const checkpointURL = 'https://storage.googleapis.com/tm-models/fRJ7LKDj/model.json';

​

// The metatadata json file contains the text labels of your model

// and additional information

const metadataURL = 'https://storage.googleapis.com/tm-models/fRJ7LKDj/metadata.json';

​

​

let model, webcamEl, maxPredictions;

​

async function init() {

  // load the model and metadata

  // Refer to tmImage.loadFromFiles() in the API to support files from a file picker

  // or files from your local hard drive

  model = await tmImage.load(checkpointURL, metadataURL);

  maxPredictions = model.getTotalClasses();

​

  // optional function for creating a webcam

  // webcam has a square ratio and is flipped by default to match training

  const webcamFlipped = true;

  webcamEl = await tmImage.getWebcam(200, 200, 'front', webcamFlipped);

  webcamEl.play();

  document.querySelector('#video').appendChild(webcamEl);

  // document.body.appendChild(webcamEl);

​

  window.requestAnimationFrame(loop); // kick of pose prediction loop

}

​

async function loop(timestamp) {

  await predict();

  window.requestAnimationFrame(loop);

}

​

​

let port;

​

async function predict() {

  // predict can take in an image, video or canvas html element

  // we set flip to true since the webcam was only flipped in CSS

  const flip = true;

  const prediction = await model.predict(webcamEl, flip, maxPredictions);

  let view = new Uint8Array(1);

  // console.log(prediction);

  const class1 = prediction.filter(objs => {

    if (objs.className === 'Class 1') {

      return objs;

    }});

  const class2 = prediction.filter(objs => {

    if (objs.className === 'Class 2') {

      return objs;

    }});

  const class3 = prediction.filter(objs => {

    if (objs.className === 'Class 3') {

      return objs;

    }});

​

  if (class1[0].probability > 0.9) {

    console.log("ceral sent");

    view[0] = 1;

    port.send(view);

  }

  else if (class2[0].probability > 0.9) {

    console.log("mallow sent");

    view[0] = 2;

    port.send(view);

  }

  const class1Bar = document.getElementById('class-1');

  const class2Bar = document.getElementById('class-2');

  class1Bar.style.width = parseInt(prediction[0].probability * 100 ) + '%';

  class2Bar.style.width = parseInt(prediction[1].probability * 100) + '%';

​

}

​

​

​

init();

​