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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 | |