import numpy as np
import torch
from NeuroFlex import NeuroFlex
from NeuroFlex.utils import Config

def main():
    """
    Main function to demonstrate the capabilities of the NeuroFlex framework.

    This function initializes the NeuroFlex system and showcases various components:
    - Core neural network for image classification
    - Quantum neural network
    - Ethical evaluation
    - Explainable AI
    - BCI data processing
    - Consciousness simulation
    - Protein structure prediction
    - Math problem solving
    - Edge AI optimization
    - UnifiedTransformer for text processing and classification

    Each component is demonstrated with example inputs and outputs.
    """
    # Initialize NeuroFlex
    config = Config.get_config()
    config['USE_UNIFIED_TRANSFORMER'] = True
    config['UNIFIED_TRANSFORMER_VOCAB_SIZE'] = 30000  # Adjust as needed
    neuroflex = NeuroFlex(config)
    neuroflex.setup()

    # Example: Using core neural network
    input_data = np.random.rand(10, 28, 28, 1)  # Example input for image classification
    labels = np.random.randint(0, 10, size=(10,))
    neuroflex.train(input_data, labels)
    predictions = neuroflex.predict(input_data)

    # Example: Quantum neural network
    if neuroflex.quantum_model:
        quantum_input = np.random.rand(4)
        quantum_output = neuroflex.quantum_model.forward(quantum_input)
        print("Quantum output:", quantum_output)

    # Example: Ethical evaluation
    action = {"type": "recommendation", "user_id": 123, "item_id": 456}
    is_ethical = neuroflex.evaluate_ethics(action)
    print("Action is ethical:", is_ethical)

    # Example: Explainable AI
    explanation = neuroflex.explain_prediction(input_data[0], predictions[0])
    print("Prediction explanation:", explanation)

    # Example: BCI data processing
    if neuroflex.bci_processor:
        bci_data = np.random.rand(neuroflex.config['BCI_NUM_CHANNELS'], 1000)
        processed_bci_data = neuroflex.process_bci_data(bci_data)
        print("Processed BCI data shape:", processed_bci_data.shape)

    # Example: Consciousness simulation
    if neuroflex.consciousness_sim:
        consciousness_input = np.random.rand(64)
        consciousness_output = neuroflex.simulate_consciousness(consciousness_input)
        print("Consciousness simulation output:", consciousness_output)

    # Example: Protein structure prediction
    if neuroflex.alphafold:
        protein_sequence = "MKWVTFISLLLLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAKTCVADESAENCDKS"
        structure = neuroflex.predict_protein_structure(protein_sequence)
        print("Predicted protein structure:", structure)

    # Example: Math problem solving
    math_problem = "Solve the equation: 2x + 5 = 15"
    solution = neuroflex.solve_math_problem(math_problem)
    print("Math problem solution:", solution)

    # Example: Edge AI optimization
    optimized_model = neuroflex.optimize_for_edge(neuroflex.core_model)
    print("Model optimized for edge deployment")

    # Example: Using UnifiedTransformer
    if neuroflex.unified_transformer:
        input_text = "This is an example sentence for the UnifiedTransformer."
        tokenized_input = neuroflex.process_text(input_text)
        input_ids = torch.tensor([tokenized_input])
        attention_mask = torch.ones_like(input_ids)

        # Fine-tune for classification task
        neuroflex.unified_transformer.fine_tune(task='classification', num_labels=2)

        # Perform classification
        output = neuroflex.unified_transformer.task_specific_forward(input_ids, attention_mask, task='classification')
        print("UnifiedTransformer classification output:", output)

if __name__ == "__main__":
    main()
