Create botlogic.py

A little extra logic to help your coder out from my coder :)

botlogic.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.")
+    )