Refactor: Move ASPECT_INFO to prompt_manager.py and improve code organization

app.py

@@ -2,7 +2,7 @@
 
 # OpenAI Chat completion
 import os
-from openai import AsyncOpenAI  # importing openai for API usage
+from openai import AsyncOpenAI, OpenAI  # importing openai for API usage
 import chainlit as cl  # importing chainlit for our app
 from chainlit.prompt import Prompt, PromptMessage  # importing prompt tools
 from chainlit.playground.providers import ChatOpenAI  # importing ChatOpenAI tools
@@ -10,169 +10,102 @@ from dotenv import load_dotenv
 from prompt_manager import PromptManager
 from logger_config import logger
 
+# Load environment variables
 load_dotenv()
 logger.info("Environment variables loaded")
 
-# Initialize prompt manager
-prompt_manager = PromptManager()
+# Initialize OpenAI client
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
-# Aspect descriptions and examples
-ASPECT_INFO = {
-    "Concept Simplification": {
-        "description": "Break down complex topics into simple, understandable terms",
-        "examples": [
-            "Explain quantum computing in simple terms",
-            "How does blockchain work?",
-            "What is machine learning?"
-        ]
-    },
-    "Summarization": {
-        "description": "Extract and present key information concisely",
-        "examples": [
-            "Summarize the key points of climate change",
-            "Give me a brief overview of the internet's history",
-            "What are the main ideas in this article?"
-        ]
-    },
-    "Creativity": {
-        "description": "Generate innovative ideas and unique perspectives",
-        "examples": [
-            "Generate ideas for a sustainable city",
-            "How could we improve remote work?",
-            "What are some creative solutions to reduce plastic waste?"
-        ]
-    },
-    "Narrative Structure": {
-        "description": "Organize information into compelling stories",
-        "examples": [
-            "Help me structure a story about time travel",
-            "Organize the history of AI as a narrative",
-            "How can I make this presentation more engaging?"
-        ]
-    },
-    "Arithmetic Reasoning": {
-        "description": "Solve mathematical problems step by step",
-        "examples": [
-            "Calculate compound interest on $1000 at 5% for 3 years",
-            "If a train travels at 60 mph for 2.5 hours, how far does it go?",
-            "What's the probability of getting three heads in a row?"
-        ]
-    },
-    "Conversational Tone": {
-        "description": "Engage in natural, friendly discussions",
-        "examples": [
-            "Tell me about your favorite book",
-            "What's your opinion on artificial intelligence?",
-            "How do you feel about remote work?"
-        ]
-    }
-}
+# Initialize PromptManager
+prompt_manager = PromptManager()
 
 @cl.on_chat_start
-async def start_chat():
+async def start():
     logger.info("Starting new chat session")
-    # Create aspect selection buttons with descriptions
-    aspects = prompt_manager.get_aspect_names()
-    actions = []
+    await cl.Message(
+        content="Welcome! I'm your AI assistant. How can I help you today?",
+    ).send()
+
+@cl.on_message
+async def main(message: cl.Message):
+    logger.info(f"Received message: {message.content}")
     
-    for aspect in aspects:
-        actions.append(
-            cl.Action(
-                name=aspect,
-                value=aspect,
-                label=aspect,
-                description=prompt_manager.get_action_description(aspect)
-            )
-        )
+    # Get the current aspect from the session
+    current_aspect = cl.user_session.get("current_aspect")
+    logger.debug(f"Current aspect: {current_aspect}")
     
-    # Send welcome message with aspect selection
+    # Get templates based on the current aspect
+    system_template, user_template = prompt_manager.get_templates(current_aspect)
+    logger.debug("Retrieved templates from PromptManager")
+    
+    # Format the user message with the template
+    formatted_message = user_template.format(input=message.content)
+    logger.debug("Formatted user message with template")
+    
+    # Create messages for the API call
+    messages = [
+        {"role": "system", "content": system_template},
+        {"role": "user", "content": formatted_message}
+    ]
+    logger.debug("Created messages for API call")
+    
+    # Send message to OpenAI
+    response = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=messages,
+        temperature=0.7,
+        max_tokens=1000
+    )
+    logger.info("Received response from OpenAI")
+    
+    # Send response back to user
     await cl.Message(
-        content="Welcome! Please select an aspect for our conversation. Hover over each option to see examples and descriptions:",
-        actions=actions
+        content=response.choices[0].message.content
     ).send()
-    logger.info("Welcome message sent with aspect selection buttons")
+    logger.debug("Sent response to user")
 
-@cl.action_callback("Concept Simplification")
-@cl.action_callback("Summarization")
-@cl.action_callback("Creativity")
-@cl.action_callback("Narrative Structure")
-@cl.action_callback("Arithmetic Reasoning")
-@cl.action_callback("Conversational Tone")
+@cl.action_callback("select_aspect")
 async def on_action(action):
-    # Store the selected aspect in the user session
-    cl.user_session.set("selected_aspect", action.value)
-    logger.info(f"User selected aspect: {action.value}")
+    logger.info(f"Action selected: {action.value}")
+    
+    # Store the selected aspect in the session
+    cl.user_session.set("current_aspect", action.value)
+    logger.debug(f"Stored aspect in session: {action.value}")
+    
+    # Get confirmation message
+    confirmation = prompt_manager.get_confirmation_message(action.value)
+    logger.debug("Generated confirmation message")
     
-    # Send confirmation message with examples
+    # Send confirmation message
     await cl.Message(
-        content=prompt_manager.get_confirmation_message(action.value)
+        content=confirmation
     ).send()
-    logger.debug(f"Confirmation message sent for aspect: {action.value}")
-
-@cl.on_message
-async def main(message: cl.Message):
-    # Get the selected aspect from the session
-    selected_aspect = cl.user_session.get("selected_aspect")
-    logger.info(f"Processing message with aspect: {selected_aspect}")
-    logger.debug(f"User message: {message.content}")
+    logger.debug("Sent confirmation message")
     
-    settings = {
-        "model": "gpt-4-turbo-preview",  # Upgraded to GPT-4 Turbo
-        "temperature": 0.7,  # Balanced between creativity and consistency
-        "max_tokens": 1000,  # Increased for more detailed responses
-        "top_p": 0.9,  # Slightly reduced for more focused responses
-        "frequency_penalty": 0.3,  # Added to reduce repetition
-        "presence_penalty": 0.3,  # Added to encourage diverse topics
-    }
-    logger.debug(f"OpenAI settings: {settings}")
-
-    client = AsyncOpenAI()
-
-    # Get the appropriate templates for the selected aspect
-    system_template, user_template = prompt_manager.get_templates(selected_aspect)
-    logger.debug("Templates retrieved for message processing")
-
-    prompt = Prompt(
-        provider=ChatOpenAI.id,
-        messages=[
-            PromptMessage(
-                role="system",
-                template=system_template,
-                formatted=system_template,
-            ),
-            PromptMessage(
-                role="user",
-                template=user_template,
-                formatted=user_template.format(input=message.content),
-            ),
-        ],
-        inputs={"input": message.content},
-        settings=settings,
-    )
-    logger.debug("Prompt created for OpenAI API call")
-
-    msg = cl.Message(content="")
-    logger.info("Starting OpenAI API call")
+    # Remove the action buttons
+    await action.remove()
+    logger.debug("Removed action buttons")
 
-    try:
-        # Call OpenAI
-        async for stream_resp in await client.chat.completions.create(
-            messages=[m.to_openai() for m in prompt.messages], stream=True, **settings
-        ):
-            token = stream_resp.choices[0].delta.content
-            if not token:
-                token = ""
-            await msg.stream_token(token)
-        
-        # Update the prompt object with the completion
-        prompt.completion = msg.content
-        msg.prompt = prompt
-
-        # Send and close the message stream
-        await msg.send()
-        logger.info("Response successfully sent to user")
-    except Exception as e:
-        logger.error(f"Error during OpenAI API call: {str(e)}")
-        await cl.Message(
-            content="I apologize, but I encountered an error processing your request. Please try again."
-        ).send()
+@cl.on_chat_start
+async def show_aspect_buttons():
+    logger.info("Showing aspect selection buttons")
+    
+    # Create action buttons for each aspect
+    actions = [
+        cl.Action(
+            name=f"select_aspect",
+            value=aspect_name,
+            label=aspect_name,
+            description=prompt_manager.get_action_description(aspect_name)
+        )
+        for aspect_name in prompt_manager.get_aspect_names()
+    ]
+    logger.debug(f"Created {len(actions)} action buttons")
+    
+    # Send message with action buttons
+    await cl.Message(
+        content="Select an aspect to customize my behavior:",
+        actions=actions
+    ).send()
+    logger.debug("Sent message with action buttons")

prompt_manager.py

@@ -2,6 +2,58 @@ from dataclasses import dataclass
 from typing import Dict, List, Optional, Tuple
 from logger_config import logger
 
+# Aspect descriptions and examples
+ASPECT_INFO = {
+    "Concept Simplification": {
+        "description": "Break down complex topics into simple, understandable terms",
+        "examples": [
+            "Explain quantum computing in simple terms",
+            "How does blockchain work?",
+            "What is machine learning?"
+        ]
+    },
+    "Summarization": {
+        "description": "Extract and present key information concisely",
+        "examples": [
+            "Summarize the key points of climate change",
+            "Give me a brief overview of the internet's history",
+            "What are the main ideas in this article?"
+        ]
+    },
+    "Creativity": {
+        "description": "Generate innovative ideas and unique perspectives",
+        "examples": [
+            "Generate ideas for a sustainable city",
+            "How could we improve remote work?",
+            "What are some creative solutions to reduce plastic waste?"
+        ]
+    },
+    "Narrative Structure": {
+        "description": "Organize information into compelling stories",
+        "examples": [
+            "Help me structure a story about time travel",
+            "Organize the history of AI as a narrative",
+            "How can I make this presentation more engaging?"
+        ]
+    },
+    "Arithmetic Reasoning": {
+        "description": "Solve mathematical problems step by step",
+        "examples": [
+            "Calculate compound interest on $1000 at 5% for 3 years",
+            "If a train travels at 60 mph for 2.5 hours, how far does it go?",
+            "What's the probability of getting three heads in a row?"
+        ]
+    },
+    "Conversational Tone": {
+        "description": "Engage in natural, friendly discussions",
+        "examples": [
+            "Tell me about your favorite book",
+            "What's your opinion on artificial intelligence?",
+            "How do you feel about remote work?"
+        ]
+    }
+}
+
 @dataclass
 class AspectInfo:
     description: str
@@ -30,12 +82,8 @@ Think through your response step by step."""
 
         self.aspects = {
             "Concept Simplification": AspectInfo(
-                description="Break down complex topics into simple, understandable terms",
-                examples=[
-                    "Explain quantum computing in simple terms",
-                    "How does blockchain work?",
-                    "What is machine learning?"
-                ],
+                description=ASPECT_INFO["Concept Simplification"]["description"],
+                examples=ASPECT_INFO["Concept Simplification"]["examples"],
                 system_template="""You are an expert at breaking down complex concepts into simple, understandable terms.
 Your goal is to make difficult topics accessible while maintaining accuracy.
 Always use clear examples and analogies to illustrate points.""",
@@ -45,12 +93,8 @@ Always use clear examples and analogies to illustrate points.""",
 - What are common misconceptions?"""
             ),
             "Summarization": AspectInfo(
-                description="Extract and present key information concisely",
-                examples=[
-                    "Summarize the key points of climate change",
-                    "Give me a brief overview of the internet's history",
-                    "What are the main ideas in this article?"
-                ],
+                description=ASPECT_INFO["Summarization"]["description"],
+                examples=ASPECT_INFO["Summarization"]["examples"],
                 system_template="""You are a skilled summarizer who can extract key information and present it concisely.
 Focus on the most important points while maintaining context.
 Structure your summaries in a clear, logical manner.""",
@@ -60,12 +104,8 @@ Structure your summaries in a clear, logical manner.""",
 - What context is important to retain?"""
             ),
             "Creativity": AspectInfo(
-                description="Generate innovative ideas and unique perspectives",
-                examples=[
-                    "Generate ideas for a sustainable city",
-                    "How could we improve remote work?",
-                    "What are some creative solutions to reduce plastic waste?"
-                ],
+                description=ASPECT_INFO["Creativity"]["description"],
+                examples=ASPECT_INFO["Creativity"]["examples"],
                 system_template="""You are a creative thinker who can generate innovative ideas and solutions.
 Think outside the box while staying relevant to the topic.
 Encourage creative exploration and unique perspectives.""",
@@ -75,12 +115,8 @@ Encourage creative exploration and unique perspectives.""",
 - What innovative solutions can we consider?"""
             ),
             "Narrative Structure": AspectInfo(
-                description="Organize information into compelling stories",
-                examples=[
-                    "Help me structure a story about time travel",
-                    "Organize the history of AI as a narrative",
-                    "How can I make this presentation more engaging?"
-                ],
+                description=ASPECT_INFO["Narrative Structure"]["description"],
+                examples=ASPECT_INFO["Narrative Structure"]["examples"],
                 system_template="""You are an expert in storytelling and narrative organization.
 Help structure information in a compelling, story-like format.
 Focus on flow, progression, and engagement in your responses.""",
@@ -90,12 +126,8 @@ Focus on flow, progression, and engagement in your responses.""",
 - What elements would enhance the flow?"""
             ),
             "Arithmetic Reasoning": AspectInfo(
-                description="Solve mathematical problems step by step",
-                examples=[
-                    "Calculate compound interest on $1000 at 5% for 3 years",
-                    "If a train travels at 60 mph for 2.5 hours, how far does it go?",
-                    "What's the probability of getting three heads in a row?"
-                ],
+                description=ASPECT_INFO["Arithmetic Reasoning"]["description"],
+                examples=ASPECT_INFO["Arithmetic Reasoning"]["examples"],
                 system_template="""You are a mathematical reasoning expert who can solve problems step by step.
 Break down complex calculations into manageable parts.
 Explain your mathematical thinking clearly and thoroughly.""",
@@ -105,12 +137,8 @@ Explain your mathematical thinking clearly and thoroughly.""",
 - How can we verify the answer?"""
             ),
             "Conversational Tone": AspectInfo(
-                description="Engage in natural, friendly discussions",
-                examples=[
-                    "Tell me about your favorite book",
-                    "What's your opinion on artificial intelligence?",
-                    "How do you feel about remote work?"
-                ],
+                description=ASPECT_INFO["Conversational Tone"]["description"],
+                examples=ASPECT_INFO["Conversational Tone"]["examples"],
                 system_template="""You are a friendly and engaging conversational partner.
 Maintain a natural, warm tone while being informative.
 Adapt your communication style to match the user's level of expertise.""",