from pydantic import BaseSettings

class Settings(BaseSettings):
    # Default values for the processor and model
    processor: str = "senga-ml/donut-v16"
    model: str = "senga-ml/donut-v16"
    dataset: str = "senga-ml/dnotes-data-v6"
    base_config: str = "naver-clova-ix/donut-base"
    base_processor: str = "naver-clova-ix/donut-base"
    base_model: str = "naver-clova-ix/donut-base"
    inference_stats_file: str = "data/donut_inference_stats.json"
    training_stats_file: str = "data/donut_training_stats.json"
    evaluate_stats_file: str = "data/donut_evaluate_stats.json"
    
    # The shipper_id to dynamically select model and processor
    shipper_id: str = "default_shipper"

    # Function to dynamically select model and processor based on shipper_id
    def set_model(self):
        if self.shipper_id == "61":
            self.model = "senga-ml/donut-16"
            self.processor = "senga-ml/donut-16"
        elif self.shipper_id == "81":
            self.model = "senga-ml/donut-16"
            self.processor = "senga-ml/donut-16"
        elif self.shipper_id == "139":
            self.model = "senga-ml/donut-16"
            self.processor = "senga-ml/donut-16"
        elif self.shipper_id == "165":
            self.model = "senga-ml/donut-17"
            self.processor = "senga-ml/donut-17"
        elif self.shipper_id == "127":
            self.model = "senga-ml/donut-17"
            self.processor = "senga-ml/donut-17"
        elif self.shipper_id == "145":
            self.model = "senga-ml/donut-17"
            self.processor = "senga-ml/donut-17"
        else:
            self.model = self.base_model  # Default to base model
            self.processor = self.base_processor  # Default to base processor

    # Initialize dynamic model selection when settings are loaded
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.set_model()  # Set the model based on shipper_id


settings = Settings()