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https://github.com/444b/sttemplate

Production ready, streamlit template repo
https://github.com/444b/sttemplate

cicd dependencies pypi python streamlit uv

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Production ready, streamlit template repo

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README 

        
# sttemplate

A minimal viable template repo for streamlit projects and publishing them on

PyPi  



The repo comes with everything you would need to get started making scalable,

maintainable python ackages.  

This summarizes alot of what I had to learn to develop and maintain [Streamlit Analytics2](https://github.com/444B/streamlit-analytics2)



>[!TIP]

> How to use this repo

> 1. make a [fork](https://github.com/444B/sttemplate/fork)

> 1. clone it to a workstation

> 1. start adding your code into the src/ folder

> 1. add your dependencies (I will add a guide to this if there is interest, just open an issue)

> 1. test it by writing unit test in the tests/ folder and running checks.sh

> 1. set up Github action CI/CD and deploy to Pypi!



Here is the layout and a description below for each section

  

├── [README.md](https://github.com/444B/sttemplate/blob/main/README.md#-readmemd)   

├── [pyproject.toml](https://github.com/444B/sttemplate/blob/main/README.md#-pyprojecttoml)  

├── [src](https://github.com/444B/sttemplate/blob/main/README.md#-src)  

│   └── [sttemplate](https://github.com/444B/sttemplate/blob/main/README.md#----sttemplate)  

│       ├── [__init__.py](https://github.com/444B/sttemplate/blob/main/README.md#--------initpy)  

│       └── [app.py](https://github.com/444B/sttemplate/blob/main/README.md#--------apppy)  

├── [tests](https://github.com/444B/sttemplate/blob/main/README.md#-tests)  

│   ├── [checks.sh](https://github.com/444B/sttemplate/blob/main/README.md#----checkssh)  

│   └── [test_calc.py](https://github.com/444B/sttemplate/blob/main/README.md#----test_calcpy)  

└── [uv.lock](https://github.com/444B/sttemplate/blob/main/README.md#-uvlock)  



---  

## ├── README.md



This is the document you are reading at this very moment. It is essential to 

sharing what your project is about and how to use it.  



--- 



## ├── pyproject.toml



This is a crucial part of your project and can be used for managing

dependancies, configurations, and if you deploy to PyPi then it is the info on

the sidepanels.  



--- 



## ├── src



This is the top level folder where your actual code will be.  



--- 



## │   └── sttemplate



This sub level folder is the name of your project and package and organizes your

code even further.  

You would have one of these for each module you write but you need at least 1.  



--- 



## │       ├── __init__.py



This very important file indicates to other python files that it organizes your

code into a module and can be imported with `import sttemplate`.  

Inside, you would define which functions and which files specifically are

availible  



--- 



## │       └── app.py



Here is where the magic happens. Your Million dollar idea, the next IYKYKaaS.

This is why you get paid the big bucks but as you might be observing that quite

alot goes on to make that magic code availible.  



--- 



## ├── tests



This folder is essential for observing the true scale of your application with

scale and complexity. `print()` and `logging.info` will only take you so far

Especially as you add features and make unpredictable changes, you will use code

here to test the `src/` code for correct behaviour.  

They also help our code be more readable and maintainable.  



--- 



## │   ├── checks.sh



This is a custom script I came up with that collects alot of python packages

used for testing:  

- black = formatting  

- isort = import sorting  

- flake8 = linting  

- mypy = type checking  

- bandit = basic security checks  

- pytest = unit testing  



--- 



## │   └── test_calc.py



This is just an example of a pytest file that demonstrates how to do a unit test

and also verifies that the imports set out in __init__.py work as intended.  



--- 



## └── uv.lock



This section is opinionated since it touches on the topic of dependancy

management. [UV](https://docs.astral.sh/uv/) is a tool for managing all these 

dependencies and in my opinion it does a fair job of it. 

There are many dependency managment tools such as pip, pipenv, poetry, tox or

even just rawdogging it with `python -m`. Try them out and find what works best for you



---