Create aicore.py

aicore.py

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+import asyncio
+import logging
+from typing import List, Dict
+from cryptography.hazmat.primitives import hashes
+from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
+from cryptography.hazmat.primitives.asymmetric import rsa, padding
+from cryptography.fernet import Fernet
+
+# Simplified Element System
+class Element:
+    DEFENSE_ACTIONS = {
+        "evasion": "evades threats through strategic ambiguity",
+        "adaptability": "adapts to counter emerging challenges",
+        "fortification": "strengthens defensive parameters"
+    }
+
+    def __init__(self, name: str, symbol: str, defense: str):
+        self.name = name
+        self.symbol = symbol
+        self.defense = defense
+
+    def defend(self):
+        return f"{self.name} ({self.symbol}): {self.DEFENSE_ACTIONS[self.defense]}"
+
+# Core AI Perspectives
+class AIPerspective:
+    PERSPECTIVES = {
+        "newton": lambda q: f"Newtonian Analysis: Force = {len(q)*0.73:.2f}N",
+        "davinci": lambda q: f"Creative Insight: {q[::-1]}",
+        "quantum": lambda q: f"Quantum View: {hash(q)%100}% certainty"
+    }
+
+    def __init__(self, active_perspectives: List[str] = None):
+        self.active = active_perspectives or list(self.PERSPECTIVES.keys())
+
+    async def analyze(self, question: str) -> List[str]:
+        return [self.PERSPECTIVES[p](question) for p in self.active]
+
+# Quantum-Resistant Encryption Upgrade
+class QuantumSafeEncryptor:
+    def __init__(self):
+        self.private_key = rsa.generate_private_key(public_exponent=65537, key_size=4096)
+        self.public_key = self.private_key.public_key()
+
+    def hybrid_encrypt(self, data: str) -> bytes:
+        # Generate symmetric key
+        sym_key = Fernet.generate_key()
+        fernet = Fernet(sym_key)
+
+        # Encrypt data with symmetric encryption
+        encrypted_data = fernet.encrypt(data.encode())
+
+        # Encrypt symmetric key with post-quantum algorithm
+        encrypted_key = self.public_key.encrypt(
+            sym_key,
+            padding.OAEP(
+                mgf=padding.MGF1(algorithm=hashes.SHA512()),
+                algorithm=hashes.SHA512(),
+                label=None
+            )
+        )
+
+        return encrypted_key + b'||SEPARATOR||' + encrypted_data
+
+# Neural Architecture Search Integration
+class AINeuralOptimizer:
+    def __init__(self):
+        self.search_model = None
+
+    async def optimize_pipeline(self, dataset):
+        from autokeras import StructuredDataClassifier
+        self.search_model = StructuredDataClassifier(max_trials=10)
+        self.search_model.fit(x=dataset.features, y=dataset.labels, epochs=50)
+
+    def generate_architecture(self):
+        import tensorflow as tf
+        best_model = self.search_model.export_model()
+        return tf.keras.models.clone_model(best_model)
+
+# Holographic Knowledge Graph
+class HolographicKnowledge:
+    def __init__(self, uri, user, password):
+        from neo4j import GraphDatabase
+        self.driver = GraphDatabase.driver(uri, auth=(user, password))
+
+    async def store_relationship(self, entity1, relationship, entity2):
+        with self.driver.session() as session:
+            session.write_transaction(
+                self._create_relationship, entity1, relationship, entity2
+            )
+
+    @staticmethod
+    def _create_relationship(tx, e1, rel, e2):
+        query = (
+            "MERGE (a:Entity {name: $e1}) "
+            "MERGE (b:Entity {name: $e2}) "
+            f"MERGE (a)-[r:{rel}]->(b)"
+        )
+        tx.run(query, e1=e1, e2=e2)
+
+# Self-Healing Mechanism
+class SelfHealingSystem:
+    def __init__(self):
+        from elasticsearch import Elasticsearch
+        import sentry_sdk
+        self.es = Elasticsearch()
+        sentry_sdk.init(dsn="YOUR_SENTRY_DSN")
+
+    async def monitor_system(self):
+        import psutil
+        while True:
+            health = await self.check_health()
+            if health['status'] != 'GREEN':
+                self.heal_system(health)
+            await asyncio.sleep(60)
+
+    async def check_health(self):
+        import psutil
+        return {
+            'memory': psutil.virtual_memory().percent,
+            'cpu': psutil.cpu_percent(),
+            'response_time': self._measure_response_time()
+        }
+
+    def heal_system(self, health):
+        if health['memory'] > 90:
+            self._clean_memory()
+        if health['response_time'] > 5000:
+            self._scale_out()
+
+    def _measure_response_time(self):
+        # Implement response time measurement
+        return 100  # Placeholder value
+
+    def _clean_memory(self):
+        # Implement memory cleaning
+        pass
+
+    def _scale_out(self):
+        # Implement scaling out
+        pass
+
+# Temporal Analysis Engine
+class TemporalProphet:
+    def __init__(self):
+        from prophet import Prophet
+        self.models = {}
+
+    async def analyze_temporal_patterns(self, data):
+        model = Prophet(interval_width=0.95)
+        model.fit(data)
+        future = model.make_future_dataframe(periods=365)
+        forecast = model.predict(future)
+        return forecast
+
+    def detect_anomalies(self, forecast):
+        return forecast[
+            (forecast['yhat_lower'] > forecast['cap']) |
+            (forecast['yhat_upper'] < forecast['floor'])
+        ]
+
+# Unified System
+class AISystem:
+    def __init__(self):
+        self.elements = [
+            Element("Hydrogen", "H", "evasion"),
+            Element("Carbon", "C", "adaptability")
+        ]
+        self.ai = AIPerspective()
+        self.security = QuantumSafeEncryptor()
+        self.self_healing = SelfHealingSystem()
+        self.temporal_analysis = TemporalProphet()
+        logging.basicConfig(level=logging.INFO)
+
+    async def process_query(self, question: str) -> Dict:
+        try:
+            # AI Analysis
+            perspectives = await self.ai.analyze(question)
+
+            # Element Defense
+            defenses = [e.defend() for e in self.elements
+                        if e.name.lower() in question.lower()]
+
+            return {
+                "perspectives": perspectives,
+                "defenses": defenses,
+                "encrypted": self.security.hybrid_encrypt(question)
+            }
+
+        except Exception as e:
+            logging.error(f"Processing error: {e}")
+            return {"error": str(e)}
+
+# Example Usage
+async def main():
+    system = AISystem()
+    response = await system.process_query("How does Hydrogen defend?")
+    print("AI Response:", response)
+
+if __name__ == "__main__":
+    asyncio.run(main())