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To set up a Telegram chatbot using your neural network model with the `python-telegram-bot` library, you can follow the provided steps along with the sample code. Here's a more comprehensive explanation of each part:

### Prerequisites

1. **Install Required Libraries:**

   Ensure you have `python-telegram-bot` and any other necessary libraries installed:

   ```bash
   pip install python-telegram-bot
   pip install gradio_client
   ```

2. **Set Up Your Bot on Telegram:**

   - Open Telegram and search for `BotFather`.
   - Start a chat with `BotFather` and follow the instructions to create a new bot.
   - Once you create the bot, you will receive an API token. Keep this token secure and treat it as a password.

### Developing the Bot

3. **Create a Python Script:**

   Create a new Python file, like `telegram_bot.py`, and include the following code:

   ```python
   from telegram import Update, ForceReply
   from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackContext
   from gemma_model import ModelClient

   # Initialize your model client
   model_client = ModelClient()

   # Dictionary to store user chat histories
   user_histories = {}

   def start(update: Update, context: CallbackContext):
       """Handles the /start command."""
       update.message.reply_text('Привет! Я ваш чат-бот. Начните разговаривать со мной!')

   def clear_history(update: Update, context: CallbackContext):
       """Clears the chat history for the user."""
       user_id = update.message.from_user.id
       user_histories[user_id] = []
       update.message.reply_text('История очищена!')

   def chat(update: Update, context: CallbackContext):
       """Handles incoming text messages."""
       user_id = update.message.from_user.id
       user_message = update.message.text

       # Retrieve or create chat history for the user
       history = user_histories.setdefault(user_id, [])

       # Generate a response using the model
       sys_prompt = "Your system prompt here"  # Customize this based on your task
       response = model_client.get_response(user_message, history, sys_prompt)

       # Store the new request and response in history
       history.append((user_message, response))

       # Send the response back to the user
       update.message.reply_text(response)

   def main():
       # Replace this with your bot's token
       telegram_api_token = "YOUR_TELEGRAM_BOT_API_TOKEN"
       
       # Initialize the Updater and pass it the bot's token
       updater = Updater(token=telegram_api_token, use_context=True)

       # Get the dispatcher to register handlers
       dispatcher = updater.dispatcher

       # Register handlers for commands and messages
       dispatcher.add_handler(CommandHandler("start", start))
       dispatcher.add_handler(CommandHandler("clear", clear_history))
       dispatcher.add_handler(MessageHandler(Filters.text & ~Filters.command, chat))

       # Start polling for updates
       updater.start_polling()
       updater.idle()

   if __name__ == '__main__':
       main()
   ```

### Running the Bot

4. **Start the Bot:**

   Make sure the `gemma_model.py` file—which should contain the logic for interacting with your neural network model—is accessible:

   - Ensure `gemma_model.py` is either in the same directory or properly importable by your script.
   - Run the Python script to start your bot:

   ```bash
   python telegram_bot.py
   ```

Now, your bot is operational. Users can interact with it, and your model will generate responses based on the conversations. The `/clear` command resets the chat history for each user, while `/start` sends a welcome message. Adjust `sys_prompt` or other logic as needed for your specific task.