cyclop/telegram_inference_bot.py

import json
import os
import importlib
import inspect
import tempfile
import base64
import logging
from telegram import Update
from telegram.ext import Application, CommandHandler, MessageHandler, filters, ContextTypes
from openai import OpenAI
from dotenv import load_dotenv
from tools.base_tool import BaseTool

# Load environment variables
load_dotenv()

client = OpenAI()

GPT_4O = "gpt-4o"
GPT_4O_MINI = "gpt-4o-mini"

# Set up logging to console and file
logging.basicConfig(level=logging.INFO, handlers=[
    logging.StreamHandler(),
    logging.FileHandler('logs/output.log', mode='a')
])

# Set up Telegram bot
TELEGRAM_BOT_TOKEN = os.getenv('TELEGRAM_BOT_TOKEN')

# Load system prompt
with open("prompts/developer_prompt.txt", "r") as file:
    system_prompt = file.read().strip()

# Dictionary to store conversation history for each user
conversation_history = {}

# Dictionary to store the last image file for each user
user_images = {}

# Load tools
tools = []
tools_dir = os.path.join(os.path.dirname(__file__), 'tools')
for filename in os.listdir(tools_dir):
    if filename.endswith('.py') and filename != '__init__.py' and filename != 'base_tool.py':
        module_name = f'tools.{filename[:-3]}'
        module = importlib.import_module(module_name)
        for name, obj in inspect.getmembers(module):
            if inspect.isclass(obj) and issubclass(obj, BaseTool) and obj != BaseTool:
                tools.append(obj())

# Collect all function definitions
functions = []
for tool in tools:
    functions.extend(tool.get_functions())

async def start(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
    logging.info("Bot started")
    await update.message.reply_text("Hello! I'm your AI assistant. How can I help you today? You can send me images and then ask questions about them.")

async def clear(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
    user_id = update.effective_user.id
    if user_id in conversation_history:
        del conversation_history[user_id]
    if user_id in user_images:
        os.remove(user_images[user_id])
        del user_images[user_id]
    logging.info(f"Cleared conversation history and image for user {user_id}")
    await update.message.reply_text("Conversation history and image cleared. Let's start fresh!")

async def handle_image(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
    user_id = update.effective_user.id
    
    # Get the largest available photo
    photo = max(update.message.photo, key=lambda x: x.file_size)
    
    # Download the photo
    photo_file = await context.bot.get_file(photo.file_id)
    
    # Create a temporary file to store the image
    with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
        await photo_file.download_to_drive(custom_path=temp_file.name)
        user_images[user_id] = temp_file.name

    logging.info(f"Received image from user {user_id}")
    await update.message.reply_text("I've received your image. What would you like to know about it?")

async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
    try:
        user_id = update.effective_user.id
        user_message = update.message.text

        logging.info(f"Message from user {user_id}: {user_message}")

        # Initialize conversation history for new users
        if user_id not in conversation_history:
            conversation_history[user_id] = []

        # Add user message to conversation history
        conversation_history[user_id].append({"role": "user", "content": user_message})

        # Prepare messages for OpenAI API
        messages = [{"role": "system", "content": system_prompt}] + conversation_history[user_id]

        # Check if there's an image to process
        if user_id in user_images:
            with open(user_images[user_id], "rb") as image_file:
                response = client.chat.completions.create(
                    model=GPT_4O_MINI,
                    messages=[
                        {
                            "role": "user",
                            "content": [
                                {"type": "text", "text": user_message},
                                {
                                    "type": "image_url",
                                    "image_url": {
                                        "url": f"data:image/jpeg;base64,{base64.b64encode(image_file.read()).decode('utf-8')}"
                                    }
                                },
                            ],
                        }
                    ],
                    max_tokens=16384
                )
            # Remove the temporary image file
            os.remove(user_images[user_id])
            del user_images[user_id]
        else:
            # Call OpenAI API for inference (text-only)
            response = get_chat_response(client, messages, 4096, GPT_4O)

        # Extract the assistant's reply
        assistant_message = response.choices[0].message
        toolUseCount = 0
        if hasattr(assistant_message, 'function_call') and assistant_message.function_call:
            while hasattr(assistant_message, 'function_call') and assistant_message.function_call and toolUseCount < 50: # Todo: put amount in env
                tool_response = call_tool(assistant_message.function_call, messages)
                
                conversation_history[user_id].append({"role": "function", "name": assistant_message.function_call.name, "content": json.dumps(tool_response)})
                messages.append({
                    "role": "function",
                    "name": assistant_message.function_call.name,
                    "content": json.dumps(tool_response)
                })

                # Call API again to get the final response
                assistant_message = get_chat_response(client, messages, 4096, GPT_4O).choices[0].message
                if not hasattr(assistant_message, 'function_call') or not assistant_message.function_call:
                    assistant_reply = assistant_message.content
                    conversation_history[user_id].append({"role": "assistant", "content": assistant_reply})
        else:
            assistant_reply = assistant_message.content
            # Add assistant's reply to conversation history
            conversation_history[user_id].append({"role": "assistant", "content": assistant_reply})
        


        # Trim conversation history if it gets too long (e.g., keep last 10 messages)
        if len(conversation_history[user_id]) > 10:
            conversation_history[user_id] = conversation_history[user_id][-10:]

        # Send the reply back to the user
        await update.message.reply_text(assistant_reply)

    except Exception as e:
        logging.error(f"An error occurred: {str(e)}")
        await update.message.reply_text("Sorry, an error occurred while processing your request.")

def call_tool(function_call, messages):
    # Execute the function
    function_name = function_call.name
    function_args = function_call.arguments
    for tool in tools:
        if function_name in [f["name"] for f in tool.get_functions()]:
            return tool.execute(function_name, **eval(function_args))

def get_chat_response(client, messages, max_tokens, model):
    response = client.chat.completions.create(
        model=model,
        messages=messages,
        functions=functions,
        function_call="auto",
        max_tokens=max_tokens
    )
    return response

def main() -> None:
    # Create the Application and pass it your bot's token
    application = Application.builder().token(TELEGRAM_BOT_TOKEN).build()

    # Add handlers
    application.add_handler(CommandHandler("start", start))
    application.add_handler(CommandHandler("clear", clear))
    application.add_handler(MessageHandler(filters.PHOTO, handle_image))
    application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message))

    # Start the Bot
    logging.info("Bot is running...")
    application.run_polling()

if __name__ == '__main__':
    main()