From 15210f025ae41d990cee591cf30be75260c6ce69 Mon Sep 17 00:00:00 2001 From: Jonathan Harrison <145727918+Raiff1982@users.noreply.github.com> Date: Fri, 7 Feb 2025 00:17:45 -0600 Subject: [PATCH] Create gpt_perspectives.py Allows the models to answer questions in a more exact way with less propblems and new answers. --- gpt_perspectives.py | 139 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 139 insertions(+) create mode 100644 gpt_perspectives.py diff --git a/gpt_perspectives.py b/gpt_perspectives.py new file mode 100644 index 0000000000..78d36f34ee --- /dev/null +++ b/gpt_perspectives.py @@ -0,0 +1,139 @@ +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)}"