Streamlit + Hamilton

This example accompanies the documentation page for Streamlit integration.

Streamlit is an open-source Python library to create web applications with minimal effort. It's an effective solution to create simple dashboards, interactive data visualizations, and proof-of-concepts for data science, machine learning, and LLM applications.

In this example, We will build a simple financial dashboard based on the Kaggle Bank Marketing Dataset.

How to run

1. Create virtual environment: python -m venv ./venv
2. Activate virtual environment: . venv/bin/activate (or source venv/bin/Scripts on Windows)
3. Install requirements: pip install -r requirements.txt
4. Launch Streamlit application: streamlit run app.py

File organization

Adding Hamilton to your Streamlit application can provide a better separation between the dataflow and the UI logic. They pair nicely together because Hamilton is also stateless. Once defined, each call to Driver.execute() is independent. Therefore, on each Streamlit rerun, you use Driver.execute() to complete computations. Using Hamilton this way allows you to write your dataflow into Python modules and outside of the Streamlit.

logic.py

Hamilton transformations are defined in the module logic.py. This includes downloading the data from the web, getting unique values for job, conducting groupby aggregates, and creating plotly figures.

app.py

The Streamlit UI is defined in app.py. Notice a few things:

• app.py doesn't have to depend on pandas and plotly.
• @cache_resource allows to create the Driver only once.
• @cache_data on _execute() will automatically cache any Hamilton result based on the combination of arguments (final_vars, inputs, and overrides)
• get_state_inputs() and get_state_overrides() will collect values from user inputs.
• execute() parses the inputs and overrides from the state and call _execute().

Benefits

• Clearer scope: the decoupling between app.py and logic.py makes it easier to add data transformations or extend UI, and debug errors associated with either.
• Reusable code: the module logic.py can be reused elsewhere with Hamilton.
  • If you are building a proof-of-concept with Streamlit, your Hamilton module will be able to grow with your project and be useful for your production pipelines.
  • If you are already building dataflows with Hamilton, using it with Streamlit ensures your dashboard metrics have the same implementation with your production pipeline (i.e., prevent implementation skew)
• Performance boost: by caching the Hamilton Driver and its execution call, we are able to effectively cache all data operations in a few lines of code. Furthermore, Hamilton can scale further by using a remote task executor on a separate machine from the Streamlit application.