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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>PrecisionVision - 99.99% Accurate Object Detection</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>
        .gradient-bg {
            background: linear-gradient(135deg, #6e8efb 0%, #a777e3 100%);
        }
        .prediction-box {
            position: absolute;
            border: 2px solid #4ade80;
            background-color: rgba(74, 222, 128, 0.2);
        }
        .confidence-meter {
            height: 6px;
            background: linear-gradient(90deg, #ef4444 0%, #f59e0b 50%, #10b981 100%);
        }
        .upload-area {
            border: 2px dashed #a5b4fc;
            transition: all 0.3s ease;
        }
        .upload-area:hover {
            border-color: #818cf8;
            background-color: rgba(129, 140, 248, 0.05);
        }
        .model-card {
            transition: transform 0.3s ease, box-shadow 0.3s ease;
        }
        .model-card:hover {
            transform: translateY(-5px);
            box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04);
        }
        @keyframes pulse {
            0%, 100% {
                opacity: 1;
            }
            50% {
                opacity: 0.5;
            }
        }
        .animate-pulse {
            animation: pulse 2s cubic-bezier(0.4, 0, 0.6, 1) infinite;
        }
    </style>
</head>
<body class="bg-gray-50 font-sans">
    <!-- Navigation -->
    <nav class="gradient-bg text-white shadow-lg">
        <div class="container mx-auto px-4 py-3 flex justify-between items-center">
            <div class="flex items-center space-x-2">
                <i class="fas fa-eye text-2xl"></i>
                <span class="text-xl font-bold">PrecisionVision</span>
            </div>
            <div class="hidden md:flex space-x-6">
                <a href="#home" class="hover:text-gray-200 transition">Home</a>
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                <a href="#api" class="hover:text-gray-200 transition">API</a>
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                <a href="#contact" class="hover:text-gray-200 transition">Contact</a>
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            <a href="#home" class="block py-2 hover:text-gray-200">Home</a>
            <a href="#model" class="block py-2 hover:text-gray-200">Model</a>
            <a href="#api" class="block py-2 hover:text-gray-200">API</a>
            <a href="https://huggingface.co/docs" target="_blank" class="block py-2 hover:text-gray-200">Docs</a>
            <a href="#contact" class="block py-2 hover:text-gray-200">Contact</a>
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    </nav>

    <!-- Hero Section -->
    <section id="home" class="gradient-bg text-white py-16">
        <div class="container mx-auto px-4 flex flex-col md:flex-row items-center">
            <div class="md:w-1/2 mb-10 md:mb-0">
                <h1 class="text-4xl md:text-5xl font-bold mb-4">99.99% Accurate Object Detection</h1>
                <p class="text-xl mb-6">Our cutting-edge computer vision model delivers near-perfect object detection for your applications.</p>
                <div class="flex flex-col sm:flex-row space-y-3 sm:space-y-0 sm:space-x-4">
                    <a href="#demo" class="bg-white text-indigo-600 px-6 py-3 rounded-lg font-semibold hover:bg-gray-100 transition text-center">
                        Try Demo <i class="fas fa-arrow-right ml-2"></i>
                    </a>
                    <a href="https://huggingface.co/models" target="_blank" class="border border-white text-white px-6 py-3 rounded-lg font-semibold hover:bg-white hover:text-indigo-600 transition text-center">
                        View on Hugging Face
                    </a>
                </div>
            </div>
            <div class="md:w-1/2 flex justify-center">
                <div class="relative w-full max-w-md">
                    <div class="bg-white rounded-xl shadow-2xl overflow-hidden">
                        <img src="https://images.unsplash.com/photo-1507146426996-ef05306b995a?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1170&q=80" 
                             alt="Object detection example" class="w-full h-auto">
                        <div class="prediction-box" style="top: 30%; left: 40%; width: 25%; height: 20%;">
                            <div class="absolute -top-6 left-0 bg-green-500 text-white text-xs px-2 py-1 rounded">Dog 99.99%</div>
                        </div>
                        <div class="prediction-box" style="top: 60%; left: 20%; width: 15%; height: 15%;">
                            <div class="absolute -top-6 left-0 bg-green-500 text-white text-xs px-2 py-1 rounded">Human 99.98%</div>
                        </div>
                        <div class="prediction-box" style="top: 50%; left: 70%; width: 20%; height: 25%;">
                            <div class="absolute -top-6 left-0 bg-green-500 text-white text-xs px-2 py-1 rounded">Grass 99.97%</div>
                        </div>
                        <div class="prediction-box" style="top: 10%; left: 10%; width: 15%; height: 15%;">
                            <div class="absolute -top-6 left-0 bg-green-500 text-white text-xs px-2 py-1 rounded">Sky 99.96%</div>
                        </div>
                        <div class="prediction-box" style="top: 75%; left: 60%; width: 10%; height: 10%;">
                            <div class="absolute -top-6 left-0 bg-green-500 text-white text-xs px-2 py-1 rounded">Leash 99.95%</div>
                        </div>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <!-- Demo Section -->
    <section id="demo" class="py-16 bg-white">
        <div class="container mx-auto px-4">
            <h2 class="text-3xl font-bold text-center mb-12 text-gray-800">Try Our Model</h2>
            
            <div class="flex flex-col lg:flex-row gap-8">
                <!-- Upload Area -->
                <div class="lg:w-1/2">
                    <div class="upload-area rounded-xl p-8 text-center cursor-pointer mb-6">
                        <input type="file" id="image-upload" class="hidden" accept="image/*">
                        <div class="flex flex-col items-center justify-center py-12">
                            <i class="fas fa-cloud-upload-alt text-4xl text-indigo-500 mb-4"></i>
                            <h3 class="text-xl font-semibold text-gray-700 mb-2">Upload an Image</h3>
                            <p class="text-gray-500 mb-4">or drag and drop</p>
                            <p class="text-sm text-gray-400">PNG, JPG, JPEG up to 10MB</p>
                        </div>
                    </div>
                    <div class="flex justify-center">
                        <button id="sample-image-btn" class="bg-indigo-600 text-white px-6 py-3 rounded-lg font-semibold hover:bg-indigo-700 transition">
                            Use Sample Image
                        </button>
                    </div>
                </div>
                
                <!-- Results Area -->
                <div class="lg:w-1/2">
                    <div class="bg-gray-100 rounded-xl p-4 min-h-96 flex items-center justify-center">
                        <div id="results-container" class="relative w-full">
                            <div id="placeholder-text" class="text-center text-gray-500">
                                <i class="fas fa-image text-4xl mb-4"></i>
                                <p>Your detected objects will appear here</p>
                            </div>
                            <canvas id="result-canvas" class="hidden w-full h-auto rounded-lg"></canvas>
                        </div>
                    </div>
                    
                    <div id="confidence-display" class="mt-6 hidden">
                        <div class="flex justify-between mb-1">
                            <span class="text-sm font-medium text-gray-700">Model Confidence</span>
                            <span id="confidence-value" class="text-sm font-medium text-green-600">99.99%</span>
                        </div>
                        <div class="confidence-meter w-full rounded-full"></div>
                        <div class="mt-2 text-sm text-gray-500">Average confidence across all detected objects</div>
                    </div>
                    
                    <div id="detections-list" class="mt-6 hidden">
                        <h4 class="font-semibold text-gray-700 mb-3">Detected Objects</h4>
                        <div class="space-y-2" id="detections-container">
                            <!-- Detection items will be added here by JavaScript -->
                        </div>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <!-- Model Features -->
    <section id="model" class="py-16 bg-gray-50">
        <div class="container mx-auto px-4">
            <h2 class="text-3xl font-bold text-center mb-12 text-gray-800">Why Our Model Stands Out</h2>
            
            <div class="grid md:grid-cols-2 lg:grid-cols-3 gap-8">
                <div class="model-card bg-white p-6 rounded-xl shadow-md">
                    <div class="text-indigo-500 text-3xl mb-4">
                        <i class="fas fa-bolt"></i>
                    </div>
                    <h3 class="text-xl font-semibold mb-2 text-gray-800">Unmatched Accuracy</h3>
                    <p class="text-gray-600">With 99.99% precision, our model outperforms all existing solutions in object detection benchmarks.</p>
                </div>
                
                <div class="model-card bg-white p-6 rounded-xl shadow-md">
                    <div class="text-indigo-500 text-3xl mb-4">
                        <i class="fas fa-rocket"></i>
                    </div>
                    <h3 class="text-xl font-semibold mb-2 text-gray-800">Real-Time Performance</h3>
                    <p class="text-gray-600">Optimized for speed without compromising accuracy, perfect for live applications.</p>
                </div>
                
                <div class="model-card bg-white p-6 rounded-xl shadow-md">
                    <div class="text-indigo-500 text-3xl mb-4">
                        <i class="fas fa-shapes"></i>
                    </div>
                    <h3 class="text-xl font-semibold mb-2 text-gray-800">1000+ Classes</h3>
                    <p class="text-gray-600">Comprehensive detection across a vast range of objects, from everyday items to specialized equipment.</p>
                </div>
                
                <div class="model-card bg-white p-6 rounded-xl shadow-md">
                    <div class="text-indigo-500 text-3xl mb-4">
                        <i class="fas fa-cloud"></i>
                    </div>
                    <h3 class="text-xl font-semibold mb-2 text-gray-800">Cloud Optimized</h3>
                    <p class="text-gray-600">Deploy seamlessly on Hugging Face with our pre-configured API endpoints.</p>
                </div>
                
                <div class="model-card bg-white p-6 rounded-xl shadow-md">
                    <div class="text-indigo-500 text-3xl mb-4">
                        <i class="fas fa-mobile-alt"></i>
                    </div>
                    <h3 class="text-xl font-semibold mb-2 text-gray-800">Edge Compatible</h3>
                    <p class="text-gray-600">Lightweight versions available for mobile and edge device deployment.</p>
                </div>
                
                <div class="model-card bg-white p-6 rounded-xl shadow-md">
                    <div class="text-indigo-500 text-3xl mb-4">
                        <i class="fas fa-cogs"></i>
                    </div>
                    <h3 class="text-xl font-semibold mb-2 text-gray-800">Custom Training</h3>
                    <p class="text-gray-600">Fine-tune the model with your custom datasets while maintaining core accuracy.</p>
                </div>
            </div>
        </div>
    </section>

    <!-- Performance Metrics -->
    <section id="api" class="py-16 bg-white">
        <div class="container mx-auto px-4">
            <h2 class="text-3xl font-bold text-center mb-12 text-gray-800">Benchmark Results</h2>
            
            <div class="overflow-x-auto">
                <table class="min-w-full bg-white rounded-lg overflow-hidden">
                    <thead class="bg-gray-100">
                        <tr>
                            <th class="py-3 px-4 text-left text-gray-700 font-semibold">Model</th>
                            <th class="py-3 px-4 text-left text-gray-700 font-semibold">mAP@0.5</th>
                            <th class="py-3 px-4 text-left text-gray-700 font-semibold">Precision</th>
                            <th class="py-3 px-4 text-left text-gray-700 font-semibold">Recall</th>
                            <th class="py-3 px-4 text-left text-gray-700 font-semibold">FPS</th>
                        </tr>
                    </thead>
                    <tbody class="divide-y divide-gray-200">
                        <tr class="hover:bg-gray-50">
                            <td class="py-4 px-4 font-medium text-gray-900">PrecisionVision (Ours)</td>
                            <td class="py-4 px-4 text-green-600 font-semibold">99.99%</td>
                            <td class="py-4 px-4 text-green-600 font-semibold">99.99%</td>
                            <td class="py-4 px-4 text-green-600 font-semibold">99.98%</td>
                            <td class="py-4 px-4">62</td>
                        </tr>
                        <tr class="hover:bg-gray-50">
                            <td class="py-4 px-4">YOLOv8</td>
                            <td class="py-4 px-4">53.9%</td>
                            <td class="py-4 px-4">66.2%</td>
                            <td class="py-4 px-4">57.9%</td>
                            <td class="py-4 px-4">78</td>
                        </tr>
                        <tr class="hover:bg-gray-50">
                            <td class="py-4 px-4">Faster R-CNN</td>
                            <td class="py-4 px-4">55.2%</td>
                            <td class="py-4 px-4">68.1%</td>
                            <td class="py-4 px-4">59.3%</td>
                            <td class="py-4 px-4">26</td>
                        </tr>
                        <tr class="hover:bg-gray-50">
                            <td class="py-4 px-4">EfficientDet</td>
                            <td class="py-4 px-4">52.2%</td>
                            <td class="py-4 px-4">64.8%</td>
                            <td class="py-4 px-4">56.1%</td>
                            <td class="py-4 px-4">56</td>
                        </tr>
                    </tbody>
                </table>
            </div>
            
            <div class="mt-8 text-center">
                <p class="text-gray-600 mb-4">Tested on COCO 2017 validation set with RTX 4090 GPU</p>
                <a href="https://huggingface.co/spaces" target="_blank" class="bg-indigo-600 text-white px-6 py-3 rounded-lg font-semibold hover:bg-indigo-700 transition inline-block">
                    View Full Benchmark Details
                </a>
            </div>
        </div>
    </section>

    <!-- Call to Action -->
    <section class="gradient-bg text-white py-16">
        <div class="container mx-auto px-4 text-center">
            <h2 class="text-3xl md:text-4xl font-bold mb-6">Ready to Integrate 99.99% Accurate Vision?</h2>
            <p class="text-xl mb-8 max-w-3xl mx-auto">Join hundreds of developers using PrecisionVision for their computer vision applications.</p>
            <div class="flex flex-col sm:flex-row justify-center space-y-4 sm:space-y-0 sm:space-x-6">
                <a href="https://huggingface.co/settings/tokens" target="_blank" class="bg-white text-indigo-600 px-8 py-4 rounded-lg font-semibold hover:bg-gray-100 transition text-lg inline-block">
                    Get API Key <i class="fas fa-key ml-2"></i>
                </a>
                <a href="https://huggingface.co/docs" target="_blank" class="border border-white text-white px-8 py-4 rounded-lg font-semibold hover:bg-white hover:text-indigo-600 transition text-lg inline-block">
                    View Documentation <i class="fas fa-book ml-2"></i>
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    <footer id="contact" class="bg-gray-900 text-white py-12">
        <div class="container mx-auto px-4">
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                <div>
                    <div class="flex items-center space-x-2 mb-4">
                        <i class="fas fa-eye text-2xl"></i>
                        <span class="text-xl font-bold">PrecisionVision</span>
                    </div>
                    <p class="text-gray-400">The most accurate object detection model available today.</p>
                    <div class="flex space-x-4 mt-4">
                        <a href="https://github.com" target="_blank" class="text-gray-400 hover:text-white"><i class="fab fa-github"></i></a>
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                <p>© 2023 PrecisionVision. All rights reserved.</p>
            </div>
        </div>
    </footer>

    <script>
        // Demo functionality
        document.addEventListener('DOMContentLoaded', function() {
            // Mobile menu toggle
            const mobileMenuButton = document.getElementById('mobile-menu-button');
            const mobileMenu = document.getElementById('mobile-menu');
            
            mobileMenuButton.addEventListener('click', function() {
                mobileMenu.classList.toggle('hidden');
            });
            
            // Image detection functionality
            const uploadArea = document.querySelector('.upload-area');
            const fileInput = document.getElementById('image-upload');
            const placeholderText = document.getElementById('placeholder-text');
            const resultCanvas = document.getElementById('result-canvas');
            const confidenceDisplay = document.getElementById('confidence-display');
            const detectionsContainer = document.getElementById('detections-container');
            const sampleImageBtn = document.getElementById('sample-image-btn');
            
            // Handle drag and drop
            uploadArea.addEventListener('click', function() {
                fileInput.click();
            });
            
            uploadArea.addEventListener('dragover', function(e) {
                e.preventDefault();
                this.classList.add('border-indigo-500', 'bg-indigo-50');
            });
            
            uploadArea.addEventListener('dragleave', function() {
                this.classList.remove('border-indigo-500', 'bg-indigo-50');
            });
            
            uploadArea.addEventListener('drop', function(e) {
                e.preventDefault();
                this.classList.remove('border-indigo-500', 'bg-indigo-50');
                
                if (e.dataTransfer.files.length) {
                    fileInput.files = e.dataTransfer.files;
                    handleImageUpload(e.dataTransfer.files[0]);
                }
            });
            
            fileInput.addEventListener('change', function() {
                if (this.files.length) {
                    handleImageUpload(this.files[0]);
                }
            });
            
            sampleImageBtn.addEventListener('click', function() {
                const sampleImageUrl = 'https://images.unsplash.com/photo-1507146426996-ef05306b995a?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1170&q=80';
                displayImageWithDetections(sampleImageUrl);
            });
            
            function handleImageUpload(file) {
                if (!file.type.match('image.*')) {
                    alert('Please upload an image file');
                    return;
                }
                
                const reader = new FileReader();
                reader.onload = function(e) {
                    displayImageWithDetections(e.target.result);
                };
                reader.readAsDataURL(file);
            }
            
            function displayImageWithDetections(imageSrc) {
                const img = new Image();
                img.onload = function() {
                    // Set canvas dimensions
                    const maxWidth = 800;
                    const scale = Math.min(maxWidth / img.width, 1);
                    resultCanvas.width = img.width * scale;
                    resultCanvas.height = img.height * scale;
                    
                    const ctx = resultCanvas.getContext('2d');
                    
                    // Draw image
                    ctx.drawImage(img, 0, 0, resultCanvas.width, resultCanvas.height);
                    
                    // Simulate detections (in a real app, this would come from your model)
                    simulateDetections(ctx, img.width * scale, img.height * scale);
                    
                    // Show results
                    placeholderText.classList.add('hidden');
                    resultCanvas.classList.remove('hidden');
                    confidenceDisplay.classList.remove('hidden');
                    
                    // Populate detections list
                    populateDetectionsList();
                };
                img.src = imageSrc;
            }
            
            function simulateDetections(ctx, imgWidth, imgHeight) {
                // These would be replaced with actual model predictions
                const simulatedDetections = [
                    { class: 'dog', confidence: 0.9999, x: 0.4, y: 0.3, width: 0.25, height: 0.2 },
                    { class: 'human', confidence: 0.9998, x: 0.2, y: 0.6, width: 0.15, height: 0.15 },
                    { class: 'grass', confidence: 0.9997, x: 0.7, y: 0.5, width: 0.2, height: 0.25 },
                    { class: 'sky', confidence: 0.9996, x: 0.1, y: 0.1, width: 0.15, height: 0.15 },
                    { class: 'leash', confidence: 0.9995, x: 0.6, y: 0.75, width: 0.1, height: 0.1 },
                    { class: 'collar', confidence: 0.9994, x: 0.45, y: 0.35, width: 0.05, height: 0.05 },
                    { class: 'fur', confidence: 0.9993, x: 0.35, y: 0.4, width: 0.3, height: 0.25 }
                ];
                
                simulatedDetections.forEach(det => {
                    const x = det.x * imgWidth;
                    const y = det.y * imgHeight;
                    const width = det.width * imgWidth;
                    const height = det.height * imgHeight;
                    
                    // Draw bounding box
                    ctx.strokeStyle = '#4ade80';
                    ctx.lineWidth = 2;
                    ctx.strokeRect(x, y, width, height);
                    
                    // Draw background for label
                    ctx.fillStyle = 'rgba(74, 222, 128, 0.8)';
                    const text = `${det.class} ${(det.confidence * 100).toFixed(2)}%`;
                    const textWidth = ctx.measureText(text).width + 10;
                    ctx.fillRect(x, y - 25, textWidth, 25);
                    
                    // Draw label text
                    ctx.fillStyle = 'white';
                    ctx.font = 'bold 12px sans-serif';
                    ctx.fillText(text, x + 5, y - 8);
                });
            }
            
            function populateDetectionsList() {
                // Simulated data - replace with actual model output
                const simulatedDetections = [
                    { class: 'Dog', confidence: 99.99, color: 'bg-green-500' },
                    { class: 'Human', confidence: 99.98, color: 'bg-blue-500' },
                    { class: 'Grass', confidence: 99.97, color: 'bg-purple-500' },
                    { class: 'Sky', confidence: 99.96, color: 'bg-indigo-500' },
                    { class: 'Leash', confidence: 99.95, color: 'bg-yellow-500' },
                    { class: 'Collar', confidence: 99.94, color: 'bg-red-500' },
                    { class: 'Fur', confidence: 99.93, color: 'bg-pink-500' }
                ];
                
                detectionsContainer.innerHTML = simulatedDetections.map(det => `
                    <div class="flex items-center justify-between p-3 bg-gray-100 rounded-lg">
                        <div class="flex items-center">
                            <span class="w-3 h-3 rounded-full ${det.color} mr-2"></span>
                            <span class="font-medium">${det.class}</span>
                        </div>
                        <span class="font-semibold text-green-600">${det.confidence.toFixed(2)}%</span>
                    </div>
                `).join('');
                
                document.getElementById('detections-list').classList.remove('hidden');
            }
        });
    </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=Jobwengi/object-detection-model" style="color: #fff;text-decoration: underline;" target="_blank" >Remix</a></p></body>
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