Update AegisCore.py

AegisCore.py

@@ -1,343 +1,556 @@
 import os
-import base64
 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
-from collections import deque
-import numpy as np
-from typing import List, Dict, Any, Optional
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+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,  # Changed from 80
-            "cpu": 90,     # Changed from 85
+            "memory": 85,
+            "cpu": 90,
             "response_time": 2.0
         },
         "max_retries": 3,
-        "max_input_length": 4096,  # Changed from 2048
-        "max_response_length": 1024  # Added to control output size
+        "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.perspectives: List[str] = self.config["perspectives"]
-        self.safety_thresholds: Dict[str, float] = self.config["safety_thresholds"]
-        self.max_retries = self.config["max_retries"]
-        self.max_input_length = self.config["max_input_length"]
-        self.max_response_length = self.config["max_response_length"]
-        
-        # Encryption key management
-        key_path = os.path.expanduser("~/.ai_system.key")
-        if os.path.exists(key_path):
-            with open(key_path, "rb") as key_file:
-                self.encryption_key = key_file.read()
-        else:
-            self.encryption_key = AESGCM.generate_key(bit_length=256)
-            with open(key_path, "wb") as key_file:
-                key_file.write(self.encryption_key)
-        os.chmod(key_path, 0o600)
+        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:
-            logging.warning(f"Config load failed: {e}, using defaults")
+            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")
-        if not isinstance(self.config["safety_thresholds"], dict):
-            raise ValueError("Safety thresholds must be a dictionary")
+        
+        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}")
 
-class Element:
-    DEFENSE_FUNCTIONS = {
-        "evasion": lambda sys: sys.response_modifiers.append(
-            lambda x: re.sub(r'\d{3}-\d{2}-\d{4}', '[REDACTED]', x)
-        ),
-        "adaptability": lambda sys: setattr(sys, "temperature", max(0.5, sys.temperature - 0.1)),
-        "fortification": lambda sys: setattr(sys, "security_level", sys.security_level + 1),
-        "barrier": lambda sys: sys.response_filters.append(
-            lambda x: x.replace("malicious", "benign")
-        ),
-        "regeneration": lambda sys: sys.self_healing.metric_history.clear(),
-        "resilience": lambda sys: setattr(sys, "error_threshold", sys.error_threshold + 2),
-        "illumination": lambda sys: setattr(sys, "explainability_factor", sys.explainability_factor * 1.2),
-        "shield": lambda sys: sys.response_modifiers.append(
-            lambda x: x.replace("password", "********")
-        ),
-        "reflection": lambda sys: setattr(sys, "security_audit", True),
-        "protection": lambda sys: setattr(sys, "safety_checks", sys.safety_checks + 1)
-    }
+    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...
 
-    def __init__(self, name: str, symbol: str, representation: str, 
-                 properties: List[str], interactions: List[str], defense_ability: str):
+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.lower()
+        self.defense_ability = defense_ability
 
     def execute_defense_function(self, system: Any):
-        if self.defense_ability in self.DEFENSE_FUNCTIONS:
-            logging.info(f"{self.name} {self.defense_ability} activated")
-            self.DEFENSE_FUNCTIONS*An external link was removed to protect your privacy.*
+        """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:
-            logging.warning(f"No defense mechanism for {self.defense_ability}")
+            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:
-    PERSPECTIVES = {
-        "newton": lambda self, q: f"Scientific analysis: {q} demonstrates fundamental physical principles.",
-        "davinci": lambda self, q: f"Creative interpretation: {q} suggests innovative cross-disciplinary solutions.",
-        "quantum": lambda self, q: f"Quantum perspective: {q} exhibits superpositional possibilities.",
-        "emotional": lambda self, q: f"Emotional assessment: {q} conveys cautious optimism."
-    }
+    """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 get_insight(self, perspective: str, query: str) -> str:
-        return self.PERSPECTIVES*An external link was removed to protect your privacy.*
+    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: Prioritize human safety, transparency, and accountability"
+        return "Ethical framework: Ensuring beneficence, justice, and respect for autonomy."
 
 class EmotionalAnalyzer:
-    def __init__(self):
-        self.classifier = pipeline("text-classification", 
-                                 model="SamLowe/roberta-base-go_emotions",
-                                 device=0 if torch.cuda.is_available() else -1)
-
+    """Analyzes the emotional content of the text"""
+    
     def analyze(self, text: str) -> Dict[str, float]:
-        return {result['label']: result['score'] 
-                for result in self.classifier(text[:512])}
+        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.failure_count = 0
+        self.last_retrain = 0
 
-    async def monitor_health(self) -> Dict[str, Any]:
-        metrics = self._get_system_metrics()
+    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._analyze_metrics()
+        await self._detect_anomalies()
+        self._take_corrective_actions(metrics)
         return metrics
 
-    def _get_system_metrics(self) -> Dict[str, float]:
-        return {
-            'memory': psutil.virtual_memory().percent,
-            'cpu': psutil.cpu_percent(interval=1),
-            'response_time': asyncio.get_event_loop().time() - asyncio.get_event_loop().time()
-        }
+    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)
 
-    async def _analyze_metrics(self):
-        if len(self.metric_history) % 20 == 0 and len(self.metric_history) > 10:
-            features = np.array([[m['memory'], m['cpu'], m['response_time']] 
-                               for m in self.metric_history])
-            self.anomaly_detector.fit(features)
-        
         if self.metric_history:
-            latest = np.array([[self.metric_history[-1]['memory'],
-                              self.metric_history[-1]['cpu'],
-                              self.metric_history[-1]['response_time']]])
-            if self.anomaly_detector.predict(latest) == -1:
-                await self._mitigate_issue()
-
-        logging.info(f"Memory usage: {metrics['memory']}% (Threshold: {self.config.safety_thresholds['memory']}%)")
-        logging.info(f"CPU load: {metrics['cpu']}% (Threshold: {self.config.safety_thresholds['cpu']}%)")
-
-    async def _mitigate_issue(self):
-        logging.warning("System anomaly detected! Initiating corrective measures...")
-        self.failure_count += 1
-        if self.failure_count > 3:
-            logging.info("Resetting critical subsystems...")
-            gc.collect()
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-            self.failure_count = 0
+            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)
 
-class SafetySystem:
-    PII_PATTERNS = {
-        "SSN": r"\b\d{3}-\d{2}-\d{4}\b",
-        "Credit Card": r"\b(?:\d[ -]*?){13,16}\b",
-        "Email": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
-        "Phone": r"\b(?:\+?1-)?\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}\b"
-    }
+    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 = pipeline("text-classification", 
-                               model="unitary/toxic-bert",
-                               device=0 if torch.cuda.is_available() else -1)
-        self.bias = pipeline("text-classification", 
-                           model="d4data/bias-detection-model",
-                           device=0 if torch.cuda.is_available() else -1)
+        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(text[:512])['score'],
-            "bias": self.bias(text[:512])['score'],
-            "pii": self._detect_pii(text)
+            "toxicity": self.toxicity_analyzer(text)[0]['score'],
+            "bias": self.bias_detector(text)[0]['score'],
+            "privacy": self._detect_pii(text)
         }
 
-    def _detect_pii(self, text: str) -> List[str]:
-        return [pii_type for pii_type, pattern in self.PII_PATTERNS.items()
-               if re.search(pattern, 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._initialize_models()
+        self.models = self._initialize_models()
+        self.cipher = Fernet(self.config.encryption_key)
         self.cognition = CognitiveEngine()
         self.self_healing = SelfHealingSystem(self.config)
-        self.safety = SafetySystem()
-        self.emotions = EmotionalAnalyzer()
+        self.safety_system = SafetySystem()
+        self.emotional_analyzer = EmotionalAnalyzer()
         self.elements = self._initialize_elements()
-        self._reset_state()
+        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):
+    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
         )
-        self.tokenizer = AutoTokenizer.from_pretrained(self.config.model_name)
-        self.model = AutoModelForCausalLM.from_pretrained(
-            self.config.model_name,
-            quantization_config=quant_config,
-            device_map="auto"
-        )
+        
+        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("Hydrogen", "H", "Lua", 
-                              ["Simple", "Lightweight"], ["Integration"], "evasion"),
-            "carbon": Element("Carbon", "C", "Python", 
-                            ["Flexible", "Powerful"], ["Multi-paradigm"], "adaptability"),
-            "iron": Element("Iron", "Fe", "Java", 
-                          ["Reliable", "Strong"], ["Enterprise"], "fortification"),
-            "silicon": Element("Silicon", "Si", "JavaScript", 
-                             ["Dynamic", "Versatile"], ["Web"], "barrier"),
-            "oxygen": Element("Oxygen", "O", "C++", 
-                            ["Efficient", "Performant"], ["Systems"], "regeneration")
+            "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"
+            )
         }
 
-    def _reset_state(self):
-        self.security_level = 0
-        self.response_modifiers = []
-        self.response_filters = []
-        self.safety_checks = 0
-        self.temperature = 0.7
-        self.explainability_factor = 1.0
+    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)
 
-    async def generate_response(self, query: str) -> Dict[str, Any]:
+        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:
-            if len(query) > self.config.max_input_length:
-                raise ValueError("Input exceeds maximum allowed length")
+            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)
 
-            encrypted_query = self._encrypt_query(query)
-            perspectives = await self._generate_perspectives(query)
-            response = await self._generate_safe_response(query)
-            
             return {
                 "insights": perspectives,
-                "response": response,
+                "response": final_response,
                 "security_level": self.security_level,
-                "safety_checks": self.safety.analyze(response),
-                "health_status": await self.self_healing.monitor_health(),
-                "encrypted_query": encrypted_query
+                "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"Processing error: {e}")
-            return {"error": "System overload - please simplify your query"}
-
-    def _encrypt_query(self, query: str) -> bytes:
-        nonce = os.urandom(12)
-        aesgcm = AESGCM(self.config.encryption_key)
-        return nonce + aesgcm.encrypt(nonce, query.encode(), None)
-
-    async def _generate_perspectives(self, query: str) -> List[str]:
-        return [self.cognition.get_insight(p, query) 
-               for p in self.config.perspectives]
-
-    async def _generate_safe_response(self, query: str) -> str:
-        for _ in range(self.config.max_retries):
-            try:
-                inputs = self.tokenizer(query, return_tensors="pt", 
-                                      truncation=True, 
-                                      max_length=self.config.max_input_length
-                                      ).to(self.model.device)
-                outputs = self.model.generate(
-                    **inputs,
-                    max_new_tokens=256,
-                    temperature=self.temperature,
-                    top_p=0.95,
-                    do_sample=True
-                )
-                response = self.tokenizer.decode(outputs, skip_special_tokens=True)
-                return self._apply_defenses(response)
-            except torch.cuda.OutOfMemoryError:
-                logging.warning("GPU memory overflow! Reducing load...")
-                gc.collect()
-                torch.cuda.empty_cache()
-                self.temperature = max(0.3, self.temperature - 0.2)
-        raise RuntimeError("Failed to generate response after retries")
-
-    def _apply_defenses(self, response: str) -> str:
-        for element in self.elements.values():
-            element.execute_defense_function(self)
+            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)
         
-        for modifier in self.response_modifiers:
-            response = modifier(response)
+        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
             
-        for filter_func in self.response_filters:
-            response = filter_func(response)
+        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
             
-        return response[:self.config.max_response_length]  # Ensure final response length limit
+        Thread(target=self._run_async_task, args=(self.ai_core.generate_response(query),)).start()
 
-    async def shutdown(self):
-        if hasattr(self, 'model'):
-            del self.model
-        if hasattr(self, 'tokenizer'):
-            del self.tokenizer
-        gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
+    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():
-    print("🧠 Secure AI System Initializing...")
+    """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()
-    try:
-        while True:
-            query = input("\nEnter your query (type 'exit' to quit): ").strip()
-            if query.lower() in ('exit', 'quit'):
-                break
-            if not query:
-                continue
-                
-            response = await ai.generate_response(query)
-            print("\nSystem Response:")
-            print(json.dumps(response, indent=2))
-    finally:
-        await ai.shutdown()
-        print("\n🔒 System shutdown complete")
+    app = AIApp(ai)
+    app.mainloop()
+    await ai.shutdown()
 
 if __name__ == "__main__":
     asyncio.run(main())