This is the Python Charmers Coffee Shop application.

1. Get the .env file from the trainers. This is configured for minimal configuration

2. In the .env file change your DB_NUMBER setting (at the top of the file) to either your personal DB number or your group number.

3. Create a new virtual environment:

   python3 -m venv venv

4. Activate the virtual environment

   OS	Command
   Windows	venv/bin/activate
   OS X / *nix	source venv/bin/activate

5. Install the application requirements

   pip install -r requirements.txt
   pip install -r dev_requirements.txt

6. Set the FLASK_APP environment variabls

   OS	Command
   Windows	set FLASK_APP=server
   OS X / *nix	export FLASK_APP=server

7. Upgrade the database with flask db upgrade

8. Run the application locally with flask run

1. Install Zappa with pip install zappa (if you're using Python 3.7 use pip install git+https://github.com/itamt/Zappa.git)
2. Run zappa init to initialise the zappa application.
   • The app's functon will be server.app
   • For now select n when asked if you want to deploy globally
3. Run zappa deploy dev to deploy the application
4. Test the application by visiting the URL you're provided

Create and activate a virtual environment, and then install the requirements:

pip install -r requirements.txt
pip install -r dev_requirements.txt

Copy sample environment file from the environment_config folder into a file .env in the base folder:

cp environment_config/sample.env .env

The following environment variables should be updated in the .env files:

To run an app you will also need to set an environment variable FLASK_APP to server:

export FLASK_APP=server

You can use Alembic to set up the database:

flask db upgrade

Ultimately this application is intended to be deployed on AWS. As such there's a dependency on the boto3 library. The application will work locally provided you have boto3 installed, but for uploading files to S3 and deployment to AWS Lambda you will need the AWS CLI installed and configured.

Installing the AWS CLI is easy (if you wish there is complete documentation available as well):

1. Outside your Virtual Environment (to install globally on your system) run:

   pip3 install awscli

2. Copy your AWS Access Key ID and your AWS Secret Access Key from your IAM role in the AWS management console
3. From the command line run:

   aws configure

   to complete the configuration process
4. Test the configuration by running:

   aws s3 ls

   to list all of your S3 buckets along with their unique identifiers

You can run the server locally using the standard flask comands.

flask run

There will be an extended exercises per day based on the coffee shop application.

The solutions will be separated into individual branches (in the form day_n_exercise), and there is a combined_solutions branch where all extended exercises are merged together.

The front page of the coffee shop application is very bare at the moment. We can do better:

1. Update the / endpoint to display the 10 most recently added shops.

   Since we haven't covered SQLAlchemy yet, use the following to generate shops to pass through to the template:

   from coffeeshop.server.shop.models import Shop
   shops = Shop.query.order_by(Shop.date_added.desc()).limit(10)

2. Update the template to show a list of these shops, linking to the individual shop page (see the search results for inspiration if you get stuck)

It would be nice to create an API for people to programatically query the coffee shops, rather than having to scrape the site.

Use Connexion to create a simple API to access the Shops model. It should have two endpoints:

1. A list endpoint which lists stores 10 at a time. It should take a page parameter which defaults to 1 and can be any positive integer, which is the query offset for which stores to return.
2. A search endpoint which lists stores matching a query passed via the q parameter, in the same way the current coffeeshop.server.shop.views.search_shop method handles queries. It should also take a page parameter.

The functions that are mapped by the app.yaml file should be store in coffeeshop/server/shop/api.py

You will also need to create a new create_connexion_app method within your coffeeshop/server/__init__.py file.

Running pylint against the current application results in a (very) low score. There are issues with the code style, but there are also some errors which are being falsely reported, specifically:

• E1101 (instance of object doesn't have member)
• R0401 (circular imports)

Generate a .pylintrc file for your project with:

pylint --generate-rcfile > .pylintrc

In the .pylintrc file add those two errors to the disabled messages list.

Re-run pylint and update the files so that the score is at least 8 out of 10.

To deploy to AWS Lambda you'll need:

1. To have the AWS CLI installed and configured
2. To install Zappa (if you're using Python 3.7 use pip install git+https://github.com/itamt/Zappa.git)
3. You will need a database setup on RDS where the application data will be stored - the connection string for an accessible PostGreSQL database will be provided via a download link by the trainers.
4. You'll need to create a new bucket on S3 to store photos. It should not block public ACLs from uploading. It will be used to store uploaded photos.
5. You'll need to update the following environment variables in your .env file (note: it's a good idea to backup your development .env file first - possibly with cp .env environment_config/dev.env):
   • SECRET_KEY should be generated for production as per the instructions above
   • SECURITY_PASSWORD_SALT likewise the password salt should be updated
   • SQLALCHEMY_DATABASE_URI the connection string for your RDS database
   • FLASK_ENV should now be set to production
   • WTF_CSRF_ENABLED should be True
6. Run the Alembic upgrade to add your database tables on the server

Your goal is to upload photos to a S3 bucket. As such you'll need to update your S3 settings accordingly (in your .env file):

Finally, to ensure the upload works, set the environment variable that will control your app:

On Windows:

set APP_SETTINGS="coffeeshop.server.config.ProductionConfig"

On Unix or OS X:

export APP_SETTINGS="coffeeshop.server.config.ProductionConfig"

Then run the app as normal. Test that when you create a new shop and add a photo that instead of being stored locally, the file is uploaded to your S3 bucket.