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

98 lines
3.3 KiB

<!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 = Array(380).fill(0);
const checks = [...document.querySelectorAll('.checkbox')];
checks.forEach(check => {
check.addEventListener('change', function() {
// toggle the state of the ingredient
// based on the checkbox's value (1 or 0)
ingredients[check.value] = check.checked ? 1 : 0;
});
});
function testCheckboxes() {
// validate if at least one checkbox is checked
return checks.some(check => check.checked);
}
async function startInference() {
let atLeastOneChecked = testCheckboxes()
if (!atLeastOneChecked) {
alert('Please select at least one ingredient.');
return;
}
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`);
console.error(e);
}
}
</script>
</body>
</html>