Update index.js

index.js

@@ -1,76 +1,127 @@
-import { pipeline } from 'https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.7.6';
+import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.6.0';
+
+// Since we will download the model from the Hugging Face Hub, we can skip the local model check
+env.allowLocalModels = false;
 
 // Reference the elements that we will need
-const status = document.getElementById('status');
-const fileUpload = document.getElementById('upload');
-const imageContainer = document.getElementById('container');
-const example = document.getElementById('example');
+const statusText = document.getElementById('status-text');
+const fileUpload = document.getElementById('file-upload');
+const uploadLabel = document.querySelector('.upload-label');
+const imagePreview = document.getElementById('image-preview');
+const labelsInput = document.getElementById('labels-input');
+const classifyBtn = document.getElementById('classify-btn');
+const resultsContainer = document.getElementById('results-container');
+const spinner = document.querySelector('.spinner');
+
+
+// Create a new zero-shot image classification pipeline
+statusText.textContent = 'Loading model...';
+spinner.style.display = 'block';
+const classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch16');
+statusText.textContent = 'Ready';
+spinner.style.display = 'none';
+
+let imageSrc = null;
+
+function handleFile(file) {
+    if (!file) {
+        return;
+    }
 
-const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
+    const reader = new FileReader();
 
-// Create a new object detection pipeline
-status.textContent = 'Loading model...';
-const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
-status.textContent = 'Ready';
+    reader.onload = function (e2) {
+        imageSrc = e2.target.result;
+        imagePreview.innerHTML = `<img src="${imageSrc}" alt="uploaded image">`;
+        resultsContainer.innerHTML = `<div class="placeholder">
+            <i class="fas fa-chart-pie"></i>
+            <h2>Results will be displayed here</h2>
+            <p>Get instant classification results</p>
+        </div>`;
+    };
+    reader.readAsDataURL(file);
+}
 
-example.addEventListener('click', (e) => {
-    e.preventDefault();
-    detect(EXAMPLE_URL);
+fileUpload.addEventListener('change', (e) => {
+    handleFile(e.target.files[0]);
 });
 
-fileUpload.addEventListener('change', function (e) {
-    const file = e.target.files[0];
-    if (!file) {
-        return;
-    }
+// Drag and drop functionality
 
-    const reader = new FileReader();
+function preventDefaults(e) {
+    e.preventDefault();
+    e.stopPropagation();
+}
 
-    // Set up a callback when the file is loaded
-    reader.onload = e2 => detect(e2.target.result);
+['dragenter', 'dragover', 'dragleave', 'drop'].forEach(eventName => {
+    uploadLabel.addEventListener(eventName, preventDefaults, false);
+    document.body.addEventListener(eventName, preventDefaults, false);
+});
 
-    reader.readAsDataURL(file);
+['dragenter', 'dragover'].forEach(eventName => {
+    uploadLabel.addEventListener(eventName, () => {
+        uploadLabel.classList.add('highlight');
+    }, false);
 });
 
+['dragleave', 'drop'].forEach(eventName => {
+    uploadLabel.addEventListener(eventName, () => {
+        uploadLabel.classList.remove('highlight');
+    }, false);
+});
 
-// Detect objects in the image
-async function detect(img) {
-    imageContainer.innerHTML = '';
-    imageContainer.style.backgroundImage = `url(${img})`;
+uploadLabel.addEventListener('drop', (e) => {
+    const dt = e.dataTransfer;
+    const files = dt.files;
+    if (files.length > 0) {
+        handleFile(files[0]);
+    }
+});
 
-    status.textContent = 'Analysing...';
-    const output = await detector(img, {
-        threshold: 0.5,
-        percentage: true,
-    });
-    status.textContent = '';
-    output.forEach(renderBox);
-}
+classifyBtn.addEventListener('click', function () {
+    if (!imageSrc) {
+        alert('Please upload an image first.');
+        return;
+    }
+    const labels = labelsInput.value.split(',').map(label => label.trim()).filter(label => label);
+    if (labels.length === 0) {
+        alert('Please enter at least one label.');
+        return;
+    }
+    classify(imageSrc, labels);
+});
 
-// Render a bounding box and label on the image
-function renderBox({ box, label }) {
-    const { xmax, xmin, ymax, ymin } = box;
-
-    // Generate a random color for the box
-    const color = '#' + Math.floor(Math.random() * 0xFFFFFF).toString(16).padStart(6, 0);
-
-    // Draw the box
-    const boxElement = document.createElement('div');
-    boxElement.className = 'bounding-box';
-    Object.assign(boxElement.style, {
-        borderColor: color,
-        left: 100 * xmin + '%',
-        top: 100 * ymin + '%',
-        width: 100 * (xmax - xmin) + '%',
-        height: 100 * (ymax - ymin) + '%',
-    })
-
-    // Draw label
-    const labelElement = document.createElement('span');
-    labelElement.textContent = label;
-    labelElement.className = 'bounding-box-label';
-    labelElement.style.backgroundColor = color;
-
-    boxElement.appendChild(labelElement);
-    imageContainer.appendChild(boxElement);
+// Classify the image
+async function classify(img, labels) {
+    statusText.textContent = 'Analysing...';
+    spinner.style.display = 'block';
+    resultsContainer.innerHTML = '';
+    const output = await classifier(img, labels);
+    statusText.textContent = '';
+    spinner.style.display = 'none';
+    renderResults(output);
 }
+
+// Render the classification results
+function renderResults(output) {
+    resultsContainer.innerHTML = '';
+    if (output.length === 0) {
+        resultsContainer.innerHTML = '<p>No results found.</p>';
+        return;
+    }
+    output.sort((a, b) => b.score - a.score);
+    output.forEach(({ label, score }) => {
+        const resultElement = document.createElement('div');
+        resultElement.className = 'result';
+
+        const labelElement = document.createElement('span');
+        labelElement.textContent = label;
+
+        const scoreElement = document.createElement('span');
+        scoreElement.textContent = `${(score * 100).toFixed(2)}%`;
+
+        resultElement.appendChild(labelElement);
+        resultElement.appendChild(scoreElement);
+        resultsContainer.appendChild(resultElement);
+    });
+}