Getting started¶

This article provides information on how to quickly set up and start working with the AI Detector Model project. It covers environment setup, dependency installation, preparing datasets, code formatting and running the initial scripts to ensure everything is working correctly.

👟 Quickstart¶

This section will guide you through setting up the project environment, installing dependencies, and preparing for development.

1️⃣ Create Python environment¶

Make sure you have Python 3.11.9 installed. Then create a virtual environment using the provided Makefile:

make create_environment

This will create a new Pipenv environment configured with the correct Python version. You can activate it with:

pipenv shell

2️⃣ Install dependencies¶

Install all required Python packages:

make requirements          # Install globally
pipenv run make requirements  # Install inside pipenv environment

3️⃣ Environment setup (Optional)¶

This project uses a .env file for configuration. Create it from the template:

cp .env.example .env

Currently .env is only used for dataset download

4️⃣ Prepare dataset¶

Download and process the dataset using the provided script:

make data

This will ensure that the raw, interim, and processed datasets are available in the correct folders.

5️⃣ Build and serve local documentation (Optional)¶

You can generate the project documentation and serve it locally for browsing:

make build_docs       # Build documentation
make serve_docs       # Serve docs at localhost:9000

The local docs are useful for quickly checking usage examples, model details, and project structure.

📝 Code formatting¶

This project follows standard Python code formatting and style conventions. To ensure consistency, the following tools are used:

Black – automatically formats Python code to a uniform style
isort – sorts imports in a consistent order
flake8 – checks for style violations and potential errors

Before submitting any changes or pull requests, make sure to run:

make format   # Format code automatically
make lint     # Check code style and errors

Following these steps helps maintain clean, readable, and consistent code throughout the project.