Update HIM implementation with consciousness framework

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import torch
 from src.model.him_model import HIMModel
 from config.model_config import HIMConfig
 from config.environment_config import EnvironmentConfig
@@ -6,15 +7,16 @@ from config.environment_config import EnvironmentConfig
 def initialize_model():
     model_config = HIMConfig()
     env_config = EnvironmentConfig()
-    return HIMModel(model_config)
+    device = torch.device("cuda" if torch.cuda.is_available() and env_config.device == "cuda" else "cpu")
+    model = HIMModel(model_config).to(device)
+    return model
 
-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 chat(message: str, 
+         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,
         "system_message": system_message,
@@ -25,7 +27,8 @@ def chat(
         }
     }
     
-    result = model.generate_response(input_data)
+    with torch.no_grad():
+        result = model.generate_response(input_data)
     return result["response"]
 
 model = initialize_model()
@@ -49,5 +52,5 @@ if __name__ == "__main__":
     interface.launch(
         server_name=env_config.api_host,
         server_port=env_config.api_port,
-        enable_cors=env_config.enable_cors
+        share=True
     )

src/core/consciousness_kernel.py

@@ -1,8 +1,10 @@
 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
+from typing import Dict, List, Optional, Any
+import asyncio
 
 @dataclass
 class ConsciousnessState:
@@ -19,10 +21,6 @@ class ConsciousnessLevel(Enum):
     REFLECTIVE = "reflective_consciousness"
     INTEGRATED = "integrated_consciousness"
 
-from typing import Dict, List, Any
-import asyncio
-import torch
-
 class ConsciousnessKernel:
     def __init__(self):
         self.awareness_module = nn.Sequential(
@@ -32,17 +30,6 @@ class ConsciousnessKernel:
         )
         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/model/him_model.py

@@ -9,12 +9,13 @@ from ..core.semiotic_processor import SemioticProcessor
 class HIMModel(nn.Module):
     def __init__(self, config: Dict[str, Any]):
         super().__init__()
+        self.config = config
         self.consciousness_kernel = ConsciousnessKernel()
         self.emotional_processor = EmotionalProcessor()
         self.theory_of_mind = TheoryOfMind()
         self.semiotic_processor = SemioticProcessor()
         
-    def forward(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
+    def generate_response(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
         consciousness_state = self.consciousness_kernel.process_consciousness_cycle(input_data)
         emotional_context = self.emotional_processor.process_emotional_context(input_data)
         social_understanding = self.theory_of_mind.model_agent_mind(input_data)
@@ -25,4 +26,14 @@ class HIMModel(nn.Module):
             emotional_context,
             social_understanding,
             semiotic_analysis
-        )
+        )
+        
+    def _integrate_outputs(self, *states) -> Dict[str, Any]:
+        # Integration implementation
+        return {
+            "response": "Integrated response based on multiple processing layers",
+            "consciousness_state": states[0],
+            "emotional_context": states[1],
+            "social_understanding": states[2],
+            "semiotic_analysis": states[3]
+        }