# Getting Started

## Prerequisites

- Docker
- Python 3.7+

## Installation

1. Clone the repository
2. Build the Docker image:

```bash
docker build -t test-score-predictor .
```

3. Run the container:

```bash
docker run -p 8000:8000 test-score-predictor
```

## Environment Variables

The application uses the following environment variables:
- `MODEL_PATH`: Path to the model file (default: `linear_regression_model.pkl`)
- `SCALER_PATH`: Path to the scaler file (default: `scaler.pkl`)
- `PORT`: Port to run the application on (default: `8000`)

These can be set in the `.env` file or passed as environment variables.

## Development

To set up a development environment:

1. Create a virtual environment:

```bash
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
```

2. Install dependencies:

```bash
pip install -r requirements.txt
```

3. Run the application:

```bash
uvicorn main:app --reload
```

The application will be available at http://localhost:8000.

## Testing

Run the tests using:

```bash
pytest test_api.py
```

## Deployment

The application includes a GitHub Actions workflow for CI/CD in the `.github/workflows/deploy.yml` file.