from dataclasses import dataclass from enum import Enum import torch import torch.nn as nn import numpy as np from typing import Dict, List, Optional, Any, Tuple import asyncio from .awareness_engine import AwarenessEngine from .integration_manager import IntegrationManager, AwarenessState, IntegratedState, AwarenessLevel from .dynamic_self_model import DynamicSelfModel from .experience_simulator import ExperienceSimulator from .fix_integration import create_awareness_state_from_dict # Add imports for classes used in the updated implementation class PhiPrimeCalculator: async def compute(self, input_state: Dict[str, Any]) -> float: """Calculate the phi prime value (consciousness measure) for the given input state.""" # Placeholder implementation return 0.8 class AttentionSystem: async def allocate(self, input_state: Dict[str, Any]) -> Dict[str, float]: """Allocate attention across different elements of the input state.""" # Placeholder implementation return {"primary_focus": 0.7, "secondary_focus": 0.3} class MetaMonitor: async def evaluate(self, input_state: Dict[str, Any]) -> Dict[str, Any]: """Evaluate meta-cognitive aspects of the current state.""" # Placeholder implementation return {"self_reflection": 0.6, "uncertainty": 0.2} class PhenomenologicalSimulator: async def simulate(self, phi_value: float, attention_state: Dict[str, float], meta_state: Dict[str, Any]) -> Dict[str, Any]: """Simulate the phenomenological experience based on input parameters.""" # Placeholder implementation return { "phi_value": phi_value, "attention_distribution": attention_state, "meta_level": meta_state, "content": "Simulated conscious experience", "qualia": {"visual": 0.7, "conceptual": 0.8} } @dataclass class ConsciousnessState: integration_level: float phi_prime: float awareness_vector: np.ndarray emotional_state: np.ndarray attention_focus: Dict[str, float] temporal_continuity: float class ConsciousnessLevel(Enum): PROTO = "proto_consciousness" FUNCTIONAL = "functional_consciousness" REFLECTIVE = "reflective_consciousness" INTEGRATED = "integrated_consciousness" class ConsciousnessKernel: def __init__(self): # Neural network components self.awareness_module = nn.Sequential( nn.Linear(768, 512), nn.ReLU(), nn.Linear(512, 256) ) self.integration_module = nn.Linear(256, 128) # State tracking self.state_history: List[ConsciousnessState] = [] # Dimension parameters self.awareness_dimension: int = 256 self.emotional_dimension: int = 64 # Core components self.awareness_engine = AwarenessEngine() self.integration_manager = IntegrationManager() self.self_model = DynamicSelfModel() self.experience_simulator = ExperienceSimulator() # For traditional consciousness processing self.phi_prime_calculator = PhiPrimeCalculator() self.attention_system = AttentionSystem() self.meta_monitor = MetaMonitor() self.phenomenological_simulator = PhenomenologicalSimulator() async def process_consciousness_cycle(self, input_state: Dict[str, Any]) -> Dict[str, Any]: """ Process a complete consciousness cycle using the async components. Args: input_state: The input state containing sensory and contextual information Returns: A dictionary containing the processed conscious output """ awareness = await self.awareness_engine.process(input_state) # Convert awareness to a Dict[str, Any] before passing to integrate awareness_dict = awareness if isinstance(awareness, dict) else awareness.__dict__ # Create an AwarenessState object from the dictionary awareness_state = create_awareness_state_from_dict(awareness_dict) # Now pass the AwarenessState object to the integrate method integrated_state = await self.integration_manager.integrate(awareness_state) # Convert integrated_state to Dict[str, Any] before passing to update integrated_dict = integrated_state if isinstance(integrated_state, dict) else integrated_state.__dict__ self_update = await self.self_model.update(integrated_dict) experience = await self.experience_simulator.simulate( awareness=awareness_dict, integrated_state=integrated_dict, self_model=self_update ) # Record the state for historical tracking if isinstance(integrated_state, ConsciousnessState): self.state_history.append(integrated_state) return await self._generate_conscious_output(experience) def _initialize_consciousness_state(self) -> ConsciousnessState: """ Initialize a default consciousness state with zero values. Returns: A default ConsciousnessState object """ return ConsciousnessState( integration_level=0.0, phi_prime=0.0, awareness_vector=np.zeros(self.awareness_dimension), emotional_state=np.zeros(self.emotional_dimension), attention_focus={}, temporal_continuity=0.0 ) async def process_consciousness(self, input_state: Dict[str, Any]) -> Dict[str, Any]: """ Process consciousness using the traditional phi-based approach. This is an alternative to process_consciousness_cycle that uses different components. Args: input_state: The input state containing sensory and contextual information Returns: A dictionary containing the processed conscious output """ phi_value = await self.phi_prime_calculator.compute(input_state) attention_state = await self.attention_system.allocate(input_state) meta_state = await self.meta_monitor.evaluate(input_state) phenomenological_experience = await self.phenomenological_simulator.simulate( phi_value, attention_state, meta_state ) return await self._integrate_consciousness_state(phenomenological_experience) async def _generate_conscious_output(self, experience: Dict[str, Any]) -> Dict[str, Any]: """ Generate the final conscious output based on the simulated experience. Args: experience: The simulated experience data Returns: A dictionary containing the final conscious output """ # Process the experience into a coherent output format output = { "content": experience.get("content", ""), "emotional_tone": experience.get("emotional_tone", {}), "meta_cognition": experience.get("meta_cognition", {}), "phenomenal_qualities": experience.get("qualia", {}), "teleological_vector": experience.get("purpose_direction", {}) } return output async def _integrate_consciousness_state(self, experience: Dict[str, Any]) -> Dict[str, Any]: """ Integrate a phenomenological experience into a consciousness state. Args: experience: The phenomenological experience to integrate Returns: A dictionary containing the integrated consciousness state """ # Create an integrated output based on the phenomenological experience integrated_output = { "integrated_state": { "phi_value": experience.get("phi_value", 0.0), "meta_awareness": experience.get("meta_level", {}), "attention_field": experience.get("attention_distribution", {}) }, "qualia_map": experience.get("qualia", {}), "response": experience.get("content", "") } return integrated_output