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from typing import Dict, Any
import torch
import torch.nn as nn
import numpy as np
from .states import AwarenessState, AwarenessLevel
class AwarenessEngine:
def __init__(self):
self.attention_network = nn.Sequential(
nn.Linear(768, 512),
nn.ReLU(),
nn.Linear(512, 256)
)
async def process(self, input_state: Dict[str, Any]) -> AwarenessState:
attention_vector = self._compute_attention(input_state)
awareness_level = self._calculate_awareness(attention_vector)
level = self._determine_awareness_level(awareness_level)
cognitive_state = {
"attention_focus": self._compute_attention_focus(attention_vector),
"processing_depth": awareness_level,
"cognitive_load": self._estimate_cognitive_load(input_state)
}
return AwarenessState(
attention_vector=attention_vector.detach().numpy(),
awareness_level=awareness_level,
cognitive_state=cognitive_state,
emotional_valence=self._compute_emotional_valence(input_state),
consciousness_level=0.8,
level=level
)
def _compute_attention_focus(self, attention_vector: torch.Tensor) -> float:
return float(torch.mean(attention_vector))
def _estimate_cognitive_load(self, input_state: Dict[str, Any]) -> float:
return 0.5 # Default mid-range load
def _determine_awareness_level(self, awareness_level: float) -> AwarenessLevel:
if awareness_level > 0.8:
return AwarenessLevel.TRANSCENDENT
elif awareness_level > 0.6:
return AwarenessLevel.INTEGRATED
elif awareness_level > 0.4:
return AwarenessLevel.REFLECTIVE
elif awareness_level > 0.2:
return AwarenessLevel.PERCEPTUAL
return AwarenessLevel.BASIC
def _compute_attention(self, input_state: Dict[str, Any]) -> torch.Tensor:
return torch.ones(256)
def _calculate_awareness(self, attention_vector: torch.Tensor) -> float:
return 0.8
def _process_cognitive_state(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
return {"state": "active", "focus_level": 0.9}
def _compute_emotional_valence(self, input_state: Dict[str, Any]) -> float:
return 0.5
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