We need to create a small application where everyone can view the movies on the schedule, separated by date. These movies should be in a list with the following information:

Title Poster Genre(s) Rating Year Release Metacritic Rating Runtime There should be one filter and one search. The filter should be by Genre. It should be a dropdown list, populated with all genres of movies currently in our list and it should hide all movies that do not match the selected genre when clicked. Bonus points if it allows for multiple selections.

The search should be by Title. It's should be a text input that, with each character, shows each movie that matches the current string and hides each movie that doesn't.

1. Set up a Python virtual environment and install the required Python dependencies:

    python3 -m venv <path-to-virtualenv>
    source <path-to-virtualenv>/bin/activate
    pip install -r requirements.txt
   

2. Create .env configuration file based on env.sample:

    cp env.sample .env
    vim .env
   

3. Set up the database

   You'll need to create the database and set DATABASE_URL in the configuration file before you can run migrations and use the code.

   To use SQLite (supported out of the box), set the DATABASE_URL to the location of database file (it will be created on the first run), either relative to the project directory:

    DATABASE_URL=sqlite:///sqlite.db
   

   Or absolutely positioned in the file system:

    DATABASE_URL=sqlite:////full/path/to/sqlite.db
   

   (Note the three or four dashes in the URL, respectively).

   To use PostgreSQL or MariaDB databases, install the appropriate driver and create database and user as needed. Example for PostgreSQL (this assumes you already have PostgreSQL installed on your system via package manager such as apt, rpm, or brew):

   1. Connect to the database as admin and create a new user and database

      CREATE USER 'appuser' WITH PASSWORD 'secretpassword'; CREATE DATABASE 'dbname' WITH OWNER 'appuser';

   2. Install Python database driver for PostgreSQL

      pip install psycopg2

   3. Set up DATABASE_URL in your .env:

      DATABASE_URL=postgres://appuser:secretpassword@localhost/dbname

4. Run migrations:

    python manage.py makemigrations
    python manage.py migrate
   

5. Run the server:

    python manage.py runserver
   

6. Visit the browsable API at http://localhost:8000/api/v1/

7. Access the Django admin at http://localhost:8000/admin/

A superuser account can be created using the Django management command:

python manage.py createsuperuser

To run the test suite:

python manage.py test

To run the test suite and get code coverage statistics:

coverage run manage.py test
coverage report

To generate HTML reports, run this and open htmlcov/index.html afterwards:

coverage html

To format the code automatically using black, run it from the project root directory:

black .

To check for common programming errors or style problems, run ruff linter in the project root directory:

ruff --fix .

To automatically run black (formatting), ruff (linter) and isort (sort/format package imports) on every git commit, set up a git pre-commit hook:

pre-commit install

Note that you'll need to have initialized your git repository for the git pre-commit hook to be available. To test it without installation, you can run:

pre-commit run --all-files

Build the docker image with:

    docker build -t movie-schedule .

The default command is to start the web server (gunicorn). Run the image with -P docker option to expose the internal port and check the exposed port with docker ps:

    docker run --env-file .env --P movie-schedule
    docker ps

Make sure you provide the correct path to the env file (this example assumes it's located in the local directory).

To run a custom command using the image (for example, db migrations):

    docker run --env-file .env movie-schedule python manage.py migrate

To run a Django shell inside the container:

    docker run --env-file .env -t movie-schedule

Note that any changes inside the container will be lost. For that reason, running collectstatic or using a SQLite database within a container will have no effect. If you want to use SQLite with docker, mount a docker volume and place the SQLite database inside it.

For more information on the docker build process, see the included Dockerfile.