Building a Personal Coffee Data App with Python and Streamlit
Coffee is more than just a drink, it's an experience, a ritual, and for many of us, a passion. As a coffee enthusiast, I've always been fascinated by the of the variables that go into making the perfect cup. From the origin of the beans to the brewing method, each factor plays a crucial role in the final taste profile.
The Birth of a Coffee Data App
Over the years, i've accumulated a mountain of notes about the different beans i've purchased and the various ways i've prepared them. These notes are scattered all over my house, scribbled on pieces of paper, sticky notes, and in random notebooks. This chaotic collection of data was begging for some organization. So, this past weekend, i decided to take matters into my own hands and create an app to capture all this information.
Using Python and Streamlit, i hacked together a simple but effective app to store and analyze my coffee data. If you are not familiar with it, Streamlit is an incredible open-source Python framework that makes it easy to build and share beautiful, custom web apps for machine learning and data science¹ A huge shoutout to the Streamlit team for their amazing work on this tool!
Building the App
The app's development started with a basic idea: i needed a place to input and store data about the coffee beans i use and the methods i employ to brew them. Initially, the database contains only the two types of beans i'm currently brewing, but my goal is to eventually input all my historical coffee notes and, of course, document any new beans and preparations moving forward.
Here’s a brief overview of the steps I took to build the app:
Setting Up the Environment: I began by setting up a Python environment and installing Streamlit. This step was straightforward, thanks to Streamlit's clear and concise documentation².
Designing the Database Schema: I created a simple schema to capture essential data points such as bean origin, roast level, grind size, brewing method, water temperature, and brewing time. The database tables include beans, equipment, brewing details, and tasting notes and can all be found in the GitHub project³ i created.
Building the User Interface: Using Streamlit's intuitive API, i developed a user-friendly interface for inputting data. The interface includes dropdown menus, sliders, and text input fields to make data entry as seamless as possible. You can checkout a working version here.
Implementing Data Storage: For data storage, i opted for a lightweight MySQL database, which is perfect for this type of personal project. It allows for quick read and write operations and can handle the growing dataset as i add more entries.
Adding Data Visualization: One of the key features of the app is its ability to visualize data. I added functionality to generate charts and graphs (currently only avaiable in the backend admin app that i built to help input and manage all of the data), which help me see trends and patterns in my coffee brewing habits. For example, i can easily compare how different grind sizes affect the taste of beans from various regions.
The Joy of Coffee Data
Creating this app has not only helped me organize my coffee data but also deepened my appreciation for the intricacies of coffee brewing. By capturing and analyzing this information, i'm able to make more informed decisions about how to brew my coffee, leading to a consistently better cup.
The combination of coffee and data is truly a match made in heaven: ☕️ + 📊 = 🥰.
One other side bonus, i’ve found that the app has also forced me to slow down and be more present when preparing and enjoy the coffee, as i have to take notes through the process and slow down enough to notice the subtle differences in texture and flavor. This is a great bonus for one who is trying to practice mindfulness.
Moving Forward
While the current version of the app is quite basic, i have big plans for its future. If i find the motivation, i'll work on entering all my random coffee notes into the database and i also have a growing list of future features i’d like to add to the app including an integration with ChatGPT that will allow app users to have a conversation with the data, something like, “I am in the mood for something light, that tastes like green apples, and comes from South America. What do you recommend?”
Also, i’ve open-sourced the frontend of this app which is available for anyone on GitHub for anyone who wants to add their ideas or fork it and make it their own.
In the world of coffee, there's always something new to learn and discover. Whether you're a casual coffee drinker or a passionate connoisseur, capturing and analyzing data about your coffee can enhance your experience and help you enjoy better coffee. If you have a passion project like this, I highly encourage you to dive in and start building. With tools like Python and Streamlit, the possibilities are endless.
P.S. Once again, a huge shoutout to the team at Streamlit for creating such an incredible framework. Your work has made this project possible, and I can't wait to see what the community builds next.
