src/ai_core_identityscan.py

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())