from pathlib import Path
from architectures.Model import Model

def _log_to_csv(model:Model, mode, accuracy, report_dict, strategy):
        """Handles directory structures, file setups, and distinct CSV column formatting."""
        arch_name = model.__class__.__name__.lower()
        save_dir = Path(f"reports/{strategy}")
        save_dir.mkdir(parents=True, exist_ok=True)
        csv_path = save_dir / f"{arch_name}-{mode}.csv"
        
        file_exists = csv_path.exists()

        headers = [
                "accuracy", 
                "macro_precision", "macro_recall", "macro_f1",
                "weighted_precision", "weighted_recall", "weighted_f1"
        ]
        row = [
            f"{accuracy / 100.0:.4f}",
            f"{report_dict['macro avg']['precision']:.4f}",
            f"{report_dict['macro avg']['recall']:.4f}",
            f"{report_dict['macro avg']['f1-score']:.4f}",
            f"{report_dict['weighted avg']['precision']:.4f}",
            f"{report_dict['weighted avg']['recall']:.4f}",
            f"{report_dict['weighted avg']['f1-score']:.4f}"
        ]
            
        with open(csv_path, "a") as f:
            if not file_exists:
                f.write(",".join(headers) + "\n")
            f.write(",".join(row) + "\n")
            
        print(f">> Direct CSV metrics appended to {csv_path}")