A step by step approach to understanding Streamlit, useful for quickly building user interfaces in Python.
The code is written in Python, utilising Pandas and Streamlit and is packaged using Poetry. It is assumend Poetry is installed on your local system.
The pyproject.toml file can be viewed to determine the dependancies in use by this code, these will automatically be installed when the installation steps below are followed.
Clone the repository and install dependencies:
git clone <this-repository>
cd <this-repository>
poetry installStreamlit concepts are taught in a step by step approach by building a simple food rating application.
The steps show how to load data from CSV files, store session data persistently, overwrite UI dynamically based on user actions and output the data to a CSV file.
streamlit_step_1.py - Simple UI. Create a selection box to choose a food type and see that food type with optional ratings displayed as a list of radio buttons. A placeholder element is used to hold the list of radio buttons, this is useful in later steps when new food types are added to the list.
streamlit_step_2.py - Updates the UI from step 1 to allow user choices to persist when food categories are changed. This step introduces session state.
streamlit_step_3.py - Updates the UI from step 2 to introduce the ability to add new food items to the list of radio buttons so that the user can update the seed data. Shows the use of input_text element and button elements.
streamlit_step_4.py - Updates the UI from step 3 to add the capability to write a CSV file based off of the selections the user has made. The CSV can be written in a long or wide format. Error handling is added to ensure if file issues occur the Streamlit outputs a useful message in an appropriate error element.
streamlit_step_5.py -Updates the UI from step 4 to allow the user to upload data from a specified CSV file. This step introduces the Streamlit sidebar element.
Execute the desired step by running
poetry run streamlit run src/streamlit_in_steps/streamlit_step_<number>.pyA local web browser will be launched on http://localhost:8502/ running the streamlit application
Output is written to $PROJECT_ROOT/output/food_ratings.csv
Streamlit official documentation - https://docs.streamlit.io/library/api-reference