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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">
// Import the library
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">
<!-- 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>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() {
// TO-Do: pipeline() 함수를 사용하여 ViT 모델 인스턴스를 classifier라는 이름으로 생성하십시오/
classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32');
}
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() {
const fileInput = document.getElementById("imageClassificationLocalFile");
const file = fileInput.files[0];
if (!file) {
alert('Please select an image file first.');
return;
}
// Create a Blob URL from the file
const url = URL.createObjectURL(file);
const labels = document.getElementById("labelsLocalText").value.trim().split(",").map(item => item.trim());
// classifier에 url을 입력하여 출력되는 결과를 result에 저장하십시오.
// 힌트: cont result = ???
const result = await classifier(url, labels);
// HTML코드 중 element Id가 'outputAreaLocal'인 요소에 resul의 값을 JSON string 형태로 text로 출력하십시오.
// 힌트: document.getElementById와 JSON.stringify 이용
document.getElementById("outputAreaLocal").innerText = JSON.stringify(result, null, 2);
}
async function classifyTopImage() {
// 코드 삭제됨
}
// Initialize the model after the DOM is completely loaded
window.addEventListener("DOMContentLoaded", initializeModel);
</script>
</body>
</html> |