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streamlit-image-select 🖼️

PyPI

An image select component for Streamlit.

This custom component works just like st.selectbox but with images. It's a great option if you want to let the user select an example image, e.g. for a computer vision app!



Installation

pip install streamlit-image-select

Usage

from streamlit_image_select import image_select
img = image_select("Label", ["image1.png", "image2.png", "image3.png"])
st.write(img)

See the demo app for a detailed guide!

Development

Note: you only need to run these steps if you want to change this component or contribute to its development!

Setup

First, clone the repository:

git clone https://github.com/jrieke/streamlit-image-select.git
cd streamlit-image-select

Install the Python dependencies:

poetry install --dev

And install the frontend dependencies:

cd streamlit_image_select/frontend
npm install

Making changes

To make changes, first go to streamlit_image_select/__init__.py and make sure the variable _RELEASE is set to False. This will make the component use the local version of the frontend code, and not the built project.

Then, start one terminal and run:

cd streamlit_image_select/frontend
npm start

This starts the frontend code on port 3001.

Open another terminal and run:

cp demo/streamlit_app.py .
poetry shell
streamlit run streamlit_app.py

This copies the demo app to the root dir (so you have something to work with and see your changes!) and then starts it. Now you can make changes to the Python or Javascript code in streamlit_image_select and the demo app should update automatically!

Publishing on PyPI

Switch the variable _RELEASE in streamlit_image_select/__init__.py to True. Increment the version number in pyproject.toml. Make sure the copy of the demo app in the root dir is deleted or merged back into the demo app in demo/streamlit_app.py.

Build the frontend code with:

cd streamlit_image_select/frontend
npm run build

After this has finished, build and upload the package to PyPI:

cd ../..
poetry build
poetry publish

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🖼️ An image select component for Streamlit

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  • TypeScript 37.7%
  • Python 33.0%
  • CSS 19.8%
  • HTML 7.3%
  • Shell 2.2%