Touch Grass Planner: A Local AI That Sends You Outside

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Touch Grass Planner is a small web app that helps you leave your screen and go outside.

You type your city, how much free time you have, and your mood. A local AI model gives you a short 3 step outdoor plan, a list of things to bring, and one tip for this time of year. Then you press one button and a countdown timer starts. The app tells you to close the tab and go.

The screen part is short on purpose. Plan in under a minute, then walk out the door.

It is for anyone who keeps saying "I will go for a walk later" and never does. Students, developers, and anyone who sits too long in front of a laptop.

Demo

The app runs fully on a local machine, so there is no hosted link. Here is how it looks and how to run it.

Run it yourself:

1. Install Ollama
2. ollama pull llama3.2:3b
3. pip install -r requirements.txt
4. python app.py
5. Open http://localhost:5000

Code

shubsolos19 / touch-grass

Touch Grass Planner

Small app that makes a short outdoor plan using a local open-weight AI model. You type your city, free time and mood. The model gives a 3 step plan. Then a timer starts and you go outside.

Why open AI

• Runs fully on your laptop with Ollama
• Works with no internet after setup
• Your city and mood stay on your machine
• Free to run
• You can swap the model with one setting

Setup

1. Install Ollama: https://ollama.com
2. Pull a small model: ollama pull llama3.2:3b
3. Install packages: pip install -r requirements.txt
4. Run: python app.py
5. Open http://localhost:5000

Change model

Windows PowerShell: $env:MODEL="gemma2:2b"; python app.py

Mac or Linux: MODEL=gemma2:2b python app.py

Stack

Python, Flask, Ollama, open-weight Llama 3.2

View on GitHub

How I Built It

• Open model: Llama 3.2 3B, an open-weight model
• Local inference: Ollama runs the model on my own laptop
• Backend: Python and Flask. One route sends the prompt to Ollama and returns the answer.
• Frontend: Plain HTML, CSS and JavaScript. No heavy framework.

The prompt tells the model to keep the answer short, follow a fixed format, and avoid made up place names. It suggests types of places like a park or a riverside instead. This keeps the plan safe and useful.

The model name is one environment variable, so I can swap to another open model without changing any code.

Why Does Open Innovation Matter?

• Works offline. After the model is downloaded, no internet is needed. Good for trails and parks with weak signal.
• Private. My city and mood never leave my laptop. No server I do not control.
• Free. No API key, no bill, no usage limit.
• Swappable. I can change the model in seconds, and use a smaller one on a slow machine.

A closed API would need internet, an account and money. For an app about going outside, that felt wrong.

How It Went Outside

WRITE 2 OR 3 TRUE LINES HERE: where you went, what plan you got, how long, how it felt.