Update fullreasoning.py

fullreasoning.py

@@ -1,376 +1,556 @@
-import asyncio
-import json
-import os
-import logging
-from typing import List, Dict, Any
-from pydantic import BaseModel, ValidationError
-from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-
-# Ensure vaderSentiment is installed
-try:
-    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-except ModuleNotFoundError:
-    import subprocess
-    import sys
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "vaderSentiment"])
-    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
-
-# Ensure nltk is installed and download required data
-try:
-    import nltk
-    from nltk.tokenize import word_tokenize
-    nltk.download('punkt', quiet=True)
-except ImportError:
-    import subprocess
-    import sys
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "nltk"])
-    import nltk
-    from nltk.tokenize import word_tokenize
-    nltk.download('punkt', quiet=True)
-
-# Import perspectives
-from perspectives import (
-    NewtonPerspective, DaVinciPerspective, HumanIntuitionPerspective,
-    NeuralNetworkPerspective, QuantumComputingPerspective, ResilientKindnessPerspective,
-    MathematicalPerspective, PhilosophicalPerspective, CopilotPerspective, BiasMitigationPerspective
-)
-
-# Load environment variables
-from dotenv import load_dotenv
-load_dotenv()
-azure_openai_api_key = os.getenv('AZURE_OPENAI_API_KEY')
-azure_openai_endpoint = os.getenv('AZURE_OPENAI_ENDPOINT')
-
-# Configuration management using pydantic
-class Config(BaseModel):
-    real_time_data_sources: List[str]
-    sensitive_keywords: List[str]
-
-# Initialize configuration
-config = Config(
-    real_time_data_sources=["https://api.example.com/data"],
-    sensitive_keywords=["password", "ssn"]
-)
-
-# Memory management
-memory = []
-
-# Sentiment analysis
-analyzer = SentimentIntensityAnalyzer()
-
-# Dependency injection
-class DependencyInjector:
-    def __init__(self):
-        self.dependencies = {}
-
-    def register(self, name, dependency):
-        self.dependencies[name] = dependency
-
-    def get(self, name):
-        return self.dependencies.get(name)
-
-injector = DependencyInjector()
-injector.register("config", config)
-injector.register("analyzer", analyzer)
-
-# Error handling and logging
-logging.basicConfig(level=logging.INFO)
-
-def handle_error(e):
-    logging.error(f"Error: {e}")
-
-# Functions to implement
-async def llm_should_continue() -> bool:
-    # Placeholder logic to determine if the goal is achieved
-    return False
-
-async def llm_get_next_action() -> str:
-    # Placeholder logic to get the next action
-    return "next_action"
-
-async def execute_action(action: str):
-    # Placeholder logic to execute an action
-    logging.info(f"Executing action: {action}")
-
-async def goal_achieved() -> bool:
-    # Placeholder logic to check if the goal is achieved
-    return False
-
-async def run():
-    while not await goal_achieved():
-        action = await llm_get_next_action()
-        await execute_action(action)
-
-def process_command(command: str):
-    # Placeholder logic to process a command
-    logging.info(f"Processing command: {command}")
-
-def analyze_sentiment(text: str) -> Dict[str, float]:
-    return analyzer.polarity_scores(text)
-
-def classify_emotion(sentiment_score: Dict[str, float]) -> str:
-    # Placeholder logic to classify emotion based on sentiment scores
-    return "neutral"
-
-def correlate_emotion_with_perspective(emotion: str) -> str:
-    # Placeholder logic to correlate emotion with perspectives
-    return "HumanIntuitionPerspective"
-
-def handle_whitespace(text: str) -> str:
-    return text.strip()
-
-def determine_next_action(memory: List[Dict[str, Any]]) -> str:
-    # Placeholder logic to determine the next action based on memory
-    return "next_action"
-
-def generate_response(question: str) -> str:
-    # Placeholder logic to generate a response to a question
-    return "response"
-
-async def fetch_real_time_data(source_url: str) -> Dict[str, Any]:
-    # Placeholder logic to fetch real-time data
-    return {"data": "real_time_data"}
-
-def save_response(response: str):
-    # Placeholder logic to save the generated response
-    logging.info(f"Response saved: {response}")
-
-def backup_response(response: str):
-    # Placeholder logic to backup the generated response
-    logging.info(f"Response backed up: {response}")
-
-def handle_voice_input():
-    # Placeholder for handling voice input
-    pass
-
-def handle_image_input(image_path: str):
-    # Placeholder for handling image input
-    pass
-
-def handle_question(question: str):
-    # Placeholder logic to handle a question and apply functions
-    pass
-
-def apply_function(function: str):
-    # Placeholder logic to apply a given function
-    pass
-
-def analyze_element_interactions(element_name1: str, element_name2: str):
-    # Placeholder logic to analyze interactions between two elements
-    pass
-
-# Setup Logging
-def setup_logging(config):
-    if config.get('logging_enabled', True):
-        log_level = config.get('log_level', 'DEBUG').upper()
-        numeric_level = getattr(logging, log_level, logging.DEBUG)
-        logging.basicConfig(
-            filename='universal_reasoning.log',
-            level=numeric_level,
-            format='%(asctime)s - %(levelname)s - %(message)s'
-        )
-    else:
-        logging.disable(logging.CRITICAL)
-
-# Load JSON configuration
-def load_json_config(file_path):
-    if not os.path.exists(file_path):
-        logging.error(f"Configuration file '{file_path}' not found.")
-        return {}
-    try:
-        with open(file_path, 'r') as file:
-            config = json.load(file)
-            logging.info(f"Configuration loaded from '{file_path}'.")
-            return config
-    except json.JSONDecodeError as e:
-        logging.error(f"Error decoding JSON from the configuration file '{file_path}': {e}")
-        return {}
-
-# Initialize NLP (basic tokenization)
-def analyze_question(question):
-    tokens = word_tokenize(question)
-    logging.debug(f"Question tokens: {tokens}")
-    return tokens
-
-# Define the Element class
-class Element:
-    def __init__(self, name, symbol, representation, properties, interactions, defense_ability):
-        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):
-        message = f"{self.name} ({self.symbol}) executes its defense ability: {self.defense_ability}"
-        logging.info(message)
-        return message
-
-# Define the CustomRecognizer class
-class CustomRecognizer:
-    def recognize(self, question):
-        # Simple keyword-based recognizer for demonstration purposes
-        if any(element_name.lower() in question.lower() for element_name in ["hydrogen", "diamond"]):
-            return RecognizerResult(question)
-        return RecognizerResult(None)
-
-    def get_top_intent(self, recognizer_result):
-        if recognizer_result.text:
-            return "ElementDefense"
-        else:
-            return "None"
-
-class RecognizerResult:
-    def __init__(self, text):
-        self.text = text
-
-# Universal Reasoning Aggregator
-class UniversalReasoning:
-    def __init__(self, config):
-        self.config = config
-        self.perspectives = self.initialize_perspectives()
-        self.elements = self.initialize_elements()
-        self.recognizer = CustomRecognizer()
-        # Initialize the sentiment analyzer
-        self.sentiment_analyzer = SentimentIntensityAnalyzer()
-
-    def initialize_perspectives(self):
-        perspective_names = self.config.get('enabled_perspectives', [
-            "newton",
-            "davinci",
-            "human_intuition",
-            "neural_network",
-            "quantum_computing",
-            "resilient_kindness",
-            "mathematical",
-            "philosophical",
-            "copilot",
-            "bias_mitigation"
-        ])
-        perspective_classes = {
-            "newton": NewtonPerspective,
-            "davinci": DaVinciPerspective,
-            "human_intuition": HumanIntuitionPerspective,
-            "neural_network": NeuralNetworkPerspective,
-            "quantum_computing": QuantumComputingPerspective,
-            "resilient_kindness": ResilientKindnessPerspective,
-            "mathematical": MathematicalPerspective,
-            "philosophical": PhilosophicalPerspective,
-            "copilot": CopilotPerspective,
-            "bias_mitigation": BiasMitigationPerspective
-        }
-        perspectives = []
-        for name in perspective_names:
-            cls = perspective_classes.get(name.lower())
-            if cls:
-                perspectives.append(cls(self.config))
-                logging.debug(f"Perspective '{name}' initialized.")
-            else:
-                logging.warning(f"Perspective '{name}' is not recognized and will be skipped.")
-        return perspectives
-
-    def initialize_elements(self):
-        elements = [
-            Element(
-                name="Hydrogen",
-                symbol="H",
-                representation="Lua",
-                properties=["Simple", "Lightweight", "Versatile"],
-                interactions=["Easily integrates with other languages and systems"],
-                defense_ability="Evasion"
-            ),
-            # You can add more elements as needed
-            Element(
-                name="Diamond",
-                symbol="D",
-                representation="Kotlin",
-                properties=["Modern", "Concise", "Safe"],
-                interactions=["Used for Android development"],
-                defense_ability="Adaptability"
-            )
-        ]
-        return elements
-
-    async def generate_response(self, question):
-        responses = []
-        tasks = []
-                # Generate responses from perspectives concurrently
-        for perspective in self.perspectives:
-            if asyncio.iscoroutinefunction(perspective.generate_response):
-                tasks.append(perspective.generate_response(question))
-            else:
-                # Wrap synchronous functions in coroutine
-                async def sync_wrapper(perspective, question):
-                    return perspective.generate_response(question)
-                tasks.append(sync_wrapper(perspective, question))
-        
-        perspective_results = await asyncio.gather(*tasks, return_exceptions=True)
-        for perspective, result in zip(self.perspectives, perspective_results):
-            if isinstance(result, Exception):
-                logging.error(f"Error generating response from {perspective.__class__.__name__}: {result}")
-            else:
-                responses.append(result)
-                logging.debug(f"Response from {perspective.__class__.__name__}: {result}")
-        
-        # Handle element defense logic
-        recognizer_result = self.recognizer.recognize(question)
-        top_intent = self.recognizer.get_top_intent(recognizer_result)
-        if top_intent == "ElementDefense":
-            element_name = recognizer_result.text.strip()
-            element = next(
-                (el for el in self.elements if el.name.lower() in element_name.lower()),
-                None
-            )
-            if element:
-                defense_message = element.execute_defense_function()
-                responses.append(defense_message)
-            else:
-                logging.info(f"No matching element found for '{element_name}'")
-        
-        ethical_considerations = self.config.get(
-            'ethical_considerations',
-            "Always act with transparency, fairness, and respect for privacy."
-        )
-        responses.append(f"**Ethical Considerations:**\n{ethical_considerations}")
-        
-        formatted_response = "\n\n".join(responses)
-        return formatted_response
-
-    def save_response(self, response):
-        if self.config.get('enable_response_saving', False):
-            save_path = self.config.get('response_save_path', 'responses.txt')
-            try:
-                with open(save_path, 'a', encoding='utf-8') as file:
-                    file.write(response + '\n')
-                logging.info(f"Response saved to '{save_path}'.")
-            except Exception as e:
-                logging.error(f"Error saving response to '{save_path}': {e}")
-
-    def backup_response(self, response):
-        if self.config.get('backup_responses', {}).get('enabled', False):
-            backup_path = self.config['backup_responses'].get('backup_path', 'backup_responses.txt')
-            try:
-                with open(backup_path, 'a', encoding='utf-8') as file:
-                    file.write(response + '\n')
-                logging.info(f"Response backed up to '{backup_path}'.")
-            except Exception as e:
-                logging.error(f"Error backing up response to '{backup_path}': {e}")
-
-# Example usage
-if __name__ == "__main__":
-    try:
-        config = load_json_config('config.json')
-        # Add Azure OpenAI configurations to the config
-        config['azure_openai_api_key'] = azure_openai_api_key
-        config['azure_openai_endpoint'] = azure_openai_endpoint
-        setup_logging(config)
-        universal_reasoning = UniversalReasoning(config)
-        question = "Tell me about Hydrogen and its defense mechanisms."
-        response = asyncio.run(universal_reasoning.generate_response(question))
-        print(response)
-        if response:
-            universal_reasoning.save_response(response)
-            universal_reasoning.backup_response(response)
-    except ValidationError as e:
-        handle_error(e)
+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())