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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.
import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';
// Make it available globally
window.pipeline = pipeline;
</script>
<link href="https://cdn.jsdelivr.net/npm/[email protected]/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 지정하십시오. 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.
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.
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.
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> |