Skip to content

Implement the CLI entry point

Notice that the CLI will still not work in the way that we want it to. In order for the CLI to work, we have to make two alterations.

Tip

There are a few different libraries that will help you handle CLI. In this project, we use typer, but argparse is also a very popular one.

Additions to the code

At this point it is worth quickly going through the code for the app.py script. Click the arrows to find out what the code does.

import sys

import typer
from cancer_prediction import streamlit_app  # (1)!
from streamlit.web import cli as stcli     

app = typer.Typer()  # (2)!

@app.command()  # (3)!
def __version__():
    typer.echo("0.1.0")

@app.command()  # (4)!
def run():
    sys.argv = ["streamlit", "run", "cancer_prediction/streamlit_app.py"]
    sys.exit(stcli.main())


if __name__ == "__main__":
    app()
  1. Since this depends on the streamlit_app.py script, we have to import it here
  2. Initialize the typer app
  3. A command that prints out the version of the app
  4. A command that essentially mimics the streamlit run cancer_prediction/streamlit_app.py command that we used earlier

We create a new folder inside cancer_prediction called cli. We also create a new __init__.py file and copy over the app.py file. The init file should contain only: 

from .app import app

__all__ = ["app"]

We also need to add the typer library. Since this is a main dependancy, we can add it using the regular uv add command.

Additions to the .toml file

We want someone to be able to do: 

pip install cancer-prediction

and then 

cancer-prediction run

We have defined our run command, but your bash terminal will not recognize the command cancer-prediction! To do this, we first need to define an entry point. We add the following lines to the bottom of pyproject.toml: 

[build-system]
requires = ["setuptools"]
build-backend = "setuptools.build_meta"

[tool.setuptools.packages.find]
where = ["."]
include = ["cancer_prediction*"]

[tool.uv]
package = true

Then we add the following lines below that: 

[project.scripts]
cancer-prediction = "cancer_prediction.cli:app"

This tells Python: "When I type the command cancer-prediction in my terminal, run the function app from cancer_prediction/cli.py" Next, install the project locally with uv by syncing changes we have just made to pyproject.toml 

uv sync

We can now try it out by running 

cancer-prediction run

and the streamlit app should open! You should be able to play around with the app in the browser. In general, streamlit is a great way to prototype new applications. Try training a model using the training data - give it a name like cancer_model_v2. Then try running inference on this model with the testing data.


Dark Souls Bonfire

Further reading