AICoreAGIX_with_TB.py

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()