robertofernandezmartinez/ai-corporate-suite
Operational AI platform integrating 🛳️ SmartPort maritime risk, ✈️ NASA RUL predictive maintenance, and 📦 retail Stockout risk prediction with FastAPI, Streamlit, Supabase, Railway and Telegram bot automation.
What's novel
Operational AI platform integrating 🛳️ SmartPort maritime risk, ✈️ NASA RUL predictive maintenance, and 📦 retail Stockout risk prediction with FastAPI, Streamlit, Supabase, Railway and Telegram bot automation.
Code Analysis
0 files read · 4 roundsA Streamlit-based dashboard that loads pre-trained pickle models for three distinct prediction tasks (NASA RUL, Maritime Risk, Retail Stockout) and displays results via a unified UI.
Strengths
Clear separation of concerns between core logic, UI pages, and database clients. The modular structure with distinct directories for `core`, `pages`, `bot`, and `db` is logical and maintainable.
Weaknesses
Lacks visible error handling, input validation, or unit tests in the available code snippets. The core implementation relies on loading `.pkl` files without showing any preprocessing or model logic, suggesting it may be a simple wrapper around pre-trained models rather than a novel algorithmic solution.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
71
Contributors
1
Files
29
Active weeks
4
Repository
Language
Python
Stars
1
Forks
0
License
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