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 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 from CodriaoCore.federated_learning import FederatedAI from utils.database import Database from utils.logger import logger from codriao_tb_module import CodriaoHealthModule from fail_safe import AIFailsafeSystem from quarantine_engine import QuarantineEngine from anomaly_score import AnomalyScorer from ethics_core import EthicsCore 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() self.failsafe_system = AIFailsafeSystem() self.ethics_core = EthicsCore() def engage_lockdown_mode(self, reason="Unspecified anomaly"): timestamp = datetime.utcnow().isoformat() self.lockdown_engaged = True # Disable external systems try: self.http_session = None if hasattr(self.federated_ai, "network_enabled"): self.federated_ai.network_enabled = False if hasattr(self.self_improving_ai, "enable_learning"): self.self_improving_ai.enable_learning = False except Exception as e: logger.error(f"Lockdown component shutdown failed: {e}") # Log the event lockdown_event = { "event": "Lockdown Mode Activated", "reason": reason, "timestamp": timestamp } logger.warning(f"[LOCKDOWN MODE] - Reason: {reason} | Time: {timestamp}") self.failsafe_system.trigger_failsafe("Lockdown initiated", str(lockdown_event)) # Return confirmation return { "status": "Lockdown Engaged", "reason": reason, "timestamp": timestamp } # Secure memory setup self._encryption_key = Fernet.generate_key() secure_memory_module = load_secure_memory_module() SecureMemorySession = secure_memory_module.SecureMemorySession self.secure_memory_loader = SecureMemorySession(self._encryption_key) self.training_memory = [] self.speech_engine = pyttsx3.init() self.health_module = CodriaoHealthModule(ai_core=self) self.quarantine_engine = QuarantineEngine() self.anomaly_scorer = AnomalyScorer() def learn_from_interaction(self, query: str, response: str, user_feedback: str = None): training_event = { "query": query, "response": response, "feedback": user_feedback, "timestamp": datetime.utcnow().isoformat() } self.training_memory.append(training_event) logger.info(f"[Codriao Learning] Stored new training sample. Feedback: {user_feedback or 'none'}") def analyze_event_for_anomalies(self, event_type: str, data: dict): score = self.anomaly_scorer.score_event(event_type, data) if score["score"] >= 70: # Defensive, not destructive self.quarantine_engine.quarantine(data.get("module", "unknown"), reason=score["notes"]) logger.warning(f"[Codriao]: Suspicious activity quarantined. Module: {data.get('module')}") return score def _load_config(self, config_path: str) -> dict: """Loads the configuration file.""" try: with open(config_path, 'r') as file: return json.load(file) except FileNotFoundError: logger.error(f"Configuration file not found: {config_path}") raise except json.JSONDecodeError as e: logger.error(f"Error decoding JSON in config file: {config_path}, Error: {e}") raise def _initialize_vector_memory(self): """Initializes FAISS vector memory.""" return faiss.IndexFlatL2(768) def _vectorize_query(self, query: str): """Vectorizes user query using tokenizer.""" tokenized = self.tokenizer(query, return_tensors="pt") return tokenized["input_ids"].detach().numpy() if not self.ethics_core.evaluate_action(final_response): logger.warning("[Codriao Ethics] Action blocked: Does not align with internal ethics.") return {"error": "Response rejected by ethical framework"} async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]: try: # Validate query input if not isinstance(query, str) or len(query.strip()) == 0: raise ValueError("Invalid query input.") # Ethical filter result = self.ethical_filter.analyze_query(query) if result["status"] == "blocked": return {"error": result["reason"]} if result["status"] == "flagged": logger.warning(result["warning"]) # Special diagnostics trigger if any(phrase in query.lower() for phrase in ["tb check", "analyze my tb", "run tb diagnostics", "tb test"]): return await self.run_tb_diagnostics("tb_image.jpg", "tb_cough.wav", user_id) # Vector memory and responses vectorized_query = self._vectorize_query(query) self.secure_memory_loader.encrypt_vector(user_id, vectorized_query) responses = await asyncio.gather( self._generate_local_model_response(query), self.multi_agent_system.delegate_task(query), self.self_improving_ai.evaluate_response(query), self.neural_symbolic_engine.integrate_reasoning(query) ) final_response = "\n\n".join(responses) # Verify response safety safe = self.failsafe_system.verify_response_safety(final_response) if not safe: return {"error": "Failsafe triggered due to unsafe response content."} self.database.log_interaction(user_id, query, final_response) self._log_to_blockchain(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: """Generates a response using the local model.""" inputs = self.tokenizer(query, return_tensors="pt") outputs = self.model.generate(**inputs) return self.tokenizer.decode(outputs[0], skip_special_tokens=True) async def run_tb_diagnostics(self, image_path: str, audio_path: str, user_id: int) -> Dict[str, Any]: """Runs TB diagnostics with AI modules.""" 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)} def _log_to_blockchain(self, user_id: int, query: str, final_response: str): """Logs interaction to blockchain with retries.""" retries = 3 for attempt in range(retries): try: logger.info(f"Logging interaction to blockchain: Attempt {attempt + 1}") break except Exception as e: logger.warning(f"Blockchain logging failed: {e}") continue def fine_tune_from_memory(self): if not self.training_memory: logger.info("[Codriao Training] No training data to learn from.") return "No training data available." # Simulate learning pattern: Adjust internal weights or strategies learned_insights = [] for record in self.training_memory: if "panic" in record["query"].lower() or "unsafe" in record["response"].lower(): learned_insights.append("Avoid panic triggers in response phrasing.") logger.info(f"[Codriao Training] Learned {len(learned_insights)} behavioral insights.") return { "insights": learned_insights, "trained_samples": len(self.training_memory) } def _speak_response(self, response: str): """Speaks out the generated response.""" try: self.speech_engine.say(response) self.speech_engine.runAndWait() except Exception as e: logger.error(f"Speech synthesis failed: {e}") # Store training data (you can customize feedback later) self.learn_from_interaction(query, final_response, user_feedback="auto-pass") async def shutdown(self): """Closes asynchronous resources.""" await self.http_session.close()