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| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Neural Vision AI</title> | |
| <link rel="preconnect" href="https://fonts.googleapis.com"> | |
| <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> | |
| <link href="https://fonts.googleapis.com/css2?family=Space+Mono:wght@400;700&family=Inter:wght@300;400;500;600;700&display=swap" rel="stylesheet"> | |
| <style> | |
| * { | |
| margin: 0; | |
| padding: 0; | |
| box-sizing: border-box; | |
| } | |
| :root { | |
| --primary: #0f1419; | |
| --surface: #1a1f2e; | |
| --accent-cyan: #00d9ff; | |
| --accent-purple: #9d4edd; | |
| --accent-pink: #ff006e; | |
| --text-primary: #ffffff; | |
| --text-secondary: #a0a9be; | |
| --glow-intensity: 0.8; | |
| } | |
| body { | |
| background: linear-gradient(135deg, var(--primary) 0%, #0d0f18 50%, var(--surface) 100%); | |
| color: var(--text-primary); | |
| font-family: 'Inter', sans-serif; | |
| min-height: 100vh; | |
| overflow-x: hidden; | |
| position: relative; | |
| } | |
| /* Animated background grid */ | |
| body::before { | |
| content: ''; | |
| position: fixed; | |
| top: 0; | |
| left: 0; | |
| width: 100%; | |
| height: 100%; | |
| background-image: | |
| linear-gradient(0deg, transparent 24%, rgba(0, 217, 255, 0.05) 25%, rgba(0, 217, 255, 0.05) 26%, transparent 27%, transparent 74%, rgba(0, 217, 255, 0.05) 75%, rgba(0, 217, 255, 0.05) 76%, transparent 77%, transparent), | |
| linear-gradient(90deg, transparent 24%, rgba(0, 217, 255, 0.05) 25%, rgba(0, 217, 255, 0.05) 26%, transparent 27%, transparent 74%, rgba(0, 217, 255, 0.05) 75%, rgba(0, 217, 255, 0.05) 76%, transparent 77%, transparent); | |
| background-size: 50px 50px; | |
| pointer-events: none; | |
| z-index: 0; | |
| animation: gridShift 20s linear infinite; | |
| } | |
| @keyframes gridShift { | |
| 0% { transform: translate(0, 0); } | |
| 100% { transform: translate(50px, 50px); } | |
| } | |
| .container { | |
| max-width: 900px; | |
| margin: 0 auto; | |
| padding: 40px 20px; | |
| position: relative; | |
| z-index: 1; | |
| } | |
| /* Header with glow */ | |
| .header { | |
| text-align: center; | |
| margin-bottom: 60px; | |
| animation: fadeInDown 0.8s cubic-bezier(0.34, 1.56, 0.64, 1); | |
| } | |
| @keyframes fadeInDown { | |
| from { | |
| opacity: 0; | |
| transform: translateY(-30px); | |
| } | |
| to { | |
| opacity: 1; | |
| transform: translateY(0); | |
| } | |
| } | |
| .logo { | |
| font-family: 'Space Mono', monospace; | |
| font-size: 14px; | |
| letter-spacing: 2px; | |
| text-transform: uppercase; | |
| color: var(--accent-cyan); | |
| margin-bottom: 8px; | |
| opacity: 0.8; | |
| font-weight: 700; | |
| text-shadow: 0 0 20px rgba(0, 217, 255, 0.3); | |
| } | |
| h1 { | |
| font-family: 'Space Mono', monospace; | |
| font-size: 48px; | |
| font-weight: 700; | |
| margin-bottom: 16px; | |
| background: linear-gradient(135deg, var(--accent-cyan) 0%, var(--accent-purple) 100%); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| background-clip: text; | |
| letter-spacing: 1px; | |
| } | |
| .subtitle { | |
| color: var(--text-secondary); | |
| font-size: 16px; | |
| font-weight: 300; | |
| letter-spacing: 0.5px; | |
| } | |
| /* Main content card */ | |
| .card { | |
| background: linear-gradient(135deg, rgba(26, 31, 46, 0.8) 0%, rgba(30, 35, 52, 0.8) 100%); | |
| border: 1px solid rgba(0, 217, 255, 0.2); | |
| border-radius: 20px; | |
| padding: 50px; | |
| backdrop-filter: blur(10px); | |
| box-shadow: | |
| 0 0 40px rgba(0, 217, 255, 0.1), | |
| inset 0 1px 0 rgba(255, 255, 255, 0.1); | |
| animation: fadeInUp 0.8s cubic-bezier(0.34, 1.56, 0.64, 1) 0.2s both; | |
| } | |
| @keyframes fadeInUp { | |
| from { | |
| opacity: 0; | |
| transform: translateY(30px); | |
| } | |
| to { | |
| opacity: 1; | |
| transform: translateY(0); | |
| } | |
| } | |
| /* Upload area */ | |
| .upload-area { | |
| margin-bottom: 40px; | |
| } | |
| .upload-label { | |
| display: block; | |
| font-size: 14px; | |
| font-weight: 600; | |
| letter-spacing: 0.5px; | |
| text-transform: uppercase; | |
| color: var(--accent-cyan); | |
| margin-bottom: 16px; | |
| opacity: 0.9; | |
| } | |
| .file-input-wrapper { | |
| position: relative; | |
| cursor: pointer; | |
| } | |
| #imageUpload { | |
| position: absolute; | |
| width: 100%; | |
| height: 100%; | |
| opacity: 0; | |
| cursor: pointer; | |
| z-index: 1; | |
| } | |
| .file-input-label { | |
| display: flex; | |
| flex-direction: column; | |
| align-items: center; | |
| justify-content: center; | |
| padding: 40px; | |
| border: 2px dashed rgba(0, 217, 255, 0.4); | |
| border-radius: 16px; | |
| background: linear-gradient(135deg, rgba(0, 217, 255, 0.05) 0%, rgba(157, 78, 221, 0.05) 100%); | |
| transition: all 0.3s cubic-bezier(0.34, 1.56, 0.64, 1); | |
| position: relative; | |
| overflow: hidden; | |
| } | |
| .file-input-label::before { | |
| content: ''; | |
| position: absolute; | |
| top: 0; | |
| left: 0; | |
| width: 100%; | |
| height: 100%; | |
| background: linear-gradient(135deg, rgba(0, 217, 255, 0.1) 0%, rgba(157, 78, 221, 0.1) 100%); | |
| opacity: 0; | |
| transition: opacity 0.3s ease; | |
| z-index: -1; | |
| } | |
| .file-input-wrapper:hover .file-input-label { | |
| border-color: rgba(0, 217, 255, 0.8); | |
| background: linear-gradient(135deg, rgba(0, 217, 255, 0.1) 0%, rgba(157, 78, 221, 0.1) 100%); | |
| box-shadow: 0 0 30px rgba(0, 217, 255, 0.2); | |
| transform: translateY(-2px); | |
| } | |
| .upload-icon { | |
| font-size: 32px; | |
| margin-bottom: 12px; | |
| opacity: 0.8; | |
| animation: float 3s ease-in-out infinite; | |
| } | |
| @keyframes float { | |
| 0%, 100% { transform: translateY(0px); } | |
| 50% { transform: translateY(-8px); } | |
| } | |
| .upload-text { | |
| color: var(--text-primary); | |
| font-size: 16px; | |
| font-weight: 600; | |
| margin-bottom: 4px; | |
| } | |
| .upload-subtext { | |
| color: var(--text-secondary); | |
| font-size: 13px; | |
| font-weight: 300; | |
| } | |
| /* Preview section */ | |
| .preview-section { | |
| margin-bottom: 40px; | |
| text-align: center; | |
| } | |
| #preview { | |
| max-width: 100%; | |
| max-height: 350px; | |
| border-radius: 12px; | |
| border: 1px solid rgba(0, 217, 255, 0.3); | |
| object-fit: contain; | |
| box-shadow: | |
| 0 0 40px rgba(0, 217, 255, 0.2), | |
| 0 0 80px rgba(157, 78, 221, 0.1), | |
| inset 0 0 30px rgba(0, 217, 255, 0.05); | |
| animation: imageGlow 2s ease-in-out infinite; | |
| display: none; | |
| } | |
| #preview.loaded { | |
| display: block; | |
| animation: slideInImage 0.5s cubic-bezier(0.34, 1.56, 0.64, 1); | |
| } | |
| @keyframes slideInImage { | |
| from { | |
| opacity: 0; | |
| transform: scale(0.95); | |
| } | |
| to { | |
| opacity: 1; | |
| transform: scale(1); | |
| } | |
| } | |
| @keyframes imageGlow { | |
| 0%, 100% { box-shadow: 0 0 40px rgba(0, 217, 255, 0.2), 0 0 80px rgba(157, 78, 221, 0.1), inset 0 0 30px rgba(0, 217, 255, 0.05); } | |
| 50% { box-shadow: 0 0 50px rgba(0, 217, 255, 0.3), 0 0 100px rgba(157, 78, 221, 0.15), inset 0 0 30px rgba(0, 217, 255, 0.08); } | |
| } | |
| /* Results section */ | |
| #label-container { | |
| min-height: 80px; | |
| display: flex; | |
| flex-direction: column; | |
| gap: 12px; | |
| justify-content: flex-start; | |
| } | |
| .prediction { | |
| padding: 16px 20px; | |
| border-radius: 12px; | |
| background: linear-gradient(135deg, rgba(0, 217, 255, 0.05) 0%, rgba(157, 78, 221, 0.05) 100%); | |
| border: 1px solid rgba(0, 217, 255, 0.2); | |
| display: flex; | |
| justify-content: space-between; | |
| align-items: center; | |
| font-family: 'Space Mono', monospace; | |
| font-size: 14px; | |
| font-weight: 500; | |
| transition: all 0.3s cubic-bezier(0.34, 1.56, 0.64, 1); | |
| animation: slideIn 0.4s cubic-bezier(0.34, 1.56, 0.64, 1); | |
| cursor: pointer; | |
| position: relative; | |
| overflow: hidden; | |
| } | |
| .prediction::before { | |
| content: ''; | |
| position: absolute; | |
| top: 0; | |
| left: 0; | |
| height: 100%; | |
| width: 0; | |
| background: linear-gradient(90deg, rgba(0, 217, 255, 0.2) 0%, transparent 100%); | |
| transition: width 0.4s ease; | |
| z-index: 0; | |
| } | |
| .prediction:hover { | |
| transform: translateX(8px); | |
| border-color: rgba(0, 217, 255, 0.6); | |
| box-shadow: 0 0 25px rgba(0, 217, 255, 0.2), inset 0 1px 0 rgba(255, 255, 255, 0.1); | |
| } | |
| .prediction:hover::before { | |
| width: 100%; | |
| } | |
| .prediction-content { | |
| position: relative; | |
| z-index: 1; | |
| flex: 1; | |
| text-align: left; | |
| } | |
| .prediction-class { | |
| color: var(--text-primary); | |
| font-weight: 700; | |
| margin-bottom: 2px; | |
| } | |
| .prediction-bar { | |
| height: 6px; | |
| background: rgba(0, 217, 255, 0.1); | |
| border-radius: 3px; | |
| overflow: hidden; | |
| margin-top: 6px; | |
| } | |
| .prediction-fill { | |
| height: 100%; | |
| background: linear-gradient(90deg, var(--accent-cyan) 0%, var(--accent-purple) 100%); | |
| border-radius: 3px; | |
| animation: fillBar 0.6s cubic-bezier(0.34, 1.56, 0.64, 1) forwards; | |
| box-shadow: 0 0 15px rgba(0, 217, 255, 0.5); | |
| } | |
| @keyframes fillBar { | |
| from { | |
| width: 0%; | |
| box-shadow: 0 0 15px rgba(0, 217, 255, 0.5); | |
| } | |
| to { | |
| width: var(--percentage); | |
| box-shadow: 0 0 10px rgba(0, 217, 255, 0.3); | |
| } | |
| } | |
| .prediction-percent { | |
| color: var(--accent-cyan); | |
| font-weight: 700; | |
| margin-left: 12px; | |
| position: relative; | |
| z-index: 1; | |
| white-space: nowrap; | |
| font-size: 13px; | |
| } | |
| @keyframes slideIn { | |
| from { | |
| opacity: 0; | |
| transform: translateX(-20px); | |
| } | |
| to { | |
| opacity: 1; | |
| transform: translateX(0); | |
| } | |
| } | |
| .loading-indicator { | |
| display: flex; | |
| align-items: center; | |
| justify-content: center; | |
| gap: 8px; | |
| color: var(--accent-cyan); | |
| font-family: 'Space Mono', monospace; | |
| font-size: 14px; | |
| font-weight: 600; | |
| animation: fadeInUp 0.4s ease; | |
| } | |
| .loading-dot { | |
| width: 8px; | |
| height: 8px; | |
| border-radius: 50%; | |
| background: var(--accent-cyan); | |
| animation: pulse 1.4s ease-in-out infinite; | |
| } | |
| .loading-dot:nth-child(2) { | |
| animation-delay: 0.2s; | |
| } | |
| .loading-dot:nth-child(3) { | |
| animation-delay: 0.4s; | |
| } | |
| @keyframes pulse { | |
| 0%, 100% { | |
| opacity: 0.3; | |
| transform: scale(0.8); | |
| } | |
| 50% { | |
| opacity: 1; | |
| transform: scale(1.2); | |
| } | |
| } | |
| .status-badge { | |
| display: inline-block; | |
| padding: 6px 12px; | |
| border-radius: 20px; | |
| font-size: 12px; | |
| font-weight: 600; | |
| letter-spacing: 0.5px; | |
| margin-top: 12px; | |
| background: linear-gradient(135deg, rgba(0, 217, 255, 0.1) 0%, rgba(157, 78, 221, 0.1) 100%); | |
| border: 1px solid rgba(0, 217, 255, 0.3); | |
| color: var(--accent-cyan); | |
| text-transform: uppercase; | |
| animation: slideInUp 0.4s ease 0.6s both; | |
| } | |
| @keyframes slideInUp { | |
| from { | |
| opacity: 0; | |
| transform: translateY(10px); | |
| } | |
| to { | |
| opacity: 1; | |
| transform: translateY(0); | |
| } | |
| } | |
| /* Responsive design */ | |
| @media (max-width: 768px) { | |
| .container { | |
| padding: 20px 16px; | |
| } | |
| .card { | |
| padding: 30px 20px; | |
| } | |
| h1 { | |
| font-size: 32px; | |
| } | |
| .prediction { | |
| flex-direction: column; | |
| align-items: flex-start; | |
| gap: 8px; | |
| } | |
| .prediction-percent { | |
| margin-left: 0; | |
| } | |
| } | |
| /* Scroll behavior */ | |
| html { | |
| scroll-behavior: smooth; | |
| } | |
| /* Selection styling */ | |
| ::selection { | |
| background: rgba(0, 217, 255, 0.3); | |
| color: var(--text-primary); | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <div class="header"> | |
| <div class="logo">⚡ Neural Vision</div> | |
| <h1>AI Image Classifier</h1> | |
| <p class="subtitle">Powered by TensorFlow & Teachable Machine</p> | |
| </div> | |
| <div class="card"> | |
| <div class="upload-area"> | |
| <label class="upload-label">Upload Image</label> | |
| <div class="file-input-wrapper"> | |
| <input type="file" id="imageUpload" accept="image/*"> | |
| <label class="file-input-label"> | |
| <div class="upload-icon">🚀</div> | |
| <div class="upload-text">Drop your image here</div> | |
| <div class="upload-subtext">or click to browse</div> | |
| </label> | |
| </div> | |
| </div> | |
| <div class="preview-section"> | |
| <img id="preview" alt="Preview" /> | |
| </div> | |
| <div id="label-container"></div> | |
| </div> | |
| </div> | |
| <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> | |
| // Model configuration | |
| const MODEL_URL = "https://huggingface.co/HedronCreeper/img-class/resolve/main/"; | |
| let model, maxPredictions; | |
| let isProcessing = false; | |
| // Load model from HF | |
| async function loadModel() { | |
| try { | |
| const modelURL = MODEL_URL + "model.json"; | |
| const metadataURL = MODEL_URL + "metadata.json"; | |
| model = await tmImage.load(modelURL, metadataURL); | |
| maxPredictions = model.getTotalClasses(); | |
| console.log("✅ Model loaded successfully"); | |
| return true; | |
| } catch (error) { | |
| console.error("❌ Model load error:", error); | |
| showError("Failed to load model. Please refresh and try again."); | |
| return false; | |
| } | |
| } | |
| // Show error state | |
| function showError(message) { | |
| const labelContainer = document.getElementById("label-container"); | |
| labelContainer.innerHTML = `<div style="color: #ff006e; padding: 16px; text-align: center; border-radius: 8px; background: rgba(255, 0, 110, 0.1); border: 1px solid rgba(255, 0, 110, 0.3);">${message}</div>`; | |
| } | |
| // Predict function | |
| async function predict(image) { | |
| if (!image.complete || image.naturalHeight === 0) { | |
| return; | |
| } | |
| try { | |
| const prediction = await model.predict(image); | |
| const labelContainer = document.getElementById("label-container"); | |
| labelContainer.innerHTML = ""; | |
| // Sort by highest probability | |
| prediction.sort((a, b) => b.probability - a.probability); | |
| // Create prediction elements | |
| prediction.forEach((pred, index) => { | |
| const percentage = (pred.probability * 100).toFixed(1); | |
| const div = document.createElement("div"); | |
| div.className = "prediction"; | |
| div.innerHTML = ` | |
| <div class="prediction-content"> | |
| <div class="prediction-class">${pred.className}</div> | |
| <div class="prediction-bar"> | |
| <div class="prediction-fill" style="--percentage: ${percentage}%; animation-delay: ${index * 0.1}s;"></div> | |
| </div> | |
| </div> | |
| <div class="prediction-percent">${percentage}%</div> | |
| `; | |
| labelContainer.appendChild(div); | |
| }); | |
| // Add status badge | |
| const badge = document.createElement("div"); | |
| badge.className = "status-badge"; | |
| badge.textContent = "✓ Classification Complete"; | |
| labelContainer.appendChild(badge); | |
| } catch (error) { | |
| console.error("Prediction error:", error); | |
| showError("Error during classification. Please try another image."); | |
| } finally { | |
| isProcessing = false; | |
| } | |
| } | |
| // Handle file upload | |
| document.getElementById("imageUpload").addEventListener("change", async function(event) { | |
| const file = event.target.files[0]; | |
| if (!file) return; | |
| if (isProcessing) return; | |
| isProcessing = true; | |
| const img = document.getElementById("preview"); | |
| const labelContainer = document.getElementById("label-container"); | |
| // Show loading state | |
| labelContainer.innerHTML = ` | |
| <div class="loading-indicator"> | |
| <span>Processing</span> | |
| <div class="loading-dot"></div> | |
| <div class="loading-dot"></div> | |
| <div class="loading-dot"></div> | |
| </div> | |
| `; | |
| // Reset and load image | |
| img.classList.remove("loaded"); | |
| img.src = ""; | |
| const objectURL = window.URL.createObjectURL(file); | |
| img.src = objectURL; | |
| img.onload = async () => { | |
| img.classList.add("loaded"); | |
| // Load model if not already loaded | |
| if (!model) { | |
| const loaded = await loadModel(); | |
| if (!loaded) { | |
| isProcessing = false; | |
| window.URL.revokeObjectURL(objectURL); | |
| return; | |
| } | |
| } | |
| // Perform prediction | |
| await predict(img); | |
| window.URL.revokeObjectURL(objectURL); | |
| }; | |
| img.onerror = () => { | |
| isProcessing = false; | |
| showError("Failed to load image. Please try another file."); | |
| window.URL.revokeObjectURL(objectURL); | |
| }; | |
| }); | |
| // Initialize model on page load | |
| window.addEventListener("load", () => { | |
| loadModel(); | |
| }); | |
| </script> | |
| </body> | |
| </html> |