import numpy as np
import jax
import jax.numpy as jnp
from jax import jit
from flax import linen as nn
from typing import Callable

class RealTimeFeedback:
    def __init__(self, signal_processor: Callable, decoder: nn.Module, feedback_generator: Callable):
        self.signal_processor = signal_processor
        self.decoder = decoder
        self.feedback_generator = feedback_generator
        self.feedback_history = []

    def process_signal(self, raw_signal):
        raw_signal = jnp.array(raw_signal)
        return self.signal_processor(raw_signal)

    def decode_signal(self, processed_signal):
        processed_signal = jnp.atleast_2d(processed_signal)
        return self.decoder(processed_signal)

    def generate_feedback(self, decoded_signal):
        return self.feedback_generator(decoded_signal)

    def closed_loop_feedback(self, raw_signal):
        processed_signal = self.process_signal(raw_signal)
        decoded_signal = self.decode_signal(processed_signal)
        feedback = self.generate_feedback(decoded_signal)
        self.feedback_history.append(feedback)
        return feedback

    def get_feedback_history(self):
        return self.feedback_history

class NeurofeedbackApplication:
    def __init__(self, real_time_feedback: RealTimeFeedback, adaptation_rate: float = 0.1):
        self.real_time_feedback = real_time_feedback
        self.adaptation_rate = adaptation_rate
        self.user_performance = []

    def update_user_performance(self, performance_metric):
        self.user_performance.append(performance_metric)

    def adapt_feedback(self):
        if len(self.user_performance) > 1:
            performance_change = self.user_performance[-1] - self.user_performance[-2]
            self.adaptation_rate *= (1 + performance_change)

    def run_session(self, raw_signal_stream, session_duration):
        for t in range(session_duration):
            raw_signal = next(raw_signal_stream)
            feedback = self.real_time_feedback.closed_loop_feedback(raw_signal)
            self.provide_user_feedback(feedback)
            user_performance = self.measure_user_performance()
            self.update_user_performance(user_performance)
            self.adapt_feedback()

    def provide_user_feedback(self, feedback):
        # Implement user interface for providing feedback
        # This could be visual, auditory, or haptic feedback
        print(f"Providing user feedback: {feedback}")

    def measure_user_performance(self):
        # Implement logic to measure user performance
        # This could be based on task completion, accuracy, or other metrics
        return np.random.random()  # Placeholder for actual performance measurement

# Example usage:
def example_signal_processor(raw_signal):
    # Implement signal processing logic
    processed_signal = jnp.array(raw_signal)  # Convert to JAX array
    # Apply a simple bandpass filter (example)
    filtered_signal = jnp.where((processed_signal > 0.2) & (processed_signal < 0.8), processed_signal, 0)
    # Normalize the signal
    normalized_signal = (filtered_signal - jnp.mean(filtered_signal)) / jnp.std(filtered_signal)
    return normalized_signal

class ExampleDecoder(nn.Module):
    @nn.compact
    def __call__(self, x):
        x = nn.Dense(features=64)(x)
        x = nn.relu(x)
        x = nn.Dense(features=32)(x)
        x = nn.relu(x)
        x = nn.Dense(features=1)(x)
        return x

def example_feedback_generator(decoded_signal):
    # Implement feedback generation logic
    return float(decoded_signal[0])  # Placeholder for actual feedback generation

if __name__ == "__main__":
    # Initialize components
    signal_processor = example_signal_processor
    decoder = ExampleDecoder()
    decoder_params = decoder.init(jax.random.PRNGKey(0), jnp.ones((1, 64)))  # Initialize with dummy input
    decoder = decoder.bind(decoder_params)
    feedback_generator = example_feedback_generator

    # Create RealTimeFeedback instance
    rtf = RealTimeFeedback(signal_processor, decoder, feedback_generator)

    # Create NeurofeedbackApplication instance
    nf_app = NeurofeedbackApplication(rtf)

    # Simulate a signal stream (replace with actual signal acquisition)
    def signal_stream():
        while True:
            yield np.random.rand(64)  # Simulating 64-channel EEG data

    # Run a neurofeedback session
    nf_app.run_session(signal_stream(), session_duration=100)

    print("Neurofeedback session completed.")
    print(f"Final user performance: {nf_app.user_performance[-1]}")
