DS Build Week scaffold

Here's a template with starter code to deploy an API for your machine learning model and data visualizations. You're encouraged (but not required) to use this template for your Build Week.

You can deploy on Heroku in 10 minutes. Here's the template deployed as-is: https://ds-bw-test.herokuapp.com/

This diagram shows two different ways to use frameworks like Flask. Both ways are good! The first way is what you learned in DS Unit 3. The second way is more common in Build Weeks & Labs.

Instead of Flask, we'll use FastAPI. It's similar, but faster, with automatic interactive docs. For more comparison, see FastAPI for Flask Users.

You'll build and deploy a Data Science API. You'll work cross-functionally with your Web teammates to connect your API to a full-stack web app!

Tech stack

FastAPI: Web framework. Like Flask, but faster, with automatic interactive docs.
Flake8: Linter, enforces PEP8 style guide.
Heroku: Platform as a service, hosts your API.
Pipenv: Reproducible virtual environment, manages dependencies.
Plotly: Visualization library, for Python & JavaScript.
Pytest: Testing framework, runs your unit tests.

Getting started

Create a new repository from this template.

Clone the repo

git clone https://github.com/YOUR-GITHUB-USERNAME/YOUR-REPO-NAME.git

cd YOUR-REPO-NAME

Install dependencies

pipenv install --dev

git add Pipfile.lock

git commit -m "Add Pipfile.lock"

Activate the virtual environment

pipenv shell

Launch the app

uvicorn app.main:app --reload

You'll see your API documentation:

Your app's title, "DS API"
Your description, "Lorem ipsum"
An endpoint for POST requests, /predict
An endpoint for GET requests, /vis/{statecode}

Click the /predict endpoint's green button.

You'll see the endpoint's documentation, including:

Your function's docstring, """Make random baseline predictions for classification problem."""
Request body example, as JSON (like a Python dictionary)
A button, "Try it out"

Click the "Try it out" button.

The request body becomes editable.

Click the "Execute" button. Then scroll down.

You'll see the server response, including:

Code 200, which means the request was successful.
The response body, as JSON, with random baseline predictions for a classification problem.

Your job is to replace these random predictions with real predictions from your model. Use this starter code and documentation to deploy your model as an API!

File structure

.
└── app
    ├── __init__.py
    ├── main.py
    ├── api
    │   ├── __init__.py
    │   ├── predict.py
    │   └── viz.py    
    └── tests
        ├── __init__.py
        ├── test_main.py
        ├── test_predict.py
        └── test_viz.py

app/main.py is where you edit your app's title and description, which are displayed at the top of the your automatically generated documentation. This file also configures "Cross-Origin Resource Sharing", which you shouldn't need to edit.

app/api/predict.py defines the Machine Learning endpoint. /predict accepts POST requests and responds with random predictions. In a notebook, train your model and pickle it. Then in this source code file, unpickle your model and edit the predict function to return real predictions.

When your API receives a POST request, FastAPI automatically parses and validates the request body JSON, using the Item class attributes and functions. Edit this class so it's consistent with the column names and types from your training dataframe.

app/api/viz.py defines the Visualization endpoint. Currently /viz/{statecode} accepts GET requests where {statecode} is a 2 character US state postal code, and responds with a Plotly figure of the state's unemployment rate, as a JSON string. Create your own Plotly visualizations in notebooks. Then add your code to this source code file. Your web developer teammates can use react-plotly.js to show the visualizations.

Lambda School docs - Data visualization with React & Plotly. This is the code for the above example. Your web teammates can reuse this as-is.
Plotly docs

app/tests/test_*.py is where you edit your pytest unit tests.

More instructions

Activate the virtual environment

pipenv shell

Install additional packages

pipenv install PYPI-PACKAGE-NAME

Launch a Jupyter notebook

jupyter notebook

Run tests

pytest

Run linter

flake8

calmcode.io videos - flake8

Deploying to Heroku

Prepare Heroku

heroku login

heroku create YOUR-APP-NAME-GOES-HERE

heroku git:remote -a YOUR-APP-NAME-GOES-HERE

Deploy to Heroku

git add --all

git commit -m "Deploy to Heroku"

git push heroku main:master

heroku open

Deactivate the virtual environment