This post aims to record something I learned from my first Hackathon experience.

Python⌗

I am responsible for setting up a set of REST APIs in the backend and building a dashboard for managing and displaying patient data in real-time.

The Tech Stack I chose for the REST APIs is Flask + SQLITE.

Flask⌗

Flask has good support for building the RESTful API. The only thing you need to do is to install flask-restful.

It enables you to implement the logic of the API in a class and provides built-in support for the argument parsing that can parse data sent via application/json.

# an example from their website
from flask import Flask
from flask_restful import Resource, Api

app = Flask(__name__)
api = Api(app)

class HelloWorld(Resource):
    def get(self):
        return {'hello': 'world'}

api.add_resource(HelloWorld, '/')

if __name__ == '__main__':
    app.run(debug=True)

It can automatically return error messages if the API requests from the clients are incorrect (missing parameters, type mismatch, or useless parameters).

To use the database, I wrap the sqlite3 library. See the code here.

As I want to reuse the same database connection in every request, I save this to Flask’s Application Context by following the instructions at the website.

Database⌗

This part documents some simple commands I learned at the Hackathon.

The following are some commands that we use:

CREATE TABLE IF NOT EXISTS users (id INTEGER PRIMARY KEY, name TEXT, age INTEGER, gender TEXT)
CREATE TABLE IF NOT EXISTS anxieties (id INTEGER PRIMARY KEY, user_id INTEGER, timestamp INTEGER, value INTEGER UNIQUE(user_id, timestamp) ON CONFLICT REPLACE, FOREIGN KEY (user_id) REFERENCES users(id))

INSERT or REPLACE into anxieties (user_id, timestamp, value) VALUES (?, ?, ?)
  • Unique based on two (or more) columns UNIQUE(col1, col2).
  • INSERT OR REPLACE inserts if the value doesn’t exist and replaces if it already exists. It’s worth noting that REPLACE is an alias of INSERT OR REPLACE in SQLite.

Frontend⌗

For convenience, we chose streamlit to visualize our data in the database.

It’s a very easy-to-use library for displaying data. You only need to write some markdown, load the data into the appropriate format (pd.DataFrame or using matplotlib or …), and write them to the rendered webpage.

Here is an example from their website:

import streamlit as st
import pandas as pd

st.write("""
# My first app
Hello *world!*
""")

df = pd.read_csv("my_data.csv")
st.line_chart(df)

They provide a variety of components to visualize your data. We utilized the line_chart and bar_chart in our project.

One thing that troubles us in the dev process is how to display our data in real-time as we don’t find anything in the official documentation for the line_chart.

We finally strived to find the solution in a discussion (though it turns out that they published a solution in their blog). By mimicking the usage of the pyplot, we find that the st.line_chart method returns a delta object. By updating the delta object, we can achieve real-time refreshing. This is how we do: a loop and a method.

Workflow⌗

Some tips when working with database and requests library next time:

  • Encapsulate database actions in functions. This saves you from calling db.commit() and hides the implementation details from the call site.
  • Encapsulate the requests library when doing network requests. This can save you a bunch of error-handling codes on the call site.

Team Management⌗

  1. Break the project into separate parts
  2. For each part, talk to every member of your team to specify their jobs
  3. For each job, specify how much time they want
  4. Setup a deadline for every part
  5. Keep Working!
  6. Synchronization: dynamically adjust based on the progress
    1. Ideally after developing every part
    2. Can be a short standup meeting
  7. Go back to 2