CatColonyWatch: Local Open AI for Community Cat Field Observation

A developer built CatColonyWatch, an open-source field-observation tool that lets volunteers monitoring community-cat colonies record factual notes and then use a locally run open-weight model to structure those observations into clearer records. The app pairs a Streamlit interface with a local Ollama server running Gemma 3, which outputs structured JSON for human review before data is stored in SQLite, JSON or CSV. The AI is deliberately scoped to organizing what humans observed, and does not diagnose animals, assess medical urgency, recommend treatments, or infer emotions or intentions.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass https://dev.to/challenges/hacktoberfest-week1-2026-10-05 I built CatColonyWatch , an open-source field-observation tool for volunteers and organisations that monitor community-cat colonies. Its core idea is simple: The AI is not watching the cats. The human is. Community-cat monitoring happens outside: at feeding points, streets, gardens, courtyards, shelters, and other shared spaces. But volunteers often need to do two things at once: pay attention to the animals and keep useful records. CatColonyWatch is designed to make the screen the shortest part of that experience. The workflow is: The goal is not to automate animal observation. The goal is to let people spend more time actually observing animals and less time organising notes. CatColonyWatch currently supports: The AI has deliberately narrow boundaries. It does not diagnose animals, assess medical urgency, recommend treatments, or infer emotions or intentions. Its job is much simpler: help humans organise what they actually observed. Streamlit demo: https://catcolonywatch.streamlit.app/ https://catcolonywatch.streamlit.app/ The Streamlit version demonstrates the field-observation workflow and interface. The complete AI workflow is intentionally designed to run locally through Ollama + Gemma 3 on the user's computer. That means the public interface and the local AI architecture serve slightly different purposes: The full local setup is documented in the repository. The complete project is open source: 🐾 CatColonyWatch Observe first. Record second. Let open AI structure the notes CatColonyWatch is an open-source field-observation tool for volunteers and organisations that monitor community-cat colonies The AI is not watching the cats. The human is. The app is designed to keep screen time short: volunteers observe cats and their environment first, record factual notes second, and then use a local open-weight AI model to structure those observations into a clearer record 🌿 How it works Repository: https://github.com/Maribele/CatColonyWatch https://github.com/Maribele/CatColonyWatch The repository includes: app.py requirements.txt Local databases and secrets are excluded from Git so real colony data is not accidentally published. CatColonyWatch is built with: The architecture is intentionally small: text Human field observation ↓ Streamlit interface ↓ Recorded visit ↓ Local Ollama server ↓ Gemma 3 ↓ Structured JSON ↓ Human review ↓ SQLite / JSON / CSV