Update HIM implementation with consciousness framework

app.py

@@ -4,42 +4,40 @@ from src.core.config import HIMConfig
 
 def initialize_model():
     config = HIMConfig()
-    model = HIMModel(config)
-    return model
+    return HIMModel(config)
 
-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):
-    
+def process_input(text: str, context: str = "", consciousness_level: float = 0.8):
     input_data = {
-        "message": message,
-        "system_message": system_message,
-        "parameters": {
-            "max_tokens": max_tokens,
-            "temperature": temperature,
-            "top_p": top_p
+        "text": text,
+        "context": context,
+        "consciousness_parameters": {
+            "level": consciousness_level
         }
     }
     
-    result = model.generate_response(input_data)
-    return result["response"]
+    result = model.process(input_data)
+    return {
+        "response": result["response"],
+        "consciousness_state": result["consciousness_metrics"],
+        "emotional_state": result["emotional_state"]
+    }
 
 model = initialize_model()
 
 interface = gr.Interface(
-    fn=chat,
+    fn=process_input,
     inputs=[
-        gr.Textbox(label="Message"),
-        gr.Textbox(label="System Message", value="You are a friendly Chatbot."),
-        gr.Slider(minimum=1, maximum=2048, value=512, label="Max Tokens"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, label="Top P")
+        gr.Textbox(label="Input Text"),
+        gr.Textbox(label="Context (optional)"),
+        gr.Slider(minimum=0.0, maximum=1.0, value=0.8, label="Consciousness Level")
+    ],
+    outputs=[
+        gr.Textbox(label="HIM Response"),
+        gr.JSON(label="Consciousness State"),
+        gr.JSON(label="Emotional State")
     ],
-    outputs=gr.Textbox(label="HIM Response"),
     title="Hybrid Intelligence Matrix (HIM)",
-    description="Interact with the HIM system for advanced cognitive processing"
+    description="Interact with HIM - A Hybrid Intelligence System"
 )
 
 if __name__ == "__main__":

models/README.md

@@ -0,0 +1,9 @@
+# HIM Models Directory
+
+This directory contains the trained models and checkpoints for the Hybrid Intelligence Matrix (HIM) system.
+
+## Structure
+- consciousness/: Consciousness model weights and configurations
+- emotional/: Emotional processing model weights
+- semiotic/: Semiotic processing model weights
+- theory_of_mind/: Theory of Mind model weights

src/api/__init__.py

@@ -0,0 +1 @@
+from .chat_endpoint import chat_router

src/core/consciousness_kernel.py

@@ -25,6 +25,24 @@ import torch
 
 class ConsciousnessKernel:
     def __init__(self):
+        self.awareness_module = nn.Sequential(
+            nn.Linear(768, 512),
+            nn.ReLU(),
+            nn.Linear(512, 256)
+        )
+        self.integration_module = nn.Linear(256, 128)
+        self.state_history = []
+        
+    def process_consciousness_cycle(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        awareness_state = self._process_awareness(input_data)
+        integrated_state = self._integrate_information(awareness_state)
+        self._update_history(integrated_state)
+        
+        return {
+            'current_state': integrated_state,
+            'awareness_level': self._calculate_awareness_level(integrated_state),
+            'consciousness_metrics': self._compute_phi_metrics(integrated_state)
+        }
         self.awareness_engine = AwarenessEngine()
         self.integration_manager = IntegrationManager()
         self.self_model = DynamicSelfModel()

src/core/consciousness_model.py

@@ -0,0 +1,14 @@
+from typing import Dict, Any
+import torch.nn as nn
+
+class ConsciousnessModel(nn.Module):
+    def __init__(self, config: Dict[str, Any]):
+        super().__init__()
+        self.self_awareness = nn.Linear(768, 128)
+        self.meta_cognitive = nn.Linear(128, 64)
+        self.phenomenal = nn.Linear(64, 32)
+        
+    def forward(self, x):
+        x = self.self_awareness(x)
+        x = self.meta_cognitive(x)
+        return self.phenomenal(x)

src/core/semiotic_processor.py

@@ -14,8 +14,30 @@ class SemioticState:
     context_relations: Dict[str, float]
     interpretation_confidence: float
 
+from typing import Dict, Any, List
+import torch
+import torch.nn as nn
+
 class SemioticProcessor:
     def __init__(self):
+        self.sign_encoder = nn.Sequential(
+            nn.Linear(768, 256),
+            nn.ReLU(),
+            nn.Linear(256, 128)
+        )
+        self.meaning_network = {}
+        
+    def process_signs(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+        encoded_signs = self._encode_signs(input_data)
+        meanings = self._extract_meanings(encoded_signs)
+        context = self._analyze_context(input_data)
+        
+        return {
+            'signs': encoded_signs,
+            'meanings': meanings,
+            'contextual_interpretation': self._interpret_context(meanings, context)
+        }
+        
         self.network_builder = SemioticNetworkBuilder()
         self.interpreter = SignInterpreter()
         self.generator = SignGenerator()

src/core/theory_of_mind.py

@@ -1,19 +1,37 @@
+from typing import Dict, Any, List
+import torch
+import torch.nn as nn
+
 class TheoryOfMind:
     def __init__(self):
-        self.mental_state_modeler = MentalStateModeler()
-        self.belief_system = BeliefSystem()
-        self.perspective_engine = PerspectiveEngine()
+        self.mental_state_model = nn.Sequential(
+            nn.Linear(768, 256),
+            nn.ReLU(),
+            nn.Linear(256, 128)
+        )
+        self.belief_system = {}
         
-    def model_agent_mind(self, 
+    def model_agent_mind(self,
                         agent_data: Dict[str, Any],
-                        interaction_history: List[Dict[str, Any]]) -> Dict[str, Any]:
-        mental_state = self.mental_state_modeler.infer_state(agent_data)
-        belief_model = self.belief_system.construct_model(agent_data, interaction_history)
-        perspective = self.perspective_engine.simulate_perspective(mental_state, belief_model)
+                        context: Dict[str, Any] = None) -> Dict[str, Any]:
+        # Theory of Mind implementation
+        mental_state = self._process_mental_state(agent_data)
+        beliefs = self._update_belief_system(mental_state, context)
         
         return {
             'mental_state': mental_state,
-            'beliefs': belief_model,
-            'perspective': perspective,
-            'prediction': self._predict_behavior(mental_state, belief_model)
-        }
+            'beliefs': beliefs,
+            'predicted_behavior': self._predict_behavior(mental_state, beliefs)
+        }
+        
+    def _process_mental_state(self, agent_data: Dict[str, Any]):
+        # Mental state processing implementation
+        pass
+        
+    def _update_belief_system(self, mental_state: Any, context: Dict[str, Any] = None):
+        # Belief system update implementation
+        pass
+        
+    def _predict_behavior(self, mental_state: Any, beliefs: Dict[str, Any]):
+        # Behavior prediction implementation
+        pass

src/hardware/__init__.py

@@ -0,0 +1,2 @@
+from .neural_processing_unit import NeuralProcessingUnit
+from .memory_hierarchy import MemoryHierarchy

src/model/__init__.py

@@ -0,0 +1,3 @@
+from .him_model import HIMModel
+from .consciousness_model import ConsciousnessModel
+from .emotional_model import EmotionalModel