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	<!DOCTYPE html>
	<html lang="en">

	<head>
	<meta charset="UTF-8">
	<title>Image Classification - Hugging Face Transformers.js</title>

	<script type="module">
	// 허깅페이스의 pipeline 모듈을 import하십시오.
	// To-Do: ???
	import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.4';

	// Make it available globally
	window.pipeline = pipeline;
	</script>

	<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.0/dist/css/bootstrap.min.css" rel="stylesheet">

	<link rel="stylesheet" href="css/styles.css">
	</head>

	<body>
	<div class="container-main">
	<!-- Page Header -->
	<div class="header">

	<div class="header-main-text">
	<h1>Hugging Face Transformers.js</h1>
	</div>

	</div>
	<hr> <!-- Separator -->

	<!-- Back to Home button -->
	<div class="row mt-5">
	<div class="col-md-12 text-center">
	<a href="index.html" class="btn btn-outline-secondary"
	style="color: #3c650b; border-color: #3c650b;">Back to Main Page</a>
	</div>
	</div>

	<!-- Content -->
	<div class="container mt-5">
	<!-- Centered Titles -->
	<div class="text-center">
	<h2>Computer Vision</h2>
	<h4>Mobilevit Image Classification</h4>
	</div>

	<!-- Actual Content of this page -->
	<div id="image-classification-container" class="container mt-4">
	<h5>Classify an Image:</h5>
	<div class="d-flex align-items-center">
	<label for="imageClassificationURLText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter
	image URL:</label>
	<input type="text" class="form-control flex-grow-1" id="imageClassificationURLText"
	value="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg"
	placeholder="Enter image" style="margin-right: 15px; margin-left: 15px;">
	<button id="ClassifyButton" class="btn btn-primary" onclick="classifyImage()">Classify</button>
	</div>
	<div class="mt-4">
	<h4>Output:</h4>
	<pre id="outputArea"></pre>
	</div>
	</div>

	<hr> <!-- Line Separator -->

	<div id="image-classification-local-container" class="container mt-4">
	<h5>Classify a Local Image:</h5>
	<div class="d-flex align-items-center">
	<label for="imageClassificationLocalFile" class="mb-0 text-nowrap"
	style="margin-right: 15px;">Select Local Image:</label>
	<input type="file" id="imageClassificationLocalFile" accept="image/*" />
	<button id="ClassifyButtonLocal" class="btn btn-primary"
	onclick="classifyImageLocal()">Classify</button>
	</div>
	<div class="mt-4">
	<h4>Output:</h4>
	<pre id="outputAreaLocal"></pre>
	</div>
	</div>

	<hr> <!-- Line Separator -->

	<div id="image-classification-top-container" class="container mt-4">
	<h5>Classify an Image and Return Top n Classes:</h5>
	<div class="d-flex align-items-center">
	<label for="imageClassificationTopURLText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter
	image URL:</label>
	<input type="text" class="form-control flex-grow-1" id="imageClassificationTopURLText"
	value="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg"
	placeholder="Enter image" style="margin-right: 15px; margin-left: 15px;">
	<button id="ClassifyTopButton" class="btn btn-primary" onclick="classifyTopImage()">Classify</button>
	</div>
	<div class="mt-4">
	<h4>Output:</h4>
	<pre id="outputAreaTop"></pre>
	</div>
	</div>

	<!-- Back to Home button -->
	<div class="row mt-5">
	<div class="col-md-12 text-center">
	<a href="index.html" class="btn btn-outline-secondary"
	style="color: #3c650b; border-color: #3c650b;">Back to Main Page</a>
	</div>
	</div>
	</div>
	</div>

	<script>
	let classifier;
	// Initialize the sentiment analysis model
	async function initializeModel() {
	// pipeline 함수를 이용하여 Xenova/mobilevit-small 모델의 인스턴스를 생성하여 이를 classifier에 저정하십시오. 인스턴스 생성 시 quantized 파라미터의 값을 false로 설정하십시오.
	classifier = await pipeline('image-classification', 'Xenova/mobilevit-small', { quantized: false });

	}
	async function classifyImage() {
	const textFieldValue = document.getElementById("imageClassificationURLText").value.trim();
	const result = await classifier(textFieldValue);
	document.getElementById("outputArea").innerText = JSON.stringify(result, null, 2);
	}
	async function classifyImageLocal() {
	// HTML DOM의 element Id가 imageClassificationLocalFile인 element의 값을 fileInput으로 저장하십시오.
	// To-Do: const fileInput = ???
	const fileInput = document.getElementById("imageClassificationLocalFile");
	const file = fileInput.files[0];
	if (!file) {
	alert('Please select an image file first.');
	return;
	}

	const url = URL.createObjectURL(file);
	// classifier에 url을 입력하여 출력된 결과를 result에 저장하십시오.
	// To-Do: ???
	const result = await classifier(url);
	document.getElementById("outputAreaLocal").innerText = JSON.stringify(result, null, 2);
	}
	async function classifyTopImage() {
	const textFieldValue = document.getElementById("imageClassificationTopURLText").value.trim();
	// classifier에 textFieldValue를 입력 변수로, topk 파라미터 값을 3으로 설정하여 classifer를 수행하고 그 결과를 result에 저장하십시오.
	// To-Do: ???
	const result = await classifier(textFieldValue, { topk: 3 });

	document.getElementById("outputAreaTop").innerText = JSON.stringify(result, null, 2);
	}
	// Initialize the model after the DOM is completely loaded
	window.addEventListener("DOMContentLoaded", initializeModel);
	</script>
	</body>

	</html>