Slack will be used for communication among the team as its great for collaboration in terms of communication and has a powerful web and mobile app. Slack also has many third party integrations like Github and Polls which make it more interactive to use. Github will be used for version control and will really help with code collaboration and team collaboration. It offers a well-structured GUI to create and view issues, milestones and projects.
For our front-end, React is chosen because it is easy to develop with due to its reusable components and state functions, in addition to a lot of community support. Because React is popular, it would be easy to hire for it here at our company MusiCore. Our team also has experience with React already. React can be written with ES6 and ES6 has a lot of popularity and versatility when it comes to creating classes and efficient functions. Node.js will be used as a runtime environment to compile the code. Node.js also has many different types of open-source packages that can help automate some of the tasks we want to do for the application. CSS 3 will be used to style components and is the standard for that.
For the backend of the app, Flask is chosen as it is written in Python and easily integratable with machine learning applications, as many ML frameworks are written in python. As well, Flask is quick and easy to setup, with many options for scaling it out to production. For the data, PostgreSQL will be used as it is integratabtle with Heroku since Heroku offers it as an add-on. It is also flexible and supports different data types such as JSON.
Heroku will be used to deploy the app as it is simple to setup and that is especially useful for an MVP. When you deploy an application, they already setup a website for you secured with HTTPS. They also have a lot of webpack and Docker support for you to bundle your app in. Github Actions will be used for the CI/CD pipeline as it easily integrates with Github. They also have “Actions” which help automate deployment workflow, and they already have one for deploying a Docker container to Heroku which we could use. Docker will be used to containerize the application as it will make our application portable and easier to deploy. This will also be beneficial for local developer environments.
Postman will be used to do integration testing with the backend API we create. It offers a clean interface to create many requests, and you can even organize these requests into collections. It helps to test the backend API first to make sure it's working before using it in the front-end. Jest can also be used for testing and is already embedded into React. Not only does it offer unit testing support in javascript, it can also do snapshot testing for the front-end to make sure components are rendering correctly. Enzyme is complementary to Jest and offers more functions such as shallow rendering. UnitTest will be used for Python testing as it is simple, has a lot of functionality and already built in with python. Sentry will be used for keeping track of errors as it is also easily integratable with Heroku because they offer it as an add-on. LogDNA will be used for tracking logs which are not errors and is also a Heroku add-on. Its good to have a separate service to record logs, monitor, track and even fix errors in real-time so our application can run more smoothly.
Google Maps will be used as an external API in order to mark locations on a map for display in the UI and it is an extensive and well-known framework. Tensorflow will be used for Machine Learning as it is open-source, customizable to different types of machine learning algorithms and lets you serve your model with a REST API. Tensorflow also has a lot of support and documentation which makes it easier for to start with it. Tensorflow is also written with Python. Python is easy to write in, efficient and commonly used in ML applications. In relation to Python, SnipsNLU (not shown on stackshare) will also be used in order to easily train NLU models. PredictHQ (not shown on stackshare) will be used for event data and has an easily accessible API. Twitter API will also be used in order to collect social media data and there are many endpoints it currently offers to query tweets in various ways (stackshare doesn't show this utility in the stack yet)
