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import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import numpy as np

class AIHelper:
    def __init__(self):
        self._initialize_memory_model()
        self._initialize_decision_model()
    
    def _initialize_memory_model(self):
        self.memory_model = Sequential([
            LSTM(50, input_shape=(10, 1), return_sequences=False),
            Dense(20, activation='relu')
        ])
        self.memory_model.compile(optimizer='adam', loss='mse')
        
    def _initialize_decision_model(self):
        self.decision_model = Sequential([
            Dense(64, input_dim=20, activation='relu'),
            Dense(32, activation='relu'),
            Dense(2, activation='softmax')
        ])
        self.decision_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    
    def train_memory_model(self, input_data, target_data, epochs=10):
        input_data_reshaped = input_data.reshape((input_data.shape[0], input_data.shape[1], 1))
        self.memory_model.fit(input_data_reshaped, target_data, epochs=epochs)
    
    def train_decision_model(self, input_data, target_data, epochs=10):
        self.decision_model.fit(input_data, target_data, epochs=epochs)
    
    def remember(self, input_data):
        input_data_reshaped = input_data.reshape((input_data.shape[0], input_data.shape[1], 1))
        return self.memory_model.predict(input_data_reshaped)
    
    def decide(self, memory_output):
        return self.decision_model.predict(memory_output)
    
    def assist(self, new_input):
        memory_output = self.remember(new_input)
        decision_output = self.decide(memory_output)
        return decision_output

# Example usage
if __name__ == "__main__":
    np.random.seed(42)
    tf.random.set_seed(42)

    ai_helper = AIHelper()

    # Simulated sensory input data
    input_data = np.random.random((1000, 10))
    target_memory_data = np.random.random((1000, 20))
    
    # Train memory model
    ai_helper.train_memory_model(input_data, target_memory_data, epochs=10)

    # Simulated decision labels
    decision_labels = np.random.randint(2, size=(1000, 1))
    decision_labels = tf.keras.utils.to_categorical(decision_labels, 2)
    
    # Generate pseudo-memory outputs
    memory_outputs = ai_helper.remember(input_data)

    # Train decision-making model
    ai_helper.train_decision_model(memory_outputs, decision_labels, epochs=10)

    # Simulated new sensory input for testing
    new_input = np.random.random((1, 10))

    # AI helper assisting with new input
    decision_output = ai_helper.assist(new_input)

    print("AI Helper decision output:", decision_output)