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Saad4web commited on Apr 29, 2025

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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>