Project Overview

This project is a FastAPI implementation of the challenge posted here. FastAPI is something I've enjoyed using recently since it supplies the simple syntax of Python, while having performance and scalability in the same ballpark as Node and Go for I/O bound APIs. Since Python is the premier language for ML/DS solutions, this also makes it an ideal candidate when developing APIs and tooling alongside machine learning engineers!

Running the Project via Docker Container

Note: You should be able to run and test via the Docker container.

1. Make sure you are at the root level of the fetchApi repository, since that is where our Dockerfile exists!
2. Build the image locally and give it the tag fetchapi like so:

docker build -t fetchapi .

3. Run the container and turn on port forwarding. For testing, I used port 8000 like so:

docker run -d -p 8000:80 fetchapi

4. At this point, you should have a running container and you should be able to get a successful liveliness check by navigating to the following in your web browser: http://127.0.0.1:8000/. It will print the following message to the screen:

{"message":"Liveliness check passed!!"}

Once the liveliness check passes, we are ready for some tests. I recommend a UI for calling this API, such as Postman. However, here are a few raw curl requests for those of us in a hurry. To post a receipt using the provided example:

curl --location 'http://127.0.0.1:8000/receipts/process' \
--header 'Content-Type: application/json' \
--data '{
  "retailer": "Target",
  "purchaseDate": "2022-01-01",
  "purchaseTime": "13:01",
  "items": [
    {
      "shortDescription": "Mountain Dew 12PK",
      "price": "6.49"
    },{
      "shortDescription": "Emils Cheese Pizza",
      "price": "12.25"
    },{
      "shortDescription": "Knorr Creamy Chicken",
      "price": "1.26"
    },{
      "shortDescription": "Doritos Nacho Cheese",
      "price": "3.35"
    },{
      "shortDescription": "   Klarbrunn 12-PK 12 FL OZ  ",
      "price": "12.00"
    }
  ],
  "total": "35.35"
}'

To fetch points for a receipt with id INSERT_ID_HERE:

curl --location 'http://127.0.0.1:8000/receipts/${INSERT_ID_HERE}/points'

Local Development

This section describes how to continue developing this app on your local machine.

1. Install Python 3.8.*. For this, 3.8.12 is recommended. Link: https://www.python.org/downloads/.
2. For good practice's sake, we should create a virtual env to create an isolated environment for our FastAPI project. Run:

$ python3 -m venv env

In order to use the environment’s packages/resources in isolation, you need to “activate” it. To do this, just run the following:

source env/bin/activate

3. Within the root folder, run:

pip install -r api/requirements.txt

4. Run the FastAPI web server with the following command: uvicorn api.main:createApp --reload
5. To test it, the directions are the same as they were when building via the Docker image! A liveliness check is exposed and can be accessed at the following URL: http://127.0.0.1:8000/

Running Unit Tests

Note: If you want to run tests, then read the 'Local Development' section first.

1. For testing the API routes:

python -m unittest api/tests/test_*.py

2. For testing our models:

python -m unittest models/tests/test_*.py

Directory Structure

• Data models, (and business logic associated with them), are stored in the models/ directory.
• API routes and all API logic is stored in the api/ directory.
• Both directories have a tests/ folder, which is where all unit test logic lives!

TODOs

1. "Productionize" the Dockerfile better and build off of an Ubuntu base image.
2. Switch to a more robust package management solution instead of just a requirements.txt file. I typically use Poetry for Python apps.
3. Address the TODOs in the codebase. These are very descriptive! One concern is that our API doesn't yet have a detection mechanism for duplicate receipts. :)
4. Set up CICD through GH Actions. Our CICD pipeline should always run our unit tests!
5. Set up reusable middleware for handling errors in our API (among other things).