Stop") {              // case-sensitive

    fill(255, 0, 0);

    circle(width / 2, height / 2, 200);

    fill(255);

    textSize(30);

    text(label, width / 2 - 30, height / 2 + 10);

  }

  else if (label == "Warning"){

    background(255);

    fill(255, 255, 0);

    circle(width / 2, height / 2, 200);

    fill(0);

    textSize(30);

    text(label, width / 2 - 52, height / 2 + 10);

  }

  // display the most confident result

  else {

    fill(255);

    textSize(30);

    text(label, width / 2, height / 2 );

  }

  //text(confidence, width / 2, height / 2 + 50);

  // display the whole results

  textSize(10);

  for (let i = 0; i < outputs.length; i++) {

    let output = outputs[i];

​

    let x = 10;

    let y = 20 + i * 30;

    text(output.label, x, y);

    text(output.confidence, x, y + 10);

  }

}

​

function modelReady() {

  console.log("Model Ready!");

  // classify sound

  classifier.classify(gotResult);

}

​

function gotResult(error, results) {

  if (error) {

    console.error(error);

  }

​

  //console.log(results);

  outputs = results;

  if (results.length > 0) {

    label = results[0].label;

    confidence = results[0].confidence;

  }

}