import aiohttp import json from transformers import AutoModelForCausalLM, AutoTokenizer from typing import List, Dict, Any from components.adaptive_learning import AdaptiveLearningEnvironment from components.ai_driven_creativity import AIDrivenCreativity from components.collaborative_ai import CollaborativeAI from components.cultural_sensitivity import CulturalSensitivityEngine from components.data_processing import AdvancedDataProcessor from components.dynamic_learning import DynamicLearner from components.ethical_governance import EthicalAIGovernance from components.explainable_ai import ExplainableAI from components.feedback_manager import ImprovedFeedbackManager from components.multimodal_analyzer import MultimodalAnalyzer from components.neuro_symbolic import NeuroSymbolicEngine from components.quantum_optimizer import QuantumInspiredOptimizer from components.real_time_data import RealTimeDataIntegrator from components.sentiment_analysis import EnhancedSentimentAnalyzer from components.self_improving_ai import SelfImprovingAI from components.user_personalization import UserPersonalizer from models.cognitive_engine import BroaderPerspectiveEngine from models.elements import Element from models.healing_system import SelfHealingSystem from models.safety_system import SafetySystem from models.user_profiles import UserProfile from utils.database import Database from utils.logger import logger class AICore: """Improved core system with cutting-edge capabilities""" def __init__(self, config_path: str = "config.json"): self.config = self._load_config(config_path) self.models = self._initialize_models() self.cognition = BroaderPerspectiveEngine() self.self_healing = SelfHealingSystem(self.config) self.safety_system = SafetySystem() self.emotional_analyzer = EnhancedSentimentAnalyzer() self.elements = self._initialize_elements() self.security_level = 0 self.http_session = aiohttp.ClientSession() self.database = Database() # Initialize database self.user_profiles = UserProfile(self.database) # Initialize user profiles self.feedback_manager = ImprovedFeedbackManager(self.database) # Initialize feedback manager self.context_manager = AdaptiveLearningEnvironment() # Initialize adaptive learning environment self.data_fetcher = RealTimeDataIntegrator() # Initialize real-time data fetcher self.sentiment_analyzer = EnhancedSentimentAnalyzer() # Initialize sentiment analyzer self.data_processor = AdvancedDataProcessor() # Initialize advanced data processor self.dynamic_learner = DynamicLearner() # Initialize dynamic learner self.multimodal_analyzer = MultimodalAnalyzer() # Initialize multimodal analyzer self.ethical_decision_maker = EthicalAIGovernance() # Initialize ethical decision maker self.user_personalizer = UserPersonalizer(self.database) # Initialize user personalizer self.ai_integrator = CollaborativeAI() # Initialize AI integrator self.neuro_symbolic_engine = NeuroSymbolicEngine() # Initialize neuro-symbolic engine self.explainable_ai = ExplainableAI() # Initialize explainable AI self.quantum_inspired_optimizer = QuantumInspiredOptimizer() # Initialize quantum-inspired optimizer self.cultural_sensitivity_engine = CulturalSensitivityEngine() # Initialize cultural sensitivity engine self.self_improving_ai = SelfImprovingAI() # Initialize self-improving AI self.ai_driven_creativity = AIDrivenCreativity() # Initialize AI-driven creativity self._validate_perspectives() def _load_config(self, config_path: str) -> dict: """Load configuration from a file""" with open(config_path, 'r') as file: return json.load(file) def _initialize_models(self): """Initialize models required by the AICore class""" models = { "mistralai": AutoModelForCausalLM.from_pretrained(self.config["model_name"]), "tokenizer": AutoTokenizer.from_pretrained(self.config["model_name"]) } return models def _initialize_elements(self): """Initialize elements with their defense abilities""" elements = { "hydrogen": Element("Hydrogen", "H", "Python", ["Lightweight", "Reactive"], ["Combustion"], "evasion"), "carbon": Element("Carbon", "C", "Java", ["Versatile", "Strong"], ["Bonding"], "adaptability"), "iron": Element("Iron", "Fe", "C++", ["Durable", "Magnetic"], ["Rusting"], "fortification"), "silicon": Element("Silicon", "Si", "JavaScript", ["Semiconductor", "Abundant"], ["Doping"], "barrier"), "oxygen": Element("Oxygen", "O", "Rust", ["Oxidizing", "Life-supporting"], ["Combustion"], "regeneration") } return elements def _validate_perspectives(self): """Ensure configured perspectives are valid""" valid = self.cognition.available_perspectives invalid = [p for p in self.config["perspectives"] if p not in valid] if invalid: logger.warning(f"Removing invalid perspectives: {invalid}") self.config["perspectives"] = [p for p in self.config["perspectives"] if p in valid] async def _process_perspectives(self, query: str) -> List[str]: """Safely process perspectives using validated methods""" perspectives = [] for p in self.config["perspectives"]: try: method = self.cognition.get_perspective_method(p) perspectives.append(method(query)) except Exception as e: logger.error(f"Perspective processing failed: {e}") return perspectives async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]: """Generate response with advanced capabilities""" try: # Initialize temporary modifiers/filters for this query response_modifiers = [] response_filters = [] # Execute element defenses for element in self.elements.values(): element.execute_defense_function(self, response_modifiers, response_filters) # Process perspectives and generate response perspectives = await self._process_perspectives(query) model_response = await self._generate_local_model_response(query) # Apply sentiment analysis sentiment = self.sentiment_analyzer.detailed_analysis(query) # Apply modifiers and filters final_response = model_response for modifier in response_modifiers: final_response = modifier(final_response) for filter_func in response_filters: final_response = filter_func(final_response) # Adjust response based on feedback feedback = self.database.get_latest_feedback(user_id) if feedback: final_response = self.feedback_manager.adjust_response_based_on_feedback(final_response, feedback) # Log user interaction for analytics self.database.log_interaction(user_id, query, final_response) # Update context self.context_manager.update_environment(user_id, {"query": query, "response": final_response}) # Personalize response final_response = self.user_personalizer.personalize_response(final_response, user_id) # Apply ethical decision-making framework final_response = self.ethical_decision_maker.enforce_policies(final_response) # Explain the decision explanation = self.explainable_ai.explain_decision(final_response, query) return { "insights": perspectives, "response": final_response, "sentiment": sentiment, "security_level": self.security_level, "health_status": await self.self_healing.check_health(), "explanation": explanation } except Exception as e: logger.error(f"Response generation failed: {e}") return {"error": "Processing failed - safety protocols engaged"} async def _generate_local_model_response(self, query: str) -> str: """Generate a response from the local model""" inputs = self.models['tokenizer'](query, return_tensors="pt") outputs = self.models['mistralai'].generate(**inputs) return self.models['tokenizer'].decode(outputs[0], skip_special_tokens=True) async def shutdown(self): """Proper async resource cleanup""" await self.http_session.close() await self.database.close() # Close the database connection