This is a submission for the Hacktoberfest Open-Source AI Challenge: Week-1
What I Built
Touch Grass Quest is an AI-powered outdoor scavenger hunt. You pick a place and a difficulty, and the app generates five photo quests. You then put the laptop down, go outside, and photograph what you find. An open-weight Gemma model checks each photo, tells you what it sees, adds a fun fact, and finally writes a short nature journal. The screen is only needed to start the hunt and to upload the photos.
Taking It Outside
I tested it in the garden in front of my house with my sister. We were out for around half an hour to forty minutes, and the laptop was only used when it was time to upload a photo. The rest of the time we just walked around and took pictures.
My five quests were:
- Find a fallen green leaf under a tree.
- Photograph something natural that is yellow or red.
- Photograph a tree.
- Capture sunlight coming down through the branches.
- Photograph the eye of an animal.
The yellow and red flowers were the easiest. The animal's eye was the hardest: I spotted a bird sitting on a branch and photographed it there. it was hard because the bird was far away or kept moving.
The AI judged almost every photo correctly. My favourite moment was the fun fact for the fallen leaf photo. The leaves were lying on a stone, and the model said it looked like leaves resting on a bald man's head. I laughed, and my sister enjoyed the whole thing too. Honestly, the best part was not the app. It was the thirty minutes of walking around and looking closely at things we usually ignore.
Demo
How I Tested It
Besides the garden walk, I tested both AI engines on all three difficulty levels (Easy, Medium and Hard), with five quests each. That is about 30 photo checks in total. I did not notice any serious mistakes. This was an informal test and not a benchmark, so I cannot give an accuracy number. But this is far better than random tools.
How It Works
- Interface: Streamlit
- AI: Gemma 4 through the Gemini API, or Gemma 3 (4B) running locally through Ollama. You can switch between them in the sidebar.
- The model returns structured JSON, both for the quests and for each photo check. The quest prompt tells the model to keep quests safe: no trespassing, no touching wild animals, no picking plants.
Why Open Innovation Matters Here
- Privacy: with the local engine, photos taken outdoors, including photos with family, stay on the laptop.
- Cost: the local engine needs no API key and costs nothing to run.
- Flexibility: changing the model is a one-line change in the code.
- My comparison: because the open innovation helps to make a new thing that builds actually something great new and better then others.
What's Next
A phone-friendly version that works offline, quests tuned to the season and local plants, and a group mode for friends and run clubs.
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