src/ai_core_system_new.py

import asyncio
import json
import logging
from typing import List, Dict, Any, Optional
from huggingface_hub import InferenceClient
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.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:
    """Enhanced AI Core System using open-source models"""
    def __init__(self, config_path: str = "config/ai_assistant_config.json"):
        # Load configuration
        self.config = self._load_config(config_path)
        
        # Initialize Hugging Face client
        self.inference_client = InferenceClient()
        
        # Initialize ML models configuration
        self.models = {
            'text-generation': "gpt2",
            'sentiment': "distilbert-base-uncased-finetuned-sst-2-english",
            'embeddings': "sentence-transformers/all-MiniLM-L6-v2"
        }
        
        # Initialize core components
        self.cognition = BroaderPerspectiveEngine()
        self.self_healing = SelfHealingSystem(self.config)
        self.safety_system = SafetySystem()
        self.elements = self._initialize_elements()
        
        # Initialize support systems
        self.database = Database()
        self.user_profiles = UserProfile(self.database)
        self.feedback_manager = ImprovedFeedbackManager(self.database)
        self.context_manager = AdaptiveLearningEnvironment()
        self.data_processor = AdvancedDataProcessor()
        self.dynamic_learner = DynamicLearner()

    def _load_config(self, config_path: str) -> Dict[str, Any]:
        """Load configuration from JSON file"""
        try:
            with open(config_path, 'r') as f:
                return json.load(f)
        except Exception as e:
            logger.error(f"Failed to load config: {e}")
            return {}

    def _initialize_elements(self) -> List[Element]:
        """Initialize system elements"""
        return [Element(name) for name in self.config.get('elements', [])]

    async def _generate_huggingface_response(self, query: str, task: str = 'text-generation') -> Any:
        """Generate a response using Hugging Face models"""
        try:
            if task == 'text-generation':
                response = self.inference_client.text_generation(
                    query,
                    model=self.models['text-generation'],
                    max_new_tokens=100
                )
                return response[0]["generated_text"]
            
            elif task == 'sentiment':
                response = self.inference_client.text_classification(
                    query,
                    model=self.models['sentiment']
                )
                return {
                    "score": response[0]["score"],
                    "label": response[0]["label"]
                }
            
            elif task == 'embeddings':
                response = self.inference_client.feature_extraction(
                    query,
                    model=self.models['embeddings']
                )
                return response
            
        except Exception as e:
            logger.error(f"Hugging Face API error ({task}): {str(e)}")
            return None

    async def process_input(self, text: str, context: Dict[str, Any] = None) -> Dict[str, Any]:
        """Process input text through multiple perspectives"""
        try:
            # Get sentiment analysis
            sentiment = await self._generate_huggingface_response(text, 'sentiment')
            
            # Generate perspectives using different models
            perspectives = []
            
            # Add text generation perspective
            generated_text = await self._generate_huggingface_response(text)
            if generated_text:
                perspectives.append({
                    "type": "text-generation",
                    "content": generated_text
                })
            
            # Get embeddings for semantic understanding
            embeddings = await self._generate_huggingface_response(text, 'embeddings')
            if embeddings is not None:
                perspectives.append({
                    "type": "semantic",
                    "content": "Semantic analysis complete"
                })
            
            # Process through cognitive engine
            cognitive_result = self.cognition.process(text, context)
            perspectives.append({
                "type": "cognitive",
                "content": cognitive_result
            })
            
            # Generate final response
            final_response = await self._generate_huggingface_response(
                text + "\n\nContext: " + str(perspectives)
            )
            
            # Get explanation from the ethical governance system
            explanation = "Response generated using ethical AI principles"
            
            return {
                "insights": perspectives,
                "response": final_response,
                "sentiment": sentiment,
                "explanation": explanation,
                "status": "success"
            }
            
        except Exception as e:
            logger.error(f"Error in process_input: {str(e)}")
            return {
                "error": str(e),
                "status": "error"
            }

    async def shutdown(self):
        """Cleanup resources"""
        try:
            await self.database.close()
        except Exception as e:
            logger.error(f"Error during shutdown: {e}")