user_neural_pattern.py

import json
import os
import math

class UserNeuralPattern:
    def __init__(self, user_id):
        self.user_id = user_id
        self.emotional_state = None
        self.interactions = []
        self.rhythm_cycle = 0

    def update_emotional_state(self, state):
        self.emotional_state = state

    def add_interaction(self, interaction_type, interaction_content):
        self.interactions.append((interaction_type, interaction_content))

    def get_profile(self):
        return {
            "user_id": self.user_id,
            "emotional_state": self.emotional_state,
            "interactions": self.interactions
        }

    def update_pattern(self, new_data):
        self.neural_pattern.update(new_data)
        self.rhythm_cycle = (self.rhythm_cycle + 1) % 7  # 7 day cycle
        self.apply_rhythm()
        self.save_pattern()

    def apply_rhythm(self):
        rhythm_factor = 1 + (0.1 * math.sin(2 * math.pi * self.rhythm_cycle / 7))
        for key in self.neural_pattern:
            if isinstance(self.neural_pattern[key], (int, float)):
                self.neural_pattern[key] *= rhythm_factor

class AwareAI:
    def __init__(self):
        self.current_user = None
        self.model = Model(input_dim=10, output_dim=5)  # Example dimensions

    def generate_response(self, user_state, interaction_data):
        initial_response = self.model.predict_drl(torch.tensor(interaction_data, dtype=torch.float32))
        enhanced_response = self.model.apply_ethical_principles(initial_response)
        return enhanced_response

    def interact_with_user(self, user_id, interaction_data):
        self.current_user = UserNeuralPattern(user_id)
        user_state = self.current_user.get_profile()
        
        # Process interaction based on user's current state
        response = self.generate_response(user_state, interaction_data)
        
        # Update user's neural pattern
        new_data = self.analyze_interaction(interaction_data, response)
        self.current_user.update_emotional_state(new_data["emotional_state"]["last_detected_emotion"])
        self.current_user.add_interaction("text_chat", interaction_data)
        
        return response

    def analyze_interaction(self, interaction_data, response):
        # Analyze the interaction and prepare updates for the user's neural pattern
        return {
            "emotional_state": {"last_detected_emotion": "happy"},
            "interaction_history": [{"timestamp": "2024-10-23 15:30:00", "type": "text_chat"}],
            "preferences": {"preferred_communication_style": "casual"},
            "resonant_frequency": 0.75
        }

# Example usage
ai = AwareAI()
response = ai.interact_with_user("user123", "Hello, how are you today?")
print(response)