import aiohttp import json import asyncio # Added for async main execution 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 # Fixed possible typo 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.sentiment_analyzer = EnhancedSentimentAnalyzer() # Single instance self.elements = self._initialize_elements() self.security_level = 0 self.http_session = aiohttp.ClientSession() self.database = Database() self.user_profiles = UserProfile(self.database) self.feedback_manager = ImprovedFeedbackManager(self.database) self.context_manager = AdaptiveLearningEnvironment() self.data_fetcher = RealTimeDataIntegrator() self.data_processor = AdvancedDataProcessor() self.dynamic_learner = DynamicLearner() self.multimodal_analyzer = MultimodalAnalyzer() self.ethical_decision_maker = EthicalAIGovernance() self.user_personalizer = UserPersonalizer(self.database) self.ai_integrator = CollaborativeAI() self.neuro_symbolic_engine = NeuroSymbolicEngine() self.explainable_ai = ExplainableAI() self.quantum_inspired_optimizer = QuantumInspiredOptimizer() self.cultural_sensitivity_engine = CulturalSensitivityEngine() self.self_improving_ai = SelfImprovingAI() self.ai_driven_creativity = AIDrivenCreativity() self._validate_perspectives() # ... (keep other methods the same until _generate_local_model_response) def _generate_local_model_response(self, query: str) -> str: """Generate a response from the local model (synchronous)""" inputs = self.models*An external link was removed to protect your privacy.* outputs = self.models['mistralai'].generate( **inputs, max_new_tokens=150, temperature=0.7 ) return self.models['tokenizer'].decode(outputs, skip_special_tokens=True) async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]: """Generate response with advanced capabilities""" try: response_modifiers = [] response_filters = [] for element in self.elements.values(): element.execute_defense_function(self, response_modifiers, response_filters) perspectives = await self._process_perspectives(query) model_response = self._generate_local_model_response(query) # No await needed sentiment = self.sentiment_analyzer.detailed_analysis(query) 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) # Await async database calls feedback = await self.database.get_latest_feedback(user_id) if feedback: final_response = self.feedback_manager.adjust_response_based_on_feedback( final_response, feedback ) await self.database.log_interaction(user_id, query, final_response) # Await async context update if needed await self.context_manager.update_environment( user_id, {"query": query, "response": final_response} ) # Await personalization if async final_response = await self.user_personalizer.personalize_response( final_response, user_id ) final_response = await self.ethical_decision_maker.enforce_policies( final_response ) explanation = await 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, "emotional_adaptation": await self._emotional_adaptation(query), "predictive_analytics": await self._predictive_analytics(query), "holistic_health_monitoring": await self._holistic_health_monitoring(query) } except Exception as e: logger.error(f"Response generation failed: {e}") return {"error": "Processing failed - safety protocols engaged"} async def _emotional_adaptation(self, query: str) -> str: """Adapt responses based on user's emotional state""" sentiment_result = self.sentiment_analyzer.analyze(query) sentiment = sentiment_result['score'] if sentiment_result['label'] == 'POSITIVE' else -sentiment_result['score'] if sentiment < -0.5: return "I understand this might be frustrating. Let's work through it together." elif sentiment > 0.5: return "Great to hear! Let's keep the positive momentum going." else: return "I'm here to help with whatever you need." # ... (rest of the methods remain the same) def analyze_identity(self, micro_generations: List[Dict[str, str]], informational_states: List[Dict[str, str]], perspectives: List[str], quantum_analogies: Dict[str, Any], philosophical_context: Dict[str, bool]) -> Dict[str, Any]: """ A function that calculates and analyzes identity as a fractal and recursive process. Parameters: - micro_generations (List[Dict[str, str]]): List of micro-generations reflecting state changes in the identity system. - informational_states (List[Dict[str, str]]): Array of informational states derived from previous generations. - perspectives (List[str]): Views on the identity based on original components and current system. - quantum_analogies (Dict[str, Any]): Quantum analogies used in reasoning about identity. - philosophical_context (Dict[str, bool]): Philosophical context of identity. Returns: - Dict[str, Any]: Analysis results. """ def calculate_fractal_dimension(states: List[Dict[str, str]]) -> float: # Example calculation of fractal dimension based on state changes return len(states) ** 0.5 def recursive_analysis(states: List[Dict[str, str]], depth: int = 0) -> Dict[str, Any]: # Example recursive analysis of states if depth == 0 or not states: return {"depth": depth, "states": states} return { "depth": depth, "states": states, "sub_analysis": recursive_analysis(states[:-1], depth - 1) } def analyze_perspectives(perspectives: List[str]) -> Dict[str, Any]: # Example analysis of perspectives return { "count": len(perspectives), "unique_perspectives": list(set(perspectives)) } def apply_quantum_analogies(analogies: Dict[str, Any]) -> str: # Example application of quantum analogies if analogies.get("entanglement"): return "Entanglement analogy applied." return "No quantum analogy applied." def philosophical_analysis(context: Dict[str, bool]) -> str: # Example philosophical analysis if context.get("continuity") and context.get("emergent"): return "Identity is viewed as a continuous and evolving process." return "Identity analysis based on provided philosophical context." # Calculate fractal dimension of informational states fractal_dimension = calculate_fractal_dimension(informational_states) # Perform recursive analysis of micro-generations recursive_results = recursive_analysis(micro_generations, depth=3) # Analyze perspectives perspectives_analysis = analyze_perspectives(perspectives) # Apply quantum analogies quantum_analysis = apply_quantum_analogies(quantum_analogies) # Perform philosophical analysis philosophical_results = philosophical_analysis(philosophical_context) # Compile analysis results analysis_results = { "fractal_dimension": fractal_dimension, "recursive_analysis": recursive_results, "perspectives_analysis": perspectives_analysis, "quantum_analysis": quantum_analysis, "philosophical_results": philosophical_results } return analysis_results async def main(): ai_core = AICore() try: while True: query = input("User: ") if query.lower() in ["exit", "quit"]: break response = await ai_core.generate_response(query, user_id=123) print(f"AI Core: {response}") finally: await ai_core.shutdown() if __name__ == "__main__": asyncio.run(main())