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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">
        <!-- Page Header -->
        <div class="header">
            
            <div class="header-main-text">
                <h1>Hugging Face Transformers.js</h1>
            </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>Mobilevit 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() {
            classifier = await pipeline('image-classification', 'Xenova/mobilevit-small', { quantized: false });
        }
        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 result = await classifier(url);
            document.getElementById("outputAreaLocal").innerText = JSON.stringify(result, null, 2);
        }
        async function classifyTopImage() {
            const textFieldValue = document.getElementById("imageClassificationTopURLText").value.trim();
            const result = await classifier(textFieldValue, { topk: 3 });
            document.getElementById("outputAreaTop").innerText = JSON.stringify(result, null, 2);
        }
        // Initialize the model after the DOM is completely loaded
        window.addEventListener("DOMContentLoaded", initializeModel);
    </script>
</body>

</html>