whackthejacker commited on
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1 Parent(s): 77d2e64

Update index.js

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  1. index.js +47 -76
index.js CHANGED
@@ -1,79 +1,50 @@
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- import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';
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-
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- // Since we will download the model from the Hugging Face Hub, we can skip the local model check
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- env.allowLocalModels = false;
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-
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- // Reference the elements that we will need
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- const status = document.getElementById('status');
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- const fileUpload = document.getElementById('upload');
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- const imageContainer = document.getElementById('container');
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- const example = document.getElementById('example');
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-
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- const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
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-
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- // Create a new object detection pipeline
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- status.textContent = 'Loading model...';
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- const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
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- status.textContent = 'Ready';
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-
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- example.addEventListener('click', (e) => {
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- e.preventDefault();
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- detect(EXAMPLE_URL);
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- });
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- fileUpload.addEventListener('change', function (e) {
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- const file = e.target.files[0];
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- if (!file) {
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- return;
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  }
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-
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- const reader = new FileReader();
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-
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- // Set up a callback when the file is loaded
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- reader.onload = e2 => detect(e2.target.result);
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-
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- reader.readAsDataURL(file);
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  });
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-
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-
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- // Detect objects in the image
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- async function detect(img) {
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- imageContainer.innerHTML = '';
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- imageContainer.style.backgroundImage = `url(${img})`;
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-
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- status.textContent = 'Analysing...';
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- const output = await detector(img, {
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- threshold: 0.5,
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- percentage: true,
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- });
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- status.textContent = '';
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- output.forEach(renderBox);
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- }
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-
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- // Render a bounding box and label on the image
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- function renderBox({ box, label }) {
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- const { xmax, xmin, ymax, ymin } = box;
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-
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- // Generate a random color for the box
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- const color = '#' + Math.floor(Math.random() * 0xFFFFFF).toString(16).padStart(6, 0);
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-
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- // Draw the box
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- const boxElement = document.createElement('div');
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- boxElement.className = 'bounding-box';
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- Object.assign(boxElement.style, {
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- borderColor: color,
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- left: 100 * xmin + '%',
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- top: 100 * ymin + '%',
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- width: 100 * (xmax - xmin) + '%',
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- height: 100 * (ymax - ymin) + '%',
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- })
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-
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- // Draw label
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- const labelElement = document.createElement('span');
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- labelElement.textContent = label;
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- labelElement.className = 'bounding-box-label';
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- labelElement.style.backgroundColor = color;
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-
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- boxElement.appendChild(labelElement);
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- imageContainer.appendChild(boxElement);
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- }
 
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+ $(document).ready(function() {
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+ const messagesDiv = $('#messages');
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+ const userInput = $('#userInput');
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+ const sendBtn = $('#sendBtn');
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+ const chartCanvas = $('#chart');
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+
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+ sendBtn.on('click', function() {
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+ const userMessage = userInput.val();
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+ if (userMessage) {
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+ messagesDiv.append(`<div><strong>You:</strong> ${userMessage}</div>`);
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+ userInput.val('');
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+ generateChart(userMessage);
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+ }
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+ });
 
 
 
 
 
 
 
 
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+ function generateChart(query) {
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+ // Simulate AI response and chart generation
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+ messagesDiv.append(`<div><strong>AI:</strong> Generating chart for "${query}"...</div>`);
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+
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+ // Simulated data for the chart
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+ const labels = ['January', 'February', 'March', 'April', 'May', 'June', 'July'];
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+ const data = [65, 59, 80, 81, 56, 55, 40];
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+
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+ // Create the chart
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+ const ctx = chartCanvas[0].getContext('2d');
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+ const chart = new Chart(ctx, {
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+ type: 'line',
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+ data: {
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+ labels: labels,
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+ datasets: [{
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+ label: 'Stock Trend',
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+ data: data,
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+ borderColor: 'rgba(75, 192, 192, 1)',
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+ borderWidth: 2,
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+ fill: false
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+ }]
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+ },
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+ options: {
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+ responsive: true,
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+ scales: {
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+ y: {
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+ beginAtZero: true
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+ }
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+ }
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+ }
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+ });
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+
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+ chartCanvas.show();
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  }
 
 
 
 
 
 
 
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  });