SlothDrip：以表格 AI 和 Gemma 判斷植物是否需要澆水

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

What I Built

I built SlothDrip — an offline-first, “lazy gardener” assistant that predicts whether your plant needs watering and gives a simple one-line action.

Instead of manually checking soil every day, users enter a few quick values:

days since last watering
average temperature
humidity
soil type

The app then returns a clear verdict:

💧 WATER
😴 DO NOTHING

This saves daily effort for home gardeners and makes watering decisions faster and more consistent.

Demo

Live App: https://slothdrip-fcc37ir38vccqekd6m7obc.streamlit.app/
GitHub Repo: https://github.com/AslamSujah/slothdrip

Code

The full source code is available here:

https://github.com/AslamSujah/slothdrip

Main components:

app.py → Streamlit UI
model.py → moisture prediction logic (TabPFN + fallback)
llm.py → local Gemma/Ollama action-note generation
db.py → SQLite logging
requirements.txt → dependencies

How I Built It

SlothDrip is built as a local-first AI pipeline:

Tabular prediction layer (TabPFN approach)
I use tabular features to estimate watering probability from structured garden conditions.
Natural-language action layer (Gemma via Ollama)
A local LLM converts numeric prediction into a short, friendly “lazy instruction.”
Offline persistence (SQLite)
Every prediction is logged locally so users keep plant history without cloud dependence.
Simple local UI (Streamlit)
A lightweight dashboard keeps interaction minimal and beginner-friendly.

Note on deployment

During deployment, I handled a TabPFN license/runtime issue by implementing an automatic fallback model (scikit-learn), so the app stays reliable in hosted environments while preserving the same user experience.

Why Does Open Innovation Matter?

Open innovation made this project possible in a practical way:

No monthly API lock-in for core functionality
Local-first architecture for privacy-sensitive home data
Flexibility to combine open-weight model workflows with fallback logic
Resilience: the app can continue working even if one model path is unavailable

A closed-only API setup would have made this more expensive and less robust for offline or low-connectivity scenarios.

My Agent Session

I built and iterated this project with AI-assisted development workflows, including deployment troubleshooting and model fallback integration.

(If needed, I can update this section with a public session link/embed.)

The Lazy Payoff

SlothDrip reduces repetitive daily checking into a quick decision flow:

input takes under a minute
output is immediate and actionable
logs build a long-term watering history automatically

So the “slow and steady” benefit is real: fewer routine checks, faster decisions, less mental overhead.

Prize Categories

I’m entering the following categories:

Best Use of TabPFN
Best Use of Gemma