Update index.js

index.js

@@ -1,35 +1,73 @@
-import { pipeline, env } from "https://cdn.jsdelivr.net/npm/@huggingface/transformers"; // import Transformers.js
+import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.6.0';
 
-env.allowLocalModels = false; // skip the local model check
+// Since we will download the model from the Hugging Face Hub, we can skip the local model check
+env.allowLocalModels = false;
 
-// create references to DOM elements
-const fileUpload = document.getElementById("file-upload");
-const imageContainer = document.getElementById("image-container");
-const status = document.getElementById("status");
+// Reference the elements that we will need
+const status = document.getElementById('status');
+const fileUpload = document.getElementById('file-upload');
+const imageContainer = document.getElementById('image-container');
 
-status.textContent = "Loading model..."; // status message for user
+// Create a new object detection pipeline
+status.textContent = 'Loading model...';
+const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
+status.textContent = 'Ready';
 
-// create object detection pipeline
-const detector = await pipeline("object-detection", "Xenova/detr-resnet-50");
+fileUpload.addEventListener('change', function (e) {
+    const file = e.target.files[0];
+    if (!file) {
+        return;
+    }
 
-status.textContent = "Ready"; // status message for user
+    const reader = new FileReader();
 
-// create image uploader
-fileUpload.addEventListener("change", function (e) {
-  const file = e.target.files[0];
-  if (!file) {
-    return;
-  }
+    // Set up a callback when the file is loaded
+    reader.onload = function (e2) {
+        imageContainer.innerHTML = '';
+        const image = document.createElement('img');
+        image.src = e2.target.result;
+        imageContainer.appendChild(image);
+        detect(image);
+    };
+    reader.readAsDataURL(file);
+});
 
-  const reader = new FileReader();
 
-  // Set up a callback when the file is loaded
-  reader.onload = function (e2) {
-    imageContainer.innerHTML = "";
-    const image = document.createElement("img");
-    image.src = e2.target.result;
-    imageContainer.appendChild(image);
-    // detect(image); // Uncomment this line to run the model
-  };
-  reader.readAsDataURL(file);
-});
+// Detect objects in the image
+async function detect(img) {
+    status.textContent = 'Analysing...';
+    const output = await detector(img.src, {
+        threshold: 0.5,
+        percentage: true,
+    });
+    status.textContent = '';
+    output.forEach(renderBox);
+}
+
+// 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);
+}