Preswald

Abstract

1. Introduction

Preswald is a static-site generator for building interactive data apps in Python. It packages compute, data access, and UI into self-contained data apps that run locally in the browser. Built on a WASM runtime with Pyodide and DuckDB, Preswald enables portable, file-based apps that are fast, reactive, and shareable.

Create interactive data apps with a full data stack that runs in the browser (no local dependencies!),runs offline, and is shareable in a single file.

This command builds your app into a static site inside dist/. The folder contains all the files needed to run your app locally or share it.

2. Objective

Preswald is a WASM packager for Python-based interactive data apps: bundle full complex data workflows, particularly visualizations, into single files, runnable completely in-browser, using Pyodide, DuckDB, Pandas, and Plotly, Matplotlib, etc. Build dashboards, reports, and notebooks that run offline, load fast, and share like a document.

This project demonstrates how Python can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Code-based. Write apps in Python, not in notebooks or JS frameworks
• File-first. One command creates a fully-packaged .html app
• Built for computation. Use Pyodide + DuckDB directly in-browser
• Composable UI. Use prebuilt components like tables, charts, forms
• Reactive engine. Only re-run what's needed, powered by a DAG of dependencies
• Local execution. No server. Runs offline, even with large data
• AI-ready. Apps are fully inspectable and modifiable by agents

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/StructuredLabs/preswald.git
cd preswald

pip install preswald

or

uv pip install preswald

pip install preswald
preswald init my_app
cd my_app
preswald run

my_app/
├── hello.py           # Your app logic
├── preswald.toml      # App metadata and config
├── secrets.toml       # Secrets (e.g. API keys)
├── data/sample.csv    # Input data files
├── images/logo.png    # Custom branding

from preswald import text, table, get_df

text("# Hello Preswald")
df = get_df("sample.csv")
table(df)
...

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by StructuredLabs and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.