from django.apps import AppConfig

class AiApiConfig(AppConfig):
    default_auto_field = 'django.db.models.BigAutoField'
    name = 'ai_api'

    def ready(self):
        from . import globals
        from deepface import DeepFace
        from ai_api.library.devlab_image import DevLabImage
        from transformers import AutoTokenizer, AutoModelForSequenceClassification
        import whisper
        import os
        from safetensors import safe_open
        import torch

        device = "cuda" if torch.cuda.is_available() else "cpu"

        globals.devlab_image = DevLabImage()

        # Load HuggingFace tokenizer and model once
        save_path = os.path.join(os.path.dirname(__file__), "ddet_classification")
        print(f"Model path: {save_path}")
        globals.save_path = save_path

        # Load tokenizer
        try:
            globals.tokenizer = AutoTokenizer.from_pretrained(save_path,device=device)
            print("Tokenizer loaded ✅")
        except Exception as e:
            print(f"Failed to load tokenizer: {e}")
            globals.tokenizer = None

        # Check .safetensors before loading model
        try:
            safetensor_file = os.path.join(save_path, "model.safetensors")
            if os.path.exists(safetensor_file):
                with safe_open(safetensor_file, framework="pt") as f:
                    print("Safetensors file checked ✅")

            globals.model = AutoModelForSequenceClassification.from_pretrained(save_path)
            globals.model.eval()
            print("Classification model loaded ✅")

        except Exception as e:
            print(f"Failed to load classification model: {e}")
            globals.model = None

        # Load Whisper model
        try:
            globals.whisper_model = whisper.load_model("large",device=device)
            print("Whisper model loaded ✅")
        except Exception as e:
            print(f"Failed to load Whisper model: {e}")
            globals.whisper_model = None

        # Load FaceNet model
        try:
            globals.facenet_model = DeepFace.build_model("Facenet")
            print("Facenet model loaded ✅")
        except Exception as e:
            print(f"Failed to load FaceNet model: {e}")
            globals.facenet_model = None