Note: This sample application has been copied and adapted from its original version here: https://github.com/Snowflake-Labs/streamlit-examples/tree/main/Dynamic%20Filters%20Snowpark

This is a Streamlit demo that showcases how filters can be generated.

The app connects to Snowflake and retrieves the data using snowpark Python, pushing the computations into a Snowflake warehouse.

• Dynamically selectable list of filters ("Select which filters to enable")
• Result preview
• Sankey chart showing the effects of selected filters
• Statement sequence showing which SQL statements are performed to get the result
• Download button to get the csv file with results

To run the app, following steps are required:

• localstack CLI with LOCALSTACK_AUTH_TOKEN environment variable set
• LocalStack Snowflake emulator
• Snowpark for Python
• Streamlit CLI

Note: Snowpark requires python 3.8.*, please ensure that you have that version available in your environment.

The project is using poetry to manage the dependencies and local CI.

To set up the project:

1. Install poetry
2. Open the project directory in a terminal emulator
3. (optional) to force the use of python 3.8, run poetry env use /path/to/python3.8
4. Run poetry install to install dependencies into project-specific virtual environment

As an alternative to poetry, the project can also be set up via pip:

1. Create a python 3.8 virtual environment
2. Switch into the created virtual environment
3. Use provided requirements.txt to install the dependencies: pip -r requirements.txt

To be able to connect to the LocalStack Slowflake emulator when developing locally Streamlit provides a mechanism for storing secrets and connection details in the project (doc link)

To run the sample against LocalStack Snowflake, your local ~/.streamlit/secrets.toml should look something like this:

[snowpark]
user = "test"
password = "test"
account = "test"
warehouse = "test"
database = "STREAMLIT_DEMO"
schema = "STREAMLIT_USER_PROFILER"
role = "test"
host = "snowflake.localhost.localstack.cloud"

The project comes with a SQL sheet to be run in the environment.

For example, if you have the snow CLI configured with a localstack connection profile, the SQL script can be executed as follows:

snow sql -c localstack -f .assets/setup.sql

After performing the setup and switching to the virtual environment, you can run the application using:

streamlit run main.py

This sample application is published under the Apache 2.0 License (see LICENSE file).