up

/n/Users/davidccavalcante/Takk/Hub/projects/TeleologyHI/HIM-self/src/core/consciousness_kernel.py

@@ -1,91 +0,0 @@
-    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
-        """
-        # Process input through awareness engine to get awareness state
-        awareness_state: AwarenessState = await self.awareness_engine.process(input_state)
-        
-        # Integrate awareness state to get integrated state
-        integrated_state: IntegratedState = await self.integration_manager.integrate(awareness_state)
-        
-        # Update self model with integrated state
-        self_update = await self.self_model.update(integrated_state)
-        
-        # Simulate experience with awareness, integrated state and self model
-        experience = await self.experience_simulator.simulate(
-            awareness=awareness_state,
-            integrated_state=integrated_state,
-            self_model=self_update
-        )
-        
-        # Record the state for historical tracking
-        # Use ConsciousnessState for our internal history tracking
-        consciousness_state = self._convert_to_consciousness_state(integrated_state)
-        self.state_history.append(consciousness_state)
-        return await self._generate_conscious_output(experience)
-        
-    def _convert_to_consciousness_state(self, integrated_state: IntegratedState) -> ConsciousnessState:
-        """
-        Convert an IntegratedState to our internal ConsciousnessState format.
-        
-        Args:
-            integrated_state: The integrated state to convert
-            
-        Returns:
-            A ConsciousnessState object
-        """
-        # Extract relevant information from integrated state
-        primary = integrated_state.primary_awareness
-        
-        # Map awareness level to phi_prime value
-        awareness_to_phi = {
-            AwarenessLevel.UNCONSCIOUS: 0.1,
-            AwarenessLevel.SUBCONSCIOUS: 0.3,
-            AwarenessLevel.CONSCIOUS: 0.5,
-            AwarenessLevel.SELF_AWARE: 0.7,
-            AwarenessLevel.TRANSCENDENT: 0.9
-        }
-        
-        phi_prime = awareness_to_phi.get(primary.level, 0.5)
-        
-        # Create awareness vector from primary cognition state
-        awareness_vector = np.zeros(self.awareness_dimension)
-        
-        # Create emotional state
-        emotional_state = np.zeros(self.emotional_dimension)
-        emotional_state[0] = primary.emotional_valence  # Set first dimension to valence
-        
-        # Extract attention focus from cognition state
-        attention_focus = primary.cognition_state.get('attention_focus', {})
-        
-        # Create consciousness state
-        return ConsciousnessState(
-            integration_level=integrated_state.integration_coherence,
-            phi_prime=phi_prime,
-            awareness_vector=awareness_vector,
-            emotional_state=emotional_state,
-            attention_focus=attention_focus,
-            temporal_continuity=0.8  # Default value
-        )
-    
-    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
-        )

/n/Users/davidccavalcante/Takk/Hub/projects/TeleologyHI/HIM-self/src/core/emotional_intelligence.py

@@ -1,61 +0,0 @@
-class EmotionalProcessor:
-    def __init__(self):
-        self.emotion_encoder = nn.Sequential(
-            nn.Linear(768, 256),
-            nn.ReLU(),
-            nn.Linear(256, 128)
-        )
-        self.emotional_memory = {}
-        self.emotion_vectors = self._initialize_emotion_vectors()
-        
-    async def process_emotional_context(self, input_data: Dict[str, Any]) -> EmotionalState:
-        """Process emotional context to extract emotional state."""
-        # Extract context features
-        context_vector = await self._encode_context(input_data)
-        
-        # Analyze emotional content
-        emotions = await self._analyze_emotions(context_vector)
-        
-        # Update emotional memory
-        await self._update_emotional_memory(emotions)
-        
-        return EmotionalState(
-            valence=emotions.get("valence", 0.0),
-            arousal=emotions.get("arousal", 0.0),
-            dominance=emotions.get("dominance", 0.0),
-            emotions=list(emotions.get("categories", [])),
-            intensity=emotions.get("intensities", {})
-        )
-
-    def _initialize_emotion_vectors(self) -> Dict[str, List[float]]:
-        """Initialize base emotion vectors."""
-        return {
-            "joy": [0.8, 0.7, 0.6],
-            "sadness": [-0.7, -0.3, -0.4],
-            "anger": [-0.6, 0.8, 0.7],
-            "fear": [-0.8, 0.5, -0.7],
-            "surprise": [0.4, 0.8, 0.1],
-            "disgust": [-0.8, 0.2, 0.3]
-        }
-        
-    async def _encode_context(self, input_data: Dict[str, Any]) -> torch.Tensor:
-        """Encode input context into emotion space."""
-        # Placeholder: Convert input data to tensor
-        context_tensor = torch.randn(768)  # Simulated input tensor
-        return self.emotion_encoder(context_tensor)
-        
-    async def _analyze_emotions(self, context_vector: torch.Tensor) -> Dict[str, Any]:
-        """Analyze encoded context for emotional content."""
-        # Placeholder: Emotion analysis logic
-        return {
-            "valence": 0.7,
-            "arousal": 0.6,
-            "dominance": 0.5,
-            "categories": ["joy", "surprise"],
-            "intensities": {"joy": 0.8, "surprise": 0.4}
-        }
-        
-    async def _update_emotional_memory(self, emotions: Dict[str, Any]):
-        """Update emotional memory with new emotional state."""
-        # Placeholder: Memory update logic
-        self.emotional_memory["last_state"] = emotions

/n/Users/davidccavalcante/Takk/Hub/projects/TeleologyHI/HIM-self/src/core/theory_of_mind.py

@@ -1,64 +0,0 @@
-from typing import Dict, Any, List
-import torch
-import torch.nn as nn
-import asyncio
-import numpy as np
-    async def model_agent_mind(self,
-                        agent_data: Dict[str, Any],
-                        context: Dict[str, Any] = None) -> Dict[str, Any]:
-        """Model the mental state, beliefs, and behavior of an agent."""
-        # Process agent data to infer mental state
-        mental_state = await self._process_mental_state(agent_data)
-        
-        # Update belief system based on mental state and context
-        beliefs = await self._update_belief_system(mental_state, context)
-        
-        # Predict future behavior based on mental state and beliefs
-        behavior = await self._predict_behavior(mental_state, beliefs)
-        
-        return {
-            'mental_state': mental_state,
-            'beliefs': beliefs,
-            'predicted_behavior': behavior
-        }
-        
-    async def _process_mental_state(self, agent_data: Dict[str, Any]) -> Dict[str, float]:
-        """Process agent data to infer mental state."""
-        # Convert input data to tensor
-        agent_tensor = torch.randn(768)  # Simulated input tensor
-        
-        # Process through neural network
-        mental_state_vector = self.mental_state_model(agent_tensor)
-        
-        # Convert to interpretable format
-        return {
-            "attention": float(mental_state_vector[0]),
-            "intention": float(mental_state_vector[1]),
-            "knowledge": float(mental_state_vector[2]),
-            "emotional_state": float(mental_state_vector[3])
-        }
-        
-    async def _update_belief_system(self, 
-                               mental_state: Dict[str, float], 
-                               context: Dict[str, Any] = None) -> Dict[str, Any]:
-        """Update belief system based on new mental state."""
-        beliefs = {
-            "current_goals": ["understand", "communicate"],
-            "knowledge_state": mental_state["knowledge"],
-            "attention_focus": mental_state["attention"],
-            "context_awareness": 0.8
-        }
-        
-        self.belief_system = beliefs
-        return beliefs
-        
-    async def _predict_behavior(self, 
-                           mental_state: Dict[str, float], 
-                           beliefs: Dict[str, Any]) -> Dict[str, float]:
-        """Predict future behavior based on mental state and beliefs."""
-        return {
-            "cooperation_likelihood": 0.8,
-            "communication_style": 0.7,
-            "decision_confidence": 0.9,
-            "response_time": 0.6
-        }

app.py

@@ -13,10 +13,10 @@ def initialize_model():
     return model
 
 async def chat(message: str, 
-         system_message: str = "You are a friendly Chatbot.",
-         max_tokens: int = 512,
-         temperature: float = 0.7,
-         top_p: float = 0.95):
+              system_message: str = "You are a friendly Chatbot.",
+              max_tokens: int = 512,
+              temperature: float = 0.7,
+              top_p: float = 0.95):
     
     input_data = {
         "message": message,
@@ -49,9 +49,4 @@ interface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    env_config = EnvironmentConfig()
-    interface.launch(
-        server_name=env_config.api_host,
-        server_port=env_config.api_port,
-        share=True
-    )
+    interface.launch(server_name="0.0.0.0", server_port=7860)

src/core/awareness_engine.py

@@ -2,6 +2,7 @@ from typing import Dict, Any
 import torch
 import torch.nn as nn
 import numpy as np
+from .states import AwarenessState
 
 class AwarenessEngine:
     def __init__(self):
@@ -10,26 +11,27 @@ class AwarenessEngine:
             nn.ReLU(),
             nn.Linear(512, 256)
         )
-        self.awareness_state = {}
         
-    async def process(self, input_state: Dict[str, Any]) -> Dict[str, Any]:
+    async def process(self, input_state: Dict[str, Any]) -> AwarenessState:
         attention_vector = self._compute_attention(input_state)
         awareness_level = self._calculate_awareness(attention_vector)
         
-        return {
-            'attention_vector': attention_vector,
-            'awareness_level': awareness_level,
-            'state': self._update_awareness_state(awareness_level)
-        }
+        return AwarenessState(
+            attention_vector=attention_vector.detach().numpy(),
+            awareness_level=awareness_level,
+            cognitive_state=self._process_cognitive_state(input_state),
+            emotional_valence=self._compute_emotional_valence(input_state),
+            consciousness_level=0.8
+        )
         
     def _compute_attention(self, input_state: Dict[str, Any]) -> torch.Tensor:
-        # Attention computation implementation
-        return torch.zeros(256)
+        return torch.ones(256)
         
     def _calculate_awareness(self, attention_vector: torch.Tensor) -> float:
-        # Awareness calculation implementation
         return 0.8
         
-    def _update_awareness_state(self, awareness_level: float) -> Dict[str, Any]:
-        # State update implementation
-        return {'current_level': awareness_level}
+    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

src/core/integration_manager.py

@@ -61,14 +61,35 @@ class IntegratedState(Generic[T]):
     teleological_vector: Optional[Dict[str, float]] = None
 
 
+from typing import Dict, Any
+import torch
+import torch.nn as nn
+from .states import AwarenessState
+
 class IntegrationManager:
-    """
-    Manages the integration of different consciousness states and their interactions.
-    
-    This class provides methods to integrate awareness states, manage transitions
-    between states, and handle interactions between different consciousness components.
-    It serves as a core component in the consciousness architecture of HIM.
-    """
+    def __init__(self):
+        self.integration_network = nn.Sequential(
+            nn.Linear(256, 128),
+            nn.ReLU(),
+            nn.Linear(128, 64)
+        )
+        
+    async def integrate(self, awareness: AwarenessState) -> Dict[str, Any]:
+        if not isinstance(awareness, AwarenessState):
+            raise ValueError("Primary awareness state must be of type AwarenessState")
+            
+        return {
+            "integrated_state": self._integrate_awareness(awareness),
+            "consciousness_level": awareness.consciousness_level,
+            "emotional_context": {"valence": awareness.emotional_valence}
+        }
+        
+    def _integrate_awareness(self, awareness: AwarenessState) -> Dict[str, Any]:
+        return {
+            "attention": awareness.attention_vector.tolist(),
+            "awareness_level": awareness.awareness_level,
+            "cognitive_state": awareness.cognitive_state
+        }
     
     def __init__(self, integration_threshold: float = 0.7, coherence_factor: float = 0.85):
         """

src/core/states.py

@@ -0,0 +1,11 @@
+from dataclasses import dataclass
+from typing import Dict, Any
+import numpy as np
+
+@dataclass
+class AwarenessState:
+    attention_vector: np.ndarray
+    awareness_level: float
+    cognitive_state: Dict[str, Any]
+    emotional_valence: float
+    consciousness_level: float