• Food delivery API which can be used by frontend clients to help access information seamlessly
• Tested in Python 3.9.5 in MacOS

• Install required dependencies using pip3 install -r requirements.txt
• To run the API, use flask run

• All the steps to extract, transform and load given json datasets into SQL database are done in app/etl.py.
• Run it using python3 etl.py
  • I have converted the given data into different tables which made sense to me (room for improvement)
  • This will create an etl_application.db file in app/data folder.
  • NOTE: It's gonna take some time (00:03:45 in my machine)
• The loaded data in database schema is explained in schema.md to understand it better.

NOTE: No need to do above ETL process steps for testing application, as there is a seperate db file called prod_application.db in app/data which is used by the application

• As few steps weren't clear on how data is requested, kindly follow the following structure to get desired output in an other terminal

1. List all restaurants that are open at a certain datetime

   • To get open restaurants at any given datetime

   curl -d '{"time": "2018-10-20 05:13:00"}' http://localhost:5002/v1/open-restaurants
   

   Sample Output:

2. List top y restaurants that have more or less than x number of dishes within a price range

   • To get list of restaurants with custom settings

   curl -d '{"limit_of_results": 5, "do_not_have_dishes": 11,"price_range":[13.13, 20.15]}' http://localhost:5002/v1/restaurants-with-budget-dishes
   

   Sample Output:

3. Search for restaurants or dishes by name, ranked by relevance to search term

   • To fetch matching details

   curl -d '{"search_string": "mul" }' http://localhost:5002/v1/search
   

   Sample Output:

4. Process a user purchasing a dish from a restaurant, handling all relevant data changes in an atomic transaction

   • Type this command or something similar

   curl -d '{"customer_id": 6, "dish_name":"German potato salad" }' http://localhost:5002/v1/purchase
   

   Sample Output: with customer id 6, 7

• To test the application, move to app folder and type pytest
• I have tested the preprocessing functions which are crucial to format data.
• To get code coverage type pytest --cov Should be 46%

My thoughts while doing the application are at thought_process.md