Update index.js

index.js

@@ -1,79 +1,48 @@
-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) {
+const codeInput = document.getElementById('codeInput');
+const resultsContainer = document.getElementById('results');
+const analyzeButton = document.querySelector('button');
+
+// Set initial status
+status.textContent = 'Ready to analyze code';
+
+// Event listener for the analyze button
+analyzeButton.addEventListener('click', async () => {
+    const code = codeInput.value.trim();
+    if (!code) {
+        status.textContent = 'Please enter some Python code.';
         return;
     }
 
-    const reader = new FileReader();
-
-    // Set up a callback when the file is loaded
-    reader.onload = e2 => detect(e2.target.result);
-
-    reader.readAsDataURL(file);
+    status.textContent = 'Analyzing...';
+
+    try {
+        const response = await fetch('/analyze', {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({ code })
+        });
+
+        if (!response.ok) {
+            throw new Error('Failed to analyze code');
+        }
+
+        const result = await response.json();
+        displayResults(result);
+        status.textContent = 'Analysis complete';
+    } catch (error) {
+        status.textContent = 'Error analyzing code';
+        console.error(error);
+    }
 });
 
+// Function to display the results
+function displayResults(results) {
+    resultsContainer.innerHTML = '';
 
-// 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;
+    const preElement = document.createElement('pre');
+    preElement.textContent = JSON.stringify(results, null, 2);
 
-    boxElement.appendChild(labelElement);
-    imageContainer.appendChild(boxElement);
+    resultsContainer.appendChild(preElement);
 }