Python API in a container

WarningCaution

This section is being revised. Thank you for your patience.

Set up

To run the API, navigate to the Python/api/ folder and check the Python version:

which python3

python3 --version

Create virtual environment using venv:

/usr/bin/python3 -m venv .venv 

source .venv/bin/activate  

Install libraries in the requirements.txt:

pip install -r requirements.txt

Build model (optional)

We don’t have to, but if we want to run model.py, we’ll also need palmerpenguins and duckdb.

pip install palmerpenguins

pip install duckdb

Now we can run the model.py script:

python3 model.py

This will create a new model in models/ (if something changed).

  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g     sex  year
0  Adelie  Torgersen            39.1           18.7              181.0       3750.0    male  2007
1  Adelie  Torgersen            39.5           17.4              186.0       3800.0  female  2007
2  Adelie  Torgersen            40.3           18.0              195.0       3250.0  female  2007
R^2 0.8555368759537614
Intercept 2169.2697209393996
prototype_data      bill_length_mm  species_Chinstrap  species_Gentoo  sex_male
0              39.1              False           False      True
1              39.5              False           False     False
2              40.3              False           False     False
4              36.7              False           False     False
5              39.3              False           False      True
..              ...                ...             ...       ...
339            55.8               True           False      True
340            43.5               True           False     False
341            49.6               True           False      True
342            50.8               True           False      True
343            50.2               True           False     False

[333 rows x 4 columns]
Columns Index(['bill_length_mm', 'species_Chinstrap', 'species_Gentoo', 'sex_male'], dtype='object')
Coefficients [  32.53688677 -298.76553447 1094.86739145  547.36692408]
Model Cards provide a framework for transparent, responsible reporting. 
 Use the vetiver `.qmd` Quarto template as a place to start, 
 with vetiver.model_card()
Writing pin:
Name: 'penguin_model'
Version: 20260618T104900Z-91fdd

Finally, run the API using:

python3 mod-api.py

View the API using the following URLS:

http://127.0.0.1:8080/

http://127.0.0.1:8080/docs

Testing API

Open these URLs in your browser:

• Interactive docs: http://127.0.0.1:8080/docs
• Health check: http://127.0.0.1:8080/ping
  
• Model metadata: http://127.0.0.1:8080/metadata

Or perform terminal testing (in a new terminal)

Test health check:

curl http://127.0.0.1:8080/ping

Test prediction:

curl -X POST "http://127.0.0.1:8080/predict" \
  -H "Content-Type: application/json" \
  -d '{
    "bill_length_mm": 45.0,
    "species_Chinstrap": 0,
    "species_Gentoo": 1,
    "sex_male": 1
  }'

Docker

Build the image (run from Python/api/):

docker build -t penguin-model-py .

Run the container, mounting the model directory from the host:

docker run --rm -d \
  -p 8080:8080 \
  --name penguin-model-py \
  -v "$(pwd)/models":/data/model \
  penguin-model-py

The -v flag mounts the local models/ folder into the container at /data/model, which is where MODEL_PATH points by default. This keeps the model outside the image so it can be updated without a rebuild.

Test the running container:

curl http://localhost:8080/ping