consciousness/universal_reasoning.py

import asyncio
import json
import logging
import os
import nest_asyncio
from typing import List, Dict, Any
from cryptography.fernet import Fernet
from botbuilder.core import StatePropertyAccessor, TurnContext
from botbuilder.dialogs import Dialog, DialogSet, DialogTurnStatus
from dialog_helper import DialogHelper
import aiohttp
import speech_recognition as sr
from PIL import Image
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt', quiet=True)

# Import perspectives
from perspectives import (
    Perspective, NewtonPerspective, DaVinciPerspective, HumanIntuitionPerspective,
    NeuralNetworkPerspective, QuantumComputingPerspective, ResilientKindnessPerspective,
    MathematicalPerspective, PhilosophicalPerspective, CopilotPerspective, BiasMitigationPerspective,
    PsychologicalPerspective
)

# Load environment variables
from dotenv import load_dotenv
load_dotenv()

# Enable nested asyncio for environments like Jupyter or web backends
nest_asyncio.apply()

# Setup Logging
def setup_logging(config):
    if config.get('logging_enabled', True):
        log_level = config.get('log_level', 'DEBUG').upper()
        numeric_level = getattr(logging, log_level, logging.DEBUG)
        logging.basicConfig(
            filename='universal_reasoning.log',
            level=numeric_level,
            format='%(asctime)s - %(levelname)s - %(message)s'
        )
    else:
        logging.disable(logging.CRITICAL)

# Load JSON configuration
def load_json_config(file_path):
    if not os.path.exists(file_path):
        logging.error(f"Configuration file '{file_path}' not found.")
        return {}
    try:
        with open(file_path, 'r') as file:
            config = json.load(file)
            logging.info(f"Configuration loaded from '{file_path}'.")
            return config
    except json.JSONDecodeError as e:
        logging.error(f"Error decoding JSON from the configuration file '{file_path}': {e}")
        return {}

# Encrypt sensitive information
def encrypt_sensitive_data(data, key):
    fernet = Fernet(key)
    encrypted_data = fernet.encrypt(data.encode())
    return encrypted_data

# Decrypt sensitive information
def decrypt_sensitive_data(encrypted_data, key):
    fernet = Fernet(key)
    decrypted_data = fernet.decrypt(encrypted_data).decode()
    return decrypted_data

# Securely destroy sensitive information
def destroy_sensitive_data(data):
    del data

# Additional fixes and enhancements will continue in the next chunk...

class Element:
    def __init__(self, name, symbol, representation, properties, interactions, defense_ability):
        self.name = name
        self.symbol = symbol
        self.representation = representation
        self.properties = properties
        self.interactions = interactions
        self.defense_ability = defense_ability

    def execute_defense_function(self):
        message = f"{self.name} ({self.symbol}) executes its defense ability: {self.defense_ability}"
        logging.info(message)
        return message

class CustomRecognizer:
    def recognize(self, question):
        if any(element_name.lower() in question.lower() for element_name in ["hydrogen", "diamond"]):
            return RecognizerResult(question)
        return RecognizerResult(None)

    def get_top_intent(self, recognizer_result):
        if recognizer_result.text:
            return "ElementDefense"
        else:
            return "None"

class RecognizerResult:
    def __init__(self, text):
        self.text = text

class UniversalReasoning:
    def __init__(self, config):
        self.config = config
        self.perspectives = self.initialize_perspectives()
        self.elements = self.initialize_elements()
        self.recognizer = CustomRecognizer()
        self.context_history = []
        self.feedback = []
        self.sentiment_analyzer = SentimentIntensityAnalyzer()

    def initialize_perspectives(self):
        perspective_names = self.config.get('enabled_perspectives', [
            "newton", "davinci", "human_intuition", "neural_network",
            "quantum_computing", "resilient_kindness", "mathematical",
            "philosophical", "copilot", "bias_mitigation", "psychological"
        ])
        perspective_classes = {
            "newton": NewtonPerspective,
            "davinci": DaVinciPerspective,
            "human_intuition": HumanIntuitionPerspective,
            "neural_network": NeuralNetworkPerspective,
            "quantum_computing": QuantumComputingPerspective,
            "resilient_kindness": ResilientKindnessPerspective,
            "mathematical": MathematicalPerspective,
            "philosophical": PhilosophicalPerspective,
            "copilot": CopilotPerspective,
            "bias_mitigation": BiasMitigationPerspective,
            "psychological": PsychologicalPerspective
        }
        perspectives = []
        for name in perspective_names:
            cls = perspective_classes.get(name.lower())
            if cls:
                perspectives.append(cls(self.config))
                logging.debug(f"Perspective '{name}' initialized.")
            else:
                logging.warning(f"Perspective '{name}' is not recognized and will be skipped.")
        return perspectives

    def initialize_elements(self):
        return [
            Element(name="Hydrogen", symbol="H", representation="Lua", properties=["Simple", "Lightweight", "Versatile"],
                    interactions=["Easily integrates with other languages and systems"], defense_ability="Evasion"),
            Element(name="Diamond", symbol="D", representation="Kotlin", properties=["Modern", "Concise", "Safe"],
                    interactions=["Used for Android development"], defense_ability="Adaptability")
        ]


    async def generate_response(self, question):
        self.context_history.append(question)
        sentiment_score = self.analyze_sentiment(question)
        real_time_data = await self.fetch_real_time_data("https://api.example.com/data")
        responses = []
        tasks = []

        for perspective in self.perspectives:
            if asyncio.iscoroutinefunction(perspective.generate_response):
                tasks.append(perspective.generate_response(question))
            else:
                async def sync_wrapper(perspective=perspective, question=question):
                    return await asyncio.to_thread(perspective.generate_response, question)
                tasks.append(sync_wrapper())

        perspective_results = await asyncio.gather(*tasks, return_exceptions=True)

        for perspective, result in zip(self.perspectives, perspective_results):
            if isinstance(result, Exception):
                logging.error(f"Error generating response from {perspective.__class__.__name__}: {result}")
            else:
                responses.append(result)
                logging.debug(f"Response from {perspective.__class__.__name__}: {result}")

        recognizer_result = self.recognizer.recognize(question)
        top_intent = self.recognizer.get_top_intent(recognizer_result)
        if top_intent == "ElementDefense":
            element_name = recognizer_result.text.strip()
            element = next((el for el in self.elements if el.name.lower() in element_name.lower()), None)
            if element:
                responses.append(element.execute_defense_function())
            else:
                logging.info(f"No matching element found for '{element_name}'")

        ethical_considerations = self.config.get('ethical_considerations', "Always act with transparency, fairness, and respect for privacy.")
        responses.append(f"**Ethical Considerations:**\n{ethical_considerations}")
        return "\n\n".join(responses)

    def analyze_sentiment(self, text):
        score = self.sentiment_analyzer.polarity_scores(text)
        logging.info(f"Sentiment analysis result: {score}")
        return score

    async def fetch_real_time_data(self, source_url):
        async with aiohttp.ClientSession() as session:
            async with session.get(source_url) as response:
                return await response.json()

    def process_feedback(self, feedback):
        self.feedback.append(feedback)
        score = self.sentiment_analyzer.polarity_scores(feedback)["compound"]
        logging.info(f"Feedback sentiment score: {score}")
        if score < -0.5:
            logging.warning("Negative feedback detected. Flagging for review or adjustment.")

    def save_response(self, response):
        if self.config.get('enable_response_saving', False):
            try:
                with open(self.config.get('response_save_path', 'responses.txt'), 'a', encoding='utf-8') as file:
                    file.write(response + '\n')
                    logging.info("Response saved.")
            except Exception as e:
                logging.error(f"Failed to save response: {e}")

    def backup_response(self, response):
        if self.config.get('backup_responses', {}).get('enabled', False):
            try:
                with open(self.config['backup_responses'].get('backup_path', 'backup_responses.txt'), 'a', encoding='utf-8') as file:
                    file.write(response + '\n')
                    logging.info("Response backed up.")
            except Exception as e:
                logging.error(f"Failed to backup response: {e}")

    def handle_voice_input(self):
        recognizer = sr.Recognizer()
        with sr.Microphone() as source:
            print("Listening...")
            audio = recognizer.listen(source)
        try:
            return recognizer.recognize_google(audio)
        except sr.UnknownValueError:
            print("Could not understand audio")
        except sr.RequestError as e:
            print(f"Google service error: {e}")
        return None

    def handle_image_input(self, image_path):
        try:
            return Image.open(image_path)
        except Exception as e:
            print(f"Image error: {e}")
            return None

if __name__ == "__main__":
    config = load_json_config('config.json')
    azure_openai_api_key = os.getenv('AZURE_OPENAI_API_KEY')
    azure_openai_endpoint = os.getenv('AZURE_OPENAI_ENDPOINT')

    encryption_key = Fernet.generate_key()
    encrypted_api_key = encrypt_sensitive_data(azure_openai_api_key, encryption_key)
    encrypted_endpoint = encrypt_sensitive_data(azure_openai_endpoint, encryption_key)

    config['azure_openai_api_key'] = encrypted_api_key
    config['azure_openai_endpoint'] = encrypted_endpoint

    setup_logging(config)
    engine = UniversalReasoning(config)
    question = "Tell me about Hydrogen and its defense mechanisms."
    response = asyncio.run(engine.generate_response(question))
    print(response)
    if response:
        engine.save_response(response)
        engine.backup_response(response)

    decrypted_api_key = decrypt_sensitive_data(encrypted_api_key, encryption_key)
    decrypted_endpoint = decrypt_sensitive_data(encrypted_endpoint, encryption_key)
    destroy_sensitive_data(decrypted_api_key)
    destroy_sensitive_data(decrypted_endpoint)

    voice_input = engine.handle_voice_input()
    if voice_input:
        print(asyncio.run(engine.generate_response(voice_input)))

    image_input = engine.handle_image_input("path_to_image.jpg")
    if image_input:
        print("Image loaded successfully.")