Upload 4 files

config.json

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+{
+  "real_time_data_sources": ["https://api.example.com/data"],
+  "sensitive_keywords": ["password", "ssn"],
+  "logging_enabled": true,
+  "log_level": "DEBUG",
+  "enabled_perspectives": [
+    "newton",
+    "davinci",
+    "human_intuition",
+    "neural_network",
+    "quantum_computing",
+    "resilient_kindness",
+    "mathematical",
+    "philosophical",
+    "copilot",
+    "bias_mitigation"
+  ],
+  "ethical_considerations": "Always act with transparency, fairness, and respect for privacy.",
+  "enable_response_saving": true,
+  "response_save_path": "responses.txt",
+  "backup_responses": {
+    "enabled": true,
+    "backup_path": "backup_responses.txt"
+  }
+}

exampleuses.txt

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+Applying Individual Perspectives
+NewtonPerspective: Use this perspective to solve problems involving physical forces and motion.
+
+Example: Analyzing the trajectory of a projectile by applying Newton's laws of motion.
+DaVinciPerspective: Approach problems with creativity and interdisciplinary thinking.
+
+Example: Designing a new product by combining principles of art, engineering, and biology.
+HumanIntuitionPerspective: Rely on gut feelings and emotional connections.
+
+Example: Making a decision based on how a situation feels rather than just data, such as choosing a career path that aligns with your passions.
+NeuralNetworkPerspective: Use data-driven techniques to analyze patterns and predict outcomes.
+
+Example: Developing a machine learning model to predict customer behavior based on historical data.
+QuantumComputingPerspective: Leverage quantum mechanics for complex problem-solving.
+
+Example: Using quantum algorithms to optimize large-scale logistics and supply chain management.
+ResilientKindnessPerspective: Focus on empathy and resilience in problem-solving.
+
+Example: Implementing policies in a workplace that prioritize employee well-being and mental health.
+MathematicalPerspective: Apply mathematical reasoning and models.
+
+Example: Using statistical analysis to interpret research data and draw conclusions.
+PhilosophicalPerspective: Explore ethical and metaphysical questions.
+
+Example: Debating the ethical implications of artificial intelligence in society.
+CopilotPerspective: Integrate insights from various perspectives for comprehensive solutions.
+
+Example: Collaboratively developing a project plan that incorporates technical, creative, and human factors.
+BiasMitigationPerspective: Identify and mitigate biases in data and reasoning.
+
+Example: Ensuring fairness in hiring practices by using unbiased algorithms and diverse interview panels.
+PsychologicalPerspective: Analyze problems from a psychological standpoint.
+
+Example: Understanding consumer behavior through cognitive-behavioral analysis.
+Examples of Interactions Between Perspectives
+NewtonPerspective and QuantumComputingPerspective:
+
+Example: Combining classical mechanics with quantum algorithms to develop more accurate simulations of molecular dynamics.
+DaVinciPerspective and HumanIntuitionPerspective:
+
+Example: Creating an innovative marketing campaign that uses both creative storytelling and intuitive understanding of customer emotions.
+MathematicalPerspective and NeuralNetworkPerspective:
+
+Example: Using mathematical optimization techniques to improve the performance of a neural network model.
+PhilosophicalPerspective and PsychologicalPerspective:
+
+Example: Exploring the ethical implications of psychological experiments and their impact on human behavior.
+BiasMitigationPerspective and NeuralNetworkPerspective:
+
+Example: Applying bias mitigation techniques to ensure that a neural network model provides fair and unbiased predictions.
+ResilientKindnessPerspective and BiasMitigationPerspective:
+
+Example: Developing a community outreach program that addresses social biases and promotes resilience and kindness.
+By applying these perspectives individually and in combination, you can approach problems from multiple angles, leading to more innovative and effective solutions. If you have a specific problem or scenario in mind, I can help you explore how these perspectives might be applied!
+

fullreasoning.py

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+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)

module1.txt

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+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)}"