This project is intended to help you tie together some important concepts and technologies from the 12-day course, including Git, Flask, JSON, Pandas, Requests, Heroku, and Bokeh for visualization.
The repository contains a basic template for a Flask configuration that will work on Heroku.
We will use docker to create a container which allows us to use a wider variety of packages than a bare heroku install and get around the heroku slug limit (useful for deploying scikit-learn models). This is motivated by python-miniconda
A finished example that demonstrates some basic functionality.
- Look up how to install docker here Docker Documentation
- Git clone the existing template repository.
app/requirements.txtandapp/conda-requirements.txtcontain some default settings which will be installed bypipandcondarespectively. - There is some boilerplate HTML in
app/templates/ - Create Heroku application with
heroku create <app_name>or leave blank to auto-generate a name. - Login to container with
heroku container:login - Deploy to Heroku:
heroku container:push web - Release image on Heroku:
heroku container:release web - You should be able to see your site at
https://<app_name>.herokuapp.com - A useful reference is the Heroku quickstart guide.
- Use the
requestslibrary to grab some data from a public API. This will often be in JSON format, in which casesimplejsonwill be useful. - Build in some interactivity by having the user submit a form which determines which data is requested.
- Create a
pandasdataframe with the data.
- Create a Bokeh plot from the dataframe.
- Consult the Bokeh documentation and examples.
- Make the plot visible on your website through embedded HTML or other methods - this is where Flask comes in to manage the interactivity and display the desired content.
- Some good references for Flask: This article, especially the links in "Starting off", and this tutorial.