feat: add language selection (ru and en)

.gitignore

@@ -129,6 +129,9 @@ ENV/
 env.bak/
 venv.bak/
 
+# Configuration
+config.py
+
 # Spyder project settings
 .spyderproject
 .spyproject
@@ -161,6 +164,7 @@ cython_debug/
 #.idea/
 
 # ---> VisualStudioCode
+.vscode
 .vscode/*
 !.vscode/settings.json
 !.vscode/tasks.json
@@ -174,3 +178,6 @@ cython_debug/
 # Built Visual Studio Code Extensions
 *.vsix
 
+# Session info
+*.session
+*.session-journal

main.py

@@ -0,0 +1,33 @@
+from src.integrations.gigachat_api_client import GigaChatClient
+from src.bot.telegram_userbot import TelegramUserBot
+from src.utils import logging
+from src.core.configuration import config
+
+
+def main() -> None:
+    """
+    Entry point for starting the Telegram user bot.
+    """
+    # Configure logging
+    logging.setup_logging()
+    
+    # Load API credentials and configuration
+    api_id: str = config.API_ID
+    api_hash: str = config.API_HASH
+    api_token: str = config.API_GIGACHAT_TOKEN
+
+    # Initialize GigaChatClient
+    gigachat_client: GigaChatClient = GigaChatClient(api_token=api_token)
+
+    # Initialize and run the Telegram user bot
+    bot: TelegramUserBot = TelegramUserBot(
+        session_name="userbot",
+        api_id=api_id,
+        api_hash=api_hash,
+        gigachat_client=gigachat_client
+    )
+    bot.run()
+
+if __name__ == "__main__":
+    main()
+    

pyproject.toml

@@ -6,7 +6,23 @@ authors = ["Factorino73 <masenkin73@xmail.ru>"]
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = "^3.13"
+python = "^3.12"
+pyrogram = "^2.0.106"
+tgcrypto = "^1.2.5"
+setuptools = "^75.6.0"
+wheel = "^0.45.1"
+langchain-gigachat = "^0.3.2"
+punq = "^0.7.0"
+pytest = "^8.3.4"
+speechrecognition = "^3.13.0"
+typing-extensions = "^4.12.2"
+pydub = "^0.25.1"
+numpy = "2.0.2"
+soundfile = "^0.13.0"
+torch = "^2.5.1"
+llvmlite = "0.43.0"
+numba = "0.60.0"
+openai-whisper = "^20240930"
 
 
 [build-system]

src/bot/telegram_userbot.py

@@ -0,0 +1,190 @@
+import logging
+from logging import Logger
+from tempfile import NamedTemporaryFile
+from typing import Optional
+
+from pyrogram import filters
+from pyrogram.client import Client
+from pyrogram.types import Message
+from pyrogram.enums import ChatAction
+
+from src.integrations.gigachat_api_client import GigaChatClient
+from src.utils import speech_recognition
+
+
+class TelegramUserBot:
+    """
+    A Telegram user bot.
+
+    Attributes:
+        app (Client): The Pyrogram client instance for the bot.
+        gigachat_client (GigaChatClient): The client instance for GigaChat integration.
+    """
+
+    def __init__(self, session_name: str, api_id: str, api_hash: str, gigachat_client: GigaChatClient) -> None:
+        """
+        Initializes the Telegram user bot.
+
+        Args:
+            session_name (str): The session name for the bot.
+            api_id (str): The API ID for the Telegram application.
+            api_hash (str): The API hash for the Telegram application.
+            gigachat_client (GigaChatClient): An instance of GigaChatClient for handling AI responses.
+        """
+        # Configure logging
+        self.logger: Logger = logging.getLogger(__name__)
+        
+        self.app: Client = Client(session_name, api_id=api_id, api_hash=api_hash)
+        self.gigachat_client: GigaChatClient = gigachat_client
+        self.register_handlers()
+
+    def register_handlers(self) -> None:
+        """
+        Registers the message handlers for the bot.
+        """
+        self.logger.debug("Registering handlers.")
+        self.app.on_message(filters.command("ai"))(self.handle_ai_command)
+        self.app.on_message(filters.command("voice"))(self.handle_voice_command)
+
+    async def handle_ai_command(self, client: Client, message: Message) -> None:
+        """
+        Handles messages that invoke the /ai command.
+
+        Args:
+            client (Client): The Pyrogram client instance.
+            message (Message): The incoming Telegram message.
+        """
+        self.logger.info(f"Received /ai command from chat_id={message.chat.id}")
+
+        # Extract the command argument
+        command_arg: Optional[str] = " ".join(message.text.split()[1:])
+
+        if not command_arg and message.reply_to_message and message.reply_to_message.text:
+            # Use the text of the replied message if no argument is provided
+            command_arg = message.reply_to_message.text
+
+        if not command_arg:
+            self.logger.warning(f"No argument or replied message provided for /ai command by chat_id={message.chat.id}")
+            await message.reply("Please provide a message after /ai or reply to a message.", quote=True)
+            return
+
+        # Send an initial message indicating processing
+        self.logger.debug(f"Processing request for chat_id={message.chat.id}")
+        processing_message: Message = await message.reply(f"{self.gigachat_client.model_name} is processing your request...", quote=True)
+
+        try:
+            # Start typing animation
+            await client.send_chat_action(message.chat.id, ChatAction.TYPING)
+
+            # Get a response from GigaChat
+            response: str = self.gigachat_client.get_response(str(message.chat.id), command_arg)
+            self.logger.debug(f"Received response for chat_id={message.chat.id}")
+
+            # Edit the processing message with the generated response
+            await processing_message.edit_text(response)
+        except Exception as e:
+            self.logger.error(f"Error processing /ai command for chat_id={message.chat.id}: {e}", exc_info=True)
+            await processing_message.edit_text("An error occurred while processing your request.")
+        finally:
+            # Stop indicating typing action
+            await client.send_chat_action(message.chat.id, ChatAction.CANCEL)
+
+    def get_language(self, input_text: str) -> str:
+        """
+        Determines the language for voice-to-text conversion based on the input parameter.
+
+        Args:
+            input_text (str): The input parameter indicating the language.
+
+        Returns:
+            str: The language code ('en' or 'ru').
+
+        Raises:
+            ValueError: If an invalid language parameter is provided.
+        """
+        language_params: dict[str, list[str]] = {
+            'en': ['en', 'eng', 'english'],
+            'ru': ['ru', 'rus', 'russian']
+        }
+        
+        input_lower: str = input_text.lower()
+        for lang_code, aliases in language_params.items():
+            if input_lower in aliases:
+                return lang_code
+        raise ValueError(
+            "Invalid language parameter. Please use one of the following:\n" +
+            "\n".join(f"{lang_code}: {', '.join(aliases)}" for lang_code, aliases in language_params.items())
+        )
+
+
+    async def handle_voice_command(self, client: Client, message: Message) -> None:
+        """
+        Handle the /voice command to convert a voice message to text with optional language selection.
+
+        Args:
+            client (Client): The Pyrogram Client instance.
+            message (Message): The incoming message containing the /voice command.
+        """
+        self.logger.info(f"Received /voice command from chat_id={message.chat.id}.")
+
+        # Parse the language parameter (default to Russian)
+        command_parts: list[str] = message.text.split()
+        try:
+            language: str = self.get_language(command_parts[1]) if len(command_parts) > 1 else 'ru'
+        except ValueError as e:
+            await message.reply(str(e), quote=True)
+            return
+
+        # Check if the reply is to a voice message
+        if not (message.reply_to_message and message.reply_to_message.voice):
+            self.logger.warning("The /voice command was not used in reply to a voice message.")
+            await message.reply("Please reply to a voice message with the /voice command.", quote=True)
+            return
+
+        # Send an initial message indicating processing
+        processing_message: Message = await message.reply_to_message.reply("Converting voice message to text...", quote=True)
+
+        with NamedTemporaryFile(delete=False) as temp_file:
+            file_path = await client.download_media(message.reply_to_message.voice.file_id, file_name=temp_file.name)
+            self.logger.info(f"Voice message downloaded to {file_path}.")
+
+            try:
+                # Start typing animation
+                await client.send_chat_action(message.chat.id, ChatAction.TYPING)
+            
+                # Attempt to convert voice to text with the selected language
+                text: str = speech_recognition.convert_voice_to_text(file_path, language=language)  # type: ignore
+                self.logger.info("Voice message successfully converted to text.")
+
+                # Format the text for sending
+                formatted_text: str = (
+                    f"<pre language=\"Conversion Result ({language})\">"
+                    f"{text}"
+                    "</pre>"
+                )
+
+                # Edit the initial processing message with the converted text
+                await processing_message.edit_text(formatted_text)
+            except FileNotFoundError:
+                self.logger.error("File not found during processing.", exc_info=True)
+                await processing_message.edit_text("An error occurred while processing the voice message. Please try again later.")
+            except RuntimeError:
+                self.logger.error("A runtime error occurred.", exc_info=True)
+                await processing_message.edit_text("An error occurred while processing the voice message. Please try again later.")
+            except Exception:
+                self.logger.error("An unexpected error occurred.", exc_info=True)
+                await processing_message.edit_text("An error occurred while processing the voice message. Please try again later.")
+            finally:
+                # Stop indicating typing action
+                await client.send_chat_action(message.chat.id, ChatAction.CANCEL)
+
+    def run(self) -> None:
+        """
+        Starts the bot.
+        """
+        self.logger.info("Bot is starting.")
+        print("Bot is running.")
+        try:
+            self.app.run()
+        except Exception as e:
+            self.logger.critical(f"Failed to start the bot: {e}", exc_info=True)

src/core/configuration/config.py.example

@@ -0,0 +1,27 @@
+"""
+config.py
+
+This file contains the configuration settings required for the Telegram bot and GigaChat integration.
+
+Configuration settings:
+    API_ID (str): The unique identifier for your Telegram application. It is required to initialize the Pyrogram client.
+    API_HASH (str): The hash associated with your Telegram application. It is required to initialize the Pyrogram client.
+    BOT_TOKEN (str): The token for the Telegram bot. It is used for authenticating the bot with the Telegram API.
+    API_GIGACHAT_TOKEN (str): The token for authenticating with the GigaChat API. It is used for communication with the GigaChat service.
+
+Note: 
+    - Make sure to keep these tokens secure and do not share them publicly.
+    - These values should be replaced with actual credentials for the bot and the GigaChat API.
+"""
+
+# The API ID for the Telegram application
+API_ID: str = ''
+
+# The API hash for the Telegram application
+API_HASH: str = ''
+
+# The bot token for authenticating the bot with Telegram
+BOT_TOKEN: str = ''
+
+# The token for authenticating with the GigaChat API
+API_GIGACHAT_TOKEN: str = ''

src/integrations/gigachat_api_client.py

@@ -0,0 +1,102 @@
+import logging
+from logging import Logger
+from typing import Dict
+
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_core.chat_history import InMemoryChatMessageHistory
+from langchain_gigachat.chat_models import GigaChat
+
+
+class GigaChatClient:
+    """
+    A client class for interacting with the GigaChat API using LangChain components.
+    """
+
+    def __init__(self, api_token: str, model_name: str = "GigaChat") -> None:
+        """
+        Initializes the GigaChatManager with API credentials and a default model.
+
+        Args:
+            api_token (str): The API token for authenticating with the GigaChat API.
+            model_name (str): The GigaChat model to use. Defaults to "GigaChat".
+        """
+        # Configure logging
+        self.logger: Logger = logging.getLogger(__name__)
+
+        self.api_token: str = api_token
+        self.model_name: str = model_name
+        self.logger.info(f"Initialize GigaChat client Using model: {self.model_name}")
+        
+        self.llm: GigaChat = self._create_llm(model_name)
+        self.store: Dict[str, InMemoryChatMessageHistory] = {}
+        self.conversation = RunnableWithMessageHistory(self.llm, self.get_session_history)
+
+    def _create_llm(self, model_name: str) -> GigaChat:
+        """
+        Creates and configures a GigaChat LLM instance.
+
+        Args:
+            model_name (str): The GigaChat model to use.
+
+        Returns:
+            GigaChat: Configured GigaChat instance.
+        """
+        self.logger.debug(f"Creating GigaChat LLM with model: {model_name}")
+        return GigaChat(
+            credentials=self.api_token,
+            scope="GIGACHAT_API_PERS",
+            model=model_name,
+            verify_ssl_certs=False,
+            streaming=False,
+        )
+
+    def get_session_history(self, session_id: str) -> InMemoryChatMessageHistory:
+        """
+        Retrieves the chat history for a given session, creating it if it does not exist.
+
+        Args:
+            session_id (str): The unique identifier for the session.
+
+        Returns:
+            InMemoryChatMessageHistory: The chat history for the session.
+        """
+        if session_id not in self.store:
+            self.logger.debug(f"Creating new session history for session_id: {session_id}")
+            self.store[session_id] = InMemoryChatMessageHistory()
+        else:
+            self.logger.debug(f"Retrieving existing session history for session_id: {session_id}")
+        return self.store[session_id]
+
+    def set_model(self, model_name: str) -> None:
+        """
+        Updates the LLM to use a different GigaChat model.
+
+        Args:
+            model_name (str): The new GigaChat model to use.
+        """
+        self.logger.info(f"Switching model to: {model_name}")
+        self.llm = self._create_llm(model_name)
+        self.conversation = RunnableWithMessageHistory(self.llm, self.get_session_history)
+
+    def get_response(self, session_id: str, text: str) -> str:
+        """
+        Get a response to the provided input text for a given session.
+
+        Args:
+            session_id (str): The unique identifier for the session.
+            text (str): The input text for which a response is needed.
+
+        Returns:
+            str: The response text.
+        """
+        self.logger.info(f"Generating response for session_id: {session_id}")
+        try:
+            response = self.conversation.invoke(
+                input=text,
+                config={"configurable": {"session_id": session_id}},
+            )
+            self.logger.debug(f"Response for session_id {session_id}")
+            return response.content
+        except Exception as e:
+            self.logger.error(f"Error while getting response for session_id: {session_id}. Error: {e}", exc_info=True)
+            raise

src/utils/logging.py

@@ -0,0 +1,62 @@
+import logging
+import logging.config
+from datetime import datetime
+
+
+def setup_logging(output_to_console=False) -> None:
+    """
+    Configures the logging system. 
+
+    This function sets up logging with optional output to the console and ensures 
+    log files are rotated daily. It creates a detailed logging format and retains 
+    log files for a week.
+
+    Args:
+        output_to_console (bool): If True, log messages will also be printed to the console. 
+                                  Defaults to False.
+    """
+    # Define the default handlers to use. Always logs to a file.
+    handlers: list[str] = ['file']
+    if output_to_console:
+        # Add console logging if requested
+        handlers.append('console')
+
+    # Generate the log file name with the current date
+    log_filename: str = f'logs/log-{datetime.now().strftime("%Y-%m-%d")}.log'
+
+    # Configure the logging settings using a dictionary
+    logging.config.dictConfig({
+        'version': 1,  # Logging configuration version
+        'disable_existing_loggers': True,  # Deny other loggers to remain active
+        'formatters': {
+            'detailed': {  # Define a detailed logging format
+                'format': (
+                    '%(asctime)s | %(levelname)-8s | '
+                    '%(filename)s.%(funcName)s, line %(lineno)d: '
+                    '%(message)s'
+                ),
+                'datefmt': '%Y-%m-%d %H:%M:%S'  # Timestamp format
+            },
+        },
+        'handlers': {
+            # Console handler outputs log messages to the console
+            'console': {
+                'class': 'logging.StreamHandler',  # Standard output stream
+                'formatter': 'detailed',  # Use the detailed formatter
+            },
+            # File handler writes log messages to a file, rotating daily
+            'file': {
+                'class': 'logging.handlers.TimedRotatingFileHandler',
+                'filename': log_filename,  # Log file path
+                'when': 'midnight',  # Rotate log files at midnight
+                'interval': 1,  # Rotate daily
+                'backupCount': 7,  # Keep up to 7 old log files
+                'formatter': 'detailed',  # Use the detailed formatter
+            },
+        },
+        # Define the root logger configuration
+        'root': {
+            'handlers': handlers,  # Handlers to use (console, file, or both)
+            'level': 'DEBUG',  # Log level (DEBUG logs all levels)
+        },
+    })

src/utils/speech_recognition.py

@@ -0,0 +1,120 @@
+import os
+import logging
+from logging import Logger
+
+from pydub import AudioSegment
+import speech_recognition as sr
+from speech_recognition.audio import AudioData
+
+
+# Configure logging
+logger: Logger = logging.getLogger(__name__)
+
+def convert_to_wav(file_path: str) -> str:
+    """
+    Converts an audio file to WAV format if it is not already in WAV format.
+
+    Args:
+        file_path (str): The path to the audio file to be converted.
+
+    Returns:
+        str: The path to the converted or original WAV file.
+
+    Raises:
+        FileNotFoundError: If the file does not exist.
+        RuntimeError: If the conversion fails for any reason.
+    """
+    # Check if the file exists
+    if not os.path.exists(file_path):
+        logger.error(f"File {file_path} does not exist.")
+        raise FileNotFoundError(f"File {file_path} does not exist.")
+
+    if file_path.lower().endswith('.wav'):
+        logger.info(f"File {file_path} is already in WAV format.")
+        return file_path
+
+    try:
+        logger.info(f"Converting {file_path} to WAV format.")
+        audio = AudioSegment.from_file(file_path)
+        wav_path: str = f"{os.path.splitext(file_path)[0]}.wav"
+        audio.export(wav_path, format="wav")
+        logger.info(f"File converted to {wav_path}.")
+        return wav_path
+    except Exception as e:
+        logger.error(f"Failed to convert file to WAV: {e}")
+        raise RuntimeError(f"Failed to convert file to WAV: {e}")
+
+def get_audio_duration(file_path: str) -> float:
+    """
+    Retrieves the duration of an audio file in seconds.
+
+    Args:
+        file_path (str): The path to the audio file.
+
+    Returns:
+        float: The duration of the audio file in seconds.
+
+    Raises:
+        FileNotFoundError: If the file does not exist.
+        RuntimeError: If unable to get the file duration.
+    """
+    # Check if the file exists
+    if not os.path.exists(file_path):
+        logger.error(f"File {file_path} does not exist.")
+        raise FileNotFoundError(f"File {file_path} does not exist.")
+
+    try:
+        logger.info(f"Getting duration of {file_path}.")
+        audio = AudioSegment.from_file(file_path)
+        duration: float = len(audio) / 1000  # Duration in seconds
+        logger.info(f"Duration of {file_path}: {duration} seconds.")
+        return duration
+    except Exception as e:
+        logger.error(f"Failed to get file duration: {e}")
+        raise RuntimeError(f"Failed to get file duration: {e}")
+
+def convert_voice_to_text(file_path: str, language='ru') -> str:
+    """
+    Converts speech from an audio file to text using OpenAI speech recognition service.
+
+    Args:
+        file_path (str): The path to the audio file to be processed.
+        language (str): The language code for speech recognition (default is 'ru').
+
+    Returns:
+        str: The transcribed text if recognition is successful.
+
+    Raises:
+        FileNotFoundError: If the file does not exist.
+        RuntimeError: For any errors encountered during processing.
+    """
+    # Check if the file exists
+    if not os.path.exists(file_path):
+        logger.error(f"File {file_path} does not exist.")
+        raise FileNotFoundError("File does not exist.")
+
+    # Convert the file to WAV format if necessary
+    try:
+        wav_path: str = convert_to_wav(file_path)
+    except RuntimeError as e:
+        logger.error(f"Error converting to WAV: {e}")
+        raise RuntimeError(f"Error converting to WAV: {e}")
+
+    recognizer = sr.Recognizer()
+
+    try:
+        logger.info(f"Processing file {wav_path} ({get_audio_duration(wav_path)} sec) for speech recognition.")
+        with sr.AudioFile(wav_path) as source:
+            audio_data: AudioData = recognizer.record(source)
+            text = recognizer.recognize_whisper(audio_data, language=language, model='medium')
+            logger.info("Speech recognition successful.")
+            return text  # type: ignore
+    except sr.UnknownValueError:
+        logger.warning(f"Speech in {wav_path} could not be recognized.")
+        raise RuntimeError("Speech could not be recognized.")
+    except sr.RequestError as e:
+        logger.error(f"Request error from the recognition service: {e}")
+        raise RuntimeError(f"Request error from the recognition service: {e}")
+    except Exception as e:
+        logger.error(f"An unexpected error occurred: {e}")
+        raise RuntimeError(f"An unexpected error occurred: {e}")

tests/integrations/test_gigachat_api_client.py

@@ -0,0 +1,55 @@
+import pytest
+from unittest.mock import MagicMock, patch
+from langchain_core.chat_history import InMemoryChatMessageHistory
+from langchain_gigachat import GigaChat
+
+from src.integrations.gigachat_api_client import GigaChatClient
+from src.core.configuration import config 
+
+
+API_GIGACHAT_TOKEN: str = config.API_GIGACHAT_TOKEN
+
+@pytest.fixture
+def gigachat_client() -> GigaChatClient:
+    """Fixture to create a GigaChatClient instance with a mock API token."""
+    return GigaChatClient(api_token=API_GIGACHAT_TOKEN)
+
+def test_initialization(gigachat_client) -> None:
+    """Test if the GigaChatClient initializes correctly."""
+    assert gigachat_client.api_token == API_GIGACHAT_TOKEN
+    assert gigachat_client.model_name == "GigaChat"
+    assert isinstance(gigachat_client.store, dict)
+    assert gigachat_client.llm is not None
+
+def test_create_llm() -> None:
+    """Test the _create_llm method for proper LLM creation."""
+    client = GigaChatClient(api_token=API_GIGACHAT_TOKEN)
+    mock_llm: GigaChat = client._create_llm("GigaChat-Pro")
+    assert mock_llm.credentials == API_GIGACHAT_TOKEN
+    assert mock_llm.model == "GigaChat-Pro"
+
+def test_get_session_history(gigachat_client) -> None:
+    """Test the get_session_history method for creating/retrieving session history."""
+    session_id = "test_session"
+    history = gigachat_client.get_session_history(session_id)
+    assert isinstance(history, InMemoryChatMessageHistory)
+    assert session_id in gigachat_client.store
+    assert gigachat_client.store[session_id] is history
+
+def test_set_model(gigachat_client) -> None:
+    """Test the set_model method for updating the LLM model."""
+    new_model = "GigaChat-Pro"
+    gigachat_client.set_model(new_model)
+    assert gigachat_client.llm.model == new_model
+
+def test_get_response() -> None:
+    """Test the get_response method by verifying the response code."""
+    with patch("langchain_core.runnables.history.RunnableWithMessageHistory") as MockRunnable:
+        mock_runnable = MagicMock()
+        mock_runnable.invoke.return_value.code = 200
+        MockRunnable.return_value = mock_runnable
+
+        client = GigaChatClient(api_token=API_GIGACHAT_TOKEN)
+        response_code = mock_runnable.invoke.return_value.code
+
+        assert response_code == 200