Update index.js

index.js

@@ -1,79 +1,32 @@
-import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.10.1';
-
-// 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 EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
-
-// Create a new object detection pipeline
-status.textContent = 'Loading model...';
-const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
-status.textContent = 'Ready';
-
-example.addEventListener('click', (e) => {
-    e.preventDefault();
-    detect(EXAMPLE_URL);
+import { pipeline } from "@huggingface/transformers";
+
+// Create a text-generation pipeline
+const generator = await pipeline(
+  "text-generation",
+  "HuggingFaceTB/SmolLM2-360M", // You can replace this with other models like "EleutherAI/gpt-neo-125M" or "facebook/opt-125m"
+  { device: "webgpu" }
+);
+
+// Generate text
+const prompts = [
+  "Once upon a time",
+  "The artificial intelligence"
+];
+
+const results = await generator(prompts, {
+  max_length: 50,
+  num_return_sequences: 1,
+  temperature: 0.7,
+  top_p: 0.9
 });
 
-fileUpload.addEventListener('change', function (e) {
-    const file = e.target.files[0];
-    if (!file) {
-        return;
-    }
-
-    const reader = new FileReader();
-
-    // Set up a callback when the file is loaded
-    reader.onload = e2 => detect(e2.target.result);
-
-    reader.readAsDataURL(file);
-});
-
-
-// Detect objects in the image
-async function detect(img) {
-    imageContainer.innerHTML = '';
-    imageContainer.style.backgroundImage = `url(${img})`;
-
-    status.textContent = 'Analysing...';
-    const output = await detector(img, {
-        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);
-}
+console.log(results);
+// Will output something like:
+// [
+//   [{
+//     "generated_text": "Once upon a time there was a young princess who lived in a castle..."
+//   }],
+//   [{
+//     "generated_text": "The artificial intelligence revolution has transformed the way we..."
+//   }]
+// ]