Update aicore.py

aicore.py

@@ -1,201 +1,123 @@
 import asyncio
+import json
 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:
+import torch
+import tkinter as tk
+from tkinter import messagebox
+from threading import Thread
+from typing import Dict, Any
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+
+class AICore:
     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
+        if not torch.cuda.is_available():
+            raise RuntimeError("GPU not available. Ensure CUDA is installed and a compatible GPU is present.")
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        logging.info(f"Using device: {self.device}")
+
+    async def generate_response(self, query: str) -> Dict[str, Any]:
+        # Simulate AI response generation
+        await asyncio.sleep(1)  # Simulate processing time
         return {
-            'memory': psutil.virtual_memory().percent,
-            'cpu': psutil.cpu_percent(),
-            'response_time': self._measure_response_time()
+            "query": query,
+            "response": f"AI response to: {query}",
+            "insights": ["Insight 1", "Insight 2"],
+            "security_level": 2,
+            "safety_analysis": {"toxicity": 0.1, "bias": 0.05, "privacy": []}
         }
 
-    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
+    async def check_health(self) -> Dict[str, Any]:
+        # Simulate health check
+        await asyncio.sleep(1)  # Simulate processing time
+        return {
+            "memory_usage": 30,  # Example memory usage percentage
+            "cpu_load": 20,      # Example CPU load percentage
+            "gpu_memory": self.get_gpu_memory(),  # Get GPU memory usage
+            "response_time": 0.5 # Example response time in seconds
+        }
 
-# 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:
+    def get_gpu_memory(self) -> float:
+        if torch.cuda.is_available():
+            return torch.cuda.memory_allocated() / 1e9  # Convert bytes to GB
+        return 0.0
+
+    async def shutdown(self):
+        # Simulate shutdown process
+        await asyncio.sleep(1)  # Simulate cleanup time
+        logging.info("AI Core shutdown complete.")
+
+class AIApp(tk.Tk):
+    def __init__(self, ai_core):
+        super().__init__()
+        self.ai_core = ai_core
+        self.title("AI System Interface")
+        self.geometry("800x600")
+        self._running = True
+        self.create_widgets()
+        self._start_health_monitoring()
+
+    def create_widgets(self):
+        self.query_label = tk.Label(self, text="Enter your query:")
+        self.query_label.pack(pady=10)
+        self.query_entry = tk.Entry(self, width=100)
+        self.query_entry.pack(pady=10)
+        self.submit_button = tk.Button(self, text="Submit", command=self.submit_query)
+        self.submit_button.pack(pady=10)
+        self.response_area = tk.Text(self, height=20, width=100)
+        self.response_area.pack(pady=10)
+        self.status_bar = tk.Label(self, text="Ready", bd=1, relief=tk.SUNKEN, anchor=tk.W)
+        self.status_bar.pack(side=tk.BOTTOM, fill=tk.X)
+
+    def submit_query(self):
+        query = self.query_entry.get()
+        self.status_bar.config(text="Processing...")
+        Thread(target=self._run_async_task, args=(self.ai_core.generate_response(query),)).start()
+
+    def _run_async_task(self, coroutine):
+        """Run async task in a separate thread"""
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
         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)
-            }
-
+            result = loop.run_until_complete(coroutine)
+            self.after(0, self._display_result, result)
         except Exception as e:
-            logging.error(f"Processing error: {e}")
-            return {"error": str(e)}
+            self.after(0, self._show_error, str(e))
+        finally:
+            loop.close()
+
+    def _display_result(self, result: Dict):
+        """Display results in the GUI"""
+        self.response_area.insert(tk.END, json.dumps(result, indent=2) + "\n\n")
+        self.status_bar.config(text="Query processed successfully")
+
+    def _show_error(self, message: str):
+        """Display error messages to the user"""
+        messagebox.showerror("Error", message)
+        self.status_bar.config(text=f"Error: {message}")
+
+    def _start_health_monitoring(self):
+        """Periodically check system health"""
+        def update_health():
+            if self._running:
+                health = asyncio.run(self.ai_core.check_health())
+                self.status_bar.config(
+                    text=f"System Health - Memory: {health['memory_usage']}% | "
+                         f"CPU: {health['cpu_load']}% | GPU: {health['gpu_memory']}GB | "
+                         f"Response Time: {health['response_time']:.2f}s"
+                )
+            self.after(5000, update_health)
+
+        update_health()
 
-# Example Usage
 async def main():
-    system = AISystem()
-    response = await system.process_query("How does Hydrogen defend?")
-    print("AI Response:", response)
+    """The main function initializes the AI system and starts the GUI."""
+    print("🧠 Hybrid AI System Initializing (Local Models)")
+    ai = AICore()  # Initialize the AI core
+    app = AIApp(ai)
+    app.mainloop()
+    await ai.shutdown()
 
 if __name__ == "__main__":
     asyncio.run(main())