from .memcomputing import MemoryCentricProcessor
import logging

# Initialize memory-centric processor
memory_processor = MemoryCentricProcessor()

# Configure logging for AI Prediction module
logging.basicConfig(level=logging.INFO, format='[AI Prediction] %(asctime)s - %(levelname)s - %(message)s')

def train_model(data):
    # Preprocess data using memory-centric processor
    processed_data = memory_processor.process(data)
    # Continue with model training using processed_data
    # ... existing training code ...

def optimize_parameters(params):
    # Optimize parameters using memory-centric processing
    optimized_params = memory_processor.optimize(params)
    return optimized_params

# ... existing code ...

"""
Module: ai_prediction.py
This module contains placeholder AI prediction routines extracted from quantum_transformer.py and quantum_algorithms.py.

Extensive Notation:
- Placeholder function predict_quantum_state uses minimal logic to simulate AI-driven quantum state prediction.
- Future updates will integrate advanced AI methods like deep reinforcement learning and Bayesian deep learning for predictive enhancements.
"""

def predict_quantum_state(input_data):
    """Predict the quantum state using AI-driven techniques.
    
    Parameters:
        input_data (dict): Dictionary containing input parameters for prediction.
        
    Returns:
        dict: Prediction result including status and input details.
    """
    # TODO: Implement advanced AI prediction logic integrating quantum-inspired algorithms
    return {"result": "Quantum state prediction complete", "input": input_data}

def predict_from_simulation(simulation_result):
    """Generate a prediction based on the simulation result.

    This function uses the output from the quantum simulation to provide an AI-driven prediction.
    The prediction logic is currently a placeholder and will be expanded to incorporate quantum-inspired algorithms and advanced AI techniques in future versions.

    Parameters:
        simulation_result (dict): Result dictionary from simulate_quantum_circuit, expected to have key 'output_state'.

    Returns:
        dict: A dictionary containing the prediction and associated metadata.

    Raises:
        ValueError: If simulation_result does not contain the required keys.
    """
    # Validate the input
    if not isinstance(simulation_result, dict) or 'output_state' not in simulation_result:
        raise ValueError("Invalid simulation result provided for prediction.")

    # Log the start of prediction generation
    logging.info("Starting prediction generation based on simulation result.")

    try:
        # Placeholder for actual prediction logic
        prediction = {
            "predicted_value": 42,  # Dummy prediction value
            "confidence": 0.95      # Dummy confidence score
        }
        
        # Log the successful generation of prediction
        logging.info("Prediction generated successfully.")
        return prediction
    except Exception as e:
        logging.error(f"Error during prediction generation: {e}")
        raise