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namecreate/tasks.py

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+import os
+import requests
+import numpy as np
+from datetime import datetime
+from celery import shared_task
+from django.conf import settings
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+from skl2onnx import convert_sklearn
+from skl2onnx.common.data_types import FloatTensorType
+from namecreate.models import TrainingJob
+
+
+def notify_go_service(model_path, metrics):
+    """Go servisine model yüklenmiş olduğunu bildirir."""
+    try:
+        go_service_url = settings.GO_SERVICE_URL
+        if not go_service_url:
+            return False
+        
+        payload = {
+            "model_path": model_path,
+            "metrics": metrics,
+            "timestamp": datetime.now().isoformat(),
+        }
+        
+        response = requests.post(
+            f"{go_service_url}/reload-model",
+            json=payload,
+            timeout=10
+        )
+        
+        return response.status_code == 200
+    except Exception as e:
+        print(f"Go servisi bildirimi başarısız: {str(e)}")
+        return False
+
+
+@shared_task(name='namecreate.tasks.train_model_task')
+def train_model_task(task_id):
+    """
+    Makine öğrenme modelini arka planda eğitir ve ONNX olarak kaydeder.
+    """
+    try:
+        job = TrainingJob.objects.get(task_id=task_id)
+        job.status = 'running'
+        job.started_at = datetime.now()
+        job.save(update_fields=['status', 'started_at'])
+        
+        # 1. Veri Seti Yükleme
+        if job.features and job.labels:
+            # Kullanıcının gönderdiği veri
+            X = np.array(job.features, dtype=np.float32)
+            y = np.array(job.labels,   dtype=np.int32)
+        else:
+            # Demo: Iris dataset
+            iris = load_iris()
+            X, y = iris.data.astype(np.float32), iris.target
+
+        X_train, X_test, y_train, y_test = train_test_split(
+            X, y, test_size=0.2, random_state=42
+        )
+        feature_count = X.shape[1]
+
+        # 2. Model Eğitimi
+        model = RandomForestClassifier(n_estimators=10, random_state=42)
+        model.fit(X_train, y_train)
+        
+        # 3. Metrikleri Hesapla
+        y_pred = model.predict(X_test)
+        accuracy = accuracy_score(y_test, y_pred)
+        precision = precision_score(y_test, y_pred, average='weighted')
+        recall = recall_score(y_test, y_pred, average='weighted')
+        f1 = f1_score(y_test, y_pred, average='weighted')
+        
+        # 4. ONNX Formatına Dönüştür (feature_count dinamik)
+        initial_type = [('float_input', FloatTensorType([None, feature_count]))]
+        onx = convert_sklearn(model, initial_types=initial_type)
+        
+        # 5. Dosyaya Kaydet (Versiyonlu - Timestamp ile)
+        timestamp = job.model_version.strftime('%Y-%m-%d_%H-%M-%S')
+        model_filename = f"model_{timestamp}.onnx"
+        model_path = os.path.join(settings.MEDIA_ROOT, 'models', model_filename)
+        
+        os.makedirs(os.path.dirname(model_path), exist_ok=True)
+        with open(model_path, "wb") as f:
+            f.write(onx.SerializeToString())
+        
+        # 6. Go Servisine Bilder
+        metrics = {
+            'accuracy': float(accuracy),
+            'precision': float(precision),
+            'recall': float(recall),
+            'f1_score': float(f1),
+        }
+        go_notified = notify_go_service(model_path, metrics)
+        
+        # 7. Veritabanına Kaydet
+        job.status = 'completed'
+        job.completed_at = datetime.now()
+        job.model_path = model_path
+        job.accuracy = accuracy
+        job.precision = precision
+        job.recall = recall
+        job.f1_score = f1
+        job.go_service_notified = go_notified
+        job.save()
+        
+        return {
+            'status': 'success',
+            'task_id': task_id,
+            'model_path': model_path,
+            'go_service_notified': go_notified,
+            'metrics': metrics
+        }
+        
+    except Exception as e:
+        job = TrainingJob.objects.get(task_id=task_id)
+        job.status = 'failed'
+        job.error_message = str(e)
+        job.save(update_fields=['status', 'error_message'])
+        
+        return {
+            'status': 'error',
+            'task_id': task_id,
+            'error': str(e)
+        }