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.