mybot.py

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())