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+ <!DOCTYPE html>
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+ <html lang="en">
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+
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+ <head>
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+ <meta charset="UTF-8">
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+ <title>Zero Shot Image Classification - Hugging Face Transformers.js</title>
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+
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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/[email protected]';
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+ // Make it available globally
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+ window.pipeline = pipeline;
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+ </script>
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+
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+ <link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet">
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+
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+ <link rel="stylesheet" href="css/styles.css">
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+ </head>
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+
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+ <body>
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+ <div class="container-main">
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+
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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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+
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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>Zero Shot Image Classification</h4>
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+ </div>
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+
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+ <!-- Actual Content of this page -->
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+ <div id="zero-shot-image-classification-container" class="container mt-4">
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+ <h5>Zero Shot Image Classification w/ Xenova/clip-vit-base-patch32:</h5>
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+ <div class="d-flex align-items-center mb-2">
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+ <label for="zeroShotImageClassificationURLText" class="mb-0 text-nowrap"
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+ 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="zeroShotImageClassificationURLText"
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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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+ </div>
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+ <div class="d-flex align-items-center">
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+ <label for="labelsText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Labels (comma
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+ separated):</label>
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+ <input type="text" class="form-control flex-grow-1" id="labelsText" value="tiger, horse, dog"
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+ placeholder="Enter labels (comma separated)" style="margin-right: 15px; margin-left: 15px;">
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+ <button id="classifyButton" class="btn btn-primary ml-2" 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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+
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+ <hr> <!-- Line Separator -->
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+
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+ <div id="zero-shot-image-classification-local-container" class="container mt-4">
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+ <h5>Zero Shot Image Classification Local File:</h5>
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+ <div class="d-flex align-items-center mb-2">
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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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+ </div>
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+ <div class="d-flex align-items-center">
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+ <label for="labelsLocalText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Labels (comma
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+ separated):</label>
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+ <input type="text" class="form-control flex-grow-1" id="labelsLocalText" value="tiger, horse, dog"
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+ placeholder="Enter labels (comma separated)" style="margin-right: 15px; margin-left: 15px;">
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+ <button id="classifyLocalButton" class="btn btn-primary ml-2" onclick="classifyLocalImage()">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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+
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+ </div>
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+
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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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+
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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('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32');
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+ }
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+ async function classifyImage() {
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+ const textFieldValue = document.getElementById("zeroShotImageClassificationURLText").value.trim();
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+ const labels = document.getElementById("labelsText").value.trim().split(",").map(item => item.trim());
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+ const result = await classifier(textFieldValue, labels);
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+ document.getElementById("outputArea").innerText = JSON.stringify(result, null, 2);
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+ }
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+ async function classifyLocalImage() {
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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 labels = document.getElementById("labelsLocalText").value.trim().split(",").map(item => item.trim());
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+ const result = await classifier(url, labels);
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+ document.getElementById("outputAreaLocal").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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+
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+ </html>