Update HIM implementation with consciousness framework

src/core/semiotic_processor.py

@@ -1,6 +1,9 @@
 from enum import Enum
 from dataclasses import dataclass
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Any
+import numpy as np
+import torch
+import torch.nn as nn
 
 class SignLevel(Enum):
     SYNTACTIC = "syntactic"
@@ -13,19 +16,8 @@ class SemioticState:
     meaning_vector: np.ndarray
     context_relations: Dict[str, float]
     interpretation_confidence: float
-
-from typing import Dict, Any, List
-import numpy as np
-import torch
-import torch.nn as nn
-from dataclasses import dataclass
-
-@dataclass
-class SemioticState:
     sign_vector: np.ndarray
-    meaning_vector: np.ndarray
     context_embedding: np.ndarray
-    interpretation_confidence: float
     semantic_relations: Dict[str, float]
 
 class SemioticProcessor:
@@ -35,22 +27,10 @@ class SemioticProcessor:
             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()
+        self.meaning_network = {}
         
     def process_signs(self, input_data: Dict[str, Any]) -> SemioticState:
         network = self.network_builder.construct(input_data)