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| import aiohttp | |
| import json | |
| import logging | |
| import torch | |
| import faiss | |
| import numpy as np | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from typing import List, Dict, Any | |
| from cryptography.fernet import Fernet | |
| from jwt import encode, decode, ExpiredSignatureError | |
| from datetime import datetime, timedelta | |
| import speech_recognition as sr | |
| import pyttsx3 | |
| import os | |
| from components.multi_model_analyzer import MultiAgentSystem | |
| from components.neuro_symbolic_engine import NeuroSymbolicEngine | |
| from components.self_improving_ai import SelfImprovingAI | |
| from modules.secure_memory_loader import load_secure_memory_module | |
| from ethical_filter import EthicalFilter | |
| from codette_openai_fallback import query_codette_with_fallback # <<< Fallback-aware | |
| from CodriaoCore.federated_learning import FederatedAI | |
| from utils.database import Database | |
| from utils.logger import logger | |
| from codriao_tb_module import CodriaoHealthModule | |
| class AICoreAGIX: | |
| def __init__(self, config_path: str = "config.json"): | |
| self.ethical_filter = EthicalFilter() | |
| self.config = self._load_config(config_path) | |
| self.tokenizer = AutoTokenizer.from_pretrained(self.config["model_name"]) | |
| self.model = AutoModelForCausalLM.from_pretrained(self.config["model_name"]) | |
| self.context_memory = self._initialize_vector_memory() | |
| self.http_session = aiohttp.ClientSession() | |
| self.database = Database() | |
| self.multi_agent_system = MultiAgentSystem() | |
| self.self_improving_ai = SelfImprovingAI() | |
| self.neural_symbolic_engine = NeuroSymbolicEngine() | |
| self.federated_ai = FederatedAI() | |
| # Secure memory setup | |
| self._encryption_key = self.config["security_settings"]["encryption_key"].encode() | |
| secure_memory_module = load_secure_memory_module() | |
| SecureMemorySession = secure_memory_module.SecureMemorySession | |
| self.secure_memory_loader = 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: | |
| result = self.ethical_filter.analyze_query(query) | |
| if result["status"] == "blocked": | |
| return {"error": result["reason"]} | |
| if result["status"] == "flagged": | |
| logger.warning(result["warning"]) | |
| 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"] | |
| } | |
| vectorized_query = self._vectorize_query(query) | |
| self.secure_memory_loader.encrypt_vector(user_id, vectorized_query) | |
| user_vectors = self.secure_memory_loader.decrypt_vectors(user_id) | |
| # === Use OpenAI w/ fallback === | |
| model_response = query_codette_with_fallback(query, user_id=str(user_id)) | |
| agent_response = self.multi_agent_system.delegate_task(query) | |
| self_reflection = self.self_improving_ai.evaluate_response(query, model_response) | |
| neural_reasoning = self.neural_symbolic_engine.integrate_reasoning(query) | |
| final_response = ( | |
| f"{model_response}\n\n" | |
| f"{agent_response}\n\n" | |
| f"{self_reflection}\n\n" | |
| f"Logic: {neural_reasoning}" | |
| ) | |
| self.database.log_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 run_tb_diagnostics(self, image_path: str, audio_path: str, user_id: int) -> Dict[str, Any]: | |
| 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) | |
| def _initialize_vector_memory(self): | |
| return faiss.IndexFlatL2(768) | |
| def _vectorize_query(self, query: str): | |
| tokenized = self.tokenizer(query, return_tensors="pt") | |
| return tokenized["input_ids"].detach().numpy() | |
| def _speak_response(self, response: str): | |
| self.speech_engine.say(response) | |
| self.speech_engine.runAndWait() |