Our final project consists of a very minimalistic Flask app that makes a request to an API (https://rapidapi.com/gox-ai-gox-ai-default/api/ott-details/) that returns a list of movies released between the years 2015 and 2020. The project is composed of three parts: Application development, CI/CD on local and CI/CD on cloud
Our app makes a REST request to a public API and retrieves the response in a JSON format, in order for the changed data to remain persisted. Everytime the user starts the application, the script checks whether a json file exists with the data, and if not it creates it. We implemented this approach in order to cut down the amount of calls made to the API.
For the user to acces the data, we implemented an endpoint called 'films', in which it returns the data in a JSON format (no cute user interface i'm afraid)
In order for the app to be later deployed to a local registry in Docker and on the cloud (with Azure), we wrote a Dockerfile script that:
- uses a python:3-alpine image
- passes the variables needed for the application to start
- uses a command that installs all the packets used in python
- exposes the app both locally and with the outside (using port 5000)
Now that we created the image of our application through the Dockerfile, we made a script that contains all the necessary commands that help us:
- build and push the image to a local registry
- deploy the application into a docker container
- send a curl request to the app's URL to check whether the connection returns a status code of 200 or not
Try it for yourself:
Builds the Docker image with the Flask application:
python3 script.py build --dockerFilePath . --imageName flask-app --imageTag latest
Pushes the created Docker image into a container registry:
python3 script.py push --username maraxd --imageName flask-app --imageTag latest
Deploys a container with the Flask application on your local machine using Docker:
python3 script.py deploy --imageName maraxd/flask-app --imageTag latest
Sends a curl request to the 'films' endpoint:
python3 script.py test --endpoint http://localhost:80/films
As a bonus, we've also wrote a command that creates a Kubernetes deployment with the Flask application in a Kubernetes cluster, that gets deployed locally:
python3 script.py deployK --deploymentFile deployment.yaml --serviceName flask-service
Next up, we made use of the Azure Devops to create a pipeline that does the exact thing explained above. The pipeline also creates a container instance in Azure for the app and a Kubernetes cluster (we could've done that directly from the GUI, but we chose to complicate our lives hihi)
