Wolt Summer 2021 Internship assignment

FastAPI python backend for restaurant discovery feature.

Live demo

https://wolt-backend-2021.azurewebsites.net/discovery?lat=24.935326&lng=60.155631

Installation

python -m venv venv

.\venv\Scripts\activate

pip install -r requirements.txt

Running locally

uvicorn main:app --reload

Specification

restaurants.json in the repository contains one hundred restaurants from the Helsinki area.

Your task is to create an API endpoint /discovery that takes coordinates of the customer as an input and then returns a page (JSON response) containing most popular, newest and nearby restaurants (based on given coordinates).

Location of a customer needs to be provided as request parameters lat (latitude) and lon (longitude), e.g. /discovery?lat=60.1709&lon=24.941. Both parameters accept float values.

An JSON object returned by the /discovery -endpoint must have the following structure:

{
   "sections": [
      {
           "title": "Popular Restaurants",
           "restaurants": [.. add max 10 restaurant objects..]
      },
      {
           "title": "New Restaurants",
           "restaurants": [..add max 10 restaurant objects..]
      },
 	{
           "title": "Nearby Restaurants",
           "restaurants": [.. add max 10 restaurant objects..]
      }

   ]
}

For each restaurants-list you need to add maximum 10 restaurant objects. A list can also contain fewer restaurants (or even be empty) if there are not enough objects matching given conditions. A section with an empty restaurants-list should be removed from the response.

So how do you know which restaurants to add to each list?

There are two main rules to follow:

All restaurants returned by the endpoint must be closer than 1.5 kilometers from given coordinates, measured as a straight line between coordinates and the location of the restaurant.
Open restaurants (online=true) are more important than closed ones. Every list must be first populated with open restaurants, and only adding closed ones if there is still capacity left.

In addition each list has a specific sorting rule:

“Popular Restaurants”: highest popularity value first (descending order)
“New Restaurants”: Newest launch_date first (descending). This list has also a special rule: launch_date must be no older than 4 months.
“Nearby Restaurants”: Closest to the given location first (ascending).

Remember to cap each list to max. 10 best matching restaurants. The same restaurant can obviously be in multiple lists (if it matches given criteria).