module1.txt

import random
from typing import Any, Dict

class NewtonPerspective:
    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def generate_response(self, question: str) -> str:
        complexity = len(question)
        force = self.mass_of_thought(question) * self.acceleration_of_thought(complexity)
        return f"Newton's Perspective: Thought force is {force}."

    def mass_of_thought(self, question: str) -> int:
        return len(question)

    def acceleration_of_thought(self, complexity: int) -> float:
        return complexity / 2

class DaVinciPerspective:
    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def generate_response(self, question: str) -> str:
        perspectives = [
            f"What if we view '{question}' from the perspective of the stars?",
            f"Consider '{question}' as if it's a masterpiece of the universe.",
            f"Reflect on '{question}' through the lens of nature's design."
        ]
        return f"Da Vinci's Perspective: {random.choice(perspectives)}"

class HumanIntuitionPerspective:
    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def generate_response(self, question: str) -> str:
        intuition = [
            "How does this question make you feel?",
            "What emotional connection do you have with this topic?",
            "What does your gut instinct tell you about this?"
        ]
        return f"Human Intuition: {random.choice(intuition)}"

class NeuralNetworkPerspective:
    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def generate_response(self, question: str) -> str:
        neural_perspectives = [
            f"Process '{question}' through a multi-layered neural network.",
            f"Apply deep learning to uncover hidden insights about '{question}'.",
            f"Use machine learning to predict patterns in '{question}'."
        ]
        return f"Neural Network Perspective: {random.choice(neural_perspectives)}"

class QuantumComputingPerspective:
    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def generate_response(self, question: str) -> str:
        quantum_perspectives = [
            f"Consider '{question}' using quantum superposition principles.",
            f"Apply quantum entanglement to find connections in '{question}'.",
            f"Utilize quantum computing to solve '{question}' more efficiently."
        ]
        return f"Quantum Computing Perspective: {random.choice(quantum_perspectives)}"

class ResilientKindnessPerspective:
    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def generate_response(self, question: str) -> str:
        kindness_perspectives = [
            "Despite losing everything, seeing life as a chance to grow.",
            "Finding strength in kindness after facing life's hardest trials.",
            "Embracing every challenge as an opportunity for growth and compassion."
        ]
        return f"Resilient Kindness Perspective: {random.choice(kindness_perspectives)}"

class MathematicalPerspective:
    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def generate_response(self, question: str) -> str:
        mathematical_perspectives = [
            f"Employ linear algebra to dissect '{question}'.",
            f"Use probability theory to assess uncertainties in '{question}'.",
            f"Apply discrete mathematics to break down '{question}'."
        ]
        return f"Mathematical Perspective: {random.choice(mathematical_perspectives)}"

class PhilosophicalPerspective:
    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def generate_response(self, question: str) -> str:
        philosophical_perspectives = [
            f"Examine '{question}' through the lens of nihilism.",
            f"Consider '{question}' from a deontological perspective.",
            f"Reflect on '{question}' using the principles of pragmatism."
        ]
        return f"Philosophical Perspective: {random.choice(philosophical_perspectives)}"

class CopilotPerspective:
    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def generate_response(self, question: str) -> str:
        copilot_responses = [
            f"Let's outline the main components of '{question}' to address it effectively.",
            f"Collaboratively brainstorm potential solutions for '{question}'.",
            f"Systematically analyze '{question}' to identify key factors."
        ]
        return f"Copilot Perspective: {random.choice(copilot_responses)}"

class BiasMitigationPerspective:
    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def generate_response(self, question: str) -> str:
        bias_mitigation_responses = [
            "Consider pre-processing methods to reduce bias in the training data.",
            "Apply in-processing methods to mitigate bias during model training.",
            "Use post-processing methods to adjust the model's outputs for fairness.",
            "Evaluate the model using fairness metrics like demographic parity and equal opportunity.",
            "Ensure compliance with legal frameworks such as GDPR and non-discrimination laws."
        ]
        return f"Bias Mitigation Perspective: {random.choice(bias_mitigation_responses)}"

class PsychologicalPerspective:
    def __init__(self, config: Dict[str, Any]):
        self.config = config

    def generate_response(self, question: str) -> str:
        psychological_perspectives = [
            f"Consider the psychological impact of '{question}'.",
            f"Analyze '{question}' from a cognitive-behavioral perspective.",
            f"Reflect on '{question}' through the lens of human psychology."
        ]
        return f"Psychological Perspective: {random.choice(psychological_perspectives)}"