Update index.js

index.js

@@ -1,3 +1,84 @@
+// 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);
+// });
+
+// 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);
+// }
+
+
 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
@@ -5,75 +86,42 @@ 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 userInput = document.getElementById('user-input');
+const outputContainer = document.getElementById('output');
+const submitButton = document.getElementById('submit-button');
 
-const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
-
-// Create a new object detection pipeline
+// Load the text-generation pipeline
 status.textContent = 'Loading model...';
-const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
-status.textContent = 'Ready';
+const generator = await pipeline('text-generation', 'meta-llama/Llama-2-7b-hf');
+status.textContent = 'Model loaded. Ready to chat!';
 
-example.addEventListener('click', (e) => {
-    e.preventDefault();
-    detect(EXAMPLE_URL);
-});
+// Add event listener to the submit button
+submitButton.addEventListener('click', async () => {
+    const inputText = userInput.value.trim();
 
-fileUpload.addEventListener('change', function (e) {
-    const file = e.target.files[0];
-    if (!file) {
+    if (!inputText) {
+        outputContainer.innerText = 'Please enter a prompt.';
         return;
     }
 
-    const reader = new FileReader();
-
-    // Set up a callback when the file is loaded
-    reader.onload = e2 => detect(e2.target.result);
+    // Update status to show the user we're processing
+    status.textContent = 'Generating response...';
+
+    try {
+        // Generate text from the user input
+        const response = await generator(inputText, {
+            max_new_tokens: 100,
+            temperature: 0.7, // Controls randomness; lower = more deterministic
+            top_p: 0.95,     // Nucleus sampling
+        });
+
+        // Display the generated response
+        outputContainer.innerText = response[0].generated_text;
+    } catch (error) {
+        console.error(error);
+        outputContainer.innerText = 'Error generating response. Please try again.';
+    }
 
-    reader.readAsDataURL(file);
+    // Reset the status
+    status.textContent = 'Model loaded. Ready to chat!';
 });
-
-
-// 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);
-}