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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')
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