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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Teachable Machine Image Model</title>
</head>
<body>
<div>Teachable Machine Image Model</div>
<div id="webcam-container"></div>
<div id="label-container"></div>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/image@latest/dist/teachablemachine-image.min.js"></script>
<script type="text/javascript">
const URL = "https://teachablemachine.withgoogle.com/models/ZPfAhDYCh/";
let model, webcam, labelContainer, maxPredictions;
async function init() {
const modelURL = URL + "model.json";
const metadataURL = URL + "metadata.json";
model = await tmImage.load(modelURL, metadataURL);
maxPredictions = model.getTotalClasses();
const flip = true;
webcam = new tmImage.Webcam(200, 200, flip);
await webcam.setup();
await webcam.play();
window.requestAnimationFrame(loop);
document.getElementById("webcam-container").appendChild(webcam.canvas);
labelContainer = document.getElementById("label-container");
for (let i = 0; i < maxPredictions; i++) {
labelContainer.appendChild(document.createElement("div"));
}
}
async function loop() {
webcam.update();
await predict();
window.requestAnimationFrame(loop);
}
async function predict() {
const prediction = await model.predict(webcam.canvas);
for (let i = 0; i < maxPredictions; i++) {
const classPrediction = prediction[i].className + ": " + prediction[i].probability.toFixed(2);
labelContainer.childNodes[i].innerHTML = classPrediction;
}
}
init(); // Automatically start the model when the page loads
</script>
</body>
</html>
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