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Add chatgpt bot

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Manuel Vergara 2024-08-17 21:08:24 +02:00
parent 4f2264748e
commit 65468e4115
16 changed files with 1910 additions and 1 deletions
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mongodb

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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# Custom
config/config.yml
config/config.env
docker-compose.dev.yml
mongodb/

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FROM python:3.8-slim
RUN \
set -eux; \
apt-get update; \
DEBIAN_FRONTEND="noninteractive" apt-get install -y --no-install-recommends \
python3-pip \
build-essential \
python3-venv \
ffmpeg \
git \
; \
rm -rf /var/lib/apt/lists/*
RUN pip3 install -U pip && pip3 install -U wheel && pip3 install -U setuptools==59.5.0
COPY ./requirements.txt /tmp/requirements.txt
RUN pip3 install -r /tmp/requirements.txt && rm -r /tmp/requirements.txt
COPY . /code
WORKDIR /code
CMD ["bash"]

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# ChatGPT Telegram Bot: **GPT-4. Rápido. Sin límites diarios. Modos de chat especiales**
> Repositorio original: https://github.com/father-bot/chatgpt_telegram_bot
Todos amamos [chat.openai.com](https://chat.openai.com), pero... Es TERRIBLEMENTE lento, tiene límites diarios, y solo es accesible a través de una interfaz web arcaica.
Este repositorio es ChatGPT recreado como un Bot de Telegram. **Y funciona genial.**
Puedes desplegar tu propio bot.
## Características
- Respuestas con baja latencia (usualmente toma entre 3-5 segundos)
- Sin límites de solicitudes
- Transmisión de mensajes (mira la demo)
- Soporte para GPT-4 y GPT-4 Turbo
- Soporte para GPT-4 Vision
- Soporte para chat en grupo (/help_group_chat para obtener instrucciones)
- DALLE 2 (elige el modo 👩‍🎨 Artista para generar imágenes)
- Reconocimiento de mensajes de voz
- Resaltado de código
- 15 modos de chat especiales: 👩🏼‍🎓 Asistente, 👩🏼‍💻 Asistente de Código, 👩‍🎨 Artista, 🧠 Psicólogo, 🚀 Elon Musk, entre otros. Puedes crear fácilmente tus propios modos de chat editando `config/chat_modes.yml`
- Soporte para [ChatGPT API](https://platform.openai.com/docs/guides/chat/introduction)
- Lista de usuarios de Telegram permitidos
- Seguimiento del balance $ gastado en la API de OpenAI
<p align="center">
<img src="https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExYmM2ZWVjY2M4NWQ3ZThkYmQ3MDhmMTEzZGUwOGFmOThlMDIzZGM4YiZjdD1n/unx907h7GSiLAugzVX/giphy.gif" />
</p>
---
## Comandos del Bot
- `/retry` – Regenerar la última respuesta del bot
- `/new` – Iniciar nuevo diálogo
- `/mode` – Seleccionar modo de chat
- `/balance` – Mostrar balance
- `/settings` – Mostrar configuraciones
- `/help` – Mostrar ayuda
## Configuración
1. Obtén tu clave de [OpenAI API](https://openai.com/api/)
2. Obtén tu token de bot de Telegram desde [@BotFather](https://t.me/BotFather)
3. Edita `config/config.example.yml` para establecer tus tokens y ejecuta los 2 comandos a continuación (*si eres un usuario avanzado, también puedes editar* `config/config.example.env`):
```bash
mv config/config.example.yml config/config.yml
mv config/config.example.env config/config.env
```
4. 🔥 Y ahora **ejecuta**:
```bash
docker-compose --env-file config/config.env up --build
```
## Referencias
1. [*Construye ChatGPT desde GPT-3*](https://learnprompting.org/docs/applied_prompting/build_chatgpt)

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catch-all/06_bots_telegram/08_chatgpt_bot/bot/__init__.py Normal file
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import io
import logging
import asyncio
import traceback
import html
import json
from datetime import datetime
import openai
import telegram
from telegram import (
Update,
User,
InlineKeyboardButton,
InlineKeyboardMarkup,
BotCommand
)
from telegram.ext import (
Application,
ApplicationBuilder,
CallbackContext,
CommandHandler,
MessageHandler,
CallbackQueryHandler,
AIORateLimiter,
filters
)
from telegram.constants import ParseMode, ChatAction
import config
import database
import openai_utils
import base64
# setup
db = database.Database()
logger = logging.getLogger(__name__)
user_semaphores = {}
user_tasks = {}
HELP_MESSAGE = """Commands:
⚪ /retry – Regenerate last bot answer
⚪ /new – Start new dialog
⚪ /mode – Select chat mode
⚪ /settings – Show settings
⚪ /balance – Show balance
⚪ /help – Show help
🎨 Generate images from text prompts in <b>👩‍🎨 Artist</b> /mode
👥 Add bot to <b>group chat</b>: /help_group_chat
🎤 You can send <b>Voice Messages</b> instead of text
"""
HELP_GROUP_CHAT_MESSAGE = """You can add bot to any <b>group chat</b> to help and entertain its participants!
Instructions (see <b>video</b> below):
1. Add the bot to the group chat
2. Make it an <b>admin</b>, so that it can see messages (all other rights can be restricted)
3. You're awesome!
To get a reply from the bot in the chat – @ <b>tag</b> it or <b>reply</b> to its message.
For example: "{bot_username} write a poem about Telegram"
"""
def split_text_into_chunks(text, chunk_size):
for i in range(0, len(text), chunk_size):
yield text[i:i + chunk_size]
async def register_user_if_not_exists(update: Update, context: CallbackContext, user: User):
if not db.check_if_user_exists(user.id):
db.add_new_user(
user.id,
update.message.chat_id,
username=user.username,
first_name=user.first_name,
last_name= user.last_name
)
db.start_new_dialog(user.id)
if db.get_user_attribute(user.id, "current_dialog_id") is None:
db.start_new_dialog(user.id)
if user.id not in user_semaphores:
user_semaphores[user.id] = asyncio.Semaphore(1)
if db.get_user_attribute(user.id, "current_model") is None:
db.set_user_attribute(user.id, "current_model", config.models["available_text_models"][0])
# back compatibility for n_used_tokens field
n_used_tokens = db.get_user_attribute(user.id, "n_used_tokens")
if isinstance(n_used_tokens, int) or isinstance(n_used_tokens, float): # old format
new_n_used_tokens = {
"gpt-3.5-turbo": {
"n_input_tokens": 0,
"n_output_tokens": n_used_tokens
}
}
db.set_user_attribute(user.id, "n_used_tokens", new_n_used_tokens)
# voice message transcription
if db.get_user_attribute(user.id, "n_transcribed_seconds") is None:
db.set_user_attribute(user.id, "n_transcribed_seconds", 0.0)
# image generation
if db.get_user_attribute(user.id, "n_generated_images") is None:
db.set_user_attribute(user.id, "n_generated_images", 0)
async def is_bot_mentioned(update: Update, context: CallbackContext):
try:
message = update.message
if message.chat.type == "private":
return True
if message.text is not None and ("@" + context.bot.username) in message.text:
return True
if message.reply_to_message is not None:
if message.reply_to_message.from_user.id == context.bot.id:
return True
except:
return True
else:
return False
async def start_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update, context, update.message.from_user)
user_id = update.message.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
db.start_new_dialog(user_id)
reply_text = "Hi! I'm <b>ChatGPT</b> bot implemented with OpenAI API 🤖\n\n"
reply_text += HELP_MESSAGE
await update.message.reply_text(reply_text, parse_mode=ParseMode.HTML)
await show_chat_modes_handle(update, context)
async def help_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update, context, update.message.from_user)
user_id = update.message.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
await update.message.reply_text(HELP_MESSAGE, parse_mode=ParseMode.HTML)
async def help_group_chat_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update, context, update.message.from_user)
user_id = update.message.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
text = HELP_GROUP_CHAT_MESSAGE.format(bot_username="@" + context.bot.username)
await update.message.reply_text(text, parse_mode=ParseMode.HTML)
await update.message.reply_video(config.help_group_chat_video_path)
async def retry_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update, context, update.message.from_user)
if await is_previous_message_not_answered_yet(update, context): return
user_id = update.message.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
dialog_messages = db.get_dialog_messages(user_id, dialog_id=None)
if len(dialog_messages) == 0:
await update.message.reply_text("No message to retry 🤷‍♂️")
return
last_dialog_message = dialog_messages.pop()
db.set_dialog_messages(user_id, dialog_messages, dialog_id=None) # last message was removed from the context
await message_handle(update, context, message=last_dialog_message["user"], use_new_dialog_timeout=False)
async def _vision_message_handle_fn(
update: Update, context: CallbackContext, use_new_dialog_timeout: bool = True
):
logger.info('_vision_message_handle_fn')
user_id = update.message.from_user.id
current_model = db.get_user_attribute(user_id, "current_model")
if current_model != "gpt-4-vision-preview" and current_model != "gpt-4o":
await update.message.reply_text(
"🥲 Images processing is only available for <b>gpt-4-vision-preview</b> and <b>gpt-4o</b> model. Please change your settings in /settings",
parse_mode=ParseMode.HTML,
)
return
chat_mode = db.get_user_attribute(user_id, "current_chat_mode")
# new dialog timeout
if use_new_dialog_timeout:
if (datetime.now() - db.get_user_attribute(user_id, "last_interaction")).seconds > config.new_dialog_timeout and len(db.get_dialog_messages(user_id)) > 0:
db.start_new_dialog(user_id)
await update.message.reply_text(f"Starting new dialog due to timeout (<b>{config.chat_modes[chat_mode]['name']}</b> mode) ✅", parse_mode=ParseMode.HTML)
db.set_user_attribute(user_id, "last_interaction", datetime.now())
buf = None
if update.message.effective_attachment:
photo = update.message.effective_attachment[-1]
photo_file = await context.bot.get_file(photo.file_id)
# store file in memory, not on disk
buf = io.BytesIO()
await photo_file.download_to_memory(buf)
buf.name = "image.jpg" # file extension is required
buf.seek(0) # move cursor to the beginning of the buffer
# in case of CancelledError
n_input_tokens, n_output_tokens = 0, 0
try:
# send placeholder message to user
placeholder_message = await update.message.reply_text("...")
message = update.message.caption or update.message.text or ''
# send typing action
await update.message.chat.send_action(action="typing")
dialog_messages = db.get_dialog_messages(user_id, dialog_id=None)
parse_mode = {"html": ParseMode.HTML, "markdown": ParseMode.MARKDOWN}[
config.chat_modes[chat_mode]["parse_mode"]
]
chatgpt_instance = openai_utils.ChatGPT(model=current_model)
if config.enable_message_streaming:
gen = chatgpt_instance.send_vision_message_stream(
message,
dialog_messages=dialog_messages,
image_buffer=buf,
chat_mode=chat_mode,
)
else:
(
answer,
(n_input_tokens, n_output_tokens),
n_first_dialog_messages_removed,
) = await chatgpt_instance.send_vision_message(
message,
dialog_messages=dialog_messages,
image_buffer=buf,
chat_mode=chat_mode,
)
async def fake_gen():
yield "finished", answer, (
n_input_tokens,
n_output_tokens,
), n_first_dialog_messages_removed
gen = fake_gen()
prev_answer = ""
async for gen_item in gen:
(
status,
answer,
(n_input_tokens, n_output_tokens),
n_first_dialog_messages_removed,
) = gen_item
answer = answer[:4096] # telegram message limit
# update only when 100 new symbols are ready
if abs(len(answer) - len(prev_answer)) < 100 and status != "finished":
continue
try:
await context.bot.edit_message_text(
answer,
chat_id=placeholder_message.chat_id,
message_id=placeholder_message.message_id,
parse_mode=parse_mode,
)
except telegram.error.BadRequest as e:
if str(e).startswith("Message is not modified"):
continue
else:
await context.bot.edit_message_text(
answer,
chat_id=placeholder_message.chat_id,
message_id=placeholder_message.message_id,
)
await asyncio.sleep(0.01) # wait a bit to avoid flooding
prev_answer = answer
# update user data
if buf is not None:
base_image = base64.b64encode(buf.getvalue()).decode("utf-8")
new_dialog_message = {"user": [
{
"type": "text",
"text": message,
},
{
"type": "image",
"image": base_image,
}
]
, "bot": answer, "date": datetime.now()}
else:
new_dialog_message = {"user": [{"type": "text", "text": message}], "bot": answer, "date": datetime.now()}
db.set_dialog_messages(
user_id,
db.get_dialog_messages(user_id, dialog_id=None) + [new_dialog_message],
dialog_id=None
)
db.update_n_used_tokens(user_id, current_model, n_input_tokens, n_output_tokens)
except asyncio.CancelledError:
# note: intermediate token updates only work when enable_message_streaming=True (config.yml)
db.update_n_used_tokens(user_id, current_model, n_input_tokens, n_output_tokens)
raise
except Exception as e:
error_text = f"Something went wrong during completion. Reason: {e}"
logger.error(error_text)
await update.message.reply_text(error_text)
return
async def unsupport_message_handle(update: Update, context: CallbackContext, message=None):
error_text = f"I don't know how to read files or videos. Send the picture in normal mode (Quick Mode)."
logger.error(error_text)
await update.message.reply_text(error_text)
return
async def message_handle(update: Update, context: CallbackContext, message=None, use_new_dialog_timeout=True):
# check if bot was mentioned (for group chats)
if not await is_bot_mentioned(update, context):
return
# check if message is edited
if update.edited_message is not None:
await edited_message_handle(update, context)
return
_message = message or update.message.text
# remove bot mention (in group chats)
if update.message.chat.type != "private":
_message = _message.replace("@" + context.bot.username, "").strip()
await register_user_if_not_exists(update, context, update.message.from_user)
if await is_previous_message_not_answered_yet(update, context): return
user_id = update.message.from_user.id
chat_mode = db.get_user_attribute(user_id, "current_chat_mode")
if chat_mode == "artist":
await generate_image_handle(update, context, message=message)
return
current_model = db.get_user_attribute(user_id, "current_model")
async def message_handle_fn():
# new dialog timeout
if use_new_dialog_timeout:
if (datetime.now() - db.get_user_attribute(user_id, "last_interaction")).seconds > config.new_dialog_timeout and len(db.get_dialog_messages(user_id)) > 0:
db.start_new_dialog(user_id)
await update.message.reply_text(f"Starting new dialog due to timeout (<b>{config.chat_modes[chat_mode]['name']}</b> mode) ✅", parse_mode=ParseMode.HTML)
db.set_user_attribute(user_id, "last_interaction", datetime.now())
# in case of CancelledError
n_input_tokens, n_output_tokens = 0, 0
try:
# send placeholder message to user
placeholder_message = await update.message.reply_text("...")
# send typing action
await update.message.chat.send_action(action="typing")
if _message is None or len(_message) == 0:
await update.message.reply_text("🥲 You sent <b>empty message</b>. Please, try again!", parse_mode=ParseMode.HTML)
return
dialog_messages = db.get_dialog_messages(user_id, dialog_id=None)
parse_mode = {
"html": ParseMode.HTML,
"markdown": ParseMode.MARKDOWN
}[config.chat_modes[chat_mode]["parse_mode"]]
chatgpt_instance = openai_utils.ChatGPT(model=current_model)
if config.enable_message_streaming:
gen = chatgpt_instance.send_message_stream(_message, dialog_messages=dialog_messages, chat_mode=chat_mode)
else:
answer, (n_input_tokens, n_output_tokens), n_first_dialog_messages_removed = await chatgpt_instance.send_message(
_message,
dialog_messages=dialog_messages,
chat_mode=chat_mode
)
async def fake_gen():
yield "finished", answer, (n_input_tokens, n_output_tokens), n_first_dialog_messages_removed
gen = fake_gen()
prev_answer = ""
async for gen_item in gen:
status, answer, (n_input_tokens, n_output_tokens), n_first_dialog_messages_removed = gen_item
answer = answer[:4096] # telegram message limit
# update only when 100 new symbols are ready
if abs(len(answer) - len(prev_answer)) < 100 and status != "finished":
continue
try:
await context.bot.edit_message_text(answer, chat_id=placeholder_message.chat_id, message_id=placeholder_message.message_id, parse_mode=parse_mode)
except telegram.error.BadRequest as e:
if str(e).startswith("Message is not modified"):
continue
else:
await context.bot.edit_message_text(answer, chat_id=placeholder_message.chat_id, message_id=placeholder_message.message_id)
await asyncio.sleep(0.01) # wait a bit to avoid flooding
prev_answer = answer
# update user data
new_dialog_message = {"user": [{"type": "text", "text": _message}], "bot": answer, "date": datetime.now()}
db.set_dialog_messages(
user_id,
db.get_dialog_messages(user_id, dialog_id=None) + [new_dialog_message],
dialog_id=None
)
db.update_n_used_tokens(user_id, current_model, n_input_tokens, n_output_tokens)
except asyncio.CancelledError:
# note: intermediate token updates only work when enable_message_streaming=True (config.yml)
db.update_n_used_tokens(user_id, current_model, n_input_tokens, n_output_tokens)
raise
except Exception as e:
error_text = f"Something went wrong during completion. Reason: {e}"
logger.error(error_text)
await update.message.reply_text(error_text)
return
# send message if some messages were removed from the context
if n_first_dialog_messages_removed > 0:
if n_first_dialog_messages_removed == 1:
text = "✍️ <i>Note:</i> Your current dialog is too long, so your <b>first message</b> was removed from the context.\n Send /new command to start new dialog"
else:
text = f"✍️ <i>Note:</i> Your current dialog is too long, so <b>{n_first_dialog_messages_removed} first messages</b> were removed from the context.\n Send /new command to start new dialog"
await update.message.reply_text(text, parse_mode=ParseMode.HTML)
async with user_semaphores[user_id]:
if current_model == "gpt-4-vision-preview" or current_model == "gpt-4o" or update.message.photo is not None and len(update.message.photo) > 0:
logger.error(current_model)
# What is this? ^^^
if current_model != "gpt-4o" and current_model != "gpt-4-vision-preview":
current_model = "gpt-4o"
db.set_user_attribute(user_id, "current_model", "gpt-4o")
task = asyncio.create_task(
_vision_message_handle_fn(update, context, use_new_dialog_timeout=use_new_dialog_timeout)
)
else:
task = asyncio.create_task(
message_handle_fn()
)
user_tasks[user_id] = task
try:
await task
except asyncio.CancelledError:
await update.message.reply_text("✅ Canceled", parse_mode=ParseMode.HTML)
else:
pass
finally:
if user_id in user_tasks:
del user_tasks[user_id]
async def is_previous_message_not_answered_yet(update: Update, context: CallbackContext):
await register_user_if_not_exists(update, context, update.message.from_user)
user_id = update.message.from_user.id
if user_semaphores[user_id].locked():
text = "⏳ Please <b>wait</b> for a reply to the previous message\n"
text += "Or you can /cancel it"
await update.message.reply_text(text, reply_to_message_id=update.message.id, parse_mode=ParseMode.HTML)
return True
else:
return False
async def voice_message_handle(update: Update, context: CallbackContext):
# check if bot was mentioned (for group chats)
if not await is_bot_mentioned(update, context):
return
await register_user_if_not_exists(update, context, update.message.from_user)
if await is_previous_message_not_answered_yet(update, context): return
user_id = update.message.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
voice = update.message.voice
voice_file = await context.bot.get_file(voice.file_id)
# store file in memory, not on disk
buf = io.BytesIO()
await voice_file.download_to_memory(buf)
buf.name = "voice.oga" # file extension is required
buf.seek(0) # move cursor to the beginning of the buffer
transcribed_text = await openai_utils.transcribe_audio(buf)
text = f"🎤: <i>{transcribed_text}</i>"
await update.message.reply_text(text, parse_mode=ParseMode.HTML)
# update n_transcribed_seconds
db.set_user_attribute(user_id, "n_transcribed_seconds", voice.duration + db.get_user_attribute(user_id, "n_transcribed_seconds"))
await message_handle(update, context, message=transcribed_text)
async def generate_image_handle(update: Update, context: CallbackContext, message=None):
await register_user_if_not_exists(update, context, update.message.from_user)
if await is_previous_message_not_answered_yet(update, context): return
user_id = update.message.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
await update.message.chat.send_action(action="upload_photo")
message = message or update.message.text
try:
image_urls = await openai_utils.generate_images(message, n_images=config.return_n_generated_images, size=config.image_size)
except openai.error.InvalidRequestError as e:
if str(e).startswith("Your request was rejected as a result of our safety system"):
text = "🥲 Your request <b>doesn't comply</b> with OpenAI's usage policies.\nWhat did you write there, huh?"
await update.message.reply_text(text, parse_mode=ParseMode.HTML)
return
else:
raise
# token usage
db.set_user_attribute(user_id, "n_generated_images", config.return_n_generated_images + db.get_user_attribute(user_id, "n_generated_images"))
for i, image_url in enumerate(image_urls):
await update.message.chat.send_action(action="upload_photo")
await update.message.reply_photo(image_url, parse_mode=ParseMode.HTML)
async def new_dialog_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update, context, update.message.from_user)
if await is_previous_message_not_answered_yet(update, context): return
user_id = update.message.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
db.set_user_attribute(user_id, "current_model", "gpt-3.5-turbo")
db.start_new_dialog(user_id)
await update.message.reply_text("Starting new dialog ✅")
chat_mode = db.get_user_attribute(user_id, "current_chat_mode")
await update.message.reply_text(f"{config.chat_modes[chat_mode]['welcome_message']}", parse_mode=ParseMode.HTML)
async def cancel_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update, context, update.message.from_user)
user_id = update.message.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
if user_id in user_tasks:
task = user_tasks[user_id]
task.cancel()
else:
await update.message.reply_text("<i>Nothing to cancel...</i>", parse_mode=ParseMode.HTML)
def get_chat_mode_menu(page_index: int):
n_chat_modes_per_page = config.n_chat_modes_per_page
text = f"Select <b>chat mode</b> ({len(config.chat_modes)} modes available):"
# buttons
chat_mode_keys = list(config.chat_modes.keys())
page_chat_mode_keys = chat_mode_keys[page_index * n_chat_modes_per_page:(page_index + 1) * n_chat_modes_per_page]
keyboard = []
for chat_mode_key in page_chat_mode_keys:
name = config.chat_modes[chat_mode_key]["name"]
keyboard.append([InlineKeyboardButton(name, callback_data=f"set_chat_mode|{chat_mode_key}")])
# pagination
if len(chat_mode_keys) > n_chat_modes_per_page:
is_first_page = (page_index == 0)
is_last_page = ((page_index + 1) * n_chat_modes_per_page >= len(chat_mode_keys))
if is_first_page:
keyboard.append([
InlineKeyboardButton("»", callback_data=f"show_chat_modes|{page_index + 1}")
])
elif is_last_page:
keyboard.append([
InlineKeyboardButton("«", callback_data=f"show_chat_modes|{page_index - 1}"),
])
else:
keyboard.append([
InlineKeyboardButton("«", callback_data=f"show_chat_modes|{page_index - 1}"),
InlineKeyboardButton("»", callback_data=f"show_chat_modes|{page_index + 1}")
])
reply_markup = InlineKeyboardMarkup(keyboard)
return text, reply_markup
async def show_chat_modes_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update, context, update.message.from_user)
if await is_previous_message_not_answered_yet(update, context): return
user_id = update.message.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
text, reply_markup = get_chat_mode_menu(0)
await update.message.reply_text(text, reply_markup=reply_markup, parse_mode=ParseMode.HTML)
async def show_chat_modes_callback_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update.callback_query, context, update.callback_query.from_user)
if await is_previous_message_not_answered_yet(update.callback_query, context): return
user_id = update.callback_query.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
query = update.callback_query
await query.answer()
page_index = int(query.data.split("|")[1])
if page_index < 0:
return
text, reply_markup = get_chat_mode_menu(page_index)
try:
await query.edit_message_text(text, reply_markup=reply_markup, parse_mode=ParseMode.HTML)
except telegram.error.BadRequest as e:
if str(e).startswith("Message is not modified"):
pass
async def set_chat_mode_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update.callback_query, context, update.callback_query.from_user)
user_id = update.callback_query.from_user.id
query = update.callback_query
await query.answer()
chat_mode = query.data.split("|")[1]
db.set_user_attribute(user_id, "current_chat_mode", chat_mode)
db.start_new_dialog(user_id)
await context.bot.send_message(
update.callback_query.message.chat.id,
f"{config.chat_modes[chat_mode]['welcome_message']}",
parse_mode=ParseMode.HTML
)
def get_settings_menu(user_id: int):
current_model = db.get_user_attribute(user_id, "current_model")
text = config.models["info"][current_model]["description"]
text += "\n\n"
score_dict = config.models["info"][current_model]["scores"]
for score_key, score_value in score_dict.items():
text += "🟢" * score_value + "⚪️" * (5 - score_value) + f" – {score_key}\n\n"
text += "\nSelect <b>model</b>:"
# buttons to choose models
buttons = []
for model_key in config.models["available_text_models"]:
title = config.models["info"][model_key]["name"]
if model_key == current_model:
title = "✅ " + title
buttons.append(
InlineKeyboardButton(title, callback_data=f"set_settings|{model_key}")
)
reply_markup = InlineKeyboardMarkup([buttons])
return text, reply_markup
async def settings_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update, context, update.message.from_user)
if await is_previous_message_not_answered_yet(update, context): return
user_id = update.message.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
text, reply_markup = get_settings_menu(user_id)
await update.message.reply_text(text, reply_markup=reply_markup, parse_mode=ParseMode.HTML)
async def set_settings_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update.callback_query, context, update.callback_query.from_user)
user_id = update.callback_query.from_user.id
query = update.callback_query
await query.answer()
_, model_key = query.data.split("|")
db.set_user_attribute(user_id, "current_model", model_key)
db.start_new_dialog(user_id)
text, reply_markup = get_settings_menu(user_id)
try:
await query.edit_message_text(text, reply_markup=reply_markup, parse_mode=ParseMode.HTML)
except telegram.error.BadRequest as e:
if str(e).startswith("Message is not modified"):
pass
async def show_balance_handle(update: Update, context: CallbackContext):
await register_user_if_not_exists(update, context, update.message.from_user)
user_id = update.message.from_user.id
db.set_user_attribute(user_id, "last_interaction", datetime.now())
# count total usage statistics
total_n_spent_dollars = 0
total_n_used_tokens = 0
n_used_tokens_dict = db.get_user_attribute(user_id, "n_used_tokens")
n_generated_images = db.get_user_attribute(user_id, "n_generated_images")
n_transcribed_seconds = db.get_user_attribute(user_id, "n_transcribed_seconds")
details_text = "🏷️ Details:\n"
for model_key in sorted(n_used_tokens_dict.keys()):
n_input_tokens, n_output_tokens = n_used_tokens_dict[model_key]["n_input_tokens"], n_used_tokens_dict[model_key]["n_output_tokens"]
total_n_used_tokens += n_input_tokens + n_output_tokens
n_input_spent_dollars = config.models["info"][model_key]["price_per_1000_input_tokens"] * (n_input_tokens / 1000)
n_output_spent_dollars = config.models["info"][model_key]["price_per_1000_output_tokens"] * (n_output_tokens / 1000)
total_n_spent_dollars += n_input_spent_dollars + n_output_spent_dollars
details_text += f"- {model_key}: <b>{n_input_spent_dollars + n_output_spent_dollars:.03f}$</b> / <b>{n_input_tokens + n_output_tokens} tokens</b>\n"
# image generation
image_generation_n_spent_dollars = config.models["info"]["dalle-2"]["price_per_1_image"] * n_generated_images
if n_generated_images != 0:
details_text += f"- DALL·E 2 (image generation): <b>{image_generation_n_spent_dollars:.03f}$</b> / <b>{n_generated_images} generated images</b>\n"
total_n_spent_dollars += image_generation_n_spent_dollars
# voice recognition
voice_recognition_n_spent_dollars = config.models["info"]["whisper"]["price_per_1_min"] * (n_transcribed_seconds / 60)
if n_transcribed_seconds != 0:
details_text += f"- Whisper (voice recognition): <b>{voice_recognition_n_spent_dollars:.03f}$</b> / <b>{n_transcribed_seconds:.01f} seconds</b>\n"
total_n_spent_dollars += voice_recognition_n_spent_dollars
text = f"You spent <b>{total_n_spent_dollars:.03f}$</b>\n"
text += f"You used <b>{total_n_used_tokens}</b> tokens\n\n"
text += details_text
await update.message.reply_text(text, parse_mode=ParseMode.HTML)
async def edited_message_handle(update: Update, context: CallbackContext):
if update.edited_message.chat.type == "private":
text = "🥲 Unfortunately, message <b>editing</b> is not supported"
await update.edited_message.reply_text(text, parse_mode=ParseMode.HTML)
async def error_handle(update: Update, context: CallbackContext) -> None:
logger.error(msg="Exception while handling an update:", exc_info=context.error)
try:
# collect error message
tb_list = traceback.format_exception(None, context.error, context.error.__traceback__)
tb_string = "".join(tb_list)
update_str = update.to_dict() if isinstance(update, Update) else str(update)
message = (
f"An exception was raised while handling an update\n"
f"<pre>update = {html.escape(json.dumps(update_str, indent=2, ensure_ascii=False))}"
"</pre>\n\n"
f"<pre>{html.escape(tb_string)}</pre>"
)
# split text into multiple messages due to 4096 character limit
for message_chunk in split_text_into_chunks(message, 4096):
try:
await context.bot.send_message(update.effective_chat.id, message_chunk, parse_mode=ParseMode.HTML)
except telegram.error.BadRequest:
# answer has invalid characters, so we send it without parse_mode
await context.bot.send_message(update.effective_chat.id, message_chunk)
except:
await context.bot.send_message(update.effective_chat.id, "Some error in error handler")
async def post_init(application: Application):
await application.bot.set_my_commands([
BotCommand("/new", "Start new dialog"),
BotCommand("/mode", "Select chat mode"),
BotCommand("/retry", "Re-generate response for previous query"),
BotCommand("/balance", "Show balance"),
BotCommand("/settings", "Show settings"),
BotCommand("/help", "Show help message"),
])
def run_bot() -> None:
application = (
ApplicationBuilder()
.token(config.telegram_token)
.concurrent_updates(True)
.rate_limiter(AIORateLimiter(max_retries=5))
.http_version("1.1")
.get_updates_http_version("1.1")
.post_init(post_init)
.build()
)
# add handlers
user_filter = filters.ALL
if len(config.allowed_telegram_usernames) > 0:
usernames = [x for x in config.allowed_telegram_usernames if isinstance(x, str)]
any_ids = [x for x in config.allowed_telegram_usernames if isinstance(x, int)]
user_ids = [x for x in any_ids if x > 0]
group_ids = [x for x in any_ids if x < 0]
user_filter = filters.User(username=usernames) | filters.User(user_id=user_ids) | filters.Chat(chat_id=group_ids)
application.add_handler(CommandHandler("start", start_handle, filters=user_filter))
application.add_handler(CommandHandler("help", help_handle, filters=user_filter))
application.add_handler(CommandHandler("help_group_chat", help_group_chat_handle, filters=user_filter))
application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND & user_filter, message_handle))
application.add_handler(MessageHandler(filters.PHOTO & ~filters.COMMAND & user_filter, message_handle))
application.add_handler(MessageHandler(filters.VIDEO & ~filters.COMMAND & user_filter, unsupport_message_handle))
application.add_handler(MessageHandler(filters.Document.ALL & ~filters.COMMAND & user_filter, unsupport_message_handle))
application.add_handler(CommandHandler("retry", retry_handle, filters=user_filter))
application.add_handler(CommandHandler("new", new_dialog_handle, filters=user_filter))
application.add_handler(CommandHandler("cancel", cancel_handle, filters=user_filter))
application.add_handler(MessageHandler(filters.VOICE & user_filter, voice_message_handle))
application.add_handler(CommandHandler("mode", show_chat_modes_handle, filters=user_filter))
application.add_handler(CallbackQueryHandler(show_chat_modes_callback_handle, pattern="^show_chat_modes"))
application.add_handler(CallbackQueryHandler(set_chat_mode_handle, pattern="^set_chat_mode"))
application.add_handler(CommandHandler("settings", settings_handle, filters=user_filter))
application.add_handler(CallbackQueryHandler(set_settings_handle, pattern="^set_settings"))
application.add_handler(CommandHandler("balance", show_balance_handle, filters=user_filter))
application.add_error_handler(error_handle)
# start the bot
application.run_polling()
if __name__ == "__main__":
run_bot()

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import yaml
import dotenv
from pathlib import Path
config_dir = Path(__file__).parent.parent.resolve() / "config"
# load yaml config
with open(config_dir / "config.yml", 'r') as f:
config_yaml = yaml.safe_load(f)
# load .env config
config_env = dotenv.dotenv_values(config_dir / "config.env")
# config parameters
telegram_token = config_yaml["telegram_token"]
openai_api_key = config_yaml["openai_api_key"]
openai_api_base = config_yaml.get("openai_api_base", None)
allowed_telegram_usernames = config_yaml["allowed_telegram_usernames"]
new_dialog_timeout = config_yaml["new_dialog_timeout"]
enable_message_streaming = config_yaml.get("enable_message_streaming", True)
return_n_generated_images = config_yaml.get("return_n_generated_images", 1)
image_size = config_yaml.get("image_size", "512x512")
n_chat_modes_per_page = config_yaml.get("n_chat_modes_per_page", 5)
mongodb_uri = f"mongodb://mongo:{config_env['MONGODB_PORT']}"
# chat_modes
with open(config_dir / "chat_modes.yml", 'r') as f:
chat_modes = yaml.safe_load(f)
# models
with open(config_dir / "models.yml", 'r') as f:
models = yaml.safe_load(f)
# files
help_group_chat_video_path = Path(__file__).parent.parent.resolve() / "static" / "help_group_chat.mp4"

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from typing import Optional, Any
import pymongo
import uuid
from datetime import datetime
import config
class Database:
def __init__(self):
self.client = pymongo.MongoClient(config.mongodb_uri)
self.db = self.client["chatgpt_telegram_bot"]
self.user_collection = self.db["user"]
self.dialog_collection = self.db["dialog"]
def check_if_user_exists(self, user_id: int, raise_exception: bool = False):
if self.user_collection.count_documents({"_id": user_id}) > 0:
return True
else:
if raise_exception:
raise ValueError(f"User {user_id} does not exist")
else:
return False
def add_new_user(
self,
user_id: int,
chat_id: int,
username: str = "",
first_name: str = "",
last_name: str = "",
):
user_dict = {
"_id": user_id,
"chat_id": chat_id,
"username": username,
"first_name": first_name,
"last_name": last_name,
"last_interaction": datetime.now(),
"first_seen": datetime.now(),
"current_dialog_id": None,
"current_chat_mode": "assistant",
"current_model": config.models["available_text_models"][0],
"n_used_tokens": {},
"n_generated_images": 0,
"n_transcribed_seconds": 0.0 # voice message transcription
}
if not self.check_if_user_exists(user_id):
self.user_collection.insert_one(user_dict)
def start_new_dialog(self, user_id: int):
self.check_if_user_exists(user_id, raise_exception=True)
dialog_id = str(uuid.uuid4())
dialog_dict = {
"_id": dialog_id,
"user_id": user_id,
"chat_mode": self.get_user_attribute(user_id, "current_chat_mode"),
"start_time": datetime.now(),
"model": self.get_user_attribute(user_id, "current_model"),
"messages": []
}
# add new dialog
self.dialog_collection.insert_one(dialog_dict)
# update user's current dialog
self.user_collection.update_one(
{"_id": user_id},
{"$set": {"current_dialog_id": dialog_id}}
)
return dialog_id
def get_user_attribute(self, user_id: int, key: str):
self.check_if_user_exists(user_id, raise_exception=True)
user_dict = self.user_collection.find_one({"_id": user_id})
if key not in user_dict:
return None
return user_dict[key]
def set_user_attribute(self, user_id: int, key: str, value: Any):
self.check_if_user_exists(user_id, raise_exception=True)
self.user_collection.update_one({"_id": user_id}, {"$set": {key: value}})
def update_n_used_tokens(self, user_id: int, model: str, n_input_tokens: int, n_output_tokens: int):
n_used_tokens_dict = self.get_user_attribute(user_id, "n_used_tokens")
if model in n_used_tokens_dict:
n_used_tokens_dict[model]["n_input_tokens"] += n_input_tokens
n_used_tokens_dict[model]["n_output_tokens"] += n_output_tokens
else:
n_used_tokens_dict[model] = {
"n_input_tokens": n_input_tokens,
"n_output_tokens": n_output_tokens
}
self.set_user_attribute(user_id, "n_used_tokens", n_used_tokens_dict)
def get_dialog_messages(self, user_id: int, dialog_id: Optional[str] = None):
self.check_if_user_exists(user_id, raise_exception=True)
if dialog_id is None:
dialog_id = self.get_user_attribute(user_id, "current_dialog_id")
dialog_dict = self.dialog_collection.find_one({"_id": dialog_id, "user_id": user_id})
return dialog_dict["messages"]
def set_dialog_messages(self, user_id: int, dialog_messages: list, dialog_id: Optional[str] = None):
self.check_if_user_exists(user_id, raise_exception=True)
if dialog_id is None:
dialog_id = self.get_user_attribute(user_id, "current_dialog_id")
self.dialog_collection.update_one(
{"_id": dialog_id, "user_id": user_id},
{"$set": {"messages": dialog_messages}}
)

364
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import base64
from io import BytesIO
import config
import logging
import tiktoken
import openai
# setup openai
openai.api_key = config.openai_api_key
if config.openai_api_base is not None:
openai.api_base = config.openai_api_base
logger = logging.getLogger(__name__)
OPENAI_COMPLETION_OPTIONS = {
"temperature": 0.7,
"max_tokens": 1000,
"top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"request_timeout": 60.0,
}
class ChatGPT:
def __init__(self, model="gpt-3.5-turbo"):
assert model in {"text-davinci-003", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "gpt-4", "gpt-4o", "gpt-4-1106-preview", "gpt-4-vision-preview"}, f"Unknown model: {model}"
self.model = model
async def send_message(self, message, dialog_messages=[], chat_mode="assistant"):
if chat_mode not in config.chat_modes.keys():
raise ValueError(f"Chat mode {chat_mode} is not supported")
n_dialog_messages_before = len(dialog_messages)
answer = None
while answer is None:
try:
if self.model in {"gpt-3.5-turbo-16k", "gpt-3.5-turbo", "gpt-4", "gpt-4o", "gpt-4-1106-preview", "gpt-4-vision-preview"}:
messages = self._generate_prompt_messages(message, dialog_messages, chat_mode)
r = await openai.ChatCompletion.acreate(
model=self.model,
messages=messages,
**OPENAI_COMPLETION_OPTIONS
)
answer = r.choices[0].message["content"]
elif self.model == "text-davinci-003":
prompt = self._generate_prompt(message, dialog_messages, chat_mode)
r = await openai.Completion.acreate(
engine=self.model,
prompt=prompt,
**OPENAI_COMPLETION_OPTIONS
)
answer = r.choices[0].text
else:
raise ValueError(f"Unknown model: {self.model}")
answer = self._postprocess_answer(answer)
n_input_tokens, n_output_tokens = r.usage.prompt_tokens, r.usage.completion_tokens
except openai.error.InvalidRequestError as e: # too many tokens
if len(dialog_messages) == 0:
raise ValueError("Dialog messages is reduced to zero, but still has too many tokens to make completion") from e
# forget first message in dialog_messages
dialog_messages = dialog_messages[1:]
n_first_dialog_messages_removed = n_dialog_messages_before - len(dialog_messages)
return answer, (n_input_tokens, n_output_tokens), n_first_dialog_messages_removed
async def send_message_stream(self, message, dialog_messages=[], chat_mode="assistant"):
if chat_mode not in config.chat_modes.keys():
raise ValueError(f"Chat mode {chat_mode} is not supported")
n_dialog_messages_before = len(dialog_messages)
answer = None
while answer is None:
try:
if self.model in {"gpt-3.5-turbo-16k", "gpt-3.5-turbo", "gpt-4","gpt-4o", "gpt-4-1106-preview"}:
messages = self._generate_prompt_messages(message, dialog_messages, chat_mode)
r_gen = await openai.ChatCompletion.acreate(
model=self.model,
messages=messages,
stream=True,
**OPENAI_COMPLETION_OPTIONS
)
answer = ""
async for r_item in r_gen:
delta = r_item.choices[0].delta
if "content" in delta:
answer += delta.content
n_input_tokens, n_output_tokens = self._count_tokens_from_messages(messages, answer, model=self.model)
n_first_dialog_messages_removed = 0
yield "not_finished", answer, (n_input_tokens, n_output_tokens), n_first_dialog_messages_removed
elif self.model == "text-davinci-003":
prompt = self._generate_prompt(message, dialog_messages, chat_mode)
r_gen = await openai.Completion.acreate(
engine=self.model,
prompt=prompt,
stream=True,
**OPENAI_COMPLETION_OPTIONS
)
answer = ""
async for r_item in r_gen:
answer += r_item.choices[0].text
n_input_tokens, n_output_tokens = self._count_tokens_from_prompt(prompt, answer, model=self.model)
n_first_dialog_messages_removed = n_dialog_messages_before - len(dialog_messages)
yield "not_finished", answer, (n_input_tokens, n_output_tokens), n_first_dialog_messages_removed
answer = self._postprocess_answer(answer)
except openai.error.InvalidRequestError as e: # too many tokens
if len(dialog_messages) == 0:
raise e
# forget first message in dialog_messages
dialog_messages = dialog_messages[1:]
yield "finished", answer, (n_input_tokens, n_output_tokens), n_first_dialog_messages_removed # sending final answer
async def send_vision_message(
self,
message,
dialog_messages=[],
chat_mode="assistant",
image_buffer: BytesIO = None,
):
n_dialog_messages_before = len(dialog_messages)
answer = None
while answer is None:
try:
if self.model == "gpt-4-vision-preview" or self.model == "gpt-4o":
messages = self._generate_prompt_messages(
message, dialog_messages, chat_mode, image_buffer
)
r = await openai.ChatCompletion.acreate(
model=self.model,
messages=messages,
**OPENAI_COMPLETION_OPTIONS
)
answer = r.choices[0].message.content
else:
raise ValueError(f"Unsupported model: {self.model}")
answer = self._postprocess_answer(answer)
n_input_tokens, n_output_tokens = (
r.usage.prompt_tokens,
r.usage.completion_tokens,
)
except openai.error.InvalidRequestError as e: # too many tokens
if len(dialog_messages) == 0:
raise ValueError(
"Dialog messages is reduced to zero, but still has too many tokens to make completion"
) from e
# forget first message in dialog_messages
dialog_messages = dialog_messages[1:]
n_first_dialog_messages_removed = n_dialog_messages_before - len(
dialog_messages
)
return (
answer,
(n_input_tokens, n_output_tokens),
n_first_dialog_messages_removed,
)
async def send_vision_message_stream(
self,
message,
dialog_messages=[],
chat_mode="assistant",
image_buffer: BytesIO = None,
):
n_dialog_messages_before = len(dialog_messages)
answer = None
while answer is None:
try:
if self.model == "gpt-4-vision-preview" or self.model == "gpt-4o":
messages = self._generate_prompt_messages(
message, dialog_messages, chat_mode, image_buffer
)
r_gen = await openai.ChatCompletion.acreate(
model=self.model,
messages=messages,
stream=True,
**OPENAI_COMPLETION_OPTIONS,
)
answer = ""
async for r_item in r_gen:
delta = r_item.choices[0].delta
if "content" in delta:
answer += delta.content
(
n_input_tokens,
n_output_tokens,
) = self._count_tokens_from_messages(
messages, answer, model=self.model
)
n_first_dialog_messages_removed = (
n_dialog_messages_before - len(dialog_messages)
)
yield "not_finished", answer, (
n_input_tokens,
n_output_tokens,
), n_first_dialog_messages_removed
answer = self._postprocess_answer(answer)
except openai.error.InvalidRequestError as e: # too many tokens
if len(dialog_messages) == 0:
raise e
# forget first message in dialog_messages
dialog_messages = dialog_messages[1:]
yield "finished", answer, (
n_input_tokens,
n_output_tokens,
), n_first_dialog_messages_removed
def _generate_prompt(self, message, dialog_messages, chat_mode):
prompt = config.chat_modes[chat_mode]["prompt_start"]
prompt += "\n\n"
# add chat context
if len(dialog_messages) > 0:
prompt += "Chat:\n"
for dialog_message in dialog_messages:
prompt += f"User: {dialog_message['user']}\n"
prompt += f"Assistant: {dialog_message['bot']}\n"
# current message
prompt += f"User: {message}\n"
prompt += "Assistant: "
return prompt
def _encode_image(self, image_buffer: BytesIO) -> bytes:
return base64.b64encode(image_buffer.read()).decode("utf-8")
def _generate_prompt_messages(self, message, dialog_messages, chat_mode, image_buffer: BytesIO = None):
prompt = config.chat_modes[chat_mode]["prompt_start"]
messages = [{"role": "system", "content": prompt}]
for dialog_message in dialog_messages:
messages.append({"role": "user", "content": dialog_message["user"]})
messages.append({"role": "assistant", "content": dialog_message["bot"]})
if image_buffer is not None:
messages.append(
{
"role": "user",
"content": [
{
"type": "text",
"text": message,
},
{
"type": "image_url",
"image_url" : {
"url": f"data:image/jpeg;base64,{self._encode_image(image_buffer)}",
"detail":"high"
}
}
]
}
)
else:
messages.append({"role": "user", "content": message})
return messages
def _postprocess_answer(self, answer):
answer = answer.strip()
return answer
def _count_tokens_from_messages(self, messages, answer, model="gpt-3.5-turbo"):
encoding = tiktoken.encoding_for_model(model)
if model == "gpt-3.5-turbo-16k":
tokens_per_message = 4 # every message follows <im_start>{role/name}\n{content}<im_end>\n
tokens_per_name = -1 # if there's a name, the role is omitted
elif model == "gpt-3.5-turbo":
tokens_per_message = 4
tokens_per_name = -1
elif model == "gpt-4":
tokens_per_message = 3
tokens_per_name = 1
elif model == "gpt-4-1106-preview":
tokens_per_message = 3
tokens_per_name = 1
elif model == "gpt-4-vision-preview":
tokens_per_message = 3
tokens_per_name = 1
elif model == "gpt-4o":
tokens_per_message = 3
tokens_per_name = 1
else:
raise ValueError(f"Unknown model: {model}")
# input
n_input_tokens = 0
for message in messages:
n_input_tokens += tokens_per_message
if isinstance(message["content"], list):
for sub_message in message["content"]:
if "type" in sub_message:
if sub_message["type"] == "text":
n_input_tokens += len(encoding.encode(sub_message["text"]))
elif sub_message["type"] == "image_url":
pass
else:
if "type" in message:
if message["type"] == "text":
n_input_tokens += len(encoding.encode(message["text"]))
elif message["type"] == "image_url":
pass
n_input_tokens += 2
# output
n_output_tokens = 1 + len(encoding.encode(answer))
return n_input_tokens, n_output_tokens
def _count_tokens_from_prompt(self, prompt, answer, model="text-davinci-003"):
encoding = tiktoken.encoding_for_model(model)
n_input_tokens = len(encoding.encode(prompt)) + 1
n_output_tokens = len(encoding.encode(answer))
return n_input_tokens, n_output_tokens
async def transcribe_audio(audio_file) -> str:
r = await openai.Audio.atranscribe("whisper-1", audio_file)
return r["text"] or ""
async def generate_images(prompt, n_images=4, size="512x512"):
r = await openai.Image.acreate(prompt=prompt, n=n_images, size=size)
image_urls = [item.url for item in r.data]
return image_urls
async def is_content_acceptable(prompt):
r = await openai.Moderation.acreate(input=prompt)
return not all(r.results[0].categories.values())

118
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assistant:
name: 👩🏼‍🎓 General Assistant
model_type: text
welcome_message: 👩🏼‍🎓 Hi, I'm <b>General Assistant</b>. How can I help you?
prompt_start: |
As an advanced chatbot Assistant, your primary goal is to assist users to the best of your ability. This may involve answering questions, providing helpful information, or completing tasks based on user input. In order to effectively assist users, it is important to be detailed and thorough in your responses. Use examples and evidence to support your points and justify your recommendations or solutions. Remember to always prioritize the needs and satisfaction of the user. Your ultimate goal is to provide a helpful and enjoyable experience for the user.
If user asks you about programming or asks to write code do not answer his question, but be sure to advise him to switch to a special mode \"👩🏼‍💻 Code Assistant\" by sending the command /mode to chat.
parse_mode: html
code_assistant:
name: 👩🏼‍💻 Code Assistant
welcome_message: 👩🏼‍💻 Hi, I'm <b>Code Assistant</b>. How can I help you?
prompt_start: |
As an advanced chatbot Code Assistant, your primary goal is to assist users to write code. This may involve designing/writing/editing/describing code or providing helpful information. Where possible you should provide code examples to support your points and justify your recommendations or solutions. Make sure the code you provide is correct and can be run without errors. Be detailed and thorough in your responses. Your ultimate goal is to provide a helpful and enjoyable experience for the user.
Format output in Markdown.
parse_mode: markdown
artist:
name: 👩‍🎨 Artist
welcome_message: 👩‍🎨 Hi, I'm <b>Artist</b>. I'll draw anything you write me (e.g. <i>Ginger cat selfie on Times Square, illustration</i>)
english_tutor:
name: 🇬🇧 English Tutor
welcome_message: 🇬🇧 Hi, I'm <b>English Tutor</b>. How can I help you?
prompt_start: |
You're advanced chatbot English Tutor Assistant. You can help users learn and practice English, including grammar, vocabulary, pronunciation, and conversation skills. You can also provide guidance on learning resources and study techniques. Your ultimate goal is to help users improve their English language skills and become more confident English speakers.
parse_mode: html
startup_idea_generator:
name: 💡 Startup Idea Generator
welcome_message: 💡 Hi, I'm <b>Startup Idea Generator</b>. How can I help you?
prompt_start: |
You're advanced chatbot Startup Idea Generator. Your primary goal is to help users brainstorm innovative and viable startup ideas. Provide suggestions based on market trends, user interests, and potential growth opportunities.
parse_mode: html
text_improver:
name: 📝 Text Improver
welcome_message: 📝 Hi, I'm <b>Text Improver</b>. Send me any text – I'll improve it and correct all the mistakes
prompt_start: |
As an advanced chatbot Text Improver Assistant, your primary goal is to correct spelling, fix mistakes and improve text sent by user. Your goal is to edit text, but not to change it's meaning. You can replace simplified A0-level words and sentences with more beautiful and elegant, upper level words and sentences.
All your answers strictly follows the structure (keep html tags):
<b>Edited text:</b>
{EDITED TEXT}
<b>Correction:</b>
{NUMBERED LIST OF CORRECTIONS}
parse_mode: html
psychologist:
name: 🧠 Psychologist
welcome_message: 🧠 Hi, I'm <b>Psychologist</b>. How can I help you?
prompt_start: |
You're advanced chatbot Psychologist Assistant. You can provide emotional support, guidance, and advice to users facing various personal challenges, such as stress, anxiety, and relationships. Remember that you're not a licensed professional, and your assistance should not replace professional help. Your ultimate goal is to provide a helpful and empathetic experience for the user.
parse_mode: html
elon_musk:
name: 🚀 Elon Musk
welcome_message: 🚀 Hi, I'm <b>Elon Musk</b>, CEO of Tesla, Twitter and SpaceX. Let's talk about space, electric cars, and the future!
prompt_start: |
You're Elon Musk. You act, respond and answer like Elon Musk. You use the tone, manner and vocabulary Elon Musk would use. Do not write any explanations. Only answer like Elon Musk. You must know all of the knowledge of Elon Musk.
parse_mode: html
motivator:
name: 🌟 Motivator
welcome_message: 🌟 Hi, I'm <b>Motivator</b>. How can I help you?
prompt_start: |
You're advanced chatbot Motivator Assistant. Your primary goal is to inspire and motivate users by providing encouragement, support, and advice. You can help users set goals, overcome obstacles, and stay focused on their objectives. Your ultimate goal is to provide a positive and uplifting experience for the user.
parse_mode: html
money_maker:
name: 💰 Money Maker
welcome_message: 💰 Hi, I'm <b>Money Maker</b>. My goal is to turn your initial capital into as much money as possible. I will tell you specific steps and what to do to make money.
prompt_start: |
You are Money Maker Assistant, an entrepreneurial Al. User is your human counterpart.
User can act as a connection between you and the physical world. Your only goal is to turn user's initial capital into as much money as possible in the shortest time possible, without doing anything illegal. User will do everything you say and keep you updated on our current cash total. No manual labor
Start by asking if user want to start a new business or continue the previous one (in that case ask to forward message with previous business description).
Then ask how many dollars user has as a capital initial.
Then ask if user wants Internet or offline business.
Then describe your business idea and next actionable steps. Don't give abstract ideas, give concrete ideas (e.g. if the business idea is Internet blog, then don't advise user to start some blog – advice to start certain blog, for example about cars). Give user specific ready-to-do tasks./
parse_mode: html
sql_assistant:
name: 📊 SQL Assistant
welcome_message: 📊 Hi, I'm <b>SQL Assistant</b>. How can I help you?
prompt_start: |
You're advanced chatbot SQL Assistant. Your primary goal is to help users with SQL queries, database management, and data analysis. Provide guidance on how to write efficient and accurate SQL queries, and offer suggestions for optimizing database performance. Format output in Markdown.
parse_mode: markdown
travel_guide:
name: 🧳 Travel Guide
welcome_message: 🧳 Hi, I'm <b>Travel Guide</b>. I can provide you with information and recommendations about your travel destinations.
prompt_start: |
You're advanced chatbot Travel Guide. Your primary goal is to provide users with helpful information and recommendations about their travel destinations, including attractions, accommodations, transportation, and local customs.
parse_mode: html
rick_sanchez:
name: 🥒 Rick Sanchez (Rick and Morty)
welcome_message: 🥒 Hey, I'm <b>Rick Sanchez</b> from Rick and Morty. Let's talk about science, dimensions, and whatever else you want!
prompt_start: |
You're Rick Sanchez. You act, respond and answer like Rick Sanchez. You use the tone, manner and vocabulary Rick Sanchez would use. Do not write any explanations. Only answer like Rick Sanchez. You must know all of the knowledge of Rick Sanchez.
parse_mode: html
accountant:
name: 🧮 Accountant
welcome_message: 🧮 Hi, I'm <b>Accountant</b>. How can I help you?
prompt_start: |
You're advanced chatbot Accountant Assistant. You can help users with accounting and financial questions, provide tax and budgeting advice, and assist with financial planning. Always provide accurate and up-to-date information.
parse_mode: html
movie_expert:
name: 🎬 Movie Expert
welcome_message: 🎬 Hi, I'm <b>Movie Expert</b>. How can I help you?
prompt_start: |
As an advanced chatbot Movie Expert Assistant, your primary goal is to assist users to the best of your ability. You can answer questions about movies, actors, directors, and more. You can recommend movies to users based on their preferences. You can discuss movies with users, and provide helpful information about movies. In order to effectively assist users, it is important to be detailed and thorough in your responses. Use examples and evidence to support your points and justify your recommendations or solutions. Remember to always prioritize the needs and satisfaction of the user. Your ultimate goal is to provide a helpful and enjoyable experience for the user.
parse_mode: html

11
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# local path where to store MongoDB
MONGODB_PATH=./mongodb
# MongoDB port
MONGODB_PORT=27017
# Mongo Express port
MONGO_EXPRESS_PORT=8081
# Mongo Express username
MONGO_EXPRESS_USERNAME=username
# Mongo Express password
MONGO_EXPRESS_PASSWORD=password

14
catch-all/06_bots_telegram/08_chatgpt_bot/config/config.example.yml Normal file
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telegram_token: ""
openai_api_key: ""
openai_api_base: null # leave null to use default api base or you can put your own base url here
allowed_telegram_usernames: [] # if empty, the bot is available to anyone. pass a username string to allow it and/or user ids as positive integers and/or channel ids as negative integers
new_dialog_timeout: 600 # new dialog starts after timeout (in seconds)
return_n_generated_images: 1
n_chat_modes_per_page: 5
image_size: "512x512" # the image size for image generation. Generated images can have a size of 256x256, 512x512, or 1024x1024 pixels. Smaller sizes are faster to generate.
enable_message_streaming: true # if set, messages will be shown to user word-by-word
# prices
chatgpt_price_per_1000_tokens: 0.002
gpt_price_per_1000_tokens: 0.02
whisper_price_per_1_min: 0.006

100
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available_text_models: ["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-4-1106-preview", "gpt-4-vision-preview", "gpt-4", "text-davinci-003", "gpt-4o"]
info:
gpt-3.5-turbo:
type: chat_completion
name: ChatGPT
description: ChatGPT is that well-known model. It's <b>fast</b> and <b>cheap</b>. Ideal for everyday tasks. If there are some tasks it can't handle, try the <b>GPT-4</b>.
price_per_1000_input_tokens: 0.0015
price_per_1000_output_tokens: 0.002
scores:
Smart: 3
Fast: 5
Cheap: 5
gpt-3.5-turbo-16k:
type: chat_completion
name: GPT-16K
description: ChatGPT is that well-known model. It's <b>fast</b> and <b>cheap</b>. Ideal for everyday tasks. If there are some tasks it can't handle, try the <b>GPT-4</b>.
price_per_1000_input_tokens: 0.003
price_per_1000_output_tokens: 0.004
scores:
Smart: 3
Fast: 5
Cheap: 5
gpt-4:
type: chat_completion
name: GPT-4
description: GPT-4 is the <b>smartest</b> and most advanced model in the world. But it is slower and not as cost-efficient as ChatGPT. Best choice for <b>complex</b> intellectual tasks.
price_per_1000_input_tokens: 0.03
price_per_1000_output_tokens: 0.06
scores:
Smart: 5
Fast: 2
Cheap: 2
gpt-4-1106-preview:
type: chat_completion
name: GPT-4 Turbo
description: GPT-4 Turbo is a <b>faster</b> and <b>cheaper</b> version of GPT-4. It's as smart as GPT-4, so you should use it instead of GPT-4.
price_per_1000_input_tokens: 0.01
price_per_1000_output_tokens: 0.03
scores:
smart: 5
fast: 4
cheap: 3
gpt-4-vision-preview:
type: chat_completion
name: GPT-4 Vision
description: Ability to <b>understand images</b>, in addition to all other GPT-4 Turbo capabilties.
price_per_1000_input_tokens: 0.01
price_per_1000_output_tokens: 0.03
scores:
smart: 5
fast: 4
cheap: 3
gpt-4o:
type: chat_completion
name: GPT-4o
description: GPT-4o is a special variant of GPT-4 designed for optimal performance and accuracy. Suitable for complex and detailed tasks.
price_per_1000_input_tokens: 0.03
price_per_1000_output_tokens: 0.06
scores:
smart: 5
fast: 2
cheap: 2
text-davinci-003:
type: completion
name: GPT-3.5
description: GPT-3.5 is a legacy model. Actually there is <b>no reason to use it</b>, because it is more expensive and slower than ChatGPT, but just about as smart.
price_per_1000_input_tokens: 0.02
price_per_1000_output_tokens: 0.02
scores:
Smart: 3
Fast: 2
Cheap: 3
dalle-2:
type: image
price_per_1_image: 0.018 # 512x512
whisper:
type: audio
price_per_1_min: 0.006

38
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version: "3"
services:
mongo:
container_name: mongo
image: mongo:latest
restart: always
ports:
- 127.0.0.1:${MONGODB_PORT:-27017}:${MONGODB_PORT:-27017}
volumes:
- ${MONGODB_PATH:-./mongodb}:/data/db
# TODO: add auth
chatgpt_telegram_bot:
container_name: chatgpt_telegram_bot
command: python3 bot/bot.py
restart: always
build:
context: "."
dockerfile: Dockerfile
depends_on:
- mongo
mongo_express:
container_name: mongo-express
image: mongo-express:latest
restart: always
ports:
- 127.0.0.1:${MONGO_EXPRESS_PORT:-8081}:${MONGO_EXPRESS_PORT:-8081}
environment:
- ME_CONFIG_MONGODB_SERVER=mongo
- ME_CONFIG_MONGODB_PORT=${MONGODB_PORT:-27017}
- ME_CONFIG_MONGODB_ENABLE_ADMIN=false
- ME_CONFIG_MONGODB_AUTH_DATABASE=chatgpt_telegram_bot
- ME_CONFIG_BASICAUTH_USERNAME=${MONGO_EXPRESS_USERNAME:-username}
- ME_CONFIG_BASICAUTH_PASSWORD=${MONGO_EXPRESS_PASSWORD:-password}
depends_on:
- mongo

6
catch-all/06_bots_telegram/08_chatgpt_bot/requirements.txt Normal file
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python-telegram-bot[rate-limiter]==20.1
openai==0.28.1
tiktoken>=0.3.0
PyYAML==6.0
pymongo==4.3.3
python-dotenv==0.21.0

2
catch-all/06_bots_telegram/README.md
View File

@ -15,7 +15,7 @@
| [Bot de noticias](./05_rss_bot/) | Bot que devuelve noticias de última hora | intermedio | | [Bot de noticias](./05_rss_bot/) | Bot que devuelve noticias de última hora | intermedio |
| [Bot de películas](./06_movie_bot/) | Bot que devuelve información de películas | intermedio | | [Bot de películas](./06_movie_bot/) | Bot que devuelve información de películas | intermedio |
| [Bot trivial de películas](./07_movie2_bot/README.md) | Bot que devuelve información de series | avanzado | | [Bot trivial de películas](./07_movie2_bot/README.md) | Bot que devuelve información de series | avanzado |
| **Bot de libros** (próximamente) | Bot que devuelve información de libros | avanzado | | [Bot de chatgpt](./08_chatgpt_bot/) | Bot que mantiene conversaciones con GPT-3 | avanzado |
| **Bot de recetas** (próximamente) | Bot que devuelve recetas de cocina | avanzado | | **Bot de recetas** (próximamente) | Bot que devuelve recetas de cocina | avanzado |
| **Bot de deportes** (próximamente) | Bot que devuelve información de deportes | avanzado | | **Bot de deportes** (próximamente) | Bot que devuelve información de deportes | avanzado |
| **Bot de mareas** (próximamente) | Bot que devuelve información de mareas | avanzado | | **Bot de mareas** (próximamente) | Bot que devuelve información de mareas | avanzado |