Flask Blood Glucose Tracker

My oldest daughter was diagnosed with Type 1 Diabetes at the age of two. Technology has come a long way in terms of tracking blood sugar levels, but I thought I would start a Python web application to do so. I’ll be using the Flask web framework for the project and, since I’m not a marketing or product naming genius, will call the project Flask Blood Glucose Tracker. I’m certainly open to other, more catchy, names.

This is somewhat of a different post for me, in that I’ll be walking through the generation of an actual application. The application will mostly be a minimal viable product (MVP) though. It should, however, be a good tutorial on how to integrate some concepts and technologies together. As such, in this first post, I’ll cover some of the project specifications and features that I’d like to include and work on implementing them in future posts.

Application for Diabetes

Diabetics have to check their blood glucose levels frequently, typically using a blood sugar monitor. These checks involve getting a small portion of blood from a finger (or arm) prick and having the monitor test it. It then returns a measurement of the amount of glucose in the blood as a number of milligrams per deciliter (mG/dL). These readings then will be the numbers we want to record in our application.

It is also important to know if the reading is taken before or after eating and the time of day the reading was taken. Therefore, the application will need to accommodate for that as well. The readings themselves, along with the time of day and relation to meal time are all factors into the amount of insulin needs to be injected.

Throughout this, and subsequent posts, I will do my best to explain diabetes-specific terms in as user-friendly of a way as possible. I am basing much of the numbers that I use, ranges of “good and bad”, etc. on managing my daughter’s diabetes over the last 14 years and the countless doctor appointments I’ve attended. As a disclaimer, I am not a medical professional. Please check with your physician about specifics with diabetes. There is a Diabetes For Dummies book which provides a decent overview as well.

Flask Blood Glucose Tracker Application Features

There are already a lot of excellent products on the market for keeping track of one’s blood sugar levels. With that in mind, this application is going to be fairly simple to start. I would like to build it with growth in mind, however, so building a REST API into the project seems like a good idea. My basic feature list to start with is:

  • Register for the BGT site, with email confirmation.
  • Login/Logout and based on role have different access.
    • Patient to access and edit my own data.
    • Physician to get a list of all current patient records.
    • Administrator for site maintenance.
  • Input blood sugar levels with date and time of reading and indication of before or after a meal or snack.
  • Display the data in a table with averages.
  • Display the data in a chart or visual format.
  • Typical CRUD operations for the data
  • REST API to expose patient blood sugars in a secure fashion
  • Data is stored in the cloud for accessibility and ease of database maintenance.

For the reporting features highlighting high and low blood sugar levels in the report would be helpful. Since what is “good” and “bad” can change for each individual, I’ll include a field for each individual to set that.

Application Stack

I have already mentioned that this application will be built with Flask. For the data store, I will use MongoDB. To keep in line with the feature request of storing data in the cloud, MongoDB offers a Database as a Service (DBaaS) called Atlas.

I haven’t quite figured out yet where I’ll ultimately host this application, perhaps Heroku? Or maybe on my own server.

Application Libraries

When it comes to libraries, there are a lot of choices. Here’s what I’ll be using, which will also be included in a requirements.txt file.

  • Flask version 0.12.2
  • Flask-Login, 0.4.0
  • Flask-PyMongo, 0.5.1
  • Bokeh, 0.12.6
  • Jinja2, 2.9.6
  • pandas, 0.20.2

There are other libraries that will but used as well, but those are the main ones of interest.

I’m also more of a fan of Zurb Foundation than Twitter Bootstrap, so I’ll be using that for styling.

Pages and Routes

Web Pages

To start with we need a way for a user to log in, enter their personal data, enter a new blood sugar record, edit their record, and view their information in both a tabular format and then in a chart format.

We’ll need the following pages, at least to start.

  • Index
  • Registration
  • Login/Logout Page
  • Profile page, login required
  • Records page (create, read, update, delete), login required
  • Chart page, login required

For a Physician we would want to be able to:

  • Display all of their patients, login required

For an Administrator, we want to be able to

  • Have the ability to manage users (patients & physicians) but not be able to see patient medical data.

From the API I want to expose the ability to securely read and write (GET and POST) data to a patient’s record. This will make it easier to, for example, write a mobile application to connect to our data. Or, with the advances in blood sugar monitors, perhaps automatically update our application with readings from a device.

Document Model

Since I will be using MongoDB to store data for this application, I’ll be leveraging the document model. This offers a lot of flexibility in how data is stored, among other benefits. I would encourage you to read my blog post on the document model if you are not familiar with it.

To start with, the basic data we want to capture and model will be as follows:

BGT Sample Document
Sample Patient Record document

In looking at this sample document, the groups field will keep track of values such as patientphysician, and admin. I have chosen to implement the postal_code and MRN values as strings instead of integers to accommodate alpha-numeric values.

Through the course of developing this application, we will see the flexibility of the document model in action. We’ll see how we can utilize some of the features from MongoDB’s aggregation pipeline to handle our data processing as well.

Wrap Up

I have outlined a nice project here which will utilize several different bits of programming. In the next few posts then, I’ll cover how to implement all of these features into an MVP application. I would definitely enjoy receiving feedback, so please leave comments below.


Follow me on Twitter @kenwalger to get the latest updates on my postings.


A Review of PyCon 2017

PyCon 2017 was held about 45 miles south of Portland, Oregon where I am fortunate enough to live. I am typically not a great conference attendee so I was a bit nervous and apprehensive about going. After walking into the Portland Convention Center and being surrounded by 3,500 fellow Python enthusiasts, however, I was super excited.

PyCon 2017 Talks

There were several amazing talks on a wide array of subjects. There were first time PyCon presenters like Jonas Neubert with a fascinating talk on using Python for factory automation. Along with some “heavy hitters” in the Python community such as Philip James and Daniele Procida. Daniele’s talk on documentation was very interesting and definitely a different way of thinking of of the documentation process.

There were some really good keynote speeches as well. I really enjoyed learning from Lisa Guo on the migration path Instagram took to go from Python 2 to Python 3. I also found Dr. Katy Huff‘s talk on some practical applications for Python in the science field to be quite interesting.

Kenneth Love gave a tutorial on the Django Admin which I would encourage people to work through as well. As usual it is a great presentation and Mr. Love provides excellent information.

There were many other great talks, from many great speakers. The conference talks are available on YouTube here. I’d encourage you to watch, listen, and learn from as many as possible.


There were some great vendors and businesses in attendance. Big companies like Google, Microsoft, Intel, and Facebook/Instagram were all there. They all offered some excellent short talks in their booths on how they were using Python for their applications or products. They also had a variety of swag they were giving away in exchange for a badge scan.

Google had an hourly trivia session and provided prizes for the correct answer. I picked up a copy of Python in a Nutshell: A Desktop Quick Reference for knowing that PyCon 2018 would be held in Cleveland, Ohio. Intel had several drawings for an Amazon Echo, along with several other vendors actually. Booz Allen Hamilton had several drawings for a Raspberry PI 3 Model B which I sadly wasn’t able to win.

Company branded socks were one of the big swag things at PyCon, beyond the t-shirts, stickers, and fidget spinners. Microsoft, O’Reilly Publishing, Citus Data, and Heroku all had custom socks. Scan your badge… get socks.

PyCon 2017 Vendor socks


In addition to the great learning opportunities the talks provided, and the interaction with sponsors, another key feature of any conference is the people. The opportunity to get to meet and talk with people like Andrew Pinkham who authored Django Unleashed, or Russell Keith-Magee of BeeWare was spectacular. Having the chance to meet and talk with other developers about industry trends was great as well.

I have heard that the Python community is second to none in terms of inclusiveness and I was able to witness first hand that is indeed the case. Due to the exceptional overall experience I had at PyCon 2017, I am sincerely hoping that I can make to the trip to Cleveland in 2018.