src/core/consciousness_kernel.py

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