"""
Production-Ready Transfer Learning Pipeline
This module contains the functions for the new, production-ready transfer
learning pipeline, which was designed and generated by OrchestratorAI itself.
"""

import json
from typing import Dict, Any

def load_model(model_path: str) -> Dict[str, Any]:
    """
    Loads a pre-trained model's parameters from a JSON file.

    Args:
        model_path (str): The path to the model's JSON file.

    Returns:
        Dict[str, Any]: The loaded model parameters.
    """
    try:
        with open(model_path, 'r') as f:
            model_params = json.load(f)
        print(f"  [TL Pipeline] Model loaded from {model_path}")
        return model_params
    except (FileNotFoundError, json.JSONDecodeError) as e:
        print(f"  [TL Pipeline] Error loading model: {e}")
        return {}

def apply_model(current_params: Any, loaded_params: Dict[str, Any]) -> Any:
    """
    Applies the loaded model parameters to the current parameters by averaging weights.

    Args:
        current_params (Any): The current parameter object (e.g., LayerParameters).
        loaded_params (Dict[str, Any]): The model parameters loaded from a file.

    Returns:
        Any: The updated parameter object.
    """
    if not loaded_params:
        print("  [TL Pipeline] No model to apply, returning current parameters.")
        return current_params

    loaded_weights = loaded_params.get('weights', {})
    for key, value in loaded_weights.items():
        if key in current_params.weights:
            # Simple averaging strategy
            current_params.weights[key] = (current_params.weights[key] + value) / 2

    print("  [TL Pipeline] Model applied successfully.")
    return current_params

def log_to_registry(model_path: str, project_type: str):
    """
    Logs the application of a transfer learning model to a registry file.

    Args:
        model_path (str): The path of the model that was applied.
        project_type (str): The project type for which the model was applied.
    """
    from datetime import datetime

    log_entry = {
        'timestamp': datetime.now().isoformat(),
        'model_path': model_path,
        'project_type': project_type
    }

    try:
        with open('model_registry.log', 'a') as f:
            f.write(json.dumps(log_entry) + '\n')
        print("  [TL Pipeline] Logged model application to registry.")
    except IOError as e:
        print(f"  [TL Pipeline] Error writing to registry: {e}")
