Update Codriaoagix.py

Codriaoagix.py

@@ -12,8 +12,9 @@ from datetime import datetime, timedelta
 import blockchain_module
 import speech_recognition as sr
 import pyttsx3
-from ethical_filter import EthicalFilter
+import os
 
+from ethical_filter import EthicalFilter
 from components.agix_reflection import SelfReflectiveAI
 from components.multi_agent import MultiAgentSystem
 from components.ar_integration import ARDataOverlay
@@ -22,12 +23,7 @@ from components.federated_learning import FederatedAI
 from utils.database import Database
 from utils.logger import logger
 from secure_memory import SecureMemorySession
-import os
-from cryptography.fernet import Fernet
-
-key = os.environ.get("CODRIAO_SECRET_KEY").encode()
-self._encryption_key = key
-self.secure_memory = SecureMemorySession(self._encryption_key)
+from codriao_tb_module import CodriaoHealthModule
 
 class AICoreAGIX:
     def __init__(self, config_path: str = "config.json"):
@@ -44,10 +40,14 @@ class AICoreAGIX:
         self.ar_overlay = ARDataOverlay()
         self.neural_symbolic_processor = NeuralSymbolicProcessor()
         self.federated_ai = FederatedAI()
-        self._encryption_key = Fernet.generate_key()
-        self.jwt_secret = "your_jwt_secret_key"
+
+        # Security + Memory
+        key = os.environ.get("CODRIAO_SECRET_KEY").encode()
+        self._encryption_key = key
         self.secure_memory = SecureMemorySession(self._encryption_key)
+
         self.speech_engine = pyttsx3.init()
+        self.health_module = CodriaoHealthModule(ai_core=self)
 
     async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
         try:
@@ -58,14 +58,26 @@ class AICoreAGIX:
             if result["status"] == "flagged":
                 logger.warning(result["warning"])
 
+            # Check if user explicitly requests TB analysis
+            if any(phrase in query.lower() for phrase in ["tb check", "analyze my tb", "run tb diagnostics", "tb test"]):
+                result = await self.run_tb_diagnostics("tb_image.jpg", "tb_cough.wav", user_id)
+                return {
+                    "response": result["ethical_analysis"],
+                    "explanation": result["explanation"],
+                    "tb_risk": result["tb_risk"],
+                    "image_analysis": result["image_analysis"],
+                    "audio_analysis": result["audio_analysis"],
+                    "system_health": result["system_health"]
+                }
+
             # Vectorize and encrypt
             vectorized_query = self._vectorize_query(query)
             self.secure_memory.encrypt_vector(user_id, vectorized_query)
 
-            # (Optional) retrieve memory for continuity
+            # (Optional) retrieve memory
             user_vectors = self.secure_memory.decrypt_vectors(user_id)
 
-            # Main pipeline
+            # Main AI processing
             model_response = await self._generate_local_model_response(query)
             agent_response = self.multi_agent_system.delegate_task(query)
             self_reflection = self.self_reflective_ai.evaluate_response(query, model_response)
@@ -88,6 +100,24 @@ class AICoreAGIX:
             logger.error(f"Response generation failed: {e}")
             return {"error": "Processing failed - safety protocols engaged"}
 
+    async def run_tb_diagnostics(self, image_path: str, audio_path: str, user_id: int) -> Dict[str, Any]:
+        """Only runs TB analysis if explicitly requested."""
+        try:
+            result = await self.health_module.evaluate_tb_risk(image_path, audio_path, user_id)
+            logger.info(f"TB Diagnostic Result: {result}")
+            return result
+        except Exception as e:
+            logger.error(f"TB diagnostics failed: {e}")
+            return {
+                "tb_risk": "ERROR",
+                "error": str(e),
+                "image_analysis": {},
+                "audio_analysis": {},
+                "ethical_analysis": "Unable to complete TB diagnostic.",
+                "explanation": None,
+                "system_health": None
+            }
+
     def _load_config(self, config_path: str) -> dict:
         with open(config_path, 'r') as file:
             return json.load(file)
@@ -112,4 +142,4 @@ class AICoreAGIX:
 
     def _speak_response(self, response: str):
         self.speech_engine.say(response)
-        self.speech_engine.runAndWait()
+        self.speech_engine.runAndWait()