import os
import cv2
from django.apps import AppConfig
from storages.backends.s3boto3 import S3Boto3Storage
from ultralytics import YOLO
import torch

class MyAppConfig(AppConfig):
    name = 'proctoring'

    def ready(self):
        from django.conf import settings
        ai_model_dir = os.path.join(settings.BASE_DIR, "ml", "models","object_detection")

        # Load models
        settings.net = cv2.dnn.readNet(
            os.path.join(ai_model_dir, 'yolov3.weights'),
            os.path.join(ai_model_dir, 'yolov3.cfg')
        )

        settings.model = YOLO(os.path.join(ai_model_dir, "yolo12x.pt"))

        # Determine device from env or fallback
        use_cuda = os.environ.get("USE_CUDA", "false").lower() == "true"
        if use_cuda and torch.cuda.is_available():
            settings.model = settings.model.cuda()
        else:
            settings.model = settings.model.cpu()

        settings.s3_storage = S3Boto3Storage()