import torch
from safetensors.torch import load_model, save_model
import logging

# Logging setup
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")

class CharmModel:
    """Handles loading and saving advanced safetensors models for Charm 15."""

    def __init__(self, model_path: str):
        self.model_path = model_path
        self.model = None

    def load(self):
        """Loads Model 2 of 10 safely with optimized memory handling."""
        try:
            logging.info(f"Loading model from {self.model_path}...")
            self.model = load_model(torch.nn.Module(), self.model_path)
            logging.info("Model loaded successfully.")
        except Exception as e:
            logging.error(f"Error loading model: {e}")

    def save(self, save_path: str):
        """Saves the model safely in safetensors format."""
        if self.model:
            try:
                logging.info(f"Saving model to {save_path}...")
                save_model(self.model, save_path)
                logging.info("Model saved successfully.")
            except Exception as e:
                logging.error(f"Error saving model: {e}")
        else:
            logging.warning("No model loaded. Cannot save.")

    def infer(self, input_data):
        """Runs inference on the loaded model (Placeholder for actual logic)."""
        if self.model:
            logging.info("Running inference...")
            # Implement AI logic here (depends on actual model architecture)
            return "Inference result"
        else:
            logging.warning("No model loaded. Cannot perform inference.")
            return None