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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 Image Enhancer</title>
	<script src="https://cdn.tailwindcss.com"></script>

	<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.min.js"></script>

	<style>
	/* Optional: Add custom styles or override Tailwind */
	body {
	font-family: sans-serif;
	}
	canvas {
	max-width: 100%;
	height: auto;
	border: 1px solid #ccc;
	}
	.loader {
	border: 5px solid #f3f3f3; /* Light grey */
	border-top: 5px solid #3498db; /* Blue */
	border-radius: 50%;
	width: 40px;
	height: 40px;
	animation: spin 1s linear infinite;
	margin: 20px auto;
	}
	@keyframes spin {
	0% { transform: rotate(0deg); }
	100% { transform: rotate(360deg); }
	}
	</style>
	</head>
	<body class="bg-gray-100 p-8">

	<div class="container mx-auto max-w-4xl bg-white p-6 rounded-lg shadow-lg">

	<h1 class="text-3xl font-bold mb-6 text-center text-blue-600">AI Image Enhancer</h1>
	<p class="text-center text-gray-600 mb-6">Increase resolution, retouch, denoise, and more using TensorFlow.js.</p>

	<div class="mb-6 p-4 border rounded-md bg-gray-50">
	<label for="imageUpload" class="block text-lg font-medium text-gray-700 mb-2">1. Upload Image:</label>
	<input type="file" id="imageUpload" accept="image/*" class="block w-full text-sm text-gray-500
	file:mr-4 file:py-2 file:px-4
	file:rounded-full file:border-0
	file:text-sm file:font-semibold
	file:bg-blue-50 file:text-blue-700
	hover:file:bg-blue-100
	"/>
	<p id="uploadError" class="text-red-500 text-sm mt-2"></p>
	</div>

	<div class="mb-6 p-4 border rounded-md bg-gray-50">
	<label for="enhancementType" class="block text-lg font-medium text-gray-700 mb-2">2. Select Enhancement:</label>
	<select id="enhancementType" class="block w-full p-2 border border-gray-300 rounded-md shadow-sm focus:ring-blue-500 focus:border-blue-500">
	<option value="upscale">Increase Resolution (Upscale 2x)</option>
	<option value="denoise">Denoise (Basic)</option>
	<option value="retouch">Retouch (Simple Filter)</option>
	</select>
	</div>

	<div class="text-center mb-6">
	<button id="enhanceButton" class="bg-blue-500 hover:bg-blue-700 text-white font-bold py-2 px-6 rounded-full text-lg disabled:opacity-50 disabled:cursor-not-allowed" disabled>
	Enhance Image
	</button>
	</div>

	<div id="status" class="text-center text-gray-600 mb-4 h-10"></div>
	<div id="loader" class="loader hidden"></div>

	<div class="grid grid-cols-1 md:grid-cols-2 gap-6">
	<div>
	<h2 class="text-xl font-semibold mb-2 text-center">Original Image</h2>
	<canvas id="originalCanvas"></canvas>
	</div>
	<div>
	<h2 class="text-xl font-semibold mb-2 text-center">Enhanced Image</h2>
	<canvas id="enhancedCanvas"></canvas>
	</div>
	</div>

	<div class="mt-8 text-center text-xs text-gray-500">
	<p>Powered by TensorFlow.js</p>
	<p>MIT License - [Your Name/Org] 2025</p>
	</div>
	</div>

	<script>
	// --- DOM Elements ---
	const imageUpload = document.getElementById('imageUpload');
	const enhanceButton = document.getElementById('enhanceButton');
	const enhancementType = document.getElementById('enhancementType');
	const originalCanvas = document.getElementById('originalCanvas');
	const enhancedCanvas = document.getElementById('enhancedCanvas');
	const statusDiv = document.getElementById('status');
	const loader = document.getElementById('loader');
	const uploadError = document.getElementById('uploadError');

	const originalCtx = originalCanvas.getContext('2d');
	const enhancedCtx = enhancedCanvas.getContext('2d');

	let originalImage = null;
	let model = null; // Placeholder for the loaded TFJS model

	// --- Event Listeners ---
	imageUpload.addEventListener('change', handleImageUpload);
	enhanceButton.addEventListener('click', handleEnhancement);

	// --- Functions ---

	async function handleImageUpload(event) {
	const file = event.target.files[0];
	uploadError.textContent = '';
	enhanceButton.disabled = true;
	originalImage = null;
	originalCtx.clearRect(0, 0, originalCanvas.width, originalCanvas.height); // Clear previous image
	enhancedCtx.clearRect(0, 0, enhancedCanvas.width, enhancedCanvas.height); // Clear previous result


	if (!file \|\| !file.type.startsWith('image/')) {
	uploadError.textContent = 'Please select a valid image file.';
	return;
	}

	statusDiv.textContent = 'Loading image...';
	try {
	originalImage = await loadImageFromFile(file);
	displayImageOnCanvas(originalImage, originalCanvas, originalCtx);
	statusDiv.textContent = 'Image loaded. Ready to enhance.';
	enhanceButton.disabled = false;
	} catch (error) {
	console.error("Error loading image:", error);
	uploadError.textContent = 'Could not load the image.';
	statusDiv.textContent = '';
	}
	}

	function loadImageFromFile(file) {
	return new Promise((resolve, reject) => {
	const reader = new FileReader();
	reader.onload = (e) => {
	const img = new Image();
	img.onload = () => resolve(img);
	img.onerror = reject;
	img.src = e.target.result;
	};
	reader.onerror = reject;
	reader.readAsDataURL(file);
	});
	}

	function displayImageOnCanvas(img, canvas, ctx) {
	// Scale canvas to image size
	canvas.width = img.naturalWidth;
	canvas.height = img.naturalHeight;
	ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
	console.log(`Displayed original image (${canvas.width}x${canvas.height})`);
	}

	async function handleEnhancement() {
	if (!originalImage) {
	statusDiv.textContent = 'Please upload an image first.';
	return;
	}

	const selectedTask = enhancementType.value;
	statusDiv.textContent = `Starting ${selectedTask}...`;
	loader.classList.remove('hidden');
	enhanceButton.disabled = true;
	enhancedCtx.clearRect(0, 0, enhancedCanvas.width, enhancedCanvas.height); // Clear previous result

	try {
	// --- AI Processing ---
	// This is where you'd load and run your specific TFJS model
	await runAIEnhancement(selectedTask, originalCanvas, enhancedCanvas, enhancedCtx);
	statusDiv.textContent = 'Enhancement complete!';
	console.log("Enhancement successful.");

	} catch (error) {
	console.error(`Error during ${selectedTask}:`, error);
	statusDiv.textContent = `Error during enhancement: ${error.message \|\| error}`;
	} finally {
	loader.classList.add('hidden');
	enhanceButton.disabled = false; // Re-enable button even on error
	}
	}

	async function runAIEnhancement(task, sourceCanvas, targetCanvas, targetCtx) {
	console.log(`Running task: ${task}`);

	// Ensure TFJS backend is ready (optional, good practice)
	await tf.ready();
	console.log(`Using TFJS backend: ${tf.getBackend()}`);

	// Get image data from the source canvas as a Tensor
	// Using tf.browser.fromPixels() is efficient
	const inputTensor = tf.browser.fromPixels(sourceCanvas);
	console.log("Input tensor shape:", inputTensor.shape);

	let outputTensor;

	// ====== IMPORTANT: MODEL LOADING AND PREDICTION LOGIC GOES HERE ======
	// You need to replace the following placeholder logic with actual
	// model loading (e.g., tf.loadGraphModel(MODEL_URL)) and prediction.
	// Pre-processing (resizing, normalizing) and post-processing (denormalizing)
	// depend heavily on the specific model you use.

	statusDiv.textContent = 'Loading AI model... (Placeholder)'; // Update status
	// Example: Simulating model load delay
	await new Promise(resolve => setTimeout(resolve, 500));

	statusDiv.textContent = 'Processing image... (Placeholder)'; // Update status

	if (task === 'upscale') {
	// --- Placeholder for Upscaling ---
	// A real upscaling model would output a larger tensor.
	// Here, we'll just draw the original image slightly larger as a visual cue.
	console.log("Simulating upscale...");
	const scale = 1.5; // Simulate 1.5x upscale for demo
	targetCanvas.width = Math.round(sourceCanvas.width * scale);
	targetCanvas.height = Math.round(sourceCanvas.height * scale);
	targetCtx.drawImage(sourceCanvas, 0, 0, targetCanvas.width, targetCanvas.height);
	// No tensor output in this simple simulation

	} else if (task === 'denoise') {
	// --- Placeholder for Denoising ---
	// A real denoising model takes the noisy tensor and outputs a cleaner one.
	// Simulate by applying a slight blur using canvas filter
	console.log("Simulating denoise...");
	targetCanvas.width = sourceCanvas.width;
	targetCanvas.height = sourceCanvas.height;
	targetCtx.filter = 'blur(1px)'; // Basic canvas blur
	targetCtx.drawImage(sourceCanvas, 0, 0);
	targetCtx.filter = 'none'; // Reset filter
	// No tensor output in this simple simulation

	} else if (task === 'retouch') {
	// --- Placeholder for Retouching ---
	// Simulate a simple filter like sepia using canvas filter
	console.log("Simulating retouch (sepia filter)...");
	targetCanvas.width = sourceCanvas.width;
	targetCanvas.height = sourceCanvas.height;
	targetCtx.filter = 'sepia(60%)';
	targetCtx.drawImage(sourceCanvas, 0, 0);
	targetCtx.filter = 'none'; // Reset filter
	// No tensor output in this simple simulation

	} else {
	console.warn("Unknown enhancement task:", task);
	// Draw original if task is unknown
	targetCanvas.width = sourceCanvas.width;
	targetCanvas.height = sourceCanvas.height;
	targetCtx.drawImage(sourceCanvas, 0, 0);
	outputTensor = inputTensor.clone(); // Just copy input
	}


	// --- IF YOU HAD A REAL MODEL, you would do something like: ---
	/*
	if (!model) { // Load model if not already loaded
	statusDiv.textContent = 'Loading AI model...';
	const modelUrl = 'URL_TO_YOUR_TFJS_MODEL/model.json'; // <-- Replace with your model URL
	model = await tf.loadGraphModel(modelUrl);
	console.log("Model loaded successfully");
	}

	statusDiv.textContent = 'Preprocessing image...';
	// 1. Preprocess the inputTensor (resize, normalize based on model needs)
	// Example: Normalize to [0, 1]
	const processedInput = tf.tidy(() => {
	// Assuming model expects float input normalized to [0, 1]
	let tensor = inputTensor.toFloat().div(tf.scalar(255));
	// Add batch dimension if needed: tensor = tensor.expandDims(0);
	// Resize if needed: tensor = tf.image.resizeBilinear(tensor, [targetH, targetW]);
	return tensor;
	});
	inputTensor.dispose(); // Dispose original tensor

	statusDiv.textContent = 'Running AI inference...';
	// 2. Run prediction
	const prediction = await model.predict(processedInput); // Use executeAsync for models with control flow ops
	processedInput.dispose(); // Dispose preprocessed tensor

	statusDiv.textContent = 'Postprocessing result...';
	// 3. Postprocess the prediction (denormalize, resize, remove batch dim)
	// Example: Assuming output is also [0, 1]
	outputTensor = tf.tidy(() => {
	let tensor = prediction;
	// Remove batch dim if added: tensor = tensor.squeeze([0]);
	// Clamp and convert back to integer range [0, 255]
	tensor = tensor.mul(tf.scalar(255)).clipByValue(0, 255).toInt();
	return tensor;
	});
	prediction.dispose(); // Dispose prediction tensor

	// 4. Draw the outputTensor to the target canvas
	await tf.browser.toPixels(outputTensor, targetCanvas);
	console.log("Drew tensor to canvas. Output shape:", outputTensor.shape);
	outputTensor.dispose(); // Dispose final output tensor
	*/

	// --- End of Placeholder/Real Model Section ---

	// Clean up the input tensor if it wasn't used by a real model or simulation
	if (inputTensor && !inputTensor.isDisposed) {
	inputTensor.dispose();
	console.log("Disposed input tensor.");
	}
	}

	// --- Initial Setup ---
	statusDiv.textContent = 'Ready. Please upload an image.';

	</script>

	</body>
	</html>