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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>AI Vision Classifier</title>
<script src="https://cdn.tailwindcss.com"></script>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
<style>
.prediction-bar {
height: 24px;
background: linear-gradient(90deg, #5b21b6 0%, #7e22ce 100%);
border-radius: 12px;
transition: width 0.3s ease;
box-shadow: 0 0 10px rgba(124, 58, 237, 0.5);
}
.prediction-item {
background-color: rgba(76, 29, 149, 0.2);
padding: 12px;
border-radius: 8px;
border: 1px solid rgba(124, 58, 237, 0.3);
}
.webcam-feed {
border-radius: 16px;
box-shadow: 0 10px 25px -5px rgba(0, 0, 0, 0.1);
transition: all 0.3s ease;
}
.webcam-feed:hover {
transform: scale(1.02);
box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04);
}
.webcam-feed img {
max-width: 100%;
max-height: 100%;
}
</style>
</head>
<body class="bg-gradient-to-br from-gray-900 to-purple-900 min-h-screen text-gray-100">
<div class="container mx-auto px-4 py-12">
<div class="max-w-4xl mx-auto">
<!-- Header -->
<div class="text-center mb-12">
<h1 class="text-4xl font-bold text-purple-300 mb-2">
<i class="fas fa-robot text-purple-400 mr-2"></i> AI Vision Classifier
</h1>
<p class="text-gray-300 max-w-lg mx-auto">
A real-time image classification system powered by Teachable Machine.
Point your camera at objects to see the AI's predictions.
</p>
</div>
<!-- Main Content -->
<div class="bg-gray-800 rounded-xl shadow-lg overflow-hidden border border-purple-800">
<div class="p-6 md:p-8">
<!-- Webcam Container -->
<div class="flex flex-col md:flex-row gap-8">
<div class="flex-1">
<div class="mb-4 flex justify-between items-center">
<h2 class="text-xl font-semibold text-purple-300">
<i class="fas fa-camera text-purple-400 mr-2"></i> Live Feed
</h2>
<div class="flex gap-2">
<button id="startBtn" onclick="init()" class="bg-purple-700 hover:bg-purple-600 text-white px-4 py-2 rounded-lg transition flex items-center">
<i class="fas fa-play mr-2"></i> Start Camera
</button>
<label for="fileUpload" class="bg-purple-800 hover:bg-purple-700 text-white px-4 py-2 rounded-lg transition flex items-center cursor-pointer">
<i class="fas fa-upload mr-2"></i> Upload Image
<input id="fileUpload" type="file" accept="image/*" class="hidden" onchange="handleImageUpload(this)">
</label>
</div>
</div>
<div id="webcam-container" class="webcam-feed bg-gray-700 w-full aspect-square flex items-center justify-center rounded-lg overflow-hidden border border-purple-800">
<div class="text-center p-4">
<i class="fas fa-camera text-purple-400 text-4xl mb-2"></i>
<p class="text-gray-300">Camera feed will appear here</p>
</div>
</div>
</div>
<!-- Predictions Container -->
<div class="flex-1">
<h2 class="text-xl font-semibold text-purple-300 mb-4">
<i class="fas fa-chart-bar text-purple-400 mr-2"></i> Predictions
</h2>
<div id="label-container" class="space-y-4">
<div class="bg-gray-100 p-6 rounded-lg text-center">
<i class="fas fa-lightbulb text-yellow-400 text-3xl mb-3"></i>
<p class="text-gray-600">Click "Start Camera" to begin classification</p>
<p class="text-sm text-gray-500 mt-2">The AI will analyze objects in view and display confidence levels here</p>
</div>
</div>
</div>
</div>
<!-- Instructions -->
<div class="mt-8 bg-purple-900/30 p-4 rounded-lg border border-purple-800">
<h3 class="font-medium text-purple-300 mb-2 flex items-center">
<i class="fas fa-info-circle text-purple-400 mr-2"></i> How to use
</h3>
<ol class="list-decimal list-inside text-purple-200 space-y-1 text-sm">
<li>Click "Start Camera" and allow access to your webcam</li>
<li>Point your camera at objects you've trained the model to recognize</li>
<li>View real-time predictions with confidence percentages</li>
<li>For best results, ensure good lighting and clear focus</li>
</ol>
</div>
</div>
</div>
<!-- Footer -->
<div class="mt-8 text-center text-gray-400 text-sm">
<p>Powered by Teachable Machine and TensorFlow.js</p>
</div>
</div>
</div>
<!-- Scripts -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/image@latest/dist/teachablemachine-image.min.js"></script>
<script type="text/javascript">
// the link to your model provided by Teachable Machine export panel
const URL = "https://teachablemachine.withgoogle.com/models/5bQ38_H5n/";
let model, webcam, labelContainer, maxPredictions;
let isRunning = false;
// Load the image model and setup the webcam
async function init() {
if (isRunning) return;
const startBtn = document.getElementById('startBtn');
startBtn.disabled = true;
startBtn.innerHTML = '<i class="fas fa-spinner fa-spin mr-2"></i> Initializing...';
try {
const modelURL = URL + "model.json";
const metadataURL = URL + "metadata.json";
// load the model and metadata
model = await tmImage.load(modelURL, metadataURL);
maxPredictions = model.getTotalClasses();
// Setup webcam
const flip = true; // whether to flip the webcam
webcam = new tmImage.Webcam(400, 400, flip); // increased resolution
await webcam.setup(); // request access to the webcam
await webcam.play();
// Update UI
const webcamContainer = document.getElementById('webcam-container');
webcamContainer.innerHTML = '';
webcamContainer.appendChild(webcam.canvas);
webcam.canvas.classList.add('webcam-feed', 'w-full', 'h-full');
// Setup predictions container
labelContainer = document.getElementById('label-container');
labelContainer.innerHTML = '';
for (let i = 0; i < maxPredictions; i++) {
const predictionElement = document.createElement('div');
predictionElement.className = 'prediction-item';
labelContainer.appendChild(predictionElement);
}
startBtn.innerHTML = '<i class="fas fa-check-circle mr-2"></i> Running';
startBtn.classList.remove('bg-purple-700', 'hover:bg-purple-600');
startBtn.classList.add('bg-purple-600', 'hover:bg-purple-500');
isRunning = true;
window.requestAnimationFrame(loop);
} catch (error) {
console.error('Error initializing:', error);
labelContainer.innerHTML = `
<div class="bg-red-50 p-4 rounded-lg text-red-700">
<i class="fas fa-exclamation-triangle mr-2"></i>
Error: ${error.message}
</div>
`;
startBtn.disabled = false;
startBtn.innerHTML = '<i class="fas fa-play mr-2"></i> Try Again';
}
}
async function loop() {
if (!isRunning) return;
webcam.update(); // update the webcam frame
await predict();
window.requestAnimationFrame(loop);
}
// run the webcam image through the image model
async function handleImageUpload(input) {
if (input.files && input.files[0]) {
const reader = new FileReader();
reader.onload = async function(e) {
const webcamContainer = document.getElementById('webcam-container');
webcamContainer.innerHTML = '';
const img = document.createElement('img');
img.src = e.target.result;
img.className = 'webcam-feed w-full h-full object-contain';
webcamContainer.appendChild(img);
// Stop webcam if running
if (isRunning) {
webcam.stop();
isRunning = false;
const startBtn = document.getElementById('startBtn');
startBtn.innerHTML = '<i class="fas fa-play mr-2"></i> Start Camera';
startBtn.classList.remove('bg-green-500', 'hover:bg-green-600');
startBtn.classList.add('bg-blue-500', 'hover:bg-blue-600');
startBtn.disabled = false;
}
// Load model if not already loaded
if (!model) {
try {
const modelURL = URL + "model.json";
const metadataURL = URL + "metadata.json";
model = await tmImage.load(modelURL, metadataURL);
maxPredictions = model.getTotalClasses();
} catch (error) {
console.error('Error loading model:', error);
return;
}
}
// Predict on the uploaded image
await predictOnImage(img);
}
reader.readAsDataURL(input.files[0]);
}
}
async function predictOnImage(imageElement) {
// Clear previous predictions
labelContainer = document.getElementById('label-container');
labelContainer.innerHTML = '';
// Create prediction elements
for (let i = 0; i < maxPredictions; i++) {
const predictionElement = document.createElement('div');
predictionElement.className = 'prediction-item';
labelContainer.appendChild(predictionElement);
}
// Predict
const prediction = await model.predict(imageElement);
for (let i = 0; i < maxPredictions; i++) {
const probability = prediction[i].probability.toFixed(2);
const percentage = Math.round(probability * 100);
const predictionElement = labelContainer.childNodes[i];
predictionElement.className = 'prediction-item mb-4';
predictionElement.innerHTML = `
<div class="flex justify-between items-center mb-1">
<span class="font-medium text-purple-300">${prediction[i].className}</span>
<span class="text-sm font-semibold text-purple-200">
${percentage}%
</span>
</div>
<div class="prediction-bar" style="width: ${percentage}%"></div>
`;
}
}
async function predict() {
if (!isRunning) return;
// predict can take in an image, video or canvas html element
const prediction = await model.predict(webcam.canvas);
for (let i = 0; i < maxPredictions; i++) {
const probability = prediction[i].probability.toFixed(2);
const percentage = Math.round(probability * 100);
const predictionElement = labelContainer.childNodes[i];
predictionElement.className = 'prediction-item mb-4';
predictionElement.innerHTML = `
<div class="flex justify-between items-center mb-1">
<span class="font-medium text-purple-300">${prediction[i].className}</span>
<span class="text-sm font-semibold text-purple-200">
${percentage}%
</span>
</div>
<div class="prediction-bar" style="width: ${percentage}%"></div>
`;
}
}
</script>
<p style="border-radius: 8px; text-align: center; font-size: 12px; color: #fff; margin-top: 16px;position: fixed; left: 8px; bottom: 8px; z-index: 10; background: rgba(0, 0, 0, 0.8); padding: 4px 8px;">Made with <img src="https://enzostvs-deepsite.hf.space/logo.svg" alt="DeepSite Logo" style="width: 16px; height: 16px; vertical-align: middle;display:inline-block;margin-right:3px;filter:brightness(0) invert(1);"><a href="https://enzostvs-deepsite.hf.space" style="color: #fff;text-decoration: underline;" target="_blank" >DeepSite</a> - 🧬 <a href="https://enzostvs-deepsite.hf.space?remix=deniztas/model" style="color: #fff;text-decoration: underline;" target="_blank" >Remix</a></p></body>
</html> |