Create refs/_mov.html
Browse files- refs/_mov.html +143 -0
refs/_mov.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<title>Image Classification - Hugging Face Transformers.js</title>
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<script type="module">
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// Import the library
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import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.4';
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// Make it available globally
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window.pipeline = pipeline;
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</script>
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<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.0/dist/css/bootstrap.min.css" rel="stylesheet">
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<link rel="stylesheet" href="css/styles.css">
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</head>
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<body>
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<div class="container-main">
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<!-- Page Header -->
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<div class="header">
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<div class="header-main-text">
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<h1>Hugging Face Transformers.js</h1>
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</div>
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</div>
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<hr> <!-- Separator -->
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<!-- Back to Home button -->
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<div class="row mt-5">
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<div class="col-md-12 text-center">
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<a href="index.html" class="btn btn-outline-secondary"
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style="color: #3c650b; border-color: #3c650b;">Back to Main Page</a>
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</div>
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</div>
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<!-- Content -->
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<div class="container mt-5">
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<!-- Centered Titles -->
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<div class="text-center">
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<h2>Computer Vision</h2>
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<h4>Mobilevit Image Classification</h4>
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</div>
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<!-- Actual Content of this page -->
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<div id="image-classification-container" class="container mt-4">
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<h5>Classify an Image:</h5>
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<div class="d-flex align-items-center">
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<label for="imageClassificationURLText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter
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image URL:</label>
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<input type="text" class="form-control flex-grow-1" id="imageClassificationURLText"
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value="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg"
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placeholder="Enter image" style="margin-right: 15px; margin-left: 15px;">
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<button id="ClassifyButton" class="btn btn-primary" onclick="classifyImage()">Classify</button>
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</div>
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<div class="mt-4">
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<h4>Output:</h4>
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<pre id="outputArea"></pre>
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</div>
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</div>
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<hr> <!-- Line Separator -->
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<div id="image-classification-local-container" class="container mt-4">
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<h5>Classify a Local Image:</h5>
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<div class="d-flex align-items-center">
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<label for="imageClassificationLocalFile" class="mb-0 text-nowrap"
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style="margin-right: 15px;">Select Local Image:</label>
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<input type="file" id="imageClassificationLocalFile" accept="image/*" />
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<button id="ClassifyButtonLocal" class="btn btn-primary"
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onclick="classifyImageLocal()">Classify</button>
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</div>
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<div class="mt-4">
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<h4>Output:</h4>
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<pre id="outputAreaLocal"></pre>
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</div>
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</div>
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<hr> <!-- Line Separator -->
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<div id="image-classification-top-container" class="container mt-4">
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<h5>Classify an Image and Return Top n Classes:</h5>
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<div class="d-flex align-items-center">
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<label for="imageClassificationTopURLText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter
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image URL:</label>
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<input type="text" class="form-control flex-grow-1" id="imageClassificationTopURLText"
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value="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg"
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placeholder="Enter image" style="margin-right: 15px; margin-left: 15px;">
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<button id="ClassifyTopButton" class="btn btn-primary" onclick="classifyTopImage()">Classify</button>
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</div>
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<div class="mt-4">
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<h4>Output:</h4>
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<pre id="outputAreaTop"></pre>
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</div>
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</div>
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<!-- Back to Home button -->
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<div class="row mt-5">
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<div class="col-md-12 text-center">
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<a href="index.html" class="btn btn-outline-secondary"
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style="color: #3c650b; border-color: #3c650b;">Back to Main Page</a>
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</div>
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</div>
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</div>
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</div>
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<script>
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let classifier;
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// Initialize the sentiment analysis model
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async function initializeModel() {
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classifier = await pipeline('image-classification', 'Xenova/mobilevit-small', { quantized: false });
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}
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async function classifyImage() {
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const textFieldValue = document.getElementById("imageClassificationURLText").value.trim();
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const result = await classifier(textFieldValue);
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document.getElementById("outputArea").innerText = JSON.stringify(result, null, 2);
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}
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async function classifyImageLocal() {
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const fileInput = document.getElementById("imageClassificationLocalFile");
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const file = fileInput.files[0];
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if (!file) {
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alert('Please select an image file first.');
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return;
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}
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// Create a Blob URL from the file
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const url = URL.createObjectURL(file);
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const result = await classifier(url);
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document.getElementById("outputAreaLocal").innerText = JSON.stringify(result, null, 2);
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}
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async function classifyTopImage() {
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const textFieldValue = document.getElementById("imageClassificationTopURLText").value.trim();
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const result = await classifier(textFieldValue, { topk: 3 });
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document.getElementById("outputAreaTop").innerText = JSON.stringify(result, null, 2);
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}
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// Initialize the model after the DOM is completely loaded
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window.addEventListener("DOMContentLoaded", initializeModel);
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</script>
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</body>
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</html>
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