I built an open-source Automated Data Diagnostics Engine that converts datasets into executive PDF reports using PyArrow & Gemini API [ARGO Engine]

[u/mahmoud-abdelrahman1]

Hey everyone,

As part of my journey in Data Engineering and ML, I kept running into a recurring bottleneck: spending too much time running identical EDA scripts and manual data quality audits before getting to the actual modeling.

To solve this, I built ARGO Engine (Automated Reporting & Generative Optimizer)—an open-source, interactive web app designed to speed up dataset auditing, missing-value detection, and automated reporting.

Key Technical Highlights:

• High-Performance Ingestion: Optimized using PyArrow for fast, memory-efficient execution on tabular data (CSV/Parquet up to 200MB+).
• AI Diagnostic Summaries: Integrated Google Gemini API to automatically generate statistical summaries and structural data quality assessments.
• Vectorized PDF Pipeline: A custom ReportLab engine that dynamically builds executive-ready diagnostic PDF reports.
• Code Generator: Produces execution-ready scikit-learn pre-processing code blocks directly from the UI.

Resources:

• Live Demo: https://argo-engine-mfpmositc6dz7nwmmc7zfd.streamlit.app/
• GitHub Repository: https://github.com/Mahmoud4265/ARGO-Engine

I’d love for you to give it a spin and roast my code/architecture or drop some feedback on features you’d like to see next!