import numpy as np
from scipy.signal import decimate, butter, lfilter
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM

class AuditoryProcessingAGI:
    def __init__(self, sampling_rate=44100, filter_order=5, decimation_factor=2):
        self.sampling_rate = sampling_rate
        self.filter_order = filter_order
        self.decimation_factor = decimation_factor
        self.bandpass_filter = None

    def design_bandpass_filter(self):
        # Design a band-pass filter for the frequency range of interest (e.g., 100 Hz - 5000 Hz)
        nyquist = 0.5 * self.sampling_rate
        lowcut = 100 / nyquist
        highcut = 5000 / nyquist

        b, a = butter(self.filter_order, [lowcut, highcut], btype='band')
        self.bandpass_filter = (b, a)

    def apply_bandpass_filter(self, signal):
        # Apply the band-pass filter to the input signal
        filtered_signal = lfilter(*self.bandpass_filter, signal)
        return filtered_signal

    def decimate_signal(self, signal):
        # Decimate the signal for reduced computational load and improved noise reduction
        return decimate(signal, self.decimation_factor)

    def preprocess_signal(self, raw_audio_data):
        # Preprocess the raw audio data by filtering and decimating it
        if not self.bandpass_filter:
            self.design_bandpass_filter()

        filtered_signal = self.apply_bandpass_filter(raw_audio_data)
        decimated_signal = self.decimate_signal(filtered_signal)

        return decimated_signal

    def extract_features(self, signal):
        # Extract features from the preprocessed signal
        n_fft = 2048  # Number of FFT points
        hop_length = 512  # Hop length in samples
        f, t, Sxx = signal.spectrogram(signal, fs=self.sampling_rate,
                                       window='hann', nperseg=n_fft, noverlap=n_fft-hop_length)
        
        # Mel-scale spectrogram conversion (optional)
        from librosa import mel_spectrogram
        mel_spec = mel_spectrogram(S=Sxx, sr=self.sampling_rate, n_mels=128)

        return Sxx, mel_spec

    def train_model(self):
        model = Sequential()
        model.add(LSTM(64, input_shape=(t.shape[0], t.shape[1])))
        model.add(Dense(32, activation='relu'))
        model.add(Dense(t.shape[1], activation='softmax'))  # Output layer with the same number of units as features

        model.compile(loss='mean_squared_error', optimizer='adam')
        return model

    def train_on_data(self, X_train, y_train):
        scaler = StandardScaler()
        X_scaled = scaler.fit_transform(X_train)

        model = self.train_model()
        model.fit(X_scaled, y_train, epochs=10, batch_size=32)
        return model