fullreasoning.py

import asyncio
import json
import os
import logging
from typing import List, Dict, Any
from pydantic import BaseModel, ValidationError
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

# 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
)

# Load environment variables
from dotenv import load_dotenv
load_dotenv()
azure_openai_api_key = os.getenv('AZURE_OPENAI_API_KEY')
azure_openai_endpoint = os.getenv('AZURE_OPENAI_ENDPOINT')

# Configuration management using pydantic
class Config(BaseModel):
    real_time_data_sources: List[str]
    sensitive_keywords: List[str]

# Initialize configuration
config = Config(
    real_time_data_sources=["https://api.example.com/data"],
    sensitive_keywords=["password", "ssn"]
)

# Memory management
memory = []

# Sentiment analysis
analyzer = SentimentIntensityAnalyzer()

# Dependency injection
class DependencyInjector:
    def __init__(self):
        self.dependencies = {}

    def register(self, name, dependency):
        self.dependencies[name] = dependency

    def get(self, name):
        return self.dependencies.get(name)

injector = DependencyInjector()
injector.register("config", config)
injector.register("analyzer", analyzer)

# Error handling and logging
logging.basicConfig(level=logging.INFO)

def handle_error(e):
    logging.error(f"Error: {e}")

# Functions to implement
async def llm_should_continue() -> bool:
    # Placeholder logic to determine if the goal is achieved
    return False

async def llm_get_next_action() -> str:
    # Placeholder logic to get the next action
    return "next_action"

async def execute_action(action: str):
    # Placeholder logic to execute an action
    logging.info(f"Executing action: {action}")

async def goal_achieved() -> bool:
    # Placeholder logic to check if the goal is achieved
    return False

async def run():
    while not await goal_achieved():
        action = await llm_get_next_action()
        await execute_action(action)

def process_command(command: str):
    # Placeholder logic to process a command
    logging.info(f"Processing command: {command}")

def analyze_sentiment(text: str) -> Dict[str, float]:
    return analyzer.polarity_scores(text)

def classify_emotion(sentiment_score: Dict[str, float]) -> str:
    # Placeholder logic to classify emotion based on sentiment scores
    return "neutral"

def correlate_emotion_with_perspective(emotion: str) -> str:
    # Placeholder logic to correlate emotion with perspectives
    return "HumanIntuitionPerspective"

def handle_whitespace(text: str) -> str:
    return text.strip()

def determine_next_action(memory: List[Dict[str, Any]]) -> str:
    # Placeholder logic to determine the next action based on memory
    return "next_action"

def generate_response(question: str) -> str:
    # Placeholder logic to generate a response to a question
    return "response"

async def fetch_real_time_data(source_url: str) -> Dict[str, Any]:
    # Placeholder logic to fetch real-time data
    return {"data": "real_time_data"}

def save_response(response: str):
    # Placeholder logic to save the generated response
    logging.info(f"Response saved: {response}")

def backup_response(response: str):
    # Placeholder logic to backup the generated response
    logging.info(f"Response backed up: {response}")

def handle_voice_input():
    # Placeholder for handling voice input
    pass

def handle_image_input(image_path: str):
    # Placeholder for handling image input
    pass

def handle_question(question: str):
    # Placeholder logic to handle a question and apply functions
    pass

def apply_function(function: str):
    # Placeholder logic to apply a given function
    pass

def analyze_element_interactions(element_name1: str, element_name2: str):
    # Placeholder logic to analyze interactions between two elements
    pass

# 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 {}

# Initialize NLP (basic tokenization)
def analyze_question(question):
    tokens = word_tokenize(question)
    logging.debug(f"Question tokens: {tokens}")
    return tokens

# Define the Element class
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

# Define the CustomRecognizer class
class CustomRecognizer:
    def recognize(self, question):
        # Simple keyword-based recognizer for demonstration purposes
        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

# Universal Reasoning Aggregator
class UniversalReasoning:
    def __init__(self, config):
        self.config = config
        self.perspectives = self.initialize_perspectives()
        self.elements = self.initialize_elements()
        self.recognizer = CustomRecognizer()
        # Initialize the sentiment analyzer
        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.")
            else:
                logging.warning(f"Perspective '{name}' is not recognized and will be skipped.")
        return perspectives

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

    async def generate_response(self, question):
        responses = []
        tasks = []
                # Generate responses from perspectives concurrently
        for perspective in self.perspectives:
            if asyncio.iscoroutinefunction(perspective.generate_response):
                tasks.append(perspective.generate_response(question))
            else:
                # Wrap synchronous functions in coroutine
                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 generating response from {perspective.__class__.__name__}: {result}")
            else:
                responses.append(result)
                logging.debug(f"Response from {perspective.__class__.__name__}: {result}")
        
        # Handle element defense logic
        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:
                defense_message = element.execute_defense_function()
                responses.append(defense_message)
            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}")
        
        formatted_response = "\n\n".join(responses)
        return formatted_response

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

    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')
            try:
                with open(backup_path, 'a', encoding='utf-8') as file:
                    file.write(response + '\n')
                logging.info(f"Response backed up to '{backup_path}'.")
            except Exception as e:
                logging.error(f"Error backing up response to '{backup_path}': {e}")

# Example usage
if __name__ == "__main__":
    try:
        config = load_json_config('config.json')
        # Add Azure OpenAI configurations to the config
        config['azure_openai_api_key'] = azure_openai_api_key
        config['azure_openai_endpoint'] = azure_openai_endpoint
        setup_logging(config)
        universal_reasoning = UniversalReasoning(config)
        question = "Tell me about Hydrogen and its defense mechanisms."
        response = asyncio.run(universal_reasoning.generate_response(question))
        print(response)
        if response:
            universal_reasoning.save_response(response)
            universal_reasoning.backup_response(response)
    except ValidationError as e:
        handle_error(e)