lip.html

<!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>