codette_reasoning.py

import asyncio
import json
import os
import logging
from typing import List

# Ensure vaderSentiment is installed
try:
    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
except ModuleNotFoundError:
    import subprocess
    import sys
    subprocess.check_call([sys.executable, "-m", "pip", "install", "vaderSentiment"])
    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

# Ensure nltk is installed and download required data
try:
    import nltk
    from nltk.tokenize import word_tokenize
    nltk.download('punkt', quiet=True)
except ImportError:
    import subprocess
    import sys
    subprocess.check_call([sys.executable, "-m", "pip", "install", "nltk"])
    import nltk
    from nltk.tokenize import word_tokenize
    nltk.download('punkt', quiet=True)

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

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)

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}'.")
            config['allow_network_calls'] = False
            return config
    except json.JSONDecodeError as e:
        logging.error(f"Error decoding JSON from the configuration file '{file_path}': {e}")
        return {}

def analyze_question(question):
    tokens = word_tokenize(question)
    logging.debug(f"Question tokens: {tokens}")
    return tokens

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):
        return "ElementDefense" if recognizer_result.text else "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.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"
        ])
        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
        }
        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.")
        return perspectives

    def initialize_elements(self):
        return [
            Element("Hydrogen", "H", "Lua", ["Simple", "Lightweight", "Versatile"],
                    ["Integrates with other languages"], "Evasion"),
            Element("Diamond", "D", "Kotlin", ["Modern", "Concise", "Safe"],
                    ["Used for Android development"], "Adaptability")
        ]

    async def generate_response(self, question):
        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, question):
                    return perspective.generate_response(question)
                tasks.append(sync_wrapper(perspective, question))

        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 from {perspective.__class__.__name__}: {result}")
            else:
                responses.append(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())

        ethical = self.config.get("ethical_considerations", "Act transparently and respectfully.")
        responses.append(f"**Ethical Considerations:**\n{ethical}")

        return "\n\n".join(responses)

    def save_response(self, response):
        if self.config.get('enable_response_saving', False):
            path = self.config.get('response_save_path', 'responses.txt')
            with open(path, 'a', encoding='utf-8') as file:
                file.write(response + '\n')

    def backup_response(self, response):
        if self.config.get('backup_responses', {}).get('enabled', False):
            backup_path = self.config['backup_responses'].get('backup_path', 'backup_responses.txt')
            with open(backup_path, 'a', encoding='utf-8') as file:
                file.write(response + '\n')