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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>Zero Shot Image Classification - Hugging Face Transformers.js</title> |
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<script type="module"> |
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import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.4'; |
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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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<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 class="container mt-5"> |
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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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<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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<hr> |
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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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</div> |
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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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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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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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window.addEventListener("DOMContentLoaded", initializeModel); |
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</script> |
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</body> |
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</html> |
