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ML-For-Beginners/4-Classification/4-Applied/solution/index.html

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<!DOCTYPE html>
<html>
<header>
<title>Cuisine Matcher</title>
</header>
<body>
<h1>Check your refrigerator. What can you create?</h1>
<div id="wrapper">
<div class="boxCont">
<input type="checkbox" value="4" class="checkbox">
<label>apple</label>
</div>
<div class="boxCont">
<input type="checkbox" value="247" class="checkbox">
<label>pear</label>
</div>
<div class="boxCont">
<input type="checkbox" value="77" class="checkbox">
<label>cherry</label>
</div>
<div class="boxCont">
<input type="checkbox" value="126" class="checkbox">
<label>fenugreek</label>
</div>
<div class="boxCont">
<input type="checkbox" value="302" class="checkbox">
<label>sake</label>
</div>
<div class="boxCont">
<input type="checkbox" value="327" class="checkbox">
<label>soy sauce</label>
</div>
<div class="boxCont">
<input type="checkbox" value="112" class="checkbox">
<label>cumin</label>
</div>
</div>
<div style="padding-top:10px">
<button onClick="startInference()">What kind of cuisine can you make?</button>
</div>
<!-- import ONNXRuntime Web from CDN -->
<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.9.0/dist/ort.min.js"></script>
<script>
const ingredients = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
const checks = [].slice.call(document.querySelectorAll('.checkbox'));
// use an async context to call onnxruntime functions.
function init() {
checks.forEach(function (checkbox, index) {
checkbox.onchange = function () {
if (this.checked) {
var index = checkbox.value;
if (index !== -1) {
ingredients[index] = 1;
}
console.log(ingredients)
}
else {
var index = checkbox.value;
if (index !== -1) {
ingredients[index] = 0;
}
console.log(ingredients)
}
}
})
}
function testCheckboxes() {
for (var i = 0; i < checks.length; i++)
if (checks[i].type == "checkbox")
if (checks[i].checked)
return true;
return false;
}
async function startInference() {
let checked = testCheckboxes()
if (checked) {
try {
// create a new session and load the model.
const session = await ort.InferenceSession.create('./model.onnx');
const input = new ort.Tensor(new Float32Array(ingredients), [1, 380]);
const feeds = { float_input: input };
// feed inputs and run
const results = await session.run(feeds);
// read from results
alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
} catch (e) {
console.log(`failed to inference ONNX model: ${e}.`);
}
}
else alert("Please check an ingredient")
}
init();
</script>
</body>
</html>