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())