Refers to the 'Special Topics' (C317) subject from Inatel in which students must develop a software product based on the 'Software development life cycle' (SDLC).
Here are some features of PickForMe:
- Enter your preferences to receive a more accurate indication;
- Generate a totally random indication by clicking in one button;
- Save/delete the recommendation to/from your collection;
- Filters (on the collection page): by name, genre, release year, etc;
- Responsiveness.
Obs.: To see details like requirements, setup and install and how to use, please refer to the docs of the respective end by just clicking on their name below.
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- TypeScript: allows type verification;
- React: hooks and components;
- NextJs: routes optimization and serverside rendering;
- TailwindCSS: styling classes;
- Axios: requests optimization;
With these we could built an application which is responsive, simple, clean and efficient;
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Back-end 🧮
- Python
- AWS Lambda
- API
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- Jest: unit testing. please refer to the frontend docs to see it runs.
- Cypress: used to test the interface of the app. please refer to the frontend docs to see it runs.
- Postman: used to test the API created in the backend. If you would like to try it you can click here to go directly to the postman page, or you can download the .JSON file here to run by yourself.
Obs.: Both on your localhost and the production version it will run the same.
The images below describe how to generate a personalized and a totally random indication using PickForMe.
Since this is a POC built for a college subject certain things were prioritized over other (there were specific milestones with activities which should be developed and released every 2 weeks during the semester, just like in an actual company), so there are stuff I would like to keep developing and improving as well. Here are a few of them:- Complete user CRUD;
- Show teaser of the recommendation;
- Like/dislike an indication (feedback for the next indications);
- Usage of AIs to improve the indication based on the user's preferences.
The technologies employed allow the app to have great performance on the most variable devices, as well as open up space to develop new features. PickForMe ensures you won´t waste time choosing what you will watch, so you an definetly chill while you watch something you actualy enjoy.
Special thanks to the professors Renzo and Christopher for all the support during the development.
I also would like to thank my coleagues Ana Clara Santos, Mariana Bassi and Raphael Freitas, who helped me a lot on the kickoff of this project.