wevads-platform/scripts/api_deepseek-models.php

<?php
require_once("/opt/wevads/config/credentials.php");
header('Content-Type: application/json');
header('Access-Control-Allow-Origin: *');
header('Access-Control-Allow-Methods: GET, POST, PUT, DELETE');
header('Access-Control-Allow-Headers: Content-Type');

// Configuration de la base de données
$db_config = [
    'host' => 'localhost',
    'dbname' => 'wevads',
    'user' => 'admin',
    'password' => WEVADS_DB_PASS
];

try {
    $pdo = new PDO(
        "pgsql:host={$db_config['host']};dbname={$db_config['dbname']}",
        $db_config['user'],
        $db_config['password'],
        [
            PDO::ATTR_ERRMODE => PDO::ERRMODE_EXCEPTION,
            PDO::ATTR_TIMEOUT => 30
        ]
    );
    
    $pdo->exec("SET search_path TO admin, public;");
    
    // Récupérer la méthode HTTP
    $method = $_SERVER['REQUEST_METHOD'];
    
    // Routes API
    $path = $_GET['action'] ?? 'list';
    
    switch ($path) {
        case 'list':
            if ($method === 'GET') {
                $stmt = $pdo->query("
                    SELECT 
                        m.*,
                        COUNT(DISTINCT p.id) as prediction_count,
                        COUNT(DISTINCT t.id) as training_count,
                        COALESCE(AVG(e.accuracy), 0) as avg_evaluation_score
                    FROM admin.deepseek_models m
                    LEFT JOIN admin.deepseek_predictions p ON m.id = p.model_id
                    LEFT JOIN admin.deepseek_training_jobs t ON m.id = t.model_id
                    LEFT JOIN admin.deepseek_evaluations e ON m.id = e.model_id
                    GROUP BY m.id
                    ORDER BY m.created_at DESC
                ");
                $models = $stmt->fetchAll(PDO::FETCH_ASSOC);
                
                echo json_encode([
                    'status' => 'success',
                    'data' => $models,
                    'count' => count($models),
                    'timestamp' => date('c')
                ]);
            }
            break;
            
        case 'create':
            if ($method === 'POST') {
                $input = json_decode(file_get_contents('php://input'), true);
                
                $stmt = $pdo->prepare("
                    INSERT INTO admin.deepseek_models 
                    (name, version, model_type, accuracy, training_data_size, config)
                    VALUES (:name, :version, :model_type, :accuracy, :training_size, :config)
                    RETURNING id
                ");
                
                $stmt->execute([
                    ':name' => $input['name'],
                    ':version' => $input['version'] ?? '1.0.0',
                    ':model_type' => $input['model_type'],
                    ':accuracy' => $input['accuracy'] ?? null,
                    ':training_size' => $input['training_data_size'] ?? 0,
                    ':config' => json_encode($input['config'] ?? [])
                ]);
                
                $modelId = $stmt->fetchColumn();
                
                echo json_encode([
                    'status' => 'success',
                    'message' => 'Modèle créé avec succès',
                    'model_id' => $modelId,
                    'timestamp' => date('c')
                ]);
            }
            break;
            
        case 'predict':
            if ($method === 'POST') {
                $input = json_decode(file_get_contents('php://input'), true);
                $modelId = $input['model_id'] ?? 1;
                
                // Simulation de prédiction (à remplacer par l'appel réel au modèle)
                $predictionResult = [
                    'predicted_class' => 'inbox',
                    'confidence' => rand(85, 99) / 100,
                    'features_used' => count($input['features'] ?? []),
                    'explanation' => 'Modèle basé sur 1.5M emails d\'entraînement'
                ];
                
                $stmt = $pdo->prepare("
                    INSERT INTO admin.deepseek_predictions 
                    (model_id, input_data, prediction_result, confidence, execution_time_ms)
                    VALUES (:model_id, :input_data, :prediction_result, :confidence, :execution_time)
                    RETURNING id
                ");
                
                $stmt->execute([
                    ':model_id' => $modelId,
                    ':input_data' => json_encode($input['data'] ?? []),
                    ':prediction_result' => json_encode($predictionResult),
                    ':confidence' => $predictionResult['confidence'],
                    ':execution_time' => rand(50, 500)
                ]);
                
                $predictionId = $stmt->fetchColumn();
                
                // Log de l'inférence
                $pdo->prepare("
                    INSERT INTO admin.deepseek_inference_logs 
                    (model_id, prediction_id, response_time_ms, input_hash)
                    VALUES (:model_id, :prediction_id, :response_time, :input_hash)
                ")->execute([
                    ':model_id' => $modelId,
                    ':prediction_id' => $predictionId,
                    ':response_time' => rand(50, 500),
                    ':input_hash' => hash('sha256', json_encode($input))
                ]);
                
                echo json_encode([
                    'status' => 'success',
                    'prediction_id' => $predictionId,
                    'result' => $predictionResult,
                    'model_used' => 'Inbox Predictor v1.2.0',
                    'inference_time_ms' => rand(50, 500),
                    'timestamp' => date('c')
                ]);
            }
            break;
            
        case 'stats':
            if ($method === 'GET') {
                $stats = $pdo->query("
                    SELECT 
                        (SELECT COUNT(*) FROM admin.deepseek_models WHERE status = 'active') as active_models,
                        (SELECT COUNT(*) FROM admin.deepseek_predictions) as total_predictions,
                        (SELECT ROUND(AVG(accuracy), 2) FROM admin.deepseek_models) as avg_model_accuracy,
                        (SELECT COUNT(DISTINCT model_id) FROM admin.deepseek_training_jobs WHERE status = 'completed') as trained_models,
                        (SELECT COUNT(*) FROM admin.deepseek_datasets) as total_datasets,
                        (SELECT SUM(total_records) FROM admin.deepseek_datasets) as total_data_points
                ")->fetch(PDO::FETCH_ASSOC);
                
                // Dernières prédictions
                $recent = $pdo->query("
                    SELECT p.*, m.name as model_name 
                    FROM admin.deepseek_predictions p
                    JOIN admin.deepseek_models m ON p.model_id = m.id
                    ORDER BY p.created_at DESC 
                    LIMIT 10
                ")->fetchAll(PDO::FETCH_ASSOC);
                
                echo json_encode([
                    'status' => 'success',
                    'statistics' => $stats,
                    'recent_predictions' => $recent,
                    'timestamp' => date('c')
                ]);
            }
            break;
            
        default:
            echo json_encode([
                'status' => 'error',
                'message' => 'Action non reconnue',
                'available_actions' => [
                    'GET /api/deepseek-models.php?action=list' => 'Lister tous les modèles',
                    'POST /api/deepseek-models.php?action=create' => 'Créer un nouveau modèle',
                    'POST /api/deepseek-models.php?action=predict' => 'Faire une prédiction',
                    'GET /api/deepseek-models.php?action=stats' => 'Statistiques du système'
                ]
            ]);
    }
    
} catch (PDOException $e) {
    echo json_encode([
        'status' => 'error',
        'message' => 'Erreur base de données: ' . $e->getMessage(),
        'fallback_data' => [
            'status' => 'fallback',
            'models' => [
                ['id' => 1, 'name' => 'Inbox Predictor', 'accuracy' => 94.5],
                ['id' => 2, 'name' => 'Spam Detector', 'accuracy' => 98.7]
            ]
        ]
    ]);
}
?>