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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
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