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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.")
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