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pigeon/index.html

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<html>
<head>
<!script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"> </script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.13.5"></script>
<script>
var wait = ms => new Promise((r, j)=>setTimeout(r, ms));
window.onload = async function()
{
//'./model/model.json'
let path = './model/model.json'
const model = await tf.loadModel( path );
document.getElementById('image_upload').onchange = function(ev)
{
var f = ev.target.files[0];
var fr = new FileReader();
var makePrediction = async function(img)
{
document.getElementById('result').innerHTML = "Loading";
// We need to ensure that the image is actually loaded before we proceed.
while(!img.complete) {
await wait(100);
}
var tensor = tf.fromPixels(img).resizeNearestNeighbor([224,224]).toFloat().expandDims();
const prediction = model.predict(tensor);
var data = prediction.dataSync();
console.log( data );
document.getElementById('result').innerHTML = data[0] == 0 ? "That's a massive pigeon!" : "Not a pigeon";
}
var fileReadComplete = function(ev2)
{
document.getElementById('image').src = ev2.target.result;
var img = new Image();
img.src = ev2.target.result;
makePrediction(img);
};
fr.onload = fileReadComplete;
fr.readAsDataURL(f);
}
}
</script>
</head>
<body>
<h1>Pigeon?<hr/></h1>
<input id="image_upload" type="file"/></br>
<h3 id="result"></h3></br>
<img id="image" style="max-height: 500px"/>
</body>
</html>