We'll be using Python, I recommend 3.7 or newer.
To keep the home environment tidy, we'll be using a virtual environment, you can use conda or virtualenv.
These instructions use virtualenv (venv).
I am using an IDE (PyCharm) to do a lot of my handling of the virtual environment. I recommend this only because it is one less thing to worry about.
In this step, we'll initialize our virtual environment and install required packages. I chose to use an IDE for this because setting up and activating the virtual environment is done automatically. This can ensure that our packages are localized and we won't run into any dependency issues.
This can be done one of two ways: 1) Manually using virtualenv (or conda) or, 2) Automatically when creating a project
in your favorite IDE.
For manual set up, please refer to: https://towardsdatascience.com/virtual-environments-104c62d48c54. This article gets into both the "how to use" and the "why we use" virtual environments with Python. If you run into a paywall, please visit https://12ft.io/ to remove it.
As for the automatic setup, this will vary slightly depending on IDE used:
- In your IDE, select "New Project" or "New Folder" or similar wording
- Usually in this screen or the next, when you select your language,
you will be given an option to create a virtual environment
- For PyCharm, it is directly under Location choice.
You can clone the repo using HTTPS using:
git clone https://github.com/arusk2/cat_api_practice.git
Alternatively, you can set up SSH keys in your Github account and clone using SSH. I prefer this method but takes set up that isn't covered here. You can clone via SSH using:
git clone git@github.com:arusk2/cat_api_practice.git
Our repo structure has three important files:
- api_final.py: a final version of the API practice code, all filled in. I'm choosing to include this as a reference.
- api.py: a skeleton from the api_final.py that is needs code added. I've tried to include notes and prompts where code is needed. Some specifics, like the way we reference our database, have been filled in so that we're focusing on the broad strokes of the API building process and not the library-specific implementations.
- requirements.txt: This we will be using to install dependencies
If you want to use git to track your changes, it will be best to do so on your own branch. In order to do this,
navigate to the project directory using
cd cat_api_test (if you haven't already). Then, use the following command:
git checkout -b YourNick
checkout is the command that will switch a tracked branch in Git and the -b flag will create a new branch with the name that follows it. The name should be all one word.
Just switch our "YourNick" with, well, your cat nick (This can also be whatever unique name you want your branch
to be named, just know I will be able to see it).
Now that we have our virtual environment created, our IDE has automatically activated it. You can double check this by
opening a terminal in your IDE and verify that you have something like:
(your environment name) shell_name_here $ . This will vary slightly based on terminal language but the important
thing to see if the name of your virtual environment in parentheses before everything. This shows that its been activated.
Now, we will be using pip to install our dependencies from the repo. pip is the default package manager for Python.
You can install dependencies using:
pip install -r requirements.txt
I have included in our repo a list of dependencies needed for the project, this will install all of them automatically to your virtual environment. This is a fresh environment, so we shouldn't run into any issues.
We're going to be working on api.py, it is a skeleton of the code and won't run successfully until we add code to it.
There is a finalized version called api_final.py that has all the filled in code.
This can be used for reference and is very useful for testing to make sure our requirements are successfully installed.
To test that we have successfully installed all requirements, run the command:
flask --app api_final.py --debug run
This will create a Flask webserver using the api_final.py code. We can then run
python test.py to start a training script that we can manually test the API. If you are getting status codes of 200 as returns, you
should be good to go!
To run the api.py file, you can use the python script flask_start.py by using the command:
python flask_start.py
NOTE: This will not work until we add code to it. You will get 404 errors.