SteelWorks is a Streamlit-powered operations dashboard designed for manufacturing teams. It connects to a PostgreSQL database of production runs and issues, letting operations analysts filter by calendar week and production line to instantly surface issue summaries, affected lot lists, and downloadable CSV reports.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/kishnahai0806/SteelWorks/llms.txt
Use this file to discover all available pages before exploring further.
Quick Start
Install dependencies, configure your database, and launch the dashboard in minutes.
Configuration
Set up your environment variables, database URL, and optional Sentry monitoring.
Dashboard Guide
Explore the filters, issue summary table, affected lots view, and CSV export features.
Data Model
Understand the PostgreSQL schema powering all production and shipping analytics.
Deployment
Deploy SteelWorks on Render using Docker with a managed PostgreSQL database.
API Reference
Integrate with the service and repository layers programmatically.
How it works
SteelWorks follows a clean three-layer architecture: a Streamlit UI that drives filter selections, anOperationsMetricsService that enforces business logic and holds fallback data for offline development, and an OperationsRepository that executes parameterized SQL against your PostgreSQL database.
Install and configure
Clone the repository, run
poetry install, and create a .env file with your DATABASE_URL.Initialize the database
Apply
db/schema.sql to create tables and the issue_occurrences view, then seed with db/seed.sql.Launch the dashboard
Run
poetry run streamlit run streamlit_app.py and open the app in your browser.Key features
Week & Line Filters
Select any calendar week and one or more active production lines to scope all dashboard data.
Issue Summary
View issue counts grouped by issue type, with an optional breakdown per production line.
Affected Lots
See every lot touched by an issue in the selected scope, with issue counts and type lists.
CSV Exports
Download issue summary and affected lots reports as CSV files for offline analysis.
PostgreSQL Backend
Production data is stored in a normalized relational schema with 11 tables and a reporting view.
Sentry Integration
Optional Sentry DSN for error monitoring and alerting in production deployments.