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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()) |