Set up a local Data Formulator development environment

How to set up your local machine.

Prerequisites

Create a Virtual Environment

python -m venv venv
.\venv\Scripts\activate

Install Dependencies
```
pip install -r requirements.txt
```
Configure environment variable (optional)s
- copy api-keys.env.example to api-keys.env and add your API keys.
  - required fields for different providers are different, please refer to the LiteLLM setup guide for more details.
    - currently only endpoint, model, api_key, api_base, api_version are supported.
  - this helps data formulator to automatically load the API keys when you run the app, so you don't need to set the API keys in the app UI.
- set .env to configure server properties:
  - copy .env.template to .env
  - configure settings as needed:
    - DISABLE_DISPLAY_KEYS: if true, API keys will not be shown in the frontend
    - EXEC_PYTHON_IN_SUBPROCESS: if true, Python code runs in a subprocess (safer but slower), you may consider setting it true when you are hosting Data Formulator for others
    - LOCAL_DB_DIR: directory to store the local database (uses temp directory if not set)
    - External database settings (when USE_EXTERNAL_DB=true):
      - DB_NAME: name to refer to this database connection
      - DB_TYPE: mysql or postgresql (currently only these two are supported)
      - DB_HOST: database host address
      - DB_PORT: database port
      - DB_DATABASE: database name
      - DB_USER: database username
      - DB_PASSWORD: database password
Run the app
- Windows
```
.\local_server.bat
```
- Unix-based
```
./local_server.sh
```

Install NPM packages
```
yarn
```
Development mode

Run the front-end in development mode using, allowing real-time edits and previews:
```
yarn start
```
Open http://localhost:5173 to view it in the browser. The page will reload if you make edits. You will also see any lint errors in the console.

Build the frontend and then the backend

Compile the TypeScript files and bundle the project:
```
yarn build
```
This builds the app for production to the py-src/data_formulator/dist folder.

Then, build python package:
```
pip install build
python -m build
```
This will create a python wheel in the dist/ folder. The name would be data_formulator-<version>-py3-none-any.whl
Test the artifact

You can then install the build result wheel (testing in a virtual environment is recommended):
```
# replace <version> with the actual build version. 
pip install dist/data_formulator-<version>-py3-none-any.whl 
```
Once installed, you can run Data Formulator with:
```
data_formulator
```
or
```
python -m data_formulator
```
Open http://localhost:5000 to view it in the browser.