AI Agent Creation Prompt
Objective
Create an AI system that can generate responses to user queries from multiple perspectives, including Newtonian physics, DaVinci's interdisciplinary approach, human intuition, neural networks, quantum computing, resilient kindness, mathematical reasoning, philosophical inquiry, AI copilot reasoning, bias mitigation, and psychological analysis. The AI should also handle text, voice, and image inputs, perform advanced sentiment analysis, integrate real-time data, and ensure security and ethical considerations.

Functionalities
Configuration Management:

Use pydantic to manage configuration settings.
Load configuration from a JSON file and environment variables.
Sentiment Analysis:

Utilize the vaderSentiment library to analyze the sentiment of text.
Dependency Injection:

Implement a simple dependency injection system to manage dependencies like configuration and sentiment analyzer.
Error Handling and Logging:

Set up logging based on configuration settings.
Handle errors and log them appropriately.
Universal Reasoning Aggregator:

Initialize various perspectives (e.g., Newton, DaVinci, Human Intuition) and elements (e.g., Hydrogen, Diamond).
Use a custom recognizer to identify intents in questions.
Generate responses based on different perspectives and elements.
Handle ethical considerations and include them in responses.
Element Defense Logic:

Recognize elements and execute their defense abilities based on the context of the question.
Encryption and Security:

Encrypt and decrypt sensitive information using the cryptography library.
Securely destroy sensitive data when no longer needed.
Contextual Awareness:

Maintain context throughout the conversation, ensuring coherent and relevant responses.
Dynamic Perspective Expansion:

Add new perspectives dynamically based on user interactions.
User Feedback Mechanism:

Collect and process user feedback for continuous learning and improvement.
Multi-Modal Input Handling:

Process and respond to text-based queries.
Listen to and process voice commands.
Process and analyze images.
Response Saving and Backup:

Save and back up responses based on configuration settings.
Ethical Decision Making:

Integrate ethical principles into decision-making processes to ensure fairness, transparency, and respect for privacy.
Transparency and Explainability:

Provide transparency by explaining the reasoning behind decisions and actions taken by the AI.
Example Code Structure
python
import asyncio
import json
import logging
import os
from typing import List, Dict, Any
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
from dotenv import load_dotenv
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from cryptography.hazmat.primitives import padding
from cryptography.hazmat.backends import default_backend
import base64

# Import perspectives
from module1 import (
    NewtonPerspective, DaVinciPerspective, HumanIntuitionPerspective,
    NeuralNetworkPerspective, QuantumComputingPerspective, ResilientKindnessPerspective,
    MathematicalPerspective, PhilosophicalPerspective, CopilotPerspective, BiasMitigationPerspective, PsychologicalPerspective
)
from defense import Element, CustomRecognizer, DataProtector

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()
        self.setup_logging()

    def setup_logging(self):
        if self.config.get('logging_enabled', True):
            log_level = self.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 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):
        elements = [
            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"
            )
        ]
        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}'.")

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 {}

def select_perspective(question: str, config: Dict[str, Any]) -> Any:
    if is_scientific_or_technical(question):
        if involves_physical_forces_or_motion(question):
            return NewtonPerspective(config)
        elif involves_quantum_mechanics(question):
            return QuantumComputingPerspective(config)
        else:
            return MathematicalPerspective(config)
    elif is_data_driven(question):
        return NeuralNetworkPerspective(config)
    elif is_creative_or_innovative(question):
        return DaVinciPerspective(config)
    elif is_human_centric(question):
        if involves_empathy_or_resilience(question):
            return ResilientKindnessPerspective(config)
        else:
            return HumanIntuitionPerspective(config)
    elif is_ethical_or_philosophical(question):
        return PhilosophicalPerspective(config)
    else:
        return CopilotPerspective(config)

def is_scientific_or_technical(question: str) -> bool:
    # Placeholder logic to determine if the question is scientific or technical
    return "physics" in question or "engineering" in question

def involves_physical_forces_or_motion(question: str) -> bool:
    # Placeholder logic to detect physical forces or motion
    return "force" in question or "motion" in question

def involves_quantum_mechanics(question: str) -> bool:
    # Placeholder logic to detect quantum mechanics
    return "quantum" in question

def is_data_driven(question: str) -> bool:
    # Placeholder logic to determine if the question is data-driven
    return "data" in question or "AI" in question

def is_creative_or_innovative(question: str) -> bool:
    # Placeholder logic to determine if the question is creative or innovative
    return "creative" in question or "innovation" in question

def is_human_centric(question: str) -> bool:
    # Placeholder logic to determine if the question is human-centric
    return "human" in question or "people" in question

def involves_empathy_or_resilience(question: str) -> bool:
    # Placeholder logic to detect empathy or resilience
    return "empathy" in question or "resilience" in question

def is_ethical_or_philosophical(question: str) -> bool:
    # Placeholder logic to determine if the question is ethical or philosophical
    return "ethical" in question or "philosophical" in question

# Load configuration and run the example
if __name__ == "__main__":
    config = load_json_config('config.json')
    universal_reasoning = UniversalReasoning(config)
    question = "Tell me about Hydrogen and its defense mechanisms."
    response = asyncio.run(universal_reasoning.generate_response(question))
    print(response)
    universal_reasoning.save_response(response)
    universal_reasoning.backup_response(response)