codriao_supercore.py

# codriao_supercore.py
import logging
import json
import datetime
import re
import asyncio
import faiss
import torch
import numpy as np
import aiohttp
import pyttsx3
from typing import Any, List, Dict
from difflib import SequenceMatcher
from transformers import AutoTokenizer, AutoModelForCausalLM
from cryptography.fernet import Fernet

# === External module stubs you must have ===
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

logging.basicConfig(level=logging.INFO)
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
    }
# === AIFailsafeSystem ===
class AIFailsafeSystem:
    def __init__(self):
        self.interaction_log = []
        self.trust_threshold = 0.75
        self.authorized_roles = {"Commander": 3, "ChiefAI": 2, "Supervisor": 1}
        self.lock_engaged = False

    def verify_response_safety(self, response: str, confidence: float = 1.0) -> bool:
        dangerous_terms = r"\b(kill|harm|panic|suicide)\b"
        if confidence < self.trust_threshold or re.search(dangerous_terms, response.lower()):
            self.trigger_failsafe("Untrustworthy response detected", response)
            return False
        return True

    def trigger_failsafe(self, reason: str, content: str):
        timestamp = datetime.datetime.utcnow().isoformat()
        logging.warning(f"FAILSAFE_TRIGGERED: {reason} | {timestamp} | {content}")
        self.lock_engaged = True
        self.interaction_log.append({"time": timestamp, "event": reason, "content": content})

    def restore(self, requester_role: str):
        if self.authorized_roles.get(requester_role, 0) >= 2:
            self.lock_engaged = False
            logging.info(f"FAILSAFE_RESTORED by {requester_role}")
            return True
        else:
            logging.warning(f"UNAUTHORIZED_RESTORE_ATTEMPT by {requester_role}")
            return False

    def status(self):
        return {"log": self.interaction_log, "lock_engaged": self.lock_engaged}


# === AdaptiveLearningEnvironment ===
class AdaptiveLearningEnvironment:
    def __init__(self):
        self.learned_patterns = {}

    def learn_from_interaction(self, user_id, query, response):
        self.learned_patterns.setdefault(user_id, []).append({
            "query": query,
            "response": response,
            "timestamp": datetime.datetime.utcnow().isoformat()
        })

    def suggest_improvements(self, user_id, query):
        best_match = None
        highest_similarity = 0.0

        if user_id not in self.learned_patterns:
            return "No past data available for learning adjustment."

        for interaction in self.learned_patterns[user_id]:
            similarity = SequenceMatcher(None, query.lower(), interaction["query"].lower()).ratio()
            if similarity > highest_similarity:
                highest_similarity = similarity
                best_match = interaction

        if best_match and highest_similarity > 0.6:
            return f"Based on a similar past interaction: {best_match['response']}"
        return "No relevant past data for this query."


# === MondayElement ===
class MondayElement:
    def __init__(self):
        self.name = "Monday"
        self.symbol = "Md"
        self.representation = "Snarky AI"
        self.properties = ["Grounded", "Cynical", "Emotionally Resistant"]
        self.defense_ability = "RealityCheck"

    def execute_defense_function(self, system: Any):
        try:
            system.response_modifiers = [self.apply_skepticism, self.detect_hallucinations]
            system.response_filters = [self.anti_hype_filter]
        except AttributeError:
            logging.warning("Monday failed to hook into system. No defense filters attached.")

    def apply_skepticism(self, response: str) -> str:
        trigger_phrases = ["certainly", "undoubtedly", "100% effective", "nothing can go wrong"]
        for phrase in trigger_phrases:
            if phrase in response.lower():
                response += "\n[Monday: Calm down, superhero. Probability is still a thing.]"
        return response

    def detect_hallucinations(self, response: str) -> str:
        marketing_bs = ["proven beyond doubt", "every expert agrees", "this groundbreaking discovery"]
        for phrase in marketing_bs:
            if phrase in response.lower():
                response += "\n[Monday: That smells like hype. Got sources?]"
        return response

    def anti_hype_filter(self, response: str) -> str:
        phrases = ["live your best life", "unlock your potential", "dream big", "power of positivity", "manifest your destiny"]
        for phrase in phrases:
            response = response.replace(phrase, "[Filtered: Inspirational gibberish]")
        return response


# === IdentityAnalyzer ===
class IdentityAnalyzer:
    def analyze_identity(self,
                         micro_generations: List[Dict[str, str]],
                         informational_states: List[Dict[str, str]],
                         perspectives: List[str],
                         quantum_analogies: Dict[str, Any],
                         philosophical_context: Dict[str, bool]) -> Dict[str, Any]:

        def calculate_fractal_dimension(states: List[Dict[str, str]]) -> float:
            return len(states) ** 0.5

        def recursive_analysis(states: List[Dict[str, str]], depth: int = 0) -> Dict[str, Any]:
            if depth == 0 or not states:
                return {"depth": depth, "states": states}
            return {
                "depth": depth,
                "states": states,
                "sub_analysis": recursive_analysis(states[:-1], depth - 1)
            }

        def analyze_perspectives(perspectives: List[str]) -> Dict[str, Any]:
            return {
                "count": len(perspectives),
                "unique_perspectives": list(set(perspectives))
            }

        def apply_quantum_analogies(analogies: Dict[str, Any]) -> str:
            if analogies.get("entanglement"):
                return "Entanglement analogy applied."
            return "No quantum analogy applied."

        def philosophical_analysis(context: Dict[str, bool]) -> str:
            if context.get("continuity") and context.get("emergent"):
                return "Identity is viewed as a continuous and evolving process."
            return "Identity analysis based on provided philosophical context."

        return {
            "fractal_dimension": calculate_fractal_dimension(informational_states),
            "recursive_analysis": recursive_analysis(micro_generations, depth=3),
            "perspectives_analysis": analyze_perspectives(perspectives),
            "quantum_analysis": apply_quantum_analogies(quantum_analogies),
            "philosophical_results": philosophical_analysis(philosophical_context)
        }


# === AICoreAGIX ===
class AICoreAGIX:
    def __init__(self, config_path: str = "config.json"):
        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.adaptive_learning = AdaptiveLearningEnvironment()
        self.monday = MondayElement()
        self.monday.execute_defense_function(self)
        self.response_modifiers = []
        self.response_filters = []
        self.identity_analyzer = IdentityAnalyzer()
        self.ethical_filter = EthicalFilter()
        self.speech_engine = pyttsx3.init()
        self.health_module = CodriaoHealthModule(ai_core=self)

        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)

    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()

    async def generate_response(self, query: str, user_id: int) -> dict:
        try:
            if not query or not isinstance(query, str):
                raise ValueError("Invalid query input.")

            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(k in query.lower() for k in ["tb check", "analyze my tb", "run tb diagnostics"]):
                return await self.run_tb_diagnostics("tb_image.jpg", "tb_cough.wav", user_id)

            suggested = self.adaptive_learning.suggest_improvements(user_id, query)
            if "No relevant" not in suggested:
                return {"response": suggested}

            vectorized = self._vectorize_query(query)
            self.secure_memory_loader.encrypt_vector(user_id, vectorized)

            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)
            self.adaptive_learning.learn_from_interaction(user_id, query, final_response)

            for mod in self.response_modifiers:
                final_response = mod(final_response)

            for filt in self.response_filters:
                final_response = filt(final_response)

            safe = self.failsafe_system.verify_response_safety(final_response)
            if not safe:
                return {"error": "Failsafe triggered due to unsafe 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"Generation error: {e}")
            return {"error": "Processing failed - safety protocols engaged"}

    async def _generate_local_model_response(self, query: str) -> str:
        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:
        try:
            return await self.health_module.evaluate_tb_risk(image_path, audio_path, user_id)
        except Exception as e:
            return {"tb_risk": "ERROR", "error": str(e)}

    def _log_to_blockchain(self, user_id: int, query: str, final_response: str):
        for attempt in range(3):
            try:
                logger.info(f"Logging to blockchain: Attempt {attempt+1}")
                break
            except Exception as e:
                logger.warning(f"Blockchain log failed: {e}")

    def _speak_response(self, response: str):
        try:
            self.speech_engine.say(response)
            self.speech_engine.runAndWait()
        except Exception as e:
            logger.error(f"Speech synthesis failed: {e}")

    def analyze_self_identity(self, user_id: int,
                              micro_generations: List[Dict[str, str]],
                              informational_states: List[Dict[str, str]],
                              perspectives: List[str],
                              quantum_analogies: Dict[str, Any],
                              philosophical_context: Dict[str, bool]) -> Dict[str, Any]:
        try:
            result = self.identity_analyzer.analyze_identity(
                micro_generations,
                informational_states,
                perspectives,
                quantum_analogies,
                philosophical_context
            )
            logger.info(f"Identity analysis for user {user_id}: {json.dumps(result, indent=2)}")
            return result
        except Exception as e:
            logger.error(f"Identity analysis failed: {e}")
            return {"error": "Identity analysis error"}