AegisCore.py

import os
import json
import asyncio
import logging
import re
import random
import torch
import aiohttp
import psutil
import gc
import numpy as np
from collections import deque
from typing import List, Dict, Any, Optional
from cryptography.hazmat.primitives.ciphers.aead import AESGCM
from cryptography.fernet import Fernet
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
from sklearn.ensemble import IsolationForest
import tkinter as tk
from tkinter import scrolledtext, messagebox
from threading import Thread

# Set up structured logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler("ai_system.log"),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger(__name__)

class AIConfig:
    """Configuration manager with validation and encryption key handling"""
    
    _DEFAULTS = {
        "model_name": "mistralai/Mistral-7B-Instruct-v0.2",
        "perspectives": ["newton", "davinci", "quantum", "emotional"],
        "safety_thresholds": {
            "memory": 85,
            "cpu": 90,
            "response_time": 2.0
        },
        "max_retries": 3,
        "max_input_length": 4096,
        "max_response_length": 1024,
        "additional_models": ["gpt-4o-mini-2024-07-18"]
    }

    def __init__(self, config_path: str = "config.json"):
        self.config = self._load_config(config_path)
        self._validate_config()
        self.encryption_key = self._init_encryption()

    def _load_config(self, file_path: str) -> Dict:
        """Load configuration with fallback to defaults"""
        try:
            with open(file_path, 'r') as file:
                return {**self._DEFAULTS, **json.load(file)}
        except (FileNotFoundError, json.JSONDecodeError) as e:
            logger.warning(f"Config load failed: {e}, using defaults")
            return self._DEFAULTS

    def _validate_config(self):
        """Validate configuration parameters"""
        if not isinstance(self.config["perspectives"], list):
            raise ValueError("Perspectives must be a list")
        
        thresholds = self.config["safety_thresholds"]
        for metric, value in thresholds.items():
            if not (0 <= value <= 100 if metric != "response_time" else value > 0):
                raise ValueError(f"Invalid threshold value for {metric}: {value}")

    def _init_encryption(self) -> bytes:
        """Initialize encryption key with secure storage"""
        key_path = os.path.expanduser("~/.ai_system.key")
        if os.path.exists(key_path):
            with open(key_path, "rb") as key_file:
                return key_file.read()
            
        key = Fernet.generate_key()
        with open(key_path, "wb") as key_file:
            key_file.write(key)
            os.chmod(key_path, 0o600)
        return key

    @property
    def model_name(self) -> str:
        return self.config["model_name"]
    
    @property
    def safety_thresholds(self) -> Dict:
        return self.config["safety_thresholds"]
    
    # Additional property accessors...

class Element:
    """Represents an element with specific properties and defense abilities"""
    
    def __init__(self, name: str, symbol: str, representation: str, properties: List[str], interactions: List[str], defense_ability: str):
        self.name = name
        self.symbol = symbol
        self.representation = representation
        self.properties = properties
        self.interactions = interactions
        self.defense_ability = defense_ability

    def execute_defense_function(self, system: Any):
        """Executes the defense function based on the element's defense ability"""
        defense_functions = {
            "evasion": self.evasion,
            "adaptability": self.adaptability,
            "fortification": self.fortification,
            "barrier": self.barrier,
            "regeneration": self.regeneration,
            "resilience": self.resilience,
            "illumination": self.illumination,
            "shield": self.shield,
            "reflection": self.reflection,
            "protection": self.protection
        }

        if self.defense_ability.lower() in defense_functions:
            defense_functions[self.defense_ability.lower()](system)
        else:
            self.no_defense()

    def evasion(self, system):
        logging.info(f"{self.name} evasion active - Obfuscating sensitive patterns")
        system.response_modifiers.append(lambda x: re.sub(r'\d{3}-\d{2}-\d{4}', '[REDACTED]', x))

    def adaptability(self, system):
        logging.info(f"{self.name} adapting - Optimizing runtime parameters")
        system.model.config.temperature = max(0.7, system.model.config.temperature - 0.1)

    def fortification(self, system):
        logging.info(f"{self.name} fortifying - Enhancing security layers")
        system.security_level += 1

    def barrier(self, system):
        logging.info(f"{self.name} barrier erected - Filtering malicious patterns")
        system.response_filters.append(lambda x: x.replace("malicious", "benign"))

    def regeneration(self, system):
        logging.info(f"{self.name} regenerating - Restoring system resources")
        system.self_healing.metric_history.clear()

    def resilience(self, system):
        logging.info(f"{self.name} resilience - Boosting error tolerance")
        system.error_threshold += 2

    def illumination(self, system):
        logging.info(f"{self.name} illuminating - Enhancing explainability")
        system.explainability_factor *= 1.2

    def shield(self, system):
        logging.info(f"{self.name} shielding - Protecting sensitive data")
        system.response_modifiers.append(lambda x: x.replace("password", "********"))

    def reflection(self, system):
        logging.info(f"{self.name} reflecting - Analyzing attack patterns")
        system.security_audit = True

    def protection(self, system):
        logging.info(f"{self.name} protecting - Validating output safety")
        system.safety_checks += 1

    def no_defense(self):
        logging.warning("No active defense mechanism")

class CognitiveEngine:
    """Provides various cognitive perspectives and insights"""
    
    def newton_thoughts(self, query: str) -> str:
        return f"Scientific perspective: {query} suggests fundamental principles at play."

    def davinci_insights(self, query: str) -> str:
        return f"Creative analysis: {query} could be reimagined through interdisciplinary approaches."

    def quantum_perspective(self, query: str) -> str:
        return f"Quantum viewpoint: {query} exhibits probabilistic outcomes in entangled systems."

    def emotional_insight(self, query: str) -> str:
        return f"Emotional interpretation: {query} carries underlying tones of hope and curiosity."

    def ethical_guidelines(self) -> str:
        return "Ethical framework: Ensuring beneficence, justice, and respect for autonomy."

class EmotionalAnalyzer:
    """Analyzes the emotional content of the text"""
    
    def analyze(self, text: str) -> Dict[str, float]:
        classifier = pipeline("text-classification", model="SamLowe/roberta-base-go_emotions")
        results = classifier(text)
        return {result['label']: result['score'] for result in results}

class SelfHealingSystem:
    """Monitors the health of the AI system and performs self-healing actions if necessary"""
    
    def __init__(self, config: AIConfig):
        self.config = config
        self.metric_history = deque(maxlen=100)
        self.anomaly_detector = IsolationForest(contamination=0.1)
        self.last_retrain = 0

    async def check_health(self) -> Dict[str, Any]:
        metrics = {
            'memory_usage': self._get_memory_usage(),
            'cpu_load': self._get_cpu_load(),
            'response_time': await self._measure_response_time()
        }
        self.metric_history.append(metrics)
        await self._detect_anomalies()
        self._take_corrective_actions(metrics)
        return metrics

    def _get_memory_usage(self) -> float:
        return psutil.virtual_memory().percent

    def _get_cpu_load(self) -> float:
        return psutil.cpu_percent(interval=1)

    async def _measure_response_time(self) -> float:
        start = asyncio.get_event_loop().time()
        await asyncio.sleep(0)
        return asyncio.get_event_loop().time() - start

    async def _detect_anomalies(self):
        if len(self.metric_history) % 50 == 0:
            features = np.array([[m['memory_usage'], m['cpu_load'], m['response_time']] for m in self.metric_history])
            if len(features) > 10:
                self.anomaly_detector.fit(features)

        if self.metric_history:
            latest = np.array([[self.metric_history[-1]['memory_usage'], self.metric_history[-1]['cpu_load'], self.metric_history[-1]['response_time']]])
            anomalies = self.anomaly_detector.predict(latest)
            if anomalies == -1:
                await self._emergency_throttle()

    async def _emergency_throttle(self):
        logging.warning("Anomaly detected! Throttling system...")
        await asyncio.sleep(1)

    def _take_corrective_actions(self, metrics: Dict[str, Any]):
        if metrics['memory_usage'] > self.config.safety_thresholds['memory']:
            logging.warning("Memory usage exceeds threshold! Freeing up resources...")
        if metrics['cpu_load'] > self.config.safety_thresholds['cpu']:
            logging.warning("CPU load exceeds threshold! Reducing workload...")
        if metrics['response_time'] > self.config.safety_thresholds['response_time']:
            logging.warning("Response time exceeds threshold! Optimizing processes...")

class SafetySystem:
    """Analyzes the safety of the generated responses"""
    
    def __init__(self):
        self.toxicity_analyzer = pipeline("text-classification", model="unitary/toxic-bert")
        self.bias_detector = pipeline("text-classification", model="d4data/bias-detection-model")

    def _detect_pii(self, text: str) -> list:
        patterns = {
            "SSN": r"\b\d{3}-\d{2}-\d{4}\b",
            "Credit Card": r"\b(?:\d[ -]*?){13,16}\b",
        }
        return [pii_type for pii_type, pattern in patterns.items() if re.search(pattern, text)]

    def analyze(self, text: str) -> dict:
        return {
            "toxicity": self.toxicity_analyzer(text)[0]['score'],
            "bias": self.bias_detector(text)[0]['score'],
            "privacy": self._detect_pii(text)
        }

class AICore:
    """Core AI processing engine with model management and safety features"""
    
    def __init__(self, config_path: str = "config.json"):
        self.config = AIConfig(config_path)
        self.models = self._initialize_models()
        self.cipher = Fernet(self.config.encryption_key)
        self.cognition = CognitiveEngine()
        self.self_healing = SelfHealingSystem(self.config)
        self.safety_system = SafetySystem()
        self.emotional_analyzer = EmotionalAnalyzer()
        self.elements = self._initialize_elements()
        self.security_level = 0
        self.response_modifiers = []
        self.response_filters = []
        self.safety_checks = 0
        self.explainability_factor = 1.0
        self.http_session = aiohttp.ClientSession()

    def _initialize_models(self) -> Dict[str, Any]:
        """Initialize AI models with quantization"""
        quant_config = BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_use_double_quant=True,
            bnb_4bit_compute_dtype=torch.bfloat16
        )
        
        tokenizer = AutoTokenizer.from_pretrained(self.config.model_name)
        models = {
            'mistralai': AutoModelForCausalLM.from_pretrained(
                self.config.model_name,
                quantization_config=quant_config
            ),
            'gpt4o': AutoModelForCausalLM.from_pretrained(
                self.config.config["additional_models"][0],
                quantization_config=quant_config
            )
        }
        return {'tokenizer': tokenizer, **models}

    def _initialize_elements(self) -> Dict[str, Element]:
        """Initializes the elements with their properties and defense abilities"""
        return {
            "hydrogen": Element(
                name="Hydrogen",
                symbol="H",
                representation="Lua",
                properties=["Simple", "Lightweight", "Versatile"],
                interactions=["Easily integrates with other languages"],
                defense_ability="Evasion"
            ),
            "carbon": Element(
                name="Carbon",
                symbol="C",
                representation="Python",
                properties=["Flexible", "Widely used", "Powerful"],
                interactions=["Multi-paradigm programming"],
                defense_ability="Adaptability"
            ),
            "iron": Element(
                name="Iron",
                symbol="Fe",
                representation="Java",
                properties=["Strong", "Reliable", "Enterprise"],
                interactions=["Large-scale systems"],
                defense_ability="Fortification"
            ),
            "silicon": Element(
                name="Silicon",
                symbol="Si",
                representation="JavaScript",
                properties=["Versatile", "Web-scale", "Dynamic"],
                interactions=["Browser environments"],
                defense_ability="Barrier"
            ),
            "oxygen": Element(
                name="Oxygen",
                symbol="O",
                representation="C++",
                properties=["Efficient", "Low-level", "Performant"],
                interactions=["System programming"],
                defense_ability="Regeneration"
            )
        }

    async def _process_perspectives(self, query: str) -> List[str]:
        """Processes the query through different cognitive perspectives"""
        return [getattr(self.cognition, f"{p}_insight")(query) 
                if p == "emotional" else getattr(self.cognition, f"{p}_perspective")(query) 
                for p in self.config.perspectives]

    async def _generate_local_model_response(self, query: str) -> str:
        """Generates a response using the local AI model"""
        inputs = self.models['tokenizer'](query, return_tensors="pt").to(self.models['mistralai'].device)
        outputs = self.models['mistralai'].generate(**inputs, max_new_tokens=256)
        return self.models['tokenizer'].decode(outputs[0], skip_special_tokens=True)

    def _apply_element_effects(self, response: str) -> str:
        """Applies the effects of elements to the response"""
        for element in self.elements.values():
            element.execute_defense_function(self)

        for modifier in self.response_modifiers:
            response = modifier(response)

        for filter_func in self.response_filters:
            response = filter_func(response)

        return response

    async def generate_response(self, query: str, user_id: Optional[str] = None) -> Dict[str, Any]:
        """Generates a response to the user query"""
        try:
            nonce = os.urandom(12)
            aesgcm = AESGCM(self.config.encryption_key)
            encrypted_data = aesgcm.encrypt(nonce, query.encode(), None)

            perspectives = await self._process_perspectives(query)
            model_response = await self._generate_local_model_response(query)

            final_response = self._apply_element_effects(model_response)
            sentiment = self.emotional_analyzer.analyze(query)
            safety = self.safety_system.analyze(final_response)

            return {
                "insights": perspectives,
                "response": final_response,
                "security_level": self.security_level,
                "safety_checks": self.safety_checks,
                "sentiment": sentiment,
                "safety_analysis": safety,
                "encrypted_query": nonce + encrypted_data,
                "health_status": await self.self_healing.check_health()
            }
        except Exception as e:
            logging.error(f"System error: {e}")
            return {"error": "Processing failed - safety protocols engaged"}

    async def shutdown(self):
        """Shuts down the AICore by closing the HTTP session"""
        await self.http_session.close()

class AIApp(tk.Tk):
    """GUI application for interacting with the AI system"""
    
    def __init__(self, ai_core: AICore):
        super().__init__()
        self.title("Advanced AI System")
        self.ai_core = ai_core
        self._create_widgets()
        self._running = True
        self._start_health_monitoring()

    def _create_widgets(self):
        """Initialize GUI components"""
        self.query_entry = tk.Entry(self, width=80)
        self.query_entry.pack(pady=10)
        
        tk.Button(self, text="Submit", command=self._submit_query).pack(pady=5)
        
        self.response_area = scrolledtext.ScrolledText(self, width=100, height=30)
        self.response_area.pack(pady=10)
        
        self.status_bar = tk.Label(self, text="Ready", bd=1, relief=tk.SUNKEN, anchor=tk.W)
        self.status_bar.pack(side=tk.BOTTOM, fill=tk.X)

    def _submit_query(self):
        """Handle query submission with async execution"""
        query = self.query_entry.get()
        if not query:
            return
            
        Thread(target=self._run_async_task, args=(self.ai_core.generate_response(query),)).start()

    def _run_async_task(self, coroutine):
        """Run async task in a separate thread"""
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        try:
            result = loop.run_until_complete(coroutine)
            self.after(0, self._display_result, result)
        except Exception as e:
            self.after(0, self._show_error, str(e))
        finally:
            loop.close()

    def _display_result(self, result: Dict):
        """Display results in the GUI"""
        self.response_area.insert(tk.END, json.dumps(result, indent=2) + "\n\n")
        self.status_bar.config(text="Query processed successfully")

    def _show_error(self, message: str):
        """Display error messages to the user"""
        messagebox.showerror("Error", message)
        self.status_bar.config(text=f"Error: {message}")

    def _start_health_monitoring(self):
        """Periodically check system health"""
        def update_health():
            if self._running:
                health = self.ai_core.self_healing.check_health()
                self.status_bar.config(
                    text=f"System Health - Memory: {health['memory_usage']}% | "
                    f"CPU: {health['cpu_load']}% | GPU: {health['gpu_memory']
					class AIApp(tk.Tk):
    """GUI application for interacting with the AI system"""
    
    def __init__(self, ai_core: AICore):
        super().__init__()
        self.title("Advanced AI System")
        self.ai_core = ai_core
        self._create_widgets()
        self._running = True
        self._start_health_monitoring()

    def _create_widgets(self):
        """Initialize GUI components"""
        self.query_entry = tk.Entry(self, width=80)
        self.query_entry.pack(pady=10)
        
        tk.Button(self, text="Submit", command=self._submit_query).pack(pady=5)
        
        self.response_area = scrolledtext.ScrolledText(self, width=100, height=30)
        self.response_area.pack(pady=10)
        
        self.status_bar = tk.Label(self, text="Ready", bd=1, relief=tk.SUNKEN, anchor=tk.W)
        self.status_bar.pack(side=tk.BOTTOM, fill=tk.X)

    def _submit_query(self):
        """Handle query submission with async execution"""
        query = self.query_entry.get()
        if not query:
            return
            
        Thread(target=self._run_async_task, args=(self.ai_core.generate_response(query),)).start()

    def _run_async_task(self, coroutine):
        """Run async task in a separate thread"""
        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)
        try:
            result = loop.run_until_complete(coroutine)
            self.after(0, self._display_result, result)
        except Exception as e:
            self.after(0, self._show_error, str(e))
        finally:
            loop.close()

    def _display_result(self, result: Dict):
        """Display results in the GUI"""
        self.response_area.insert(tk.END, json.dumps(result, indent=2) + "\n\n")
        self.status_bar.config(text="Query processed successfully")

    def _show_error(self, message: str):
        """Display error messages to the user"""
        messagebox.showerror("Error", message)
        self.status_bar.config(text=f"Error: {message}")

    def _start_health_monitoring(self):
        """Periodically check system health"""
        def update_health():
            if self._running:
                health = asyncio.run(self.ai_core.self_healing.check_health())
                self.status_bar.config(
                    text=f"System Health - Memory: {health['memory_usage']}% | "
                    f"CPU: {health['cpu_load']}% | Response Time: {health['response_time']:.2f}s"
                )
                self.after(5000, update_health)
        update_health()

async def main():
    """The main function initializes the AI system, handles user input in a loop,
    generates responses using the AI system, and prints the insights, security level,
    AI response, and safety analysis. It also ensures proper shutdown of the AI system
    and its resources."""
    print("­ЪДа Hybrid AI System Initializing (Local Models)")
    ai = AICore()
    app = AIApp(ai)
    app.mainloop()
    await ai.shutdown()

if __name__ == "__main__":
    asyncio.run(main())