AutoTweet is a Python application that leverages the Llama 3 language model to generate technical tweets on various topics. It integrates with ntfy.sh for a push notification-based confirmation workflow before posting to X (formerly Twitter).

• Features
• How it Works
• Setup
• Usage
• Configuration
• Dependencies
• Running as a Systemd Service (Linux)
• Contributing

• Automated Tweet Generation: Uses the meta-llama/Meta-Llama-3-8B-Instruct model to create concise technical tweets.
• X Integration: Posts approved tweets directly to your X account.
• Notification-based Confirmation:
  • Sends a push notification via a self-hosted or public ntfy.sh server to your device.
  • Allows you to "Approve ✅", "Discard ❌", or "Re-generate 🔁" the tweet directly from the notification.
• Direct Posting Option: A command-line argument (--force-post) allows bypassing the confirmation step for direct posting
• Configurable Topics: Easily customize the list of topics for tweet generation.
• Adjustable Tweet Frequency: Control how often the script attempts to generate and post a tweet.
• CUDA Support: Utilizes GPU for faster model inference if a CUDA-enabled GPU is available.

1. Model Loading: The Llama 3 model and tokenizer are loaded from Hugging Face.
2. Topic Selection: A random topic is chosen from a predefined list.
3. Tweet Generation: A prompt is constructed, and the Llama 3 model generates a short technical tweet (under 280 characters) about the selected topic.
4. Confirmation Request: Unless --force-post is used, a notification with action buttons is sent to your configured ntfy.sh topic. The script then listens on the response topic for your decision. If ntfy.sh environment variables are not correctly set, the script will raise an error.
5. Action Based on Confirmation:
   • Approve: The tweet is posted to X.
   • Discard: The tweet is not posted.
   • Re-generate: The script attempts to generate a new tweet for the same topic.
6. Scheduling: After an action (or inaction), the script waits for a configurable interval (TWEET_TIMEGAP_SECS) before starting the cycle again.

1. Clone the Repository:

   git clone https://github.com/akshayxml/autotweet.git
   cd autotweet

2. Install Dependencies: Make sure you have Python 3.x installed. It's highly recommended to create and activate a virtual environment first:

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

   Then, install the required packages:

   pip install -r requirements.txt
   # For CUDA support with PyTorch, ensure your PyTorch installation matches your CUDA version.
   # See: https://pytorch.org/get-started/locally/

3. Hugging Face Login/Token: To download and use the Llama 3 model, you need to:

   • Accept the Llama 3 license on its Hugging Face model card.
   • Log in via the Hugging Face CLI:

     huggingface-cli login

   • Alternatively, you can set the HF_TOKEN environment variable with your Hugging Face access token.

4. Environment Variables: Create a .env file in the project root by copying the provided .env.example file (cp .env.example .env) and then fill in your actual values. Alternatively, you can set these environment variables directly in your system.

   • X API Credentials (Required):

     X_CONSUMER_KEY="your_consumer_key"
     X_CONSUMER_SECRET="your_consumer_secret"
     X_ACCESS_TOKEN="your_access_token"
     X_ACCESS_TOKEN_SECRET="your_access_token_secret"
     X_BEARER_TOKEN="your_bearer_token"
     

   • ntfy.sh Configuration (Required, unless using --force-post):

     NTFY_SERVER="https://ntfy.sh" # Optional, defaults to public ntfy.sh. Use your own if self-hosting.
     NTFY_CONFIRM_TOPIC="your_unique_confirm_topic_name" # e.g., autotweet_confirm_myuser
     NTFY_RESPONSE_TOPIC="your_unique_response_topic_name" # e.g., autotweet_response_myuser
     

     Note: Ensure NTFY_CONFIRM_TOPIC and NTFY_RESPONSE_TOPIC are unique and private to you if using the public ntfy.sh server.

   • Hugging Face Token (Optional, if not using huggingface-cli login):

     HF_TOKEN="your_hugging_face_read_token"
     

1. Ensure all prerequisites and environment variables are set up.
2. Run the main script:
   • With ntfy.sh confirmation (default): Make sure your ntfy.sh environment variables are set.

     python main.py

   • To skip confirmation and post directly:

     python main.py --force-post

3. The script will start generating tweets.
4. If not using --force-post, subscribe to your NTFY_CONFIRM_TOPIC on your phone/device using the ntfy app or web client. You will receive notifications to approve, reject, or regenerate tweets.

You can modify the following in main.py:

• LLAMA3_MODEL_NAME: Change the Llama 3 model variant if needed (ensure compatibility).
• TWEET_TIMEGAP_SECS: Adjust the time interval (in seconds) between tweet generation attempts. Default is 12 hours.

  TWEET_TIMEGAP_SECS = 60 * 60 * 12 # 12 hours

• topics: Add or remove topics for tweet generation.

  topics = [
      "kubernetes", "docker", "c++", "golang", "java", "nodejs", "redis",
      "python", "system design", "data structures and algorithms",
      "operating system", "computer networking", "databases", "kafka", "javascript"
  ]

All Python dependencies are listed in requirements.txt. The main dependencies include:

• transformers: For interacting with Hugging Face models.
• torch: The deep learning framework used by the model.
• tweepy: For interacting with the X API.
• requests: For making HTTP requests (e.g., to ntfy.sh).
• python-dotenv: For managing environment variables from a .env file.

To run AutoTweet automatically on system boot, you can set it up as a systemd service.

1. Create the service file:

   Create a file named autotweet.service in /etc/systemd/system/ with the following content. Make sure to adjust User, WorkingDirectory, and ExecStart paths to match your setup.

   [Unit]
   Description=AutoTweet Service
   After=network.target
   
   [Service]
   User=your_username
   WorkingDirectory=/path/to/your/autotweet_project
   ExecStart=/path/to/your/autotweet_project/venv/bin/python3 /path/to/your/autotweet_project/main.py
   Restart=always
   RestartSec=10
   
   [Install]
   WantedBy=multi-user.target

   Note:

   • Replace your_username with the appropriate user for running the script.
   • Replace /path/to/your/autotweet_project with the absolute path to your project directory.
   • Ensure the Python interpreter path in ExecStart is correct for your virtual environment.

2. Reload systemd, enable, and start the service:

   sudo systemctl daemon-reload
   sudo systemctl enable autotweet.service
   sudo systemctl start autotweet.service

3. Check the status:

   You can check the status of the service using:

   sudo systemctl status autotweet.service

   And view logs with:

   journalctl -u autotweet.service -f

Contributions are welcome! Please feel free to submit a pull request or open an issue.