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)
        }