PICKR is the second project built in IronHack's WebDevelopment program.
The following requirements had to be met:
- Use React as a foundation.
- Use Axios or the Fetch API to fire HTTP requests to an external API, and update the screen with the received data.
- Make use of one or more external data APIs, or simulate a fake backend with the json-server package or the Ironrest API.
- Have Routes for all components that should represent a single page in the screen. There should be at least one route that uses route parameters.
- Have a repo on GitHub.
- Have at least 1 commit per day that you worked on it.
- Be deployed online using Heroku so that anybody could use your app.
The authors came up with the idea for the app while discussing that there are moments when you spend way too much time choosing what you want to eat or what you are going to watch (or even a combination of both), and it can be a burden, specially on those tough days after work or school. In order to ease the movie/food picking process, PICKR was created. The app gives up to five random suggestions of either food or movie, based on filters the user chooses.
Right in the Homepage, you will be presented two buttons: one for MoviePICKR and another for FoodPICKR.
In the MoviePICKR page, the two possible filters are for genre and the movie's original language. The user may select multiple genres for search refining purposes. After the filters are selected, PICKR will show the results it found in the following format: movie poster, title, release year, rating and synopsis.
In the FoodPICKR page, the filtering possibilities are: cuisine (food's origin country), diet (i.e. vegan or keto) and meal type (i.e. main course), and after selecting the desired options, the results will be brought in the following format: a picture and name of the food option. In the future, a link will be displayed below the food's name in which the user will be redirected to a search engine with search results for the food's recipe. The idea for FoodPICKR is only to give suggestions on what the user may want to eat in that moment based on selected filters.