<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Zero Shot 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"> <!-- Page Header --> <div class="header"> <div class="header-logo"> <img src="images/logo.png" alt="logo"> </div> <div class="header-main-text"> <h1>Hugging Face Transformers.js</h1> </div> <div class="header-sub-text"> <h3>Free AI Models for JavaScript Web Development</h3> </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>Zero Shot Image Classification</h4> </div> <!-- Actual Content of this page --> <div id="zero-shot-image-classification-container" class="container mt-4"> <h5>Zero Shot Image Classification w/ Xenova/clip-vit-base-patch32:</h5> <div class="d-flex align-items-center mb-2"> <label for="zeroShotImageClassificationURLText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter image URL:</label> <input type="text" class="form-control flex-grow-1" id="zeroShotImageClassificationURLText" value="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg" placeholder="Enter image" style="margin-right: 15px; margin-left: 15px;"> </div> <div class="d-flex align-items-center"> <label for="labelsText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Labels (comma separated):</label> <input type="text" class="form-control flex-grow-1" id="labelsText" value="tiger, horse, dog" placeholder="Enter labels (comma separated)" style="margin-right: 15px; margin-left: 15px;"> <button id="classifyButton" class="btn btn-primary ml-2" onclick="classifyImage()">Classify</button> </div> <div class="mt-4"> <h4>Output:</h4> <pre id="outputArea"></pre> </div> </div> <hr> <!-- Line Separator --> <div id="zero-shot-image-classification-local-container" class="container mt-4"> <h5>Zero Shot Image Classification Local File:</h5> <div class="d-flex align-items-center mb-2"> <label for="imageClassificationLocalFile" class="mb-0 text-nowrap" style="margin-right: 15px;">Select Local Image:</label> <input type="file" id="imageClassificationLocalFile" accept="image/*" /> </div> <div class="d-flex align-items-center"> <label for="labelsLocalText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter Labels (comma separated):</label> <input type="text" class="form-control flex-grow-1" id="labelsLocalText" value="tiger, horse, dog" placeholder="Enter labels (comma separated)" style="margin-right: 15px; margin-left: 15px;"> <button id="classifyLocalButton" class="btn btn-primary ml-2" onclick="classifyLocalImage()">Classify</button> </div> <div class="mt-4"> <h4>Output:</h4> <pre id="outputAreaLocal"></pre> </div> </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() { classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32'); } async function classifyImage() { const textFieldValue = document.getElementById("zeroShotImageClassificationURLText").value.trim(); const labels = document.getElementById("labelsText").value.trim().split(",").map(item => item.trim()); const result = await classifier(textFieldValue, labels); document.getElementById("outputArea").innerText = JSON.stringify(result, null, 2); } async function classifyLocalImage() { 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()); const result = await classifier(url, labels); document.getElementById("outputAreaLocal").innerText = JSON.stringify(result, null, 2); } // Initialize the model after the DOM is completely loaded window.addEventListener("DOMContentLoaded", initializeModel); </script> </body> </html>