I’m building a few FastAPI apps to throw in docker and run on my homelab… I wanted to add healthchecks and here’s a simple way to do it

Make sure to install curl in the dockerfile (near the top for effeciency)

# Install curl with minimal dependencies
RUN apt-get update && \
    apt-get install -y --no-install-recommends curl && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

Then I recommend making compose files even for single image deployments

services:
  app:
    build: .
    volumes:
      - type: bind
        source: .
        target: /app
    environment:
      - PYTHONPATH=/app
      - DOCKER_ENV=true
      - UV_VIRTUALENV=/opt/app-env
    user: "1000:1000"
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s

Then finally you’ll need a /health endpoint

@app.get("/health", response_class=HTMLResponse)
async def health_check():
    """
    A health check endpoint that returns a status message.
    """
    return "<html><body><h1>Service is healthy</h1></body></html>"

Logging instead of printing

I am trying to adopt logger.debug instead of print but ran into a confusing thing in ipython during Advent of Code… I riddled by script with logger.debug (yes after setting logging.setLevel('DEBUG')) but in ipython none of my log messages showed up!

import logging

logger = logging.getLogger(__name__)
logger.setLevel("DEBUG")

Turns out what I was missing was a call to basicConfig

import logging

# forget this and your messages are in the ether! or at least not seen in ipython...
logging.basicConfig()

logger = logging.getLogger(__name__)
logger.setLevel("DEBUG")

Bonus

Want your new messages to show up while iterating on something without killing the ipython kernel?

from importlib import reload
reload(logging) # to make sure you get new log messages you add while developing!

import this; print(this); print("what is taking so long black!!")

Mike Driscoll recently tweeted about making colored out with pandas DataFrames and I just had to try it for myself

Use Case

First though… why? My biggest use case is a monitoring pipeline of mine… The details aside, the output of my pipeline is a dataframe where each row has information about a failed pipeline that I need to go look into. I dump that result to a simle html file that’s hosted on an internal site and the file is updated every couple of hours. Adding some colored indicators automatically to the rows to help me assess severity of each record would be a handy way to quickly get an understanding the state of our pipelines.

How?

The docs for the applymap method state simply:

Apply a CSS-styling function elementwise.

Updates the HTML representation with the result.

So we can write a function that returns color: {color} based on the dataframe values and when we drop that dataframe to html we’ll have some simple css styling applied automagically!

By default the function will be applied to all columns of the dataframe, but that’s not useful if the columns are different types which is usually the case. Luckily there is a subset keyword to only apply to the columns you need!

Consider my example

sandbox   main via 3.8.11(sandbox) ipython
❯ df = pd.read_csv("cars.csv")

sandbox   main via 3.8.11(sandbox) ipython
❯ def mpg_color(val: float):
...:     color = "red" if val < 21 else "green"
...:     return f"color: {color}"

sandbox   main via 3.8.11(sandbox) ipython
❯ df.style.applymap(mpg_color, subset="mpg").to_html("color.html")

I want to quickly see if the mpg is any good for the cars in the cars dataset and I’ll define “good” as better than 21 mpg (not great I know but just for the sake of discussion…)

The function returns an appropriate css string and after I style.applymap on just the mpg column we get this!

I have list [True, False, False, True] and another list [1, 2, 3, 4] and a use case where I want to filter list 2 based on list 1 to remove values that line up with the element False in list 1…. so the outcome will be [1, 4]. list(compress(list2, list1)) will do it. As long as you can create a mask for the filter than itertool.compress will be your friend!

I just started using FastAPI for a home project and needed to pass back a dynamic number of values from a form rendered with jinja…

Dynamic Values

The jinja templating for rendering HTML based on something like a python iterable is nice and easy

data is the result of a database query, and item is each row, so the dot notation is the value of each column basically in that row

<form method="post">
  {% for item in data %}
    <div class="form-check ">
        <input class="form-check-input"  name="item_{{ item.id }}" id="{{ item.name }}" value="{{ item.id }}" type="checkbox">
        <label class="form-check-label" for="{{ item.id }}" > Label for: {{ item.name }} </label>
    </div>
  {% endfor %}

<button type="submit" class="submit btn btn-xl btn-outline-danger" >Remove</button>
</form>

This form generates a row with a checkbox for every item in data (in my case each item is an existing row in my table). it?

The way to pass back all those values is pretty straight forward (after hours of messing around that is!)

# I hate it when tutorials don't show ALL relevant pieces to the blurb
import starlette.status as status
from fastapi import APIRouter, Depends, Form, Request
from fastapi.encoders import jsonable_encoder
from fastapi.responses import HTMLResponse, RedirectResponse
from fastapi.templating import Jinja2Templates
from sqlalchemy.orm import Session

from app.session.session import create_get_session

router = APIRouter()
templates = Jinja2Templates(directory="templates/")

@router.post("/my_route/do_something_with_form", response_class=HTMLResponse)
async def delete_rows(
    request: Request,
    db: Session = Depends(create_get_session),
):
    form_data = await request.get_form()
    data = jsonable_encoder(form_data)
    # data = {"item_1": 1, "item_2": 2, ... "item_N": N}
    return RedirectResponse("/", status_code=status.HTTP_302_FOUND)

We await request.get_form() and after encoding the data we get a dictionary with key/value pairs of the name/value from the form!

This took me quite a long time to figure out in part because most of the Google-able resources are still on Flask…