import ollama  # Import Ollama for local Llama 3 inference

class AICoreAGIX:
    def __init__(self, config_path: str = "config.json"):
        self.config = self._load_config(config_path)
        self.http_session = aiohttp.ClientSession()
        self.database = Database()
        self.multi_agent_system = MultiAgentSystem()
        self.self_reflective_ai = SelfReflectiveAI()
        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"
        self.speech_engine = pyttsx3.init()

    async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
        try:
            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)
            ar_data = self.ar_overlay.fetch_augmented_data(query)
            neural_reasoning = self.neural_symbolic_processor.process_query(query)

            final_response = f"{model_response}\n\n{agent_response}\n\n{self_reflection}\n\nAR Insights: {ar_data}\n\nLogic: {neural_reasoning}"
            self.database.log_interaction(user_id, query, final_response)
            blockchain_module.store_interaction(user_id, query, final_response)
            self._speak_response(final_response)

            return {
                "response": final_response,
                "real_time_data": self.federated_ai.get_latest_data(),
                "context_enhanced": True,
                "security_status": "Fully Secure"
            }
        except Exception as e:
            logger.error(f"Response generation failed: {e}")
            return {"error": "Processing failed - safety protocols engaged"}

    async def _generate_local_model_response(self, query: str) -> str:
        """Use Ollama (Llama 3) for local AI inference."""
        response = ollama.chat(model="llama3", messages=[{"role": "user", "content": query}])
        return response["message"]["content"]

    def _speak_response(self, response: str):
        self.speech_engine.say(response)
        self.speech_engine.runAndWait()