ml5js
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+<html>
+
+<head>
+  <meta charset="UTF-8">
+  <title>Webcam Image Classification using a pre-trianed customized model and p5.js</title>
+
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.8.0/p5.min.js"></script>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.8.0/addons/p5.dom.min.js"></script>
+
+  <script src="http://localhost:8080/ml5.js" type="text/javascript"></script>
+</head>
+
+<body>
+  <h1>Webcam Image Classification using a pre-trianed customized model and p5.js</h1>
+  <p>If you cover your webcam, this model will classify it as "1", otherwise will classify anything else else "0" </p>
+  <script src="sketch.js"></script>
+</body>
+
+</html>
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+// Copyright (c) 2018 ml5
+//
+// This software is released under the MIT License.
+// https://opensource.org/licenses/MIT
+
+/* ===
+ml5 Example
+Webcam Image Classification using a pre-trianed customized model and p5.js
+This example uses p5 preload function to create the classifier
+=== */
+
+const checkpoint = 'https://storage.googleapis.com/tm-pro-a6966.appspot.com/eyeo-test-yining/model.json';
+let classifier;
+let video;
+let resultsP;
+
+function preload() {
+  // Create a camera input
+  video = createCapture(VIDEO);
+  // Initialize the Image Classifier method with a pre-trained customized model and the video as the second argument
+  classifier = ml5.imageClassifier(checkpoint);
+}
+
+function setup() {
+  noCanvas();
+  // ml5 also supports using callback pattern to create the classifier
+  // classifier = ml5.imageClassifier(checkpoint, video, modelReady);
+  // If you would like to load the model from local files
+  // classifier = ml5.imageClassifier('model/image-model.json', video, modelReady);
+  resultsP = createP('Loading model and video...');
+  classifyVideo();
+}
+
+// Get a prediction for the current video frame
+function classifyVideo() {
+  classifier.classify(video, gotResult);
+}
+
+// If you use callback pattern to create the classifier, you can use the following callback function
+// function modelReady() {
+//   console.log('Model Ready');
+//   classifyVideo();
+// }
+
+// When we get a result
+function gotResult(err, results) {
+  // The results are in an array ordered by confidence.
+  resultsP.html('Label: ' + results[0].label + ' ' + nf(results[0].confidence, 0, 2));
+  classifyVideo();
+}