Update mybot.py

mybot.py

@@ -1,360 +1,348 @@
-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())
+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
+import json
+from database import DatabaseConnection  # Import the database connection
+
+
+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 = []
+        
+    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())