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Awesome Streamlit Awesome

The fastest way to build Awesome Tools and Apps! Powered by Python!

The purpose of this project is to share knowledge on how Awesome Streamlit is and can become. Pull requests are very welcome!

Streamlit has just been announced (Oct 2019) but I see the potential of becoming the Iphone of Data Science Apps. And maybe it can even become the Iphone of Technical Writing, Code, Micro Apps and Python.

This project provides

  • A curated list of Awesome Streamlit resources. See below.
  • An awesome Streamlit application with a gallery of Awesome Streamlit Apps.
    • Feel free to add your awesome app to the gallery via a Pull request. It's easy (see below).
  • A vision on how awesome Streamlit is and can become.
  • A best practices example and starter template of an awesome, multipage app with an automated CI/ CD pipeline, deployed to the cloud and running in a Docker container.

Visit the app at awesome-streamlit.org!

Awesome Streamlit Org Animation

The Magic of Streamlit

The only way to truly understand how magical Streamlit is to play around with it. But if you need to be convinced first, then here is the 4 minute introduction to Streamlit!

Afterwards you can go to the Streamlit docs to get started. You might also visit Awesome Streamlit docs.

Introduction to Streamlit

Awesome Resources

A curated list of awesome streamlit resources. Inspired by awesome-python and awesome-pandas.

Alternative

App

Article

Awesome-Streamlit.org

Code

Guide

Sister Sites

Social

Streamlit.io

Technical

Tutorial

Governance

This repo is maintained by me :-)

I'm Marc, Skov, Madsen, PhD, CFA®, Lead Data Scientist Developer at Ørsted

You can learn more about me at datamodelsanalytics.com

I try my best to govern and maintain this project in the spirit of the Zen of Python.

But i'm not an experienced open source maintainer so helpfull suggestions are appreciated.

Thanks

Contribute

GitHub Issues and Pull requests are very welcome!

If you believe Awesome Streamlit is awesome and would like to join as a Core Developer feel free to reach out via datamodelsanalytics.com

How to contribute awesome links

The best way to contribute an awesome link is via a Pull request.

In the pull request you should

Thanks.

How to contribute awesome apps

The best way to contribute an awesome app is via a Pull request.

In the pull request you should

  • describe why your contribution is awesome and should be included.
  • create a new folder gallery/<your_app_name> and app file gallery/<your_app_name>/<your_app_name.py>.
  • Add your app code conforming to the template
"""
## APP NAME

DESCRIPTION

Author: [YOUR NAME](https://URL_TO_YOU))\n
Source: [Github](https://github.com/URL_TO_CODE)
"""
import streamlit as st

# Your imports goes below

def main():
    st.title("APP NAME")
    st.markdown("DESCRIPTION")

    # Your code goes below

if __name__ == "__main__":
    main()
  • Please note magic in sub pages does not work. So don't use magic.
  • add the your_app_name to the
  • update the requirements_base.txt file. Please specify the required versions.
  • Run the automated tests using invoke test.all and fix all errors from your app
  • Run the full app via streamlit run app.py and manually test your contribution.

Please note that your app should not require high compute power as we are running on one of the cheapest tiers available on Azure.

Feel free to reach out if you have comments, questions or need help.

Thanks.

How to contribute to the Streamlit Community

Please sign up to and participate in the community at discuss.streamlit.io

How to contribute to the Streamlit Package

Please contribute to improving the Streamlit package at GitHub/streamlit/streamlit

How to contribute to Streamlit.io

Streamlit.io is in the position of trying to balance building an awesome, succesfull business and providing an awesome product to the open source community.

If you are in a Team please consider signing up for the beta of

How to sponsor the Awesome Streamlit project

If you would like to sponsor my time or the infrastructure the platform is running on, feel free to reach out via datamodelsanalytics.com.

You can also appreciate the work I have already done if you

Buy me a coffee

Thanks

Marc

LICENSE

Attribution-ShareAlike 4.0 International

Getting Started with the Awesome Streamlit Repository

Prerequisites

  • An Operating System like Windows, OsX or Linux
  • A working Python installation.
    • We recommend using 64bit Python 3.7.4.
  • a Shell
    • We recommend Git Bash for Windows 8.1
    • We recommend wsl for For Windows 10
  • an Editor
  • The Git cli

Installation

Clone the repo

git clone https://github.com/MarcSkovMadsen/awesome-streamlit.git

cd into the project root folder

cd awesome-streamlit

Create virtual environment

via python

Then you should create a virtual environment named .venv

python -m venv .venv

and activate the environment.

On Linux, OsX or in a Windows Git Bash terminal it's

source .venv/Scripts/activate

or alternatively

source .venv/bin/activate

In a Windows terminal it's

.venv/Scripts/activate.bat
or via anaconda

Create virtual environment named awesome-streamlit

conda create -n awesome-streamlit python=3.7.4

and activate environment.

activate awesome-streamlit

If you are on windows you need to install some things required by GeoPandas by following these instructions.

Then you should install the local requirements

pip install -r requirements_local.txt

Finally you need to install some spacy dependencies

python -m spacy download en_core_web_sm
python -m spacy download en_core_web_md
python -m spacy download de_core_news_sm

Build and run the Application Locally

streamlit run app.py

or as a Docker container via

invoke docker.build --rebuild
invoke docker.run-server

Run the Application using the image on Dockerhub

If you don't wan't to clone the repo and build the docker container you can just use docker run to run the image from Dockerhub

To run bash interactively

docker run -it -p 80:80 --entrypoint "/bin/bash" marcskovmadsen/awesome-streamlit:latest

To run the streamlit interactively on port 80

docker run -it -p 80:80 --entrypoint "streamlit" marcskovmadsen/awesome-streamlit:latest run app.py

Code quality and Tests

We use

  • isort for sorting import statements
  • autoflake to remove unused imports and unused variables
  • black the opinionated code formatter
  • pylint for static analysis
  • mypy for static type checking
  • pytest for unit to functional tests

to ensure a high quality of our code and application.

You can run all tests using

invoke test.all

Streamlit Tests

I've created a first version of an awesome streamlit test runner. You run it via

streamlit run test_runner_app.py

or in Docker

docker run -it -p 80:80 --entrypoint "streamlit" marcskovmadsen/awesome-streamlit:latest run test_runner_app.py

Awesome Streamlit Test Runner

Workflow

We use the power of Invoke to semi-automate the local workflow. You can see the list of available commands using

$ invoke --list
Available tasks:

  docker.build                            Build Docker image
  docker.push                             Push the Docker container
  docker.run                              Run the Docker container interactively.
  docker.run-server                       Run the Docker container interactively
  docker.system-prune                     The docker system prune command will free up space
  test.all (test.pre-commit, test.test)   Runs isort, autoflake, black, pylint, mypy and pytest
  test.autoflake                          Runs autoflake to remove unused imports on all .py files recursively
  test.bandit                             Runs Bandit the security linter from PyCQA.
  test.black                              Runs black (autoformatter) on all .py files recursively
  test.isort                              Runs isort (import sorter) on all .py files recursively
  test.mypy                               Runs mypy (static type checker) on all .py files recursively
  test.pylint                             Runs pylint (linter) on all .py files recursively to identify coding errors
  test.pytest                             Runs pytest to identify failing tests

Configuration

You can configure the app in the config.py file.

Please note that Streamlit has its own config files in the ~/.streamlit folder.

CI/ CD and Hosting

The application is

  • build as a Docker image and tested via Azure Pipelines builds
    • You find the Dockerfiles here and the Azure pipelines yml files here

Azure Pipelines

Dockerhub

  • released via Azure Pipelines

Azure Pipelines

  • to a web app for containers service on Azure on the cheapest non-free pricing tier

Azure Pipelines

The Awesome-Streamlit Package

You can build the package using

cd package
python setup.py sdist bdist_wheel

If you wan't to publish the package to PyPi you should first

update the version number in the setup.py file. The format is YYYYmmdd.version. For example 20191014.2

Then you run

twine upload dist/awesome-streamlit-YYYYmmdd.version.tar.gz -u <the-pypi-username> -p <the-pypi-password>

For more info see the package README.md

Project Layout

The basic layout of a application is as simple as

.
└── app.py

As our application grows we would refactor our app.py file into multiple folders and files.

  • assets here we keep our css and images assets.
  • models - Defines the layout of our data in the form of
    • Classes: Name, attribute names, types
    • DataFrame Schemas: column and index names, dtypes
    • SQLAlchemy Tables: columns names, types
  • pages - Defines the different pages of the Streamlit app
  • services - Organizes and shares business logic, models, data and functions with different pages of the Streamlit App.
    • Database interactions: Select, Insert, Update, Delete
    • REST API interactions, get, post, put, delete
    • Pandas transformations

and end up with a project structure like

.
├── app.py
└── src
    └── assets
    |    └── css
    |    |   ├── app.css
    |    |   ├── component1.css
    |    |   ├── component2.css
    |    |   ├── page1.css
    |    |   └── page2.css
    |    └── images
    |    |   ├── image1.png
    |    |   └── image2.png
    ├── core
    |   └── services
    |       ├── service1.py
    |       └── service2.py
    └── pages
    |   └── pages
    |       ├── page1.py
    |       └── page2.py
    └── shared
        └── models
        |   ├── model1.py
        |   └── model2.py
        └── components
            ├── component1.py
            └── component2.py

Further refactoring is guided by by this blog post and the Angular Style Guide.

We place our tests in a test folder in the root folder organized with folders similar to the app folder and file names with a test_ prefix.

.
└── test
    ├── test_app.py
    ├── core
    |   └── services
    |       ├── test_service1.py
    |       └── test_service2.py
    └── pages
    |   └── pages
    |       ├── page1
    |       |   └── test_page1.py
    |       └── page2
    └── shared
        └── models
        |   ├── test_model1.py
        |   └── test_model2.py
        └── components
            ├── test_component1.py
            └── test_component2.py