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<html>
<head>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
</head>
<body>
Make sure to run this via a server
<br>
<input accept="image/*" type='file' id="input_button" />
<br>
<img id="img1" src="./image.jpg" style="width: 300px " />
<br> <br>
<button onclick="predict()">predict</button>
<br>
<p id="result"> </p>
<script>
let image = document.getElementById('img1');
let input_button = document.getElementById('input_button');
input_button.onchange = evt => {
const [file] = input_button.files
if (file) {
image.src = URL.createObjectURL(file)
}
}
async function predict() {
var model = await tf.loadGraphModel('./model.json');
let example = tf.browser.fromPixels(document.getElementById("img1") , 3 ).cast('float32');
console.log( example.shape )
example = example.reshape([1,example.shape[0], example.shape[1] ,example.shape[2]]);
let prediction = await model.predict(example);
let class_scores = await prediction.data();
let max_score_id = class_scores.indexOf(Math.max(...class_scores));
let classes = [ "amina mostafa2024020001" , "misaa babnjki2024020002" , ] ;
console.log(class_scores);
document.getElementById("result").innerHTML = classes[max_score_id].toString();
}
</script>
</body>
</html> |