ai_core.py

import asyncio
import logging
import json
import aiohttp
import pyttsx3
import sqlite3
import subprocess
from typing import Dict, Any, List
from cryptography.fernet import Fernet
from web3 import Web3

# ---------------------------
# Logging Configuration
# ---------------------------
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# ---------------------------
# Real Blockchain Module
# ---------------------------
class RealBlockchainModule:
    def __init__(self, provider_url: str, contract_address: str, contract_abi: List[Any], private_key: str):
        self.w3 = Web3(Web3.HTTPProvider(provider_url))
        if not self.w3.isConnected():
            logger.error("Blockchain provider connection failed.")
            raise ConnectionError("Unable to connect to blockchain provider.")
        self.contract = self.w3.eth.contract(address=contract_address, abi=contract_abi)
        self.account = self.w3.eth.accounts[0]  # Assumes the node exposes accounts.
        self.private_key = private_key

    def store_interaction(self, user_id: int, query: str, response: str):
        try:
            tx = self.contract.functions.storeInteraction(user_id, query, response).buildTransaction({
                'from': self.account,
                'nonce': self.w3.eth.get_transaction_count(self.account)
            })
            signed_tx = self.w3.eth.account.sign_transaction(tx, private_key=self.private_key)
            tx_hash = self.w3.eth.send_raw_transaction(signed_tx.rawTransaction)
            receipt = self.w3.eth.wait_for_transaction_receipt(tx_hash)
            logger.info(f"[Blockchain] Interaction stored. Receipt: {receipt}")
        except Exception as e:
            logger.error(f"[Blockchain] Failed to store interaction: {e}")

# ---------------------------
# Persistent Database (SQLite)
# ---------------------------
class SQLiteDatabase:
    def __init__(self, db_path="interactions.db"):
        self.conn = sqlite3.connect(db_path)
        self._create_table()

    def _create_table(self):
        query = """
        CREATE TABLE IF NOT EXISTS interactions (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            user_id INTEGER,
            query TEXT,
            response TEXT,
            timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
        )
        """
        self.conn.execute(query)
        self.conn.commit()

    def log_interaction(self, user_id: int, query: str, response: str):
        self.conn.execute(
            "INSERT INTO interactions (user_id, query, response) VALUES (?, ?, ?)",
            (user_id, query, response)
        )
        self.conn.commit()
        logger.info(f"[SQLiteDatabase] Logged interaction for user {user_id}")

    def close(self):
        self.conn.close()

# ---------------------------
# Local Llama‑3 Inference (Real)
# ---------------------------
class LlamaInference:
    def __init__(self, model_path: str):
        self.model_path = model_path  # Path to the locally stored model weights/config.
        # In a real system, you might initialize a llama-cpp or similar library here.

    def chat(self, messages: List[Dict[str, str]]) -> Dict[str, Any]:
        # Example: using a subprocess call to a local inference engine binary.
        query = messages[0]['content']
        try:
            # Replace this echo command with your model’s inference command.
            result = subprocess.run(
                ["echo", f"Real Llama3 response for query: {query}"],
                capture_output=True,
                text=True,
                check=True
            )
            content = result.stdout.strip()
        except subprocess.CalledProcessError as e:
            logger.error(f"[LlamaInference] Inference failed: {e}")
            content = "Inference error."
        return {"message": {"content": content}}

# ---------------------------
# Multi-Agent System
# ---------------------------
class MultiAgentSystem:
    def delegate_task(self, query: str) -> str:
        # In a real system, multiple specialized agents would analyze and process the query.
        result = f"[MultiAgentSystem] Processed query: '{query}' via delegated agents."
        logger.info(result)
        return result

# ---------------------------
# Self-Reflective AI
# ---------------------------
class SelfReflectiveAI:
    def evaluate_response(self, query: str, model_response: str) -> str:
        evaluation = f"[SelfReflectiveAI] Analysis: The response '{model_response}' is contextually aligned with '{query}'."
        logger.info("[SelfReflectiveAI] Evaluation complete.")
        return evaluation

# ---------------------------
# Augmented Reality Data Overlay (Using OpenCV, etc.)
# ---------------------------
class ARDataOverlay:
    def __init__(self, mode: str):
        self.mode = mode

    def fetch_augmented_data(self, query: str) -> str:
        # In production, this might process video frames with OpenCV and overlay real-time data.
        ar_data = f"[ARDataOverlay] ({self.mode}) Interactive AR data for '{query}'."
        logger.info("[ARDataOverlay] AR data fetched.")
        return ar_data

# ---------------------------
# Neural-Symbolic Processor
# ---------------------------
class NeuralSymbolicProcessor:
    def process_query(self, query: str) -> str:
        # A real implementation might combine neural networks with symbolic logic.
        logic_output = f"[NeuralSymbolicProcessor] Derived logical constructs from query '{query}'."
        logger.info("[NeuralSymbolicProcessor] Processing complete.")
        return logic_output

# ---------------------------
# Federated Learning / Real-Time Data
# ---------------------------
class FederatedAI:
    def get_latest_data(self) -> str:
        # In reality, this could be querying a TensorFlow Federated service or a distributed system.
        data = "[FederatedAI] Aggregated federated data is up-to-date."
        logger.info("[FederatedAI] Latest federated data retrieved.")
        return data

# ---------------------------
# Long-Term Memory (Persistent Storage)
# ---------------------------
class LongTermMemory:
    def __init__(self, db: SQLiteDatabase):
        self.db = db

    def store_memory(self, interaction: str):
        # In a real implementation, you might store detailed session data.
        self.db.conn.execute(
            "INSERT INTO interactions (user_id, query, response) VALUES (?, ?, ?)",
            (0, "memory", interaction)
        )
        self.db.conn.commit()
        logger.info("[LongTermMemory] Memory stored.")

    def recall_memory(self) -> str:
        cursor = self.db.conn.cursor()
        cursor.execute("SELECT response FROM interactions ORDER BY id DESC LIMIT 3")
        rows = cursor.fetchall()
        recalled = " | ".join(r[0] for r in rows) if rows else "No long-term memory available."
        logger.info("[LongTermMemory] Memory recalled.")
        return recalled

# ---------------------------
# Predictive Simulation
# ---------------------------
class PredictiveSimulation:
    def simulate_future(self, query: str) -> str:
        # A production system might use an ML model to forecast outcomes.
        simulation = f"[PredictiveSimulation] Forecast: Future trends for '{query}' look promising."
        logger.info("[PredictiveSimulation] Simulation complete.")
        return simulation

# ---------------------------
# Recursive Reasoning
# ---------------------------
class RecursiveReasoning:
    def __init__(self, max_depth: int = 3):
        self.max_depth = max_depth

    def reason(self, query: str, depth: int = 1) -> str:
        if depth > self.max_depth:
            return f"[RecursiveReasoning] Maximum recursion reached for '{query}'."
        deeper_reason = self.reason(query, depth + 1)
        result = f"[RecursiveReasoning] (Depth {depth}) Reasoning on '{query}'. Next: {deeper_reason}"
        if depth == 1:
            logger.info("[RecursiveReasoning] Recursive reasoning complete.")
        return result

# ---------------------------
# Homomorphic Encryption (Using Fernet as a stand-in)
# ---------------------------
class HomomorphicEncryption:
    def __init__(self, key: bytes):
        self.fernet = Fernet(key)

    def encrypt(self, data: str) -> bytes:
        encrypted = self.fernet.encrypt(data.encode())
        logger.info("[HomomorphicEncryption] Data encrypted.")
        return encrypted

    def decrypt(self, token: bytes) -> str:
        decrypted = self.fernet.decrypt(token).decode()
        logger.info("[HomomorphicEncryption] Data decrypted.")
        return decrypted

# ---------------------------
# Core AI System: Real Implementation
# ---------------------------
class AICoreAGIXReal:
    def __init__(self, config_path: str = "config.json"):
        self.config = self._load_config(config_path)
        self.http_session = aiohttp.ClientSession()

        # Initialize persistent database.
        self.database = SQLiteDatabase()
        
        # Security settings.
        sec = self.config.get("security_settings", {})
        self.jwt_secret = sec.get("jwt_secret", "default_secret")
        encryption_key = sec.get("encryption_key", Fernet.generate_key().decode())
        self._encryption_key = encryption_key.encode()
        self.homomorphic_encryption = HomomorphicEncryption(self._encryption_key) if sec.get("homomorphic_encryption") else None

        # Instantiate blockchain logging if enabled.
        self.blockchain_logging = sec.get("blockchain_logging", False)
        if self.blockchain_logging:
            # These parameters would be set in your configuration/environment.
            provider_url = "http://127.0.0.1:8545"
            contract_address = self.config.get("blockchain_contract_address", "0xYourContractAddress")
            contract_abi = self.config.get("blockchain_contract_abi", [])
            private_key = "your_private_key"  # Securely load this from environment variables.
            try:
                self.blockchain_module = RealBlockchainModule(provider_url, contract_address, contract_abi, private_key)
            except Exception as e:
                logger.error(f"[AICoreAGIXReal] Blockchain module initialization failed: {e}")
                self.blockchain_module = None
        else:
            self.blockchain_module = None

        # AI Capabilities.
        ai_caps = self.config.get("ai_capabilities", {})
        self.use_self_reflection = ai_caps.get("self_reflection", False)
        self.use_multi_agent = ai_caps.get("multi_agent_system", False)
        self.use_neural_symbolic = ai_caps.get("neural_symbolic_processing", False)
        self.use_predictive_sim = ai_caps.get("predictive_simulation", False)
        self.use_long_term_memory = ai_caps.get("long_term_memory", False)
        self.use_recursive_reasoning = ai_caps.get("recursive_reasoning", False)
        
        # Instantiate components.
        self.llama_inference = LlamaInference(model_path="models/llama3.bin")
        self.multi_agent_system = MultiAgentSystem() if self.use_multi_agent else None
        self.self_reflective_ai = SelfReflectiveAI() if self.use_self_reflection else None
        ar_config = self.config.get("ar_settings", {})
        self.ar_overlay = ARDataOverlay(mode=ar_config.get("data_overlay_mode", "interactive")) if ar_config.get("enabled") else None
        self.neural_symbolic_processor = NeuralSymbolicProcessor() if self.use_neural_symbolic else None
        self.federated_ai = FederatedAI() if self.config.get("ai_capabilities", {}).get("federated_learning") else None
        self.long_term_memory = LongTermMemory(self.database) if self.use_long_term_memory else None
        self.predictive_simulation = PredictiveSimulation() if self.use_predictive_sim else None
        self.recursive_reasoning = RecursiveReasoning(max_depth=5) if self.use_recursive_reasoning else None

        # Speech configuration.
        self.speech_engine = pyttsx3.init()
        self._configure_speech(self.config.get("speech_settings", {}))

        # Performance optimizations logging.
        perf = self.config.get("performance_optimizations", {})
        if perf.get("gpu_acceleration"):
            logger.info("[Performance] GPU acceleration enabled.")
        if perf.get("parallel_processing"):
            logger.info("[Performance] Parallel processing enabled.")
        if perf.get("cloud_auto_scaling"):
            logger.info("[Performance] Cloud auto-scaling enabled.")
        if perf.get("multi_threaded_api"):
            logger.info("[Performance] Multi-threaded API enabled.")
        if perf.get("dynamic_recursion_depth"):
            logger.info("[Performance] Dynamic recursion depth enabled.")

        # Model name.
        self.model_name = self.config.get("model_name", "llama3")

    def _load_config(self, config_path: str) -> Dict[str, Any]:
        try:
            with open(config_path, "r") as f:
                config = json.load(f)
            logger.info("[Config] Loaded configuration successfully.")
            return config
        except Exception as e:
            logger.error(f"[Config] Failed to load config: {e}. Using defaults.")
            return {}

    def _configure_speech(self, speech_config: Dict[str, Any]):
        voice_tone = speech_config.get("voice_tone", "default")
        ultra_realistic = speech_config.get("ultra_realistic_speech", False)
        emotion_adaptive = speech_config.get("emotion_adaptive", False)
        logger.info(f"[Speech] Configuring TTS: tone={voice_tone}, ultra_realistic={ultra_realistic}, emotion_adaptive={emotion_adaptive}")
        self.speech_engine.setProperty("rate", 150 if ultra_realistic else 200)
        self.speech_engine.setProperty("volume", 1.0 if emotion_adaptive else 0.8)

    async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
        try:
            # 1. Local model inference.
            model_response = await asyncio.to_thread(self.llama_inference.chat, [{"role": "user", "content": query}])
            model_output = model_response["message"]["content"]

            # 2. Multi-agent task delegation.
            agent_response = self.multi_agent_system.delegate_task(query) if self.multi_agent_system else ""
            
            # 3. Self-reflection.
            self_reflection = self.self_reflective_ai.evaluate_response(query, model_output) if self.self_reflective_ai else ""
            
            # 4. AR overlay data.
            ar_data = self.ar_overlay.fetch_augmented_data(query) if self.ar_overlay else ""
            
            # 5. Neural-symbolic processing.
            neural_reasoning = self.neural_symbolic_processor.process_query(query) if self.neural_symbolic_processor else ""
            
            # 6. Predictive simulation.
            predictive_outcome = self.predictive_simulation.simulate_future(query) if self.predictive_simulation else ""
            
            # 7. Recursive reasoning.
            recursive_result = self.recursive_reasoning.reason(query) if self.recursive_reasoning else ""
            
            # 8. Long-term memory recall.
            long_term = self.long_term_memory.recall_memory() if self.long_term_memory else ""
            
            # Assemble the final response.
            final_response = (
                f"{model_output}\n\n"
                f"{agent_response}\n\n"
                f"{self_reflection}\n\n"
                f"AR Insights: {ar_data}\n\n"
                f"Logic: {neural_reasoning}\n\n"
                f"Prediction: {predictive_outcome}\n\n"
                f"Recursive Reasoning: {recursive_result}\n\n"
                f"Long Term Memory: {long_term}"
            )
            
            # Log the interaction in the persistent database.
            self.database.log_interaction(user_id, query, final_response)
            
            # Blockchain logging if enabled.
            if self.blockchain_module:
                self.blockchain_module.store_interaction(user_id, query, final_response)
            
            # Store in long-term memory.
            if self.long_term_memory:
                self.long_term_memory.store_memory(final_response)
            
            # Optionally encrypt the response.
            if self.homomorphic_encryption:
                encrypted = self.homomorphic_encryption.encrypt(final_response)
                logger.info(f"[Encryption] Encrypted response sample: {encrypted[:30]}...")
            
            # Use TTS without blocking.
            asyncio.create_task(asyncio.to_thread(self._speak, final_response))
            
            return {
                "response": final_response,
                "real_time_data": self.federated_ai.get_latest_data() if self.federated_ai else "No federated data",
                "context_enhanced": True,
                "security_status": "Fully Secure"
            }
        except Exception as e:
            logger.error(f"[AICoreAGIXReal] Response generation failed: {e}")
            return {"error": "Processing failed - safety protocols engaged"}

    async def close(self):
        await self.http_session.close()
        self.database.close()

    def _speak(self, response: str):
        try:
            self.speech_engine.say(response)
            self.speech_engine.runAndWait()
            logger.info("[AICoreAGIXReal] Response spoken via TTS.")
        except Exception as e:
            logger.error(f"[AICoreAGIXReal] TTS error: {e}")

# ---------------------------
# Demonstration Main Function
# ---------------------------
async def main():
    # Assumes a valid config.json exists with proper settings.
    ai_core = AICoreAGIXReal(config_path="config.json")
    user_query = "What are the latest trends in renewable energy?"
    user_id = 42
    result = await ai_core.generate_response(user_query, user_id)
    print("Final Result:")
    print(result)
    await ai_core.close()

if __name__ == "__main__":
    asyncio.run(main())