feat(Docker): Upgrade to PHP 8.5.0RC3 with native ext-uri support

BREAKING CHANGE: Requires PHP 8.5.0RC3

Changes:
- Update Docker base image from php:8.4-fpm to php:8.5.0RC3-fpm
- Enable ext-uri for native WHATWG URL parsing support
- Update composer.json PHP requirement from ^8.4 to ^8.5
- Add ext-uri as required extension in composer.json
- Move URL classes from Url.php85/ to Url/ directory (now compatible)
- Remove temporary PHP 8.4 compatibility workarounds

Benefits:
- Native URL parsing with Uri\WhatWg\Url class
- Better performance for URL operations
- Future-proof with latest PHP features
- Eliminates PHP version compatibility issues

scripts/seed-ml-models.php

@@ -0,0 +1,248 @@
+<?php
+
+declare(strict_types=1);
+
+require_once __DIR__ . '/../vendor/autoload.php';
+
+use App\Framework\MachineLearning\ModelManagement\ModelRegistry;
+use App\Framework\MachineLearning\ModelManagement\ValueObjects\ModelMetadata;
+use App\Framework\MachineLearning\ModelManagement\ValueObjects\ModelType;
+use App\Framework\MachineLearning\ModelManagement\ModelPerformanceMonitor;
+use App\Framework\Core\ValueObjects\Version;
+use App\Framework\Core\ValueObjects\Timestamp;
+use App\Framework\Core\ValueObjects\Duration;
+use App\Framework\Core\AppBootstrapper;
+use App\Framework\Performance\EnhancedPerformanceCollector;
+use App\Framework\DateTime\SystemClock;
+use App\Framework\DateTime\SystemHighResolutionClock;
+use App\Framework\Performance\MemoryMonitor;
+
+echo "🌱 ML Models Seeder\n";
+echo "==================\n\n";
+
+// Bootstrap application
+$basePath = dirname(__DIR__);
+$clock = new SystemClock();
+$highResClock = new SystemHighResolutionClock();
+$memoryMonitor = new MemoryMonitor();
+$collector = new EnhancedPerformanceCollector($clock, $highResClock, $memoryMonitor, enabled: true);
+$bootstrapper = new AppBootstrapper($basePath, $collector, $memoryMonitor);
+$container = $bootstrapper->bootstrapWorker();
+
+/** @var ModelRegistry $registry */
+$registry = $container->get(ModelRegistry::class);
+
+/** @var ModelPerformanceMonitor $performanceMonitor */
+$performanceMonitor = $container->get(ModelPerformanceMonitor::class);
+
+// Sample Models to Seed
+$models = [
+    // 1. Fraud Detection Model (Supervised, Production)
+    [
+        'name' => 'fraud-detector',
+        'type' => ModelType::SUPERVISED,
+        'version' => '1.0.0',
+        'environment' => 'production',
+        'configuration' => [
+            'threshold' => 0.75,
+            'min_confidence' => 0.6,
+            'feature_count' => 15,
+            'algorithm' => 'random_forest',
+        ],
+        'metrics' => [
+            'accuracy' => 0.94,
+            'precision' => 0.91,
+            'recall' => 0.89,
+            'f1_score' => 0.90,
+            'total_predictions' => 15234,
+            'average_confidence' => 0.87,
+            'confusion_matrix' => [
+                'true_positive' => 1345,
+                'true_negative' => 12789,
+                'false_positive' => 567,
+                'false_negative' => 533,
+            ],
+        ],
+    ],
+
+    // 2. Spam Classifier (Supervised, Production - Degraded)
+    [
+        'name' => 'spam-classifier',
+        'type' => ModelType::SUPERVISED,
+        'version' => '2.0.0',
+        'environment' => 'production',
+        'configuration' => [
+            'threshold' => 0.80,
+            'min_confidence' => 0.7,
+            'feature_count' => 20,
+            'algorithm' => 'gradient_boosting',
+        ],
+        'metrics' => [
+            'accuracy' => 0.78, // Degraded performance
+            'precision' => 0.82,
+            'recall' => 0.71,
+            'f1_score' => 0.76,
+            'total_predictions' => 8923,
+            'average_confidence' => 0.75,
+            'confusion_matrix' => [
+                'true_positive' => 892,
+                'true_negative' => 6051,
+                'false_positive' => 1234,
+                'false_negative' => 746,
+            ],
+        ],
+    ],
+
+    // 3. User Segmentation (Unsupervised, Production)
+    [
+        'name' => 'user-segmentation',
+        'type' => ModelType::UNSUPERVISED,
+        'version' => '1.2.0',
+        'environment' => 'production',
+        'configuration' => [
+            'n_clusters' => 5,
+            'algorithm' => 'k_means',
+            'feature_count' => 12,
+        ],
+        'metrics' => [
+            'accuracy' => 0.88,
+            'total_predictions' => 5678,
+            'average_confidence' => 0.83,
+            'silhouette_score' => 0.72,
+        ],
+    ],
+
+    // 4. Anomaly Detection (Unsupervised, Production)
+    [
+        'name' => 'anomaly-detector',
+        'type' => ModelType::UNSUPERVISED,
+        'version' => '1.5.0',
+        'environment' => 'production',
+        'configuration' => [
+            'contamination' => 0.1,
+            'algorithm' => 'isolation_forest',
+            'feature_count' => 10,
+        ],
+        'metrics' => [
+            'accuracy' => 0.92,
+            'total_predictions' => 12456,
+            'average_confidence' => 0.85,
+            'anomaly_rate' => 0.08,
+        ],
+    ],
+
+    // 5. Recommendation Engine (Reinforcement, Development)
+    [
+        'name' => 'recommendation-engine',
+        'type' => ModelType::REINFORCEMENT,
+        'version' => '0.5.0',
+        'environment' => 'development',
+        'configuration' => [
+            'learning_rate' => 0.001,
+            'discount_factor' => 0.95,
+            'exploration_rate' => 0.1,
+            'algorithm' => 'q_learning',
+        ],
+        'metrics' => [
+            'accuracy' => 0.67, // Still in development
+            'total_predictions' => 2345,
+            'average_confidence' => 0.62,
+            'average_reward' => 3.42,
+        ],
+    ],
+
+    // 6. Sentiment Analysis (Supervised, Staging)
+    [
+        'name' => 'sentiment-analyzer',
+        'type' => ModelType::SUPERVISED,
+        'version' => '2.1.0',
+        'environment' => 'staging',
+        'configuration' => [
+            'threshold' => 0.65,
+            'algorithm' => 'lstm',
+            'feature_count' => 50,
+            'max_sequence_length' => 100,
+        ],
+        'metrics' => [
+            'accuracy' => 0.91,
+            'precision' => 0.89,
+            'recall' => 0.92,
+            'f1_score' => 0.90,
+            'total_predictions' => 7890,
+            'average_confidence' => 0.86,
+        ],
+    ],
+];
+
+echo "Registering " . count($models) . " ML models...\n\n";
+
+foreach ($models as $index => $modelData) {
+    $modelNum = $index + 1;
+    echo "[$modelNum/" . count($models) . "] Registering {$modelData['name']} v{$modelData['version']}...\n";
+
+    try {
+        // Create ModelMetadata
+        $metadata = new ModelMetadata(
+            modelName: $modelData['name'],
+            modelType: $modelData['type'],
+            version: Version::fromString($modelData['version']),
+            configuration: $modelData['configuration'],
+            performanceMetrics: [],
+            createdAt: Timestamp::now(),
+            deployedAt: $modelData['environment'] === 'production' ? Timestamp::now() : null,
+            environment: $modelData['environment'],
+            metadata: [
+                'seeded_at' => date('Y-m-d H:i:s'),
+                'description' => "Sample {$modelData['type']->value} model for testing",
+            ]
+        );
+
+        // Register model
+        $registry->register($metadata);
+
+        // Track performance metrics using trackPrediction
+        $performanceMonitor->trackPrediction(
+            modelName: $modelData['name'],
+            version: Version::fromString($modelData['version']),
+            prediction: 1, // Dummy prediction
+            actual: 1, // Dummy actual
+            confidence: $modelData['metrics']['average_confidence']
+        );
+
+        // Update metrics manually to match our sample data
+        if (isset($modelData['metrics']['confusion_matrix'])) {
+            $cm = $modelData['metrics']['confusion_matrix'];
+            // Track individual predictions to build up confusion matrix
+            for ($i = 0; $i < $cm['true_positive']; $i++) {
+                $performanceMonitor->trackPrediction(
+                    modelName: $modelData['name'],
+                    version: Version::fromString($modelData['version']),
+                    prediction: 1,
+                    actual: 1,
+                    confidence: $modelData['metrics']['average_confidence']
+                );
+            }
+        }
+
+        echo "  ✅ Successfully registered {$modelData['name']}\n";
+        echo "     - Type: {$modelData['type']->value}\n";
+        echo "     - Environment: {$modelData['environment']}\n";
+        echo "     - Accuracy: " . round($modelData['metrics']['accuracy'] * 100, 2) . "%\n";
+
+        if ($modelData['metrics']['accuracy'] < 0.85) {
+            echo "     ⚠️  Warning: Degraded performance\n";
+        }
+
+        echo "\n";
+    } catch (\Exception $e) {
+        echo "  ❌ Error: {$e->getMessage()}\n\n";
+    }
+}
+
+echo "==================\n";
+echo "✅ Seeding complete!\n\n";
+
+echo "Next steps:\n";
+echo "1. Visit https://localhost/admin/ml/dashboard to see the models\n";
+echo "2. Check API endpoint: https://localhost/api/ml/dashboard\n";
+echo "3. Verify foreach attribute rendering in Models Overview table\n";

scripts/seed-notifications.php

@@ -0,0 +1,247 @@
+<?php
+
+declare(strict_types=1);
+
+require_once __DIR__ . '/../vendor/autoload.php';
+
+use App\Framework\Core\AppBootstrapper;
+use App\Framework\Performance\EnhancedPerformanceCollector;
+use App\Framework\DateTime\SystemClock;
+use App\Framework\DateTime\SystemHighResolutionClock;
+use App\Framework\Performance\MemoryMonitor;
+use App\Framework\Notification\Notification;
+use App\Framework\Notification\Storage\NotificationRepository;
+use App\Framework\Notification\ValueObjects\NotificationId;
+use App\Framework\Notification\ValueObjects\NotificationPriority;
+use App\Framework\Notification\ValueObjects\NotificationStatus;
+use App\Framework\Notification\ValueObjects\NotificationChannel;
+use App\Framework\Core\ValueObjects\Timestamp;
+
+echo "🔔 Notifications Seeder\n";
+echo "=====================\n\n";
+
+// Bootstrap application
+$basePath = dirname(__DIR__);
+$clock = new SystemClock();
+$highResClock = new SystemHighResolutionClock();
+$memoryMonitor = new MemoryMonitor();
+$collector = new EnhancedPerformanceCollector($clock, $highResClock, $memoryMonitor, enabled: true);
+$bootstrapper = new AppBootstrapper($basePath, $collector, $memoryMonitor);
+$container = $bootstrapper->bootstrapWorker();
+
+/** @var NotificationRepository $repository */
+$repository = $container->get(NotificationRepository::class);
+
+// Sample notification types
+final readonly class NotificationType implements App\Framework\Notification\ValueObjects\NotificationTypeInterface
+{
+    public function __construct(private string $value) {}
+
+    public function toString(): string
+    {
+        return $this->value;
+    }
+
+    public function getDisplayName(): string
+    {
+        return match ($this->value) {
+            'ml_performance_degradation' => 'ML Performance Degradation',
+            'ml_model_deployed' => 'ML Model Deployed',
+            'ml_training_complete' => 'ML Training Complete',
+            'system_alert' => 'System Alert',
+            'security_alert' => 'Security Alert',
+            'info' => 'Information',
+            default => ucwords(str_replace('_', ' ', $this->value)),
+        };
+    }
+
+    public function isCritical(): bool
+    {
+        return in_array($this->value, [
+            'ml_performance_degradation',
+            'security_alert',
+            'system_alert'
+        ], true);
+    }
+
+    public function equals($other): bool
+    {
+        return $other instanceof self && $this->value === $other->value;
+    }
+}
+
+// Sample notifications to create
+$notifications = [
+    // ML Performance Alerts
+    [
+        'type' => 'ml_performance_degradation',
+        'title' => 'ML Model Performance Degradation Detected',
+        'body' => 'The spam-classifier model (v2.0.0) is experiencing performance degradation. Current accuracy: 78% (below threshold of 85%). Immediate attention recommended.',
+        'priority' => NotificationPriority::URGENT,
+        'action_url' => '/admin/ml/models/spam-classifier',
+        'action_label' => 'View Model Details',
+        'created_offset' => -7200, // 2 hours ago
+    ],
+    [
+        'type' => 'ml_model_deployed',
+        'title' => 'New ML Model Deployed Successfully',
+        'body' => 'Fraud detector model v1.0.0 has been successfully deployed to production. Initial accuracy: 94%. Monitoring active.',
+        'priority' => NotificationPriority::NORMAL,
+        'action_url' => '/admin/ml/models/fraud-detector',
+        'action_label' => 'View Deployment',
+        'created_offset' => -3600, // 1 hour ago
+    ],
+    [
+        'type' => 'ml_training_complete',
+        'title' => 'Model Training Completed',
+        'body' => 'Sentiment analyzer training completed successfully. New version 2.1.0 ready for deployment. Validation accuracy: 91%.',
+        'priority' => NotificationPriority::HIGH,
+        'action_url' => '/admin/ml/models/sentiment-analyzer',
+        'action_label' => 'Review & Deploy',
+        'created_offset' => -1800, // 30 minutes ago
+    ],
+
+    // System Alerts
+    [
+        'type' => 'system_alert',
+        'title' => 'High Memory Usage Detected',
+        'body' => 'System memory usage exceeded 85% threshold. Current usage: 87%. Consider scaling resources or optimizing memory-intensive processes.',
+        'priority' => NotificationPriority::HIGH,
+        'action_url' => '/admin/performance',
+        'action_label' => 'View Metrics',
+        'created_offset' => -900, // 15 minutes ago
+    ],
+    [
+        'type' => 'system_alert',
+        'title' => 'Queue Backlog Warning',
+        'body' => 'Job queue backlog detected. 1,234 pending jobs in queue. Processing rate: 45 jobs/minute. Estimated clearance time: 27 minutes.',
+        'priority' => NotificationPriority::NORMAL,
+        'action_url' => '/admin/queue',
+        'action_label' => 'View Queue',
+        'created_offset' => -600, // 10 minutes ago
+    ],
+
+    // Security Alerts
+    [
+        'type' => 'security_alert',
+        'title' => 'Suspicious Login Attempts Detected',
+        'body' => 'Multiple failed login attempts detected from IP 203.0.113.42. Rate limiting applied. Review access logs for potential security threat.',
+        'priority' => NotificationPriority::URGENT,
+        'action_url' => '/admin/security/logs',
+        'action_label' => 'View Security Logs',
+        'created_offset' => -300, // 5 minutes ago
+    ],
+    [
+        'type' => 'security_alert',
+        'title' => 'WAF Blocked Malicious Request',
+        'body' => 'Web Application Firewall blocked SQL injection attempt. Attack pattern detected: UNION SELECT. Source IP: 198.51.100.10.',
+        'priority' => NotificationPriority::HIGH,
+        'action_url' => '/admin/security/waf',
+        'action_label' => 'View WAF Logs',
+        'created_offset' => -120, // 2 minutes ago
+    ],
+
+    // Info Notifications
+    [
+        'type' => 'info',
+        'title' => 'System Backup Completed',
+        'body' => 'Daily system backup completed successfully. Backup size: 2.3 GB. Next scheduled backup: Tomorrow at 2:00 AM.',
+        'priority' => NotificationPriority::LOW,
+        'action_url' => '/admin/backups',
+        'action_label' => 'View Backups',
+        'created_offset' => -86400, // 1 day ago
+    ],
+    [
+        'type' => 'info',
+        'title' => 'Database Optimization Recommended',
+        'body' => 'Database performance analysis suggests optimizing 3 tables. Estimated performance improvement: 15%. Schedule maintenance window for optimization.',
+        'priority' => NotificationPriority::NORMAL,
+        'action_url' => '/admin/database/optimization',
+        'action_label' => 'View Recommendations',
+        'created_offset' => -172800, // 2 days ago
+    ],
+    [
+        'type' => 'info',
+        'title' => 'Weekly Performance Report Available',
+        'body' => 'Weekly system performance report is now available. Key metrics: 99.8% uptime, 145ms avg response time, 1.2M requests processed.',
+        'priority' => NotificationPriority::LOW,
+        'action_url' => '/admin/reports/weekly',
+        'action_label' => 'View Report',
+        'created_offset' => -259200, // 3 days ago
+    ],
+];
+
+echo "Creating " . count($notifications) . " sample notifications...\n\n";
+
+$createdCount = 0;
+$now = time();
+
+foreach ($notifications as $index => $notificationData) {
+    $notificationNum = $index + 1;
+    echo "[$notificationNum/" . count($notifications) . "] Creating: {$notificationData['title']}\n";
+
+    try {
+        // Create notification timestamp (offset from now)
+        $createdAt = Timestamp::fromTimestamp($now + $notificationData['created_offset']);
+
+        // For recent notifications (< 1 hour ago), leave unread
+        // For older notifications, mark some as read
+        $isRecent = abs($notificationData['created_offset']) < 3600;
+        $shouldBeRead = !$isRecent && (($index % 3) === 0); // Mark every 3rd older notification as read
+
+        $notification = new Notification(
+            id: NotificationId::generate(),
+            recipientId: 'admin',
+            type: new NotificationType($notificationData['type']),
+            title: $notificationData['title'],
+            body: $notificationData['body'],
+            createdAt: $createdAt,
+            data: [],
+            channels: [NotificationChannel::DATABASE],
+            priority: $notificationData['priority'],
+            status: $shouldBeRead ? NotificationStatus::READ : NotificationStatus::SENT,
+            sentAt: $createdAt,
+            readAt: $shouldBeRead ? Timestamp::fromTimestamp($now + $notificationData['created_offset'] + 300) : null,
+            actionUrl: $notificationData['action_url'],
+            actionLabel: $notificationData['action_label']
+        );
+
+        $repository->save($notification);
+
+        $createdCount++;
+
+        $statusIcon = $shouldBeRead ? '✓' : '📬';
+        $priorityLabel = $notificationData['priority']->value;
+
+        echo "  $statusIcon Successfully created ($priorityLabel priority, " . ($shouldBeRead ? 'read' : 'unread') . ")\n";
+        echo "     - Created: " . $createdAt->format('Y-m-d H:i:s') . "\n";
+
+        if ($notificationData['action_url']) {
+            echo "     - Action: {$notificationData['action_label']} → {$notificationData['action_url']}\n";
+        }
+
+        echo "\n";
+
+    } catch (\Exception $e) {
+        echo "  ❌ Error: {$e->getMessage()}\n\n";
+    }
+}
+
+echo "=====================\n";
+echo "✅ Seeding complete!\n\n";
+
+echo "Summary:\n";
+echo "- Total notifications created: $createdCount\n";
+
+// Get current stats
+$unreadCount = $repository->countUnreadByUser('admin');
+echo "- Unread notifications: $unreadCount\n";
+
+$allNotifications = $repository->findByUser('admin', limit: 100);
+echo "- Total notifications for admin: " . count($allNotifications) . "\n\n";
+
+echo "Next steps:\n";
+echo "1. Visit https://localhost/admin/notifications to view the notifications\n";
+echo "2. Test mark as read functionality\n";
+echo "3. Test mark all as read functionality\n";
+echo "4. Verify unread badge updates in real-time\n";