project_structure_plan.md


                                         1. Project Structure

First, let's organize the project into logical modules. Here's a suggested directory structure:


 Chatter/
 ├── .gitignore
 ├── .env
 ├── requirements.txt
 ├── app.py
 ├── ChatAI/
 │   ├── chat_ai.py
 │   └── __init__.py
 ├── core/
 │   ├── __init__.py
 │   ├── bot.py
 │   │   └── __init__.py
 │   ├── message_handler.py
 │   └── __init__.py
 ├── logging/
 │   ├── __init__.py
 │   └── handlers.py
 ├── utils/
 │   ├── __init__.py
 │   ├── constants.py
 │   └── decorators.py
 └── .env.local


                                        2. Move Code to Modules

Let's break down the existing code into these modules:

                                            a) core/bot.py

Move the bot initialization, command setup, and basic functionality here.


 import commands
 from .message_handler import on_message, respond_to_chat
 from .utils import split_string, generate, generate_response

 bot = commands.Bot(command_prefix="!")

 def setup_commands():
     # Command definitions
     pass

 async def on_ready():
     print(f'Logged in as {bot.user}')
     # Initialize channels and message counts
     pass

 @bot.event
 async def on_message(message):
     # Verification logic
     pass

 @bot.event
 async def on_ready():
     # Bot initialization and setup
     pass

 # Initialize message counts and channels
 message_counts = {}
 channel = get_default_channel()


                                      b) core/message_handler.py

Move the message handling logic here.


 import discord
 from .utils import split_string, generate, generate_response

 @bot.event
 async def on_message(message):
     guild = message.guild
     channel = get_default_channel()

     if message.channel != channel or message.author.bot:
         return

     if message.channel.id not in message_counts:
         message_counts[message.channel.id] = 0

     message_counts[message.channel.id] += 1
     print(message_counts[message.channel.id])

     await respond_to_chat(message.content)

     if message_counts[message.channel.id] >= 10:
         messages = [message.content]
         # Get previous messages from history
         async for msg in channel.history(limit=10):
             messages.append(msg.content)
         messages = "\n".join(messages)
         await respond_to_chat(messages)
         message_counts[message.channel.id] = 0

 async def respond_to_chat(content: str) -> str:
     response = generate(content)
     parts = split_string(response)
     # Send parts to channel
     await send_split_message(parts, channel)
     return "\n".join(parts)

 def split_string(text: str) -> list[str]:
     return [text[i:i+1900] for i in range(0, len(text), 1900)]

 def generate(
     prompt, temperature=0.0, max_new_tokens=2048, top_p=0.95, repetition_penalty=1.0
 ):
     # Use MistralAI model
     pass

 def generate_response(prompt):
     # Use MistralAI model
     pass

 async def send_split_message(parts, channel):
     for part in parts:
         await channel.send(part)


                                           c) core/utils.py

Move the utility functions here.


 import os
 from huggingface_hub import InferenceClient

 def split_string(text: str) -> list[str]:
     """Helper function to split text into chunks"""
     return [text[i:i+1900] for i in range(0, len(text), 1900)]

 def generate(
     prompt, temperature=0.0, max_new_tokens=2048, top_p=0.95, repetition_penalty=1.0
 ):
     """Generate response using MistralAI model"""
     if temperature == 0.0:
         temperature = random.uniform(1e-2, 0.9)
     temperature = float(temperature)

     top_p = float(top_p)

     generate_kwargs = dict(
         temperature=temperature,
         max_new_tokens=max_new_tokens,
         top_p=top_p,
         repetition_penalty=repetition_penalty,
         do_sample=True,
         seed=42,
     )

     return ai.text_generation(prompt, **generate_kwargs, stream=False, details=False,
 return_full_text=True)

 def generate_response(prompt):
     """Generate and format response"""
     response = generate(prompt)
     formatted_response = "\n".join(response)
     return formatted_response

 def get_default_channel():
     return discord.utils.get(guild.channels, name="PrzebieralniaKoedukacyjna")


                                         d) ChatAI/chat_ai.py

Move the AI configuration here.


 from huggingface_hub import InferenceClient

 class ChatAI:
     def __init__(self):
         self.pipe = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")

     async def generate(self, prompt):
         return self.pipe.generate(prompt)


                                       3. Dependency Management

Update the requirements.txt to use a modern package manager like Poetry:

requirements.txt


 <<<<<<< SEARCH
 transformers
 huggingface_hub==0.25.2
 discord.py~=2.4.0
 discord~=2.3.2
 torch
 six
 python-dotenv~=1.0.1
 huggingface-hub~=0.25.2
 =======
 discord~=2.4.0
 discord.py~=2.4.0
 transformers
 huggingface_hub>=0.25.0
 torch
 six
 python-dotenv>=1.0.0
 poetry
 >>>>>>> REPLACE


Run:


 poetry install


                                            4. Add Logging

Create a logging module:

utils/logging.py


 import logging
 from datetime import datetime

 logging.basicConfig(
     level=logging.INFO,
     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
     handlers=[logging.FileHandler('chat.log')]
 )

 logger = logging.getLogger(__name__)


                                         5. Initialize the Bot

Create a main.py to initialize and run the bot:

main.py


 from core.bot import commands
 from core.message_handler import on_ready, on_message, respond_to_chat
 from core.utils import split_string, generate, generate_response
 from ChatAI.chat_ai import ChatAI
 import logging

 # Initialize logging
 logging.basicConfig(
     level=logging.INFO,
     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
     handlers=[logging.FileHandler('chat.log')]
 )

 logger = logging.getLogger(__name__)

 # Initialize bot
 bot = commands.Bot(command_prefix="!")

 # Initialize AI
 ai = ChatAI()

 # Initialize message handler
 async def setup():
     logger.info("Initializing bot and message handler")
     await on_ready()
     logger.info("Bot initialized")

 async def run_bot():
     try:
         await setup()
         logger.info("Bot running...")
         while True:
             await bot.process_commands()
     except Exception as e:
         logger.error(f"Error in bot loop: {str(e)}")
         logger.info("Bot shutdown")
     finally:
         logger.info("Bot shutdown complete")

 if __name__ == "__main__":
     try:
         logger.info("Starting bot")
         await bot.start(DISCORD_TOKEN_CHATTER)
         logger.info("Bot running")
     except Exception as e:
         logger.error(f"Error starting bot: {str(e)}")
         logger.info("Bot shutdown")


                                              6. Testing

Add tests to ensure the bot works as expected.

tests/ ├── .gitignore ├── test_message_handler.py └── test_chat_ai.py

                                             7. Deployment

For production, consider:

 • Using a server (like Heroku or Railway)
 • Implementing proper security
 • Adding monitoring
 • Implementing rate limiting

                                           8. Documentation

Add documentation to your project for future developers.

                                          9. Deployment Setup

Set up a deployment environment with:


 poetry run --build


Then deploy to your chosen platform.

                                                Summary

This structured approach organizes the project into modular components, manages dependencies
effectively, and provides logging and error handling for reliability. By following these steps, you'll
have a maintainable, scalable, and robust project structure.