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 Copyright (c) 2018 ml5
 This software is released under the MIT License.


  <meta charset="UTF-8">
  <title>Image Regression using Feature Extraction with MobileNet. Built with p5.js</title>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.6.0/p5.min.js"></script>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.6.0/addons/p5.dom.min.js"></script>
  <script src="https://unpkg.com/ml5@0.1.1/dist/ml5.min.js" type="text/javascript"></script>



  style="text-align: center;"
  <h6 style="visibility: hidden"><span id="modelStatus">Loading base model...</span> | <span id="videoStatus">Loading video...</span></h6>
  <div style="display: flex; align-items: center; justify-content: center;">
      <input type="range" name="slider" id="slider" min="0.01" max="1.0" step="0.01" value="0.5">
      <button id="addSample">Add Sample</button>
      <p><span id="amountOfSamples">0</span> Sample Images</p>
    <p><button id="train">Train</button><span id="loss"></span></p>
      <button id="buttonPredict">Start predicting!</button><br>
  <div id="videoContainer"></div>
  <div id="canvasContainer"></div>
  <div id="font-container" >Can you read this?</div>


button {
    margin: 2px;
    padding: 4px;
    width: 300;
    height: 300;
    display: inline;
    font-size: 14px;
    margin: 4px;
    font-weight: lighter;
    font-size: 14px;
    margin-bottom: 10px;

#font-container {
  transition: .25s all ease-in-out;

  @font-face {
    font-family: 'Avenir Next Variable';
    src: url('https://s3.amazonaws.com/codepen-mh/assets/fonts/AvenirNext_Variable.ttf') 

  @font-face {
    font-family: 'Amstelvar-Roman';
    src: url('https://s3.amazonaws.com/codepen-mh/assets/fonts/Amstelvar-Roman-parametric-VF.ttf') 

  @font-face {
    font-family: 'Decovar Alpha';
    src: url('https://s3.amazonaws.com/codepen-mh/assets/fonts/DecovarAlpha-VF.ttf') 

  body {
    font-family: 'Avenir Next Variable';
              // Copyright (c) 2018 ml5
// This software is released under the MIT License.
// https://opensource.org/licenses/MIT

/* ===
ml5 Example
Creating a regression extracting features of MobileNet. Build with p5js.
=== */

let featureExtractor;
let regressor;
let video;
let loss;
let slider;
let samples = 0;
let fontSize = 10;
let fontWidth = 100;
let lastResult = .5;
let resultThreshold = .25;

function setup() {
  var canvas = createCanvas(340, 280);

  // Create a video element
  video = createCapture(VIDEO);
  // Append it to the videoContainer DOM element
  // Extract the features from MobileNet
  featureExtractor = ml5.featureExtractor('MobileNet', modelReady);
  // Create a new regressor using those features and give the video we want to use
  regressor = featureExtractor.regression(video, videoReady);
  // Create the UI buttons

function draw() {
  image(video, 0, 0, 340, 280);
  //select('#font-container').style('font-variation-settings', `'SKLA' ${fontWidth}`);
  select('#font-container').style('font-variation-settings', `'wght' ${fontWidth}`);

// A function to be called when the model has been loaded
function modelReady() {
  select('#modelStatus').html('Model loaded!');

// A function to be called when the video has loaded
function videoReady() {
  select('#videoStatus').html('Video ready!');

// Classify the current frame.
function predict() {

// A util function to create UI buttons
function setupButtons() {
  slider = select('#slider');

  select('#addSample').mousePressed(function() {

  // Train Button
  select('#train').mousePressed(function() {
    regressor.train(function(lossValue) {
      if (lossValue) {
        loss = lossValue;
        select('#loss').html('Loss: ' + loss);
      } else {
        select('#loss').html('Done Training! Final Loss: ' + loss);

  // Predict Button

// Show the results
function gotResults(err, result) {
  if (Math.abs(result-lastResult) > resultThreshold){
    fontWidth = map(result, 0, 1, 0, 1000);
    fontSize = map(result, 0, 1, 10, 100);
    lastResult = result;
  if (err) {
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