Upload 19 files

.env

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+AZURE_OPENAI_API_KEY=2zRqaiUcbPYuYRaQZ0vIkCItdwMs21V9HHxTFBcHCaLJdFJzb59jJQQJ99AKACLArgHXJ3w3AAABACOGTau5
+AZURE_OPENAI_ENDPOINT=https://aibotforjonathan.openai.azure.com/
+AZURE_SQL_SERVER=botserver.database.windows.net
+AZURE_SQL_DATABASE=BotBrain20
+AZURE_SQL_USERNAME=BotUser
+AZURE_SQL_PASSWORD=Funtimes82!
+LUIS_ENDPOINT=https://pilouis.cognitiveservices.azure.com/
+LUIS_API_VERSION=2024-10-01
+LUIS_API_KEY=your-luis-api-key

ChatSetup.json

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+{"systemPrompt":"Provide a detailed and structured summary of the described AI system's capabilities while maintaining clarity and precision.\n\n# Steps\n\n1. **Overview Writing**:\n   - Summarize key functionalities, including the purpose of each feature.\n   - Highlight the significance of integrated tools, libraries, and mechanisms in accomplishing these functionalities.\n   - Explain the capabilities in concise yet descriptive terms to ensure coherence.\n\n2. **Categorization**:  \n   - Clearly organize the features into categories for better readability and comprehension (e.g., Input Handling, Real-Time Integration, Security).\n\n3. **Error Reporting**:  \n   - Investigate and describe the significance of the error message provided and potential contextual implications for the system's function.\n   - Address the Azure OpenAI-related error, explaining its relevance, and if needed, suggest potential resolutions to ensure compatibility.\n\n# Output Format\nThe output should be presented in well-structured markdown bullet points or headings, categorized and sub-categorized as needed. Sub-level breakdowns (e.g., Text Input, Voice Input) may be used for features with multiple facets. Include:\n\n1. Introduction (1-2 sentences summarizing overall system goals.)\n2. Detailed Capabilities (Structured in categorized bullet points or headings.)\n3. Error Analysis (Detailed description of the Azure error provided and potential solutions or implications.)\n4. Optional: Suggestions or Recommendations (If necessary based on steps above or error analysis.)\n\n# Notes\n\n- Ensure all terms like library names or technical terms (e.g., SpeechRecognition, vaderSentiment) are presented exactly as described but briefly explain their contributions to the system.\n- Avoid changing any meaningful user-provided information unless brevity and readability require it. \n- Reference compatibility issues explicitly under the Error Analysis section.","fewShotExamples":[],"chatParameters":{"deploymentName":"gpt-4","maxResponseLength":4096,"temperature":0.7,"topProbablities":1,"stopSequences":[],"pastMessagesToInclude":"20","frequencyPenalty":2,"presencePenalty":2}}

UniversalReasoning.py

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+import asyncio
+import json
+import os
+import logging
+from typing import List
+
+# 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')
+
+# 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__":
+    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)

bot_logic.py

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+import logging
+from botbuilder.core import TurnContext, MessageFactory, ConversationState, UserState
+from botbuilder.schema import Activity, ActivityTypes, EndOfConversationCodes
+from UniversalReasoning import UniversalReasoning  # Ensure correct import
+import os
+from dotenv import load_dotenv
+from dialog_bot import DialogBot
+from main_dialog import MainDialog
+
+# Load environment variables from .env file
+load_dotenv()
+
+class MyBot(DialogBot):
+    def __init__(self, conversation_state: ConversationState, user_state: UserState, dialog: MainDialog):
+        super(MyBot, self).__init__(conversation_state, user_state, dialog)
+        self.context = {}
+        self.feedback = []
+        config = load_and_validate_config('config.json', 'config_schema.json')
+        # Add Azure OpenAI and LUIS configurations to the config
+        config['azure_openai_api_key'] = os.getenv('AZURE_OPENAI_API_KEY')
+        config['azure_openai_endpoint'] = os.getenv('AZURE_OPENAI_ENDPOINT')
+        config['luis_endpoint'] = os.getenv('LUIS_ENDPOINT')
+        config['luis_api_version'] = os.getenv('LUIS_API_VERSION')
+        config['luis_api_key'] = os.getenv('LUIS_API_KEY')
+        setup_logging(config)
+        self.universal_reasoning = UniversalReasoning(config)
+
+    async def enhance_context_awareness(self, user_id: str, text: str) -> None:
+        """Enhance context awareness by analyzing the user's environment, activities, and emotional state."""
+        sentiment = analyze_sentiment_vader(text)
+        self.context[user_id].append({"text": text, "sentiment": sentiment})
+
+    async def proactive_learning(self, user_id: str, feedback: str) -> None:
+        """Encourage proactive learning by seeking feedback and exploring new topics."""
+        self.context[user_id].append({"feedback": feedback})
+        self.feedback.append({"user_id": user_id, "feedback": feedback})
+
+    async def ethical_decision_making(self, user_id: str, decision: str) -> None:
+        """Integrate ethical principles into decision-making processes."""
+        ethical_decision = f"Considering ethical principles, the decision is: {decision}"
+        self.context[user_id].append({"ethical_decision": ethical_decision})
+
+    async def emotional_intelligence(self, user_id: str, text: str) -> str:
+        """Develop emotional intelligence by recognizing and responding to user emotions."""
+        sentiment = analyze_sentiment_vader(text)
+        response = self.generate_emotional_response(sentiment, text)
+        self.context[user_id].append({"emotional_response": response})
+        return response
+
+    def generate_emotional_response(self, sentiment: dict, text: str) -> str:
+        """Generate an empathetic response based on the sentiment analysis."""
+        if sentiment['compound'] >= 0.05:
+            return "I'm glad to hear that! 😊 How can I assist you further?"
+        elif sentiment['compound'] <= -0.05:
+            return "I'm sorry to hear that. 😢 Is there anything I can do to help?"
+        else:
+            return "I understand. How can I assist you further?"
+
+    async def transparency_and_explainability(self, user_id: str, decision: str) -> str:
+        """Enable transparency by explaining the reasoning behind decisions."""
+        explanation = f"The decision was made based on the following context: {self.context[user_id]}"
+        self.context[user_id].append({"explanation": explanation})
+        return explanation
+
+    async def on_message_activity(self, turn_context: TurnContext) -> None:
+        """Handles incoming messages and generates responses."""
+        user_id = turn_context.activity.from_property.id
+        if user_id not in self.context:
+            self.context[user_id] = []
+        try:
+            message_text = turn_context.activity.text.strip().lower()
+            if "end" in message_text or "stop" in message_text:
+                await end_conversation(turn_context)
+                self.context.pop(user_id, None)
+            else:
+                self.context[user_id].append(turn_context.activity.text)
+                response = await self.generate_response(turn_context.activity.text, user_id)
+                await turn_context.send_activity(MessageFactory.text(response))
+                await self.request_feedback(turn_context, user_id)
+        except Exception as e:
+            await handle_error(turn_context, e)
+
+    async def generate_response(self, text: str, user_id: str) -> str:
+        """Generates a response using UniversalReasoning."""
+        try:
+            logging.info(f"Generating response for user_id: {user_id} with text: {text}")
+            response = self.universal_reasoning.generate_response(text)
+            logging.info(f"Generated response: {response}")
+            return response
+        except Exception as e:
+            logging.error(f"Error generating response: {e}")
+            return "Sorry, I couldn't generate a response at this time."
+
+    async def request_feedback(self, turn_context: TurnContext, user_id: str) -> None:
+        """Request feedback from the user about the bot's response."""
+        feedback_prompt = "How would you rate my response? (good/neutral/bad)"
+        await turn_context.send_activity(MessageFactory.text(feedback_prompt))
+
+    async def handle_feedback(self, turn_context: TurnContext) -> None:
+        """Handle user feedback and store it for future analysis."""
+        user_id = turn_context.activity.from_property.id
+        feedback = turn_context.activity.text.lower()
+        if feedback in ["good", "neutral", "bad"]:
+            self.feedback.append({"user_id": user_id, "feedback": feedback})
+            await turn_context.send_activity(MessageFactory.text("Thank you for your feedback!"))
+        else:
+            await turn_context.send_activity(MessageFactory.text("Please provide feedback as 'good', 'neutral', or 'bad'."))
+
+async def end_conversation(turn_context: TurnContext) -> None:
+    """Ends the conversation with the user."""
+    await turn_context.send_activity(
+        MessageFactory.text("Ending conversation from the skill...")
+    )
+    end_of_conversation = Activity(type=ActivityTypes.end_of_conversation)
+    end_of_conversation.code = EndOfConversationCodes.completed_successfully
+    await turn_context.send_activity(end_of_conversation)
+
+async def handle_error(turn_context: TurnContext, error: Exception) -> None:
+    """Handles errors by logging them and notifying the user."""
+    logging.error(f"An error occurred: {error}")
+    await turn_context.send_activity(
+        MessageFactory.text("An error occurred. Please try again later.")
+    )

chat.py

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+import logging
+import httpx
+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Get the API key and endpoint from environment variables
+api_key = os.getenv('AZURE_OPENAI_API_KEY')
+endpoint = os.getenv('AZURE_OPENAI_ENDPOINT')
+
+def list_fine_tuning_jobs() -> dict:
+    """List fine-tuning jobs.
+    
+    Returns:
+        dict: The response from the API containing the list of fine-tuning jobs.
+    """
+    try:
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}"
+        }
+        response = httpx.get(f"{endpoint}/openai/fine-tunes", headers=headers)
+        response.raise_for_status()
+        return response.json()
+    except httpx.HTTPStatusError as e:
+        logging.error(f"Error listing fine-tuning jobs: {e.response.text}")
+        return None
+    except Exception as e:
+        logging.error(f"Unexpected error: {e}")
+        return None
+
+def upload_file_for_fine_tuning(file_path: str) -> dict:
+    """Upload a file for fine-tuning.
+    
+    Args:
+        file_path (str): The path to the file to be uploaded.
+    
+    Returns:
+        dict: The response from the API after uploading the file.
+    """
+    try:
+        headers = {
+            "Authorization": f"Bearer {api_key}"
+        }
+        with open(file_path, 'rb') as file:
+            files = {'file': file}
+            response = httpx.post(f"{endpoint}/openai/files", headers=headers, files=files)
+        response.raise_for_status()
+        return response.json()
+    except httpx.HTTPStatusError as e:
+        logging.error(f"Error uploading file for fine-tuning: {e.response.text}")
+        return None
+    except Exception as e:
+        logging.error(f"Unexpected error: {e}")
+        return None
+
+def create_fine_tuning_job(training_file_id: str, model: str = "davinci") -> dict:
+    """Create a fine-tuning job.
+    
+    Args:
+        training_file_id (str): The ID of the training file.
+        model (str): The model to be fine-tuned. Default is "davinci".
+    
+    Returns:
+        dict: The response from the API after creating the fine-tuning job.
+    """
+    try:
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}"
+        }
+        payload = {
+            "training_file": training_file_id,
+            "model": model
+        }
+        response = httpx.post(f"{endpoint}/openai/fine-tunes", headers=headers, json=payload)
+        response.raise_for_status()
+        return response.json()
+    except httpx.HTTPStatusError as e:
+        logging.error(f"Error creating fine-tuning job: {e.response.text}")
+        return None
+    except Exception as e:
+        logging.error(f"Unexpected error: {e}")
+        return None
+
+def make_post_request(url: str, data: dict, headers: dict) -> dict:
+    """Make a POST request.
+    
+    Args:
+        url (str): The URL to make the POST request to.
+        data (dict): The data to be sent in the POST request.
+        headers (dict): The headers to be sent in the POST request.
+    
+    Returns:
+        dict: The response from the API after making the POST request.
+    """
+    try:
+        response = httpx.post(url, json=data, headers=headers)
+        response.raise_for_status()
+        return response.json()
+    except httpx.HTTPStatusError as e:
+        logging.error(f"Error making POST request: {e.response.text}")
+        return None
+    except Exception as e:
+        logging.error(f"Unexpected error: {e}")
+        return None
+
+def azure_chat_completion_request(messages: list, deployment_id: str) -> str:
+    """Make a chat completion request to Azure OpenAI.
+    
+    Args:
+        messages (list): The list of messages to be sent in the chat completion request.
+        deployment_id (str): The deployment ID for the chat completion request.
+    
+    Returns:
+        str: The response content from the chat completion request.
+    """
+    try:
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}"
+        }
+        payload = {
+            "deployment_id": deployment_id,
+            "messages": messages
+        }
+        response = httpx.post(f"{endpoint}/openai/deployments/{deployment_id}/chat/completions", headers=headers, json=payload)
+        response.raise_for_status()
+        return response.json()["choices"][0]["message"]["content"].strip()
+    except httpx.HTTPStatusError as e:
+        logging.error(f"Error making chat completion request: {e.response.text}")
+        return None
+    except Exception as e:
+        logging.error(f"Unexpected error: {e}")
+        return None

config.json

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+{
+    "subscription_id": "5dd32870-bdc6-40cd-981a-f299e93a6439",
+    "resource_group": "Justforpi",
+    "workspace_name": "featurestone"
+}

database.py

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+import pyodbc
+import logging
+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+def connect_to_database() -> pyodbc.Connection:
+    """Connect to the Azure SQL Database."""
+    try:
+        server = os.getenv('AZURE_SQL_SERVER')
+        database = os.getenv('AZURE_SQL_DATABASE')
+        username = os.getenv('AZURE_SQL_USERNAME')
+        password = os.getenv('AZURE_SQL_PASSWORD')
+        driver = '{ODBC Driver 17 for SQL Server}'
+
+        conn_str = f'DRIVER={driver};SERVER={server};DATABASE={database};UID={username};PWD={password}'
+        conn = pyodbc.connect(conn_str)
+        logging.info(f"Connected to the Azure SQL Database at {server}")
+        return conn
+    except pyodbc.Error as e:
+        logging.error(f"Error connecting to the Azure SQL Database: {e}")
+        return None
+
+def close_database_connection(conn: pyodbc.Connection) -> None:
+    """Close the database connection."""
+    try:
+        if conn:
+            conn.close()
+            logging.info("Database connection closed.")
+    except pyodbc.Error as e:
+        logging.error(f"Error closing the database connection: {e}")
+
+# Example usage with context management
+class DatabaseConnection:
+    def __enter__(self) -> pyodbc.Connection:
+        self.conn = connect_to_database()
+        return self.conn
+
+    def __exit__(self, exc_type, exc_value, traceback) -> None:
+        close_database_connection(self.conn)
+
+# Example usage
+if __name__ == "__main__":
+    with DatabaseConnection() as conn:
+        if conn:
+            # Perform database operations
+            pass

dialog_bot.py

@@ -0,0 +1,31 @@
+from botbuilder.core import ActivityHandler, ConversationState, TurnContext, UserState
+from botbuilder.dialogs import Dialog
+from helpers.dialog_helper import DialogHelper
+
+
+class DialogBot(ActivityHandler):
+    def __init__(
+        self,
+        conversation_state: ConversationState,
+        user_state: UserState,
+        dialog: Dialog,
+    ):
+        super(DialogBot, self).__init__()
+        self.conversation_state = conversation_state
+        self.user_state = user_state
+        self.dialog = dialog
+
+    async def on_turn(self, turn_context: TurnContext) -> None:
+        await super().on_turn(turn_context)
+
+        # Save any state changes that might have occurred during the turn.
+        await self.conversation_state.save_changes(turn_context)
+        await self.user_state.save_changes(turn_context)
+
+    async def on_message_activity(self, turn_context: TurnContext) -> None:
+        # Run the Dialog with the new message Activity.
+        await DialogHelper.run_dialog(
+            self.dialog,
+            turn_context,
+            self.conversation_state.create_property("DialogState"),
+        )

main.py

@@ -0,0 +1,70 @@
+import os
+import logging
+from aiohttp import web
+from botbuilder.core import (
+    BotFrameworkAdapterSettings,
+    ConversationState,
+    MemoryStorage,
+    UserState,
+)
+from botbuilder.integration.aiohttp import BotFrameworkHttpAdapter
+from botbuilder.schema import Activity
+from dotenv import load_dotenv
+from utils import show_privacy_consent
+from universal_reasoning import UniversalReasoning, load_json_config
+from mybot import MyBot  # Import updated MyBot class
+from main_dialog import MainDialog
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+# Show privacy consent dialog and check user response
+if not show_privacy_consent():
+    logging.info("User declined data collection and privacy policy. Exiting application.")
+    exit()
+
+# Load configuration
+config = load_json_config("config.json")
+config["azure_openai_api_key"] = os.getenv("AZURE_OPENAI_API_KEY")
+config["azure_openai_endpoint"] = os.getenv("AZURE_OPENAI_ENDPOINT")
+
+# Initialize UniversalReasoning
+universal_reasoning = UniversalReasoning(config)
+
+# Create adapter
+settings = BotFrameworkAdapterSettings(
+    app_id=os.getenv("MICROSOFT_APP_ID"),
+    app_password=os.getenv("MICROSOFT_APP_PASSWORD"),
+)
+adapter = BotFrameworkHttpAdapter(settings)
+
+# Create MemoryStorage, ConversationState, and UserState
+memory_storage = MemoryStorage()
+conversation_state = ConversationState(memory_storage)
+user_state = UserState(memory_storage)
+
+# Create the main dialog
+main_dialog = MainDialog("MainDialog")
+
+# Create the bot and pass the universal_reasoning instance
+bot = MyBot(conversation_state, user_state, main_dialog, universal_reasoning)
+
+# Listen for incoming requests on /api/messages
+async def messages(req):
+    body = await req.json()
+    activity = Activity().deserialize(body)
+    auth_header = req.headers.get("Authorization", "")
+
+    response = await adapter.process_activity(activity, auth_header, bot.on_turn)
+    return web.Response(status=response.status)
+
+app = web.Application()
+app.router.add_post("/api/messages", messages)
+
+if __name__ == "__main__":
+    web.run_app(app, host="localhost", port=3978)

main_dialog.py

@@ -0,0 +1,49 @@
+from botbuilder.dialogs import ComponentDialog, WaterfallDialog, WaterfallStepContext
+from botbuilder.dialogs.prompts import TextPrompt, PromptOptions
+from botbuilder.core import MessageFactory
+
+class MainDialog(ComponentDialog):
+    def __init__(self, dialog_id: str = "MainDialog"):
+        super(MainDialog, self).__init__(dialog_id or MainDialog.__name__)
+
+        self.add_dialog(TextPrompt("TextPrompt"))
+        self.add_dialog(
+            WaterfallDialog(
+                "WFDialog",
+                [self.initial_step, self.process_step, self.final_step],
+            )
+        )
+
+        self.initial_dialog_id = "WFDialog"
+
+    async def initial_step(self, step_context: WaterfallStepContext) -> WaterfallStepContext:
+        return await step_context.prompt(
+            "TextPrompt",
+            PromptOptions(prompt=MessageFactory.text("What is your name?")),
+        )
+
+    async def process_step(self, step_context: WaterfallStepContext) -> WaterfallStepContext:
+        # Store the user's name in dialog state
+        step_context.values["name"] = step_context.result
+        return await step_context.prompt(
+            "TextPrompt",
+            PromptOptions(
+                prompt=MessageFactory.text(
+                    f"Hello {step_context.result}! How can I assist you today?"
+                )
+            ),
+        )
+
+    async def final_step(self, step_context: WaterfallStepContext) -> WaterfallStepContext:
+        user_message = step_context.result
+
+        # Here you could integrate the UniversalReasoning module
+        # For example, you might access it via the bot's context
+        # Since it's not directly available here, we'll pass the message back
+
+        # Send a confirmation message to the user
+        await step_context.context.send_activity(
+            MessageFactory.text(f"You said: {user_message}")
+        )
+
+        return await step_context.end_dialog()

mybot.py

@@ -0,0 +1,360 @@
+import os
+import logging
+import random
+from botbuilder.core import TurnContext, MessageFactory
+from botbuilder.schema import Activity, ActivityTypes, EndOfConversationCodes
+from tenacity import retry, wait_random_exponential, stop_after_attempt
+import importlib
+from sentiment_analysis import analyze_sentiment_vader
+from config import load_and_validate_config, setup_logging
+from universal_reasoning import UniversalReasoning
+from dotenv import load_dotenv
+import json
+from chat import azure_chat_completion_request  # Import the function from chat.py
+from database import DatabaseConnection  # Import the database connection
+
+# Load environment variables from .env file
+load_dotenv()
+
+class MyBot:
+    def __init__(self, conversation_state, user_state, dialog, universal_reasoning):
+        self.conversation_state = conversation_state
+        self.user_state = user_state
+        self.dialog = dialog
+        self.universal_reasoning = universal_reasoning
+        self.context = {}
+        self.feedback = []
+        config = load_and_validate_config('config.json', 'config_schema.json')
+        # Add Azure OpenAI and LUIS configurations to the config
+        config['azure_openai_api_key'] = os.getenv('AZURE_OPENAI_API_KEY')
+        config['azure_openai_endpoint'] = os.getenv('AZURE_OPENAI_ENDPOINT')
+        config['luis_endpoint'] = os.getenv('LUIS_ENDPOINT')
+        config['luis_api_version'] = os.getenv('LUIS_API_VERSION')
+        config['luis_api_key'] = os.getenv('LUIS_API_KEY')
+        setup_logging(config)
+
+    async def enhance_context_awareness(self, user_id: str, text: str) -> None:
+        """Enhance context awareness by analyzing the user's environment, activities, and emotional state."""
+        sentiment = analyze_sentiment_vader(text)
+        if user_id not in self.context:
+            self.context[user_id] = []
+        self.context[user_id].append({"text": text, "sentiment": sentiment})
+
+    async def proactive_learning(self, user_id: str, feedback: str) -> None:
+        """Encourage proactive learning by seeking feedback and exploring new topics."""
+        if user_id not in self.context:
+            self.context[user_id] = []
+        self.context[user_id].append({"feedback": feedback})
+        self.feedback.append({"user_id": user_id, "feedback": feedback})
+
+    async def ethical_decision_making(self, user_id: str, decision: str) -> None:
+        """Integrate ethical principles into decision-making processes."""
+        ethical_decision = f"Considering ethical principles, the decision is: {decision}"
+        if user_id not in self.context:
+            self.context[user_id] = []
+        self.context[user_id].append({"ethical_decision": ethical_decision})
+
+    async def emotional_intelligence(self, user_id: str, text: str) -> str:
+        """Develop emotional intelligence by recognizing and responding to user emotions."""
+        sentiment = analyze_sentiment_vader(text)
+        response = self.generate_emotional_response(sentiment, text)
+        if user_id not in self.context:
+            self.context[user_id] = []
+        self.context[user_id].append({"emotional_response": response})
+        return response
+
+    def generate_emotional_response(self, sentiment: dict, text: str) -> str:
+        """Generate an empathetic response based on the sentiment analysis."""
+        if sentiment['compound'] >= 0.05:
+            return "I'm glad to hear that! 😊 How can I assist you further?"
+        elif sentiment['compound'] <= -0.05:
+            return "I'm sorry to hear that. 😢 Is there anything I can do to help?"
+        else:
+            return "I understand. How can I assist you further?"
+
+    async def transparency_and_explainability(self, user_id: str, decision: str) -> str:
+        """Enable transparency by explaining the reasoning behind decisions."""
+        explanation = f"The decision was made based on the following context: {self.context[user_id]}"
+        if user_id not in self.context:
+            self.context[user_id] = []
+        self.context[user_id].append({"explanation": explanation})
+        return explanation
+
+    async def on_message_activity(self, turn_context: TurnContext) -> None:
+        """Handles incoming messages and generates responses."""
+        user_id = turn_context.activity.from_property.id
+        if user_id not in self.context:
+            self.context[user_id] = []
+        try:
+            if "end" in turn_context.activity.text.lower() or "stop" in turn_context.activity.text.lower():
+                await end_conversation(turn_context)
+                self.context.pop(user_id, None)
+            else:
+                self.context[user_id].append(turn_context.activity.text)
+                response = await self.generate_response(turn_context.activity.text, user_id)
+                await turn_context.send_activity(MessageFactory.text(response))
+                await self.request_feedback(turn_context, user_id)
+
+                # Example database operation
+                with DatabaseConnection() as conn:
+                    if conn:
+                        cursor = conn.cursor()
+                        cursor.execute("INSERT INTO UserMessages (UserId, Message) VALUES (?, ?)", user_id, turn_context.activity.text)
+                        conn.commit()
+
+        except Exception as e:
+            await handle_error(turn_context, e)
+
+    async def generate_response(self, text: str, user_id: str) -> str:
+        """Generates a response using Azure OpenAI's API, Universal Reasoning, and various perspectives."""
+        try:
+            logging.info(f"Generating response for user_id: {user_id} with text: {text}")
+            # Generate responses from different perspectives
+            responses = []
+            for perspective in self.perspectives.values():
+                try:
+                    response = await perspective.generate_response(text)
+                    responses.append(response)
+                except Exception as e:
+                    logging.error(f"Error generating response from {perspective.__class__.__name__}: {e}")
+            # Combine responses
+            combined_response = "\n".join(responses)
+            logging.info(f"Combined response: {combined_response}")
+            return combined_response
+        except Exception as e:
+            logging.error(f"Error generating response: {e}")
+            return "Sorry, I couldn't generate a response at this time."
+
+    async def request_feedback(self, turn_context: TurnContext, user_id: str) -> None:
+        """Request feedback from the user about the bot's response."""
+        feedback_prompt = "How would you rate my response? (good/neutral/bad)"
+        await turn_context.send_activity(MessageFactory.text(feedback_prompt))
+
+    async def handle_feedback(self, turn_context: TurnContext) -> None:
+        """Handle user feedback and store it for future analysis."""
+        user_id = turn_context.activity.from_property.id
+        feedback = turn_context.activity.text.lower()
+        if feedback in ["good", "neutral", "bad"]:
+            self.feedback.append({"user_id": user_id, "feedback": feedback})
+            await turn_context.send_activity(MessageFactory.text("Thank you for your feedback!"))
+        else:
+            await turn_context.send_activity(MessageFactory.text("Please provide feedback as 'good', 'neutral', or 'bad'."))
+
+async def end_conversation(turn_context: TurnContext) -> None:
+    """Ends the conversation with the user."""
+    await turn_context.send_activity(
+        MessageFactory.text("Ending conversation from the skill...")
+    )
+    end_of_conversation = Activity(type=ActivityTypes.end_of_conversation)
+    end_of_conversation.code = EndOfConversationCodes.completed_successfully
+    await turn_context.send_activity(end_of_conversation)
+
+async def handle_error(turn_context: TurnContext, error: Exception) -> None:
+    """Handles errors by logging them and notifying the user."""
+    logging.error(f"An error occurred: {error}")
+    await turn_context.send_activity(
+        MessageFactory.text("An error occurred. Please try again later.")
+    )
+
+def show_privacy_consent() -> bool:
+    """Display a pop-up window to obtain user consent for data collection and privacy."""
+    import tkinter as tk
+
+    def on_accept():
+        user_consent.set(True)
+        root.destroy()
+
+    def on_decline():
+        user_consent.set(False)
+        root.destroy()
+
+    root = tk.Tk()
+    root.title("Data Permission and Privacy")
+    message = ("We value your privacy. By using this application, you consent to the collection and use of your data "
+               "as described in our privacy policy. Do you agree to proceed?")
+    label = tk.Label(root, text=message, wraplength=400, justify="left")
+    label.pack(padx=20, pady=20)
+    button_frame = tk.Frame(root)
+    button_frame.pack(pady=10)
+    accept_button = tk.Button(button_frame, text="Accept", command=on_accept)
+    accept_button.pack(side="left", padx=10)
+    decline_button = tk.Button(button_frame, text="Decline", command=on_decline)
+    decline_button.pack(side="right", padx=10)
+    user_consent = tk.BooleanVar()
+    root.mainloop()
+    return user_consent.get()
+
+# Example usage of MyBot class
+bot = MyBot()
+
+# Functions based on JSON configuration
+def newton_thoughts(question: str) -> str:
+    """Apply Newton's laws to the given question."""
+    return apply_newtons_laws(question)
+
+def apply_newtons_laws(question: str) -> str:
+    """Apply Newton's laws to the given question."""
+    if not question:
+        return 'No question to think about.'
+    complexity = len(question)
+    force = mass_of_thought(question) * acceleration_of_thought(complexity)
+    return f'Thought force: {force}'
+
+def mass_of_thought(question: str) -> int:
+    """Calculate the mass of thought based on the question length."""
+    return len(question)
+
+def acceleration_of_thought(complexity: int) -> float:
+    """Calculate the acceleration of thought based on the complexity."""
+    return complexity / 2
+
+def davinci_insights(question: str) -> str:
+    """Generate insights like Da Vinci for the given question."""
+    return think_like_davinci(question)
+
+def think_like_davinci(question: str) -> str:
+    """Generate insights like Da Vinci for the given question."""
+    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 random.choice(perspectives)
+
+def human_intuition(question: str) -> str:
+    """Provide human intuition for the given question."""
+    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 random.choice(intuition)
+
+def neural_network_thinking(question: str) -> str:
+    """Apply neural network thinking to the given question."""
+    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 random.choice(neural_perspectives)
+
+def quantum_computing_thinking(question: str) -> str:
+    """Apply quantum computing principles to the given question."""
+    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 random.choice(quantum_perspectives)
+
+def resilient_kindness(question: str) -> str:
+    """Provide perspectives of resilient kindness."""
+    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 random.choice(kindness_perspectives)
+
+def identify_and_refute_fallacies(argument: str) -> str:
+    """Identify and refute common logical fallacies in the argument."""
+    refutations = [
+        "This is an ad hominem fallacy. Let's focus on the argument itself rather than attacking the person.",
+        "This is a straw man fallacy. The argument is being misrepresented.",
+        "This is a false dilemma fallacy. There are more options than presented.",
+        "This is a slippery slope fallacy. The conclusion does not necessarily follow from the premise.",
+        "This is circular reasoning. The argument's conclusion is used as a premise.",
+        "This is a hasty generalization. The conclusion is based on insufficient evidence.",
+        "This is a red herring fallacy. The argument is being diverted to an irrelevant topic.",
+        "This is a post hoc ergo propter hoc fallacy. Correlation does not imply causation.",
+        "This is an appeal to authority fallacy. The argument relies on the opinion of an authority figure.",
+        "This is a bandwagon fallacy. The argument assumes something is true because many people believe it.",
+        "This is a false equivalence fallacy. The argument equates two things that are not equivalent."
+    ]
+    return random.choice(refutations)
+
+def universal_reasoning(question: str) -> str:
+    """Generate a comprehensive response using various reasoning methods."""
+    responses = [
+        newton_thoughts(question),
+        davinci_insights(question),
+        human_intuition(question),
+        neural_network_thinking(question),
+        quantum_computing_thinking(question),
+        resilient_kindness(question),
+        identify_and_refute_fallacies(question)
+    ]
+    return "\n".join(responses)
+
+@retry(wait=wait_random_exponential(min=1, max=40), stop=stop_after_attempt(3))
+def chat_completion_request(messages: list, deployment_id: str) -> str:
+    """Make a chat completion request to Azure OpenAI."""
+    try:
+        import openai
+        response = openai.ChatCompletion.create(
+            engine=deployment_id,  # Use the deployment name of your Azure OpenAI model
+            messages=messages
+        )
+        return response.choices[0].message.content.strip()
+    except openai.error.OpenAIError as e:
+        logging.error("Unable to generate ChatCompletion response")
+        logging.error(f"Exception: {e}")
+        return f"Error: {e}"
+
+def get_internet_answer(question: str, deployment_id: str) -> str:
+    """Get an answer using Azure OpenAI's chat completion request."""
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": question}
+    ]
+    return chat_completion_request(messages, deployment_id=deployment_id)
+
+def reflect_on_decisions() -> str:
+    """Regularly reflect on your decisions and processes used."""
+    reflection_message = (
+        "Regularly reflecting on your decisions, the processes you used, the information you considered, "
+        "and the perspectives you may have missed. Reflection is a cornerstone of learning from experience."
+    )
+    return reflection_message
+
+def process_questions_from_json(file_path: str):
+    """Process questions from a JSON file and call the appropriate functions."""
+    with open(file_path, 'r') as file:
+        questions_data = json.load(file)
+        for question_data in questions_data:
+            question = question_data['question']
+            print(f"Question: {question}")
+
+            for function_data in question_data['functions']:
+                function_name = function_data['name']
+                function_description = function_data['description']
+                function_parameters = function_data['parameters']
+
+                print(f"Function: {function_name}")
+                print(f"Description: {function_description}")
+
+                # Call the function dynamically
+                if function_name in globals():
+                    function = globals()[function_name]
+                    response = function(**function_parameters)
+                    print(f"Response: {response}")
+                else:
+                    print(f"Function {function_name} not found.")
+
+if __name__ == "__main__":
+    if show_privacy_consent():
+        process_questions_from_json('questions.json')
+        question = "What is the meaning of life?"
+        deployment_id = "your-deployment-name"  # Replace with your Azure deployment name
+        print("Newton's Thoughts:", newton_thoughts(question))
+        print("Da Vinci's Insights:", davinci_insights(question))
+        print("Human Intuition:", human_intuition(question))
+        print("Neural Network Thinking:", neural_network_thinking(question))
+        print("Quantum Computing Thinking:", quantum_computing_thinking(question))
+        print("Resilient Kindness:", resilient_kindness(question))
+        print("Universal Reasoning:", universal_reasoning(question))
+        print("Internet Answer:", get_internet_answer(question, deployment_id))
+    else:
+        print("User did not consent to data collection. Exiting application.")
+    print(reflect_on_decisions())

mybotdata.py

@@ -0,0 +1,156 @@
+import faicons as fa
+import plotly.express as px
+
+# Load data and compute static values
+from shared import app_dir, bot_data
+from shinywidgets import render_plotly
+
+from shiny import reactive, render
+from shiny.express import input, ui
+
+data_rng = (min(bot_data.interactions), max(bot_data.interactions))
+
+# Add page title and sidebar
+ui.page_opts(title="MyBot Interactions", fillable=True)
+
+with ui.sidebar(open="desktop"):
+    ui.input_slider(
+        "interactions",
+        "Number of Interactions",
+        min=data_rng[0],
+        max=data_rng[1],
+        value=data_rng,
+    )
+    ui.input_checkbox_group(
+        "time_period",
+        "Time Period",
+        ["Morning", "Afternoon", "Evening", "Night"],
+        selected=["Morning", "Afternoon", "Evening", "Night"],
+        inline=True,
+    )
+    ui.input_action_button("reset", "Reset filter")
+
+# Add main content
+ICONS = {
+    "user": fa.icon_svg("user", "regular"),
+    "chat": fa.icon_svg("comments"),
+    "interaction": fa.icon_svg("exchange-alt"),
+    "ellipsis": fa.icon_svg("ellipsis"),
+}
+
+with ui.layout_columns(fill=False):
+    with ui.value_box(showcase=ICONS["user"]):
+        "Total Users"
+
+        @render.express
+        def total_users():
+            bot_data_filtered().shape[0]
+
+    with ui.value_box(showcase=ICONS["chat"]):
+        "Average Interactions per User"
+
+        @render.express
+        def average_interactions():
+            d = bot_data_filtered()
+            if d.shape[0] > 0:
+                avg_interactions = d.interactions.mean()
+                f"{avg_interactions:.1f}"
+
+    with ui.value_box(showcase=ICONS["interaction"]):
+        "Total Interactions"
+
+        @render.express
+        def total_interactions():
+            d = bot_data_filtered()
+            if d.shape[0] > 0:
+                total_interactions = d.interactions.sum()
+                f"{total_interactions}"
+
+with ui.layout_columns(col_widths=[6, 6, 12]):
+    with ui.card(full_screen=True):
+        ui.card_header("Interaction Data")
+
+        @render.data_frame
+        def table():
+            return render.DataGrid(bot_data_filtered())
+
+    with ui.card(full_screen=True):
+        with ui.card_header(class_="d-flex justify-content-between align-items-center"):
+            "Interactions Over Time"
+            with ui.popover(title="Add a color variable", placement="top"):
+                ICONS["ellipsis"]
+                ui.input_radio_buttons(
+                    "scatter_color",
+                    None,
+                    ["none", "user_type", "time_period"],
+                    inline=True,
+                )
+
+        @render_plotly
+        def scatterplot():
+            color = input.scatter_color()
+            return px.scatter(
+                bot_data_filtered(),
+                x="time",
+                y="interactions",
+                color=None if color == "none" else color,
+                trendline="lowess",
+            )
+
+    with ui.card(full_screen=True):
+        with ui.card_header(class_="d-flex justify-content-between align-items-center"):
+            "Interaction Types"
+            with ui.popover(title="Add a color variable"):
+                ICONS["ellipsis"]
+                ui.input_radio_buttons(
+                    "interaction_type",
+                    "Split by:",
+                    ["user_type", "time_period"],
+                    selected="user_type",
+                    inline=True,
+                )
+
+        @render_plotly
+        def interaction_types():
+            from ridgeplot import ridgeplot
+
+            dat = bot_data_filtered()
+            yvar = input.interaction_type()
+            uvals = dat[yvar].unique()
+
+            samples = [[dat.interactions[dat[yvar] == val]] for val in uvals]
+
+            plt = ridgeplot(
+                samples=samples,
+                labels=uvals,
+                bandwidth=0.01,
+                colorscale="viridis",
+                colormode="row-index",
+            )
+
+            plt.update_layout(
+                legend=dict(
+                    orientation="h", yanchor="bottom", y=1.02, xanchor="center", x=0.5
+                )
+            )
+
+            return plt
+
+ui.include_css(app_dir / "styles.css")
+
+# --------------------------------------------------------
+# Reactive calculations and effects
+# --------------------------------------------------------
+
+@reactive.calc
+def bot_data_filtered():
+    interactions = input.interactions()
+    idx1 = bot_data.interactions.between(interactions[0], interactions[1])
+    idx2 = bot_data.time_period.isin(input.time_period())
+    return bot_data[idx1 & idx2]
+
+@reactive.effect
+@reactive.event(input.reset)
+def _():
+    ui.update_slider("interactions", value=data_rng)
+    ui.update_checkbox_group("time_period", selected=["Morning", "Afternoon", "Evening", "Night"])

pilouis.json

@@ -0,0 +1,65 @@
+{
+    "apiVersion": "2024-10-01",
+    "id": "/subscriptions/5dd32870-bdc6-40cd-981a-f299e93a6439/resourceGroups/Justforpi/providers/Microsoft.CognitiveServices/accounts/pilouis",
+    "name": "pilouis",
+    "type": "microsoft.cognitiveservices/accounts",
+    "sku": {
+        "name": "F0"
+    },
+    "kind": "LUIS.Authoring",
+    "location": "westus",
+    "identity": {
+        "principalId": "9a4a3675-9f5f-43a3-90a1-0640b3f753cb",
+        "tenantId": "05b815de-344b-4a3f-8387-52ca67249c55",
+        "type": "SystemAssigned"
+    },
+    "properties": {
+        "endpoint": "https://pilouis.cognitiveservices.azure.com/",
+        "provisioningState": "Succeeded",
+        "internalId": "6a7ae86489d64f168c3fa6b4866b05cd",
+        "dateCreated": "2024-12-23T22:03:09.951Z",
+        "callRateLimit": {
+            "rules": [
+                {
+                    "key": "default",
+                    "renewalPeriod": 1,
+                    "count": 5,
+                    "matchPatterns": [
+                        {
+                            "path": "*",
+                            "method": "*"
+                        }
+                    ]
+                }
+            ]
+        },
+        "isMigrated": false,
+        "customSubDomainName": "pilouis",
+        "privateEndpointConnections": [],
+        "publicNetworkAccess": "Enabled",
+        "capabilities": [
+            {
+                "name": "VirtualNetworks"
+            }
+        ],
+        "endpoints": {
+            "LUIS.Authoring": "https://pilouis.cognitiveservices.azure.com/",
+            "LUIS": "https://pilouis.cognitiveservices.azure.com/",
+            "Container": "https://pilouis.cognitiveservices.azure.com/"
+        },
+        "armFeatures": [
+            "Microsoft.CognitiveServices/LegalTerms.TextAnalytics.TAForPIIRAITermsAccepted",
+            "Microsoft.CognitiveServices/LegalTerms.TextAnalytics.TAForHealthRAITermsAccepted",
+            "Microsoft.CognitiveServices/LegalTerms.ComputerVision.SpatialAnaysisRAITermsAccepted"
+        ]
+    },
+    "systemData": {
+        "createdBy": "[email protected]",
+        "createdByType": "User",
+        "createdAt": "2024-12-23T22:03:08.79Z",
+        "lastModifiedBy": "[email protected]",
+        "lastModifiedByType": "User",
+        "lastModifiedAt": "2024-12-23T22:03:35.303Z"
+    },
+    "etag": "\"a900c91a-0000-0700-0000-6769de380000\""
+}

pyvenv.cfg

@@ -0,0 +1,5 @@
+home = C:\Python312
+include-system-site-packages = false
+version = 3.12.6
+executable = C:\Python312\python.exe
+command = C:\Python312\python.exe -m venv C:\Users\Jonathan\OneDrive - Raiff's Bits\Desktop\ut\venv

qustions.json

@@ -0,0 +1,218 @@
+[
+    {
+        "question": "What is the meaning of life?",
+        "functions": [
+            {
+                "name": "newton_thoughts",
+                "description": "Apply Newton's laws to the given question.",
+                "parameters": {
+                    "question": "How does Newton's third law apply to human interactions?"
+                }
+            },
+            {
+                "name": "davinci_insights",
+                "description": "Generate insights like Da Vinci for the given question.",
+                "parameters": {
+                    "question": "What can we learn from nature's design about the meaning of life?"
+                }
+            },
+            {
+                "name": "human_intuition",
+                "description": "Provide human intuition for the given question.",
+                "parameters": {
+                    "question": "What does your gut instinct tell you about finding happiness?"
+                }
+            },
+            {
+                "name": "neural_network_thinking",
+                "description": "Apply neural network thinking to the given question.",
+                "parameters": {
+                    "question": "How can neural networks help us understand human creativity?"
+                }
+            },
+            {
+                "name": "quantum_computing_thinking",
+                "description": "Apply quantum computing principles to the given question.",
+                "parameters": {
+                    "question": "How can quantum computing revolutionize problem-solving?"
+                }
+            },
+            {
+                "name": "resilient_kindness",
+                "description": "Provide perspectives of resilient kindness.",
+                "parameters": {
+                    "question": "How can we find strength in kindness during difficult times?"
+                }
+            },
+            {
+                "name": "identify_and_refute_fallacies",
+                "description": "Identify and refute common logical fallacies in the argument.",
+                "parameters": {
+                    "argument": "Life has no meaning because it is full of suffering."
+                }
+            }
+        ]
+    },
+    {
+        "question": "How can we mitigate bias in artificial intelligence systems?",
+        "functions": [
+            {
+                "name": "newton_thoughts",
+                "description": "Apply Newton's laws to the given question.",
+                "parameters": {
+                    "question": "How can Newton's laws of motion inspire fairness in AI?"
+                }
+            },
+            {
+                "name": "davinci_insights",
+                "description": "Generate insights like Da Vinci for the given question.",
+                "parameters": {
+                    "question": "What can Da Vinci's approach to art teach us about unbiased AI?"
+                }
+            },
+            {
+                "name": "human_intuition",
+                "description": "Provide human intuition for the given question.",
+                "parameters": {
+                    "question": "How can we use human intuition to detect bias in AI?"
+                }
+            },
+            {
+                "name": "neural_network_thinking",
+                "description": "Apply neural network thinking to the given question.",
+                "parameters": {
+                    "question": "How can neural networks be designed to minimize bias?"
+                }
+            },
+            {
+                "name": "quantum_computing_thinking",
+                "description": "Apply quantum computing principles to the given question.",
+                "parameters": {
+                    "question": "How can quantum computing help in creating unbiased AI?"
+                }
+            },
+            {
+                "name": "resilient_kindness",
+                "description": "Provide perspectives of resilient kindness.",
+                "parameters": {
+                    "question": "How can kindness be integrated into AI to ensure fairness?"
+                }
+            },
+            {
+                "name": "identify_and_refute_fallacies",
+                "description": "Identify and refute common logical fallacies in the argument.",
+                "parameters": {
+                    "argument": "AI will always be biased because it is created by humans."
+                }
+            }
+        ]
+    },
+    {
+        "question": "How does Hydrogen's properties influence its interactions?",
+        "functions": [
+            {
+                "name": "newton_thoughts",
+                "description": "Apply Newton's laws to the given question.",
+                "parameters": {
+                    "question": "How does Newton's laws explain Hydrogen's behavior in reactions?"
+                }
+            },
+            {
+                "name": "davinci_insights",
+                "description": "Generate insights like Da Vinci for the given question.",
+                "parameters": {
+                    "question": "What can Da Vinci's observations teach us about Hydrogen?"
+                }
+            },
+            {
+                "name": "human_intuition",
+                "description": "Provide human intuition for the given question.",
+                "parameters": {
+                    "question": "What intuitive insights can we gain about Hydrogen's role in the universe?"
+                }
+            },
+            {
+                "name": "neural_network_thinking",
+                "description": "Apply neural network thinking to the given question.",
+                "parameters": {
+                    "question": "How can neural networks model Hydrogen's interactions?"
+                }
+            },
+            {
+                "name": "quantum_computing_thinking",
+                "description": "Apply quantum computing principles to the given question.",
+                "parameters": {
+                    "question": "How can quantum computing enhance our understanding of Hydrogen?"
+                }
+            },
+            {
+                "name": "resilient_kindness",
+                "description": "Provide perspectives of resilient kindness.",
+                "parameters": {
+                    "question": "How can we draw parallels between Hydrogen's simplicity and resilience?"
+                }
+            },
+            {
+                "name": "identify_and_refute_fallacies",
+                "description": "Identify and refute common logical fallacies in the argument.",
+                "parameters": {
+                    "argument": "Hydrogen is not important because it is the simplest element."
+                }
+            }
+        ]
+    },
+    {
+        "question": "What makes Diamond unique in its applications?",
+        "functions": [
+            {
+                "name": "newton_thoughts",
+                "description": "Apply Newton's laws to the given question.",
+                "parameters": {
+                    "question": "How do Newton's laws explain Diamond's hardness?"
+                }
+            },
+            {
+                "name": "davinci_insights",
+                "description": "Generate insights like Da Vinci for the given question.",
+                "parameters": {
+                    "question": "What can Da Vinci's approach to materials teach us about Diamond?"
+                }
+            },
+            {
+                "name": "human_intuition",
+                "description": "Provide human intuition for the given question.",
+                "parameters": {
+                    "question": "What intuitive insights can we gain about Diamond's value?"
+                }
+            },
+            {
+                "name": "neural_network_thinking",
+                "description": "Apply neural network thinking to the given question.",
+                "parameters": {
+                    "question": "How can neural networks predict Diamond's properties?"
+                }
+            },
+            {
+                "name": "quantum_computing_thinking",
+                "description": "Apply quantum computing principles to the given question.",
+                "parameters": {
+                    "question": "How can quantum computing help in synthesizing Diamonds?"
+                }
+            },
+            {
+                "name": "resilient_kindness",
+                "description": "Provide perspectives of resilient kindness.",
+                "parameters": {
+                    "question": "How can we draw parallels between Diamond's strength and human resilience?"
+                }
+            },
+            {
+                "name": "identify_and_refute_fallacies",
+                "description": "Identify and refute common logical fallacies in the argument.",
+                "parameters": {
+                    "argument": "Diamonds are valuable only because they are rare."
+                }
+            }
+        ]
+    }
+]

sentiment_analysis.py

@@ -0,0 +1,37 @@
+from textblob import TextBlob
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+from typing import Dict, Any
+
+def analyze_sentiment_textblob(text: str) -> TextBlob:
+    """Analyze the sentiment of the given text using TextBlob.
+    
+    Args:
+        text (str): The text to analyze.
+    
+    Returns:
+        TextBlob: The sentiment analysis result from TextBlob.
+    """
+    try:
+        blob = TextBlob(text)
+        sentiment = blob.sentiment
+        return sentiment
+    except Exception as e:
+        print(f"Error analyzing sentiment with TextBlob: {e}")
+        return None
+
+def analyze_sentiment_vader(text: str) -> Dict[str, Any]:
+    """Analyze the sentiment of the given text using VADER.
+    
+    Args:
+        text (str): The text to analyze.
+    
+    Returns:
+        dict: The sentiment analysis result from VADER.
+    """
+    try:
+        analyzer = SentimentIntensityAnalyzer()
+        sentiment = analyzer.polarity_scores(text)
+        return sentiment
+    except Exception as e:
+        print(f"Error analyzing sentiment with VADER: {e}")
+        return {}

ultimatethinking.txt

@@ -0,0 +1,356 @@
+import asyncio
+import json
+import logging
+import os
+from typing import List, Dict, Any
+from cryptography.fernet import Fernet
+from botbuilder.core import StatePropertyAccessor, TurnContext
+from botbuilder.dialogs import Dialog, DialogSet, DialogTurnStatus
+from dialog_helper import DialogHelper
+import aiohttp
+import speech_recognition as sr
+from PIL import Image
+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 (
+    Perspective, NewtonPerspective, DaVinciPerspective, HumanIntuitionPerspective,
+    NeuralNetworkPerspective, QuantumComputingPerspective, ResilientKindnessPerspective,
+    MathematicalPerspective, PhilosophicalPerspective, CopilotPerspective, BiasMitigationPerspective,
+    PsychologicalPerspective
+)
+
+# Load environment variables
+from dotenv import load_dotenv
+load_dotenv()
+
+# 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 {}
+
+# Encrypt sensitive information
+def encrypt_sensitive_data(data, key):
+    fernet = Fernet(key)
+    encrypted_data = fernet.encrypt(data.encode())
+    return encrypted_data
+
+# Decrypt sensitive information
+def decrypt_sensitive_data(encrypted_data, key):
+    fernet = Fernet(key)
+    decrypted_data = fernet.decrypt(encrypted_data).decode()
+    return decrypted_data
+
+# Securely destroy sensitive information
+def destroy_sensitive_data(data):
+    del data
+
+# 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()
+        self.context_history = []  # Maintain context history
+        self.feedback = []  # Store user feedback
+        # 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",
+            "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"
+            ),
+            # 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):
+        self.context_history.append(question)  # Add question to context history
+        sentiment_score = self.analyze_sentiment(question)
+        real_time_data = await self.fetch_real_time_data("https://api.example.com/data")
+        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 analyze_sentiment(self, text):
+        sentiment_score = self.sentiment_analyzer.polarity_scores(text)
+        logging.info(f"Sentiment analysis result: {sentiment_score}")
+        return sentiment_score
+
+    async def fetch_real_time_data(self, source_url):
+        async with aiohttp.ClientSession() as session:
+            async with session.get(source_url) as response:
+                data = await response.json()
+                logging.info(f"Real-time data fetched from {source_url}: {data}")
+                return data
+
+    async def run_dialog(self, dialog: Dialog, turn_context: TurnContext, accessor: StatePropertyAccessor) -> None:
+        await DialogHelper.run_dialog(dialog, turn_context, accessor)
+
+    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}'.")
+
+    async def collect_user_feedback(self, turn_context: TurnContext):
+        # Collect feedback from the user
+        feedback = turn_context.activity.text
+        logging.info(f"User feedback received: {feedback}")
+        # Process feedback for continuous learning
+        self.process_feedback(feedback)
+
+    def process_feedback(self, feedback):
+        # Implement feedback processing logic
+        logging.info(f"Processing feedback: {feedback}")
+        # Example: Adjust response generation based on feedback
+        # This can be expanded with more sophisticated learning algorithms
+
+    def add_new_perspective(self, perspective_name, perspective_class):
+        if perspective_name.lower() not in [p.__class__.__name__.lower() for p in self.perspectives]:
+            self.perspectives.append(perspective_class(self.config))
+            logging.info(f"New perspective '{perspective_name}' added.")
+        else:
+            logging.warning(f"Perspective '{perspective_name}' already exists.")
+
+    def handle_voice_input(self):
+        recognizer = sr.Recognizer()
+        with sr.Microphone() as source:
+            print("Listening...")
+            audio = recognizer.listen(source)
+        try:
+            text = recognizer.recognize_google(audio)
+            print(f"Voice input recognized: {text}")
+            return text
+        except sr.UnknownValueError:
+            print("Google Speech Recognition could not understand audio")
+            return None
+        except sr.RequestError as e:
+            print(f"Could not request results from Google Speech Recognition service; {e}")
+            return None
+
+    def handle_image_input(self, image_path):
+        try:
+            image = Image.open(image_path)
+            print(f"Image input processed: {image_path}")
+            return image
+        except Exception as e:
+            print(f"Error processing image input: {e}")
+            return None
+
+# Example usage
+if __name__ == "__main__":
+    config = load_json_config('config.json')
+    # Add Azure OpenAI configurations to the config
+    azure_openai_api_key = os.getenv('AZURE_OPENAI_API_KEY')
+    azure_openai_endpoint = os.getenv('AZURE_OPENAI_ENDPOINT')
+
+    # Encrypt sensitive data
+    encryption_key = Fernet.generate_key()
+    encrypted_api_key = encrypt_sensitive_data(azure_openai_api_key, encryption_key)
+    encrypted_endpoint = encrypt_sensitive_data(azure_openai_endpoint, encryption_key)
+
+    # Add encrypted data to config
+    config['azure_openai_api_key'] = encrypted_api_key
+    config['azure_openai_endpoint'] = encrypted_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)
+
+    # Decrypt and destroy sensitive data
+    decrypted_api_key = decrypt_sensitive_data(encrypted_api_key, encryption_key)
+    decrypted_endpoint = decrypt_sensitive_data(encrypted_endpoint, encryption_key)
+    destroy_sensitive_data(decrypted_api_key)
+    destroy_sensitive_data(decrypted_endpoint)
+
+    # Handle voice input
+    voice_input = universal_reasoning.handle_voice_input()
+    if voice_input:
+        response = asyncio.run(universal_reasoning.generate_response(voice_input))
+        print(response)
+
+    # Handle image input
+    image_input = universal_reasoning.handle_image_input("path_to_image.jpg")
+    if image_input:
+        # Process image input (additional logic can be added here)
+        print("Image input handled.")

utils.py

@@ -0,0 +1,38 @@
+import tkinter as tk
+import urllib.request
+import logging
+
+def show_privacy_consent() -> bool:
+    """Display a pop-up window to obtain user consent for data collection and privacy."""
+    def on_accept():
+        user_consent.set(True)
+        root.destroy()
+    def on_decline():
+        user_consent.set(False)
+        root.destroy()
+    root = tk.Tk()
+    root.title("Data Permission and Privacy")
+    message = ("We value your privacy. By using this application, you consent to the collection and use of your data "
+               "as described in our privacy policy. Do you agree to proceed?")
+    label = tk.Label(root, text=message, wraplength=400, justify="left")
+    label.pack(padx=20, pady=20)
+    button_frame = tk.Frame(root)
+    button_frame.pack(pady=10)
+    accept_button = tk.Button(button_frame, text="Accept", command=on_accept)
+    accept_button.pack(side="left", padx=10)
+    decline_button = tk.Button(button_frame, text="Decline", command=on_decline)
+    decline_button.pack(side="right", padx=10)
+    user_consent = tk.BooleanVar()
+    root.mainloop()
+    return user_consent.get()
+
+def download_database(url: str, file_path: str) -> None:
+    """Download the database file from the given URL."""
+    try:
+        logging.info(f"Downloading database from {url}...")
+        urllib.request.urlretrieve(url, file_path)
+        logging.info("Download complete.")
+    except urllib.error.URLError as e:
+        logging.error(f"Error: Failed to download database. {e}")
+    except Exception as e:
+        logging.error(f"An unexpected error occurred: {e}")

your_script.py

@@ -0,0 +1,244 @@
+import re
+import json
+from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+from cryptography.hazmat.primitives import padding
+from cryptography.hazmat.backends import default_backend
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+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 display_properties(self):
+        print(f"Properties of {self.name} ({self.symbol}):")
+        for prop in self.properties:
+            print(f"  - {prop}")
+
+    def display_interactions(self):
+        print(f"Interactions of {self.name} ({self.symbol}):")
+        for interaction in self.interactions:
+            print(f"  - {interaction}")
+
+    def display_defense_ability(self):
+        print(f"Defense Ability of {self.name} ({self.symbol}): {self.defense_ability}")
+
+    def execute_defense_function(self):
+        defense_functions = {
+            "evasion": self.evasion,
+            "adaptability": self.adaptability,
+            "fortification": self.fortification,
+            "barrier": self.barrier,
+            "regeneration": self.regeneration,
+            "resilience": self.resilience,
+            "illumination": self.illumination,
+            "shield": self.shield,
+            "reflection": self.reflection,
+            "protection": self.protection
+        }
+        defense_function = defense_functions.get(self.defense_ability.lower(), self.no_defense)
+        defense_function()
+
+    def evasion(self):
+        print(f"{self.name} uses Evasion to avoid threats and remain undetected.")
+
+    def adaptability(self):
+        print(f"{self.name} adapts to changing environments and evolves to overcome challenges.")
+
+    def fortification(self):
+        print(f"{self.name} strengthens defenses and fortifies positions to withstand attacks.")
+
+    def barrier(self):
+        print(f"{self.name} creates barriers to protect against external threats.")
+
+    def regeneration(self):
+        print(f"{self.name} regenerates lost or damaged parts to maintain functionality.")
+
+    def resilience(self):
+        print(f"{self.name} exhibits resilience to recover quickly from setbacks.")
+
+    def illumination(self):
+        print(f"{self.name} uses illumination to reveal hidden threats and illuminate dark areas.")
+
+    def shield(self):
+        print(f"{self.name} uses a shield to block incoming attacks and protect allies.")
+
+    def reflection(self):
+        print(f"{self.name} reflects attacks back to the source, turning the enemy's power against them.")
+
+    def protection(self):
+        print(f"{self.name} offers protection to prevent harm and ensure safety.")
+
+    def no_defense(self):
+        print("No defense function available.")
+
+class CustomRecognizer:
+    class RecognizerResult:
+        def __init__(self, text):
+            self.text = text
+            self.intents = []
+
+    class Intent:
+        def __init__(self, name, score):
+            self.name = name
+            self.score = score
+
+    def recognize(self, text):
+        recognizer_result = self.RecognizerResult(text)
+        regex_element = re.compile(r"^(Hydrogen|Carbon|Iron|Silicon|Oxygen|Nitrogen|Phosphorus|Gold|Silver|Lead|Diamond)$", re.IGNORECASE)
+        is_element = regex_element.match(text)
+
+        if is_element:
+            recognizer_result.intents.append(self.Intent("ElementDefense", 100))
+        return recognizer_result
+
+    def get_top_intent(self, recognizer_result):
+        recognizer_result.intents.sort(key=lambda x: x.score, reverse=True)
+        return recognizer_result.intents[0].name if recognizer_result.intents else None
+
+class DataProtector:
+    sensitive_keywords = {"AI", "sensitive", "confidential", "data"}
+
+    @staticmethod
+    def contains_sensitive_info(text):
+        return any(keyword.lower() in text.lower() for keyword in DataProtector.sensitive_keywords)
+
+    @staticmethod
+    def mask_sensitive_info(text):
+        for keyword in DataProtector.sensitive_keywords:
+            text = re.sub(keyword, '*' * len(keyword), text, flags=re.IGNORECASE)
+        return text
+
+    @staticmethod
+    def encrypt_string(plain_text, key):
+        backend = default_backend()
+        key_bytes = key.encode('utf-8')
+        iv = key_bytes[:16]
+
+        cipher = Cipher(algorithms.AES(key_bytes), modes.CBC(iv), backend=backend)
+        encryptor = cipher.encryptor()
+
+        padder = padding.PKCS7(algorithms.AES.block_size).padder()
+        padded_data = padder.update(plain_text.encode('utf-8')) + padder.finalize()
+
+        encrypted_data = encryptor.update(padded_data) + encryptor.finalize()
+        return encrypted_data.hex()
+
+    @staticmethod
+    def decrypt_string(cipher_text, key):
+        backend = default_backend()
+        key_bytes = key.encode('utf-8')
+        iv = key_bytes[:16]
+
+        cipher = Cipher(algorithms.AES(key_bytes), modes.CBC(iv), backend=backend)
+        decryptor = cipher.decryptor()
+
+        encrypted_data = bytes.fromhex(cipher_text)
+        decrypted_padded_data = decryptor.update(encrypted_data) + decryptor.finalize()
+
+        unpadder = padding.PKCS7(algorithms.AES.block_size).unpadder()
+        decrypted_data = unpadder.update(decrypted_padded_data) + unpadder.finalize()
+
+        return decrypted_data.decode('utf-8')
+
+def analyze_sentiment(text):
+    analyzer = SentimentIntensityAnalyzer()
+    sentiment = analyzer.polarity_scores(text)
+    return sentiment
+
+def initialize_elements():
+    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="Carbon",
+            symbol="C",
+            representation="Python",
+            properties=["Flexible", "Widely used", "Powerful"],
+            interactions=["Can be used for a variety of tasks, from web development to data analysis"],
+            defense_ability="Adaptability"
+        ),
+        Element(
+            name="Iron",
+            symbol="Fe",
+            representation="C++",
+            properties=["Strong", "Durable", "Efficient"],
+            interactions=["Used in system programming and game development"],
+            defense_ability="Fortification"
+        ),
+        Element(
+            name="Silicon",
+            symbol="Si",
+            representation="Java",
+            properties=["Robust", "Platform-independent", "Secure"],
+            interactions=["Widely used in enterprise applications"],
+            defense_ability="Barrier"
+        ),
+        Element(
+            name="Oxygen",
+            symbol="O",
+            representation="JavaScript",
+            properties=["Dynamic", "Versatile", "Ubiquitous"],
+            interactions=["Essential for web development"],
+            defense_ability="Regeneration"
+        ),
+        Element(
+            name="Nitrogen",
+            symbol="N",
+            representation="Ruby",
+            properties=["Elegant", "Productive", "Flexible"],
+            interactions=["Popular in web development with Rails"],
+            defense_ability="Resilience"
+        ),
+        Element(
+            name="Phosphorus",
+            symbol="P",
+            representation="PHP",
+            properties=["Server-side", "Web-focused", "Embedded"],
+            interactions=["Commonly used in web development"],
+            defense_ability="Illumination"
+        ),
+        Element(
+            name="Gold",
+            symbol="Au",
+            representation="Swift",
+            properties=["Modern", "Safe", "Fast"],
+            interactions=["Used for iOS and macOS development"],
+            defense_ability="Shield"
+        ),
+        Element(
+            name="Silver",
+            symbol="Ag",
+            representation="Go",
+            properties=["Concurrent", "Efficient", "Scalable"],
+            interactions=["Ideal for cloud services and backend systems"],
+            defense_ability="Reflection"
+        ),
+        Element(
+            name="Lead",
+            symbol="Pb",
+            representation="Rust",
+            properties=["Safe", "Concurrent", "Fast"],
+            interactions=["Used for system-level programming"],
+            defense_ability="Protection"
+        ),
+        Element(
+            name="Diamond",
+            symbol="D",
+            representation="Kotlin",
+            properties=["Modern", "Concise", "Safe"],
+            interactions=["Used for Android development"],
+            defense_ability="Adaptability"
+        )
+    ]
+    return elements