What I Built
Touch Grass Planner is a small web app that gets you off your screen. You pick your free time, your mood, the weather, and where you live. It suggests one free outdoor activity with 3 steps, and the minutes add up to the time you chose.
It is for anyone who sits at a screen too long. I built it because my days are busy, and when I finally get some free time, I never know what to do. This app decides for me, so I can just go outside.
Live app: https://touch-grass-planner-euqcu6hwuipckakidznvt2.streamlit.app
Code
https://github.com/Rahulgupta-63/touch-grass-planner
How I Built It
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Model: GPT-OSS 20B (
openai/gpt-oss-20b), an open-weight model, called through the Groq API. - App: Python and Streamlit, deployed on Streamlit Community Cloud.
- Prompt design: the part that took most of my time. My first version ignored my time limit and suggested a jog in hot weather. I fixed it with clear rules: the step times must add up to the minutes chosen, hot weather means shade and water, rain means no hard exercise, and no health claims.
Limits, honestly: the model runs on Groq's servers through an API, not on my laptop, so it needs internet. You choose the weather by hand, because the app has no weather data. The suggestions are AI-generated, so use your own judgment.
Why Does Open Innovation Matter?
I started with a Llama model on Groq. While I was building, that model was no longer available. Because I was using open-weight models, I changed one line of code and moved to GPT-OSS, and the app kept working. With a single closed model, I would have been stuck with whatever the one company decided to offer. Open weights also mean I can swap models, compare them, and later try running one locally. That is my next step.
I used an AI assistant (Claude) to help write the code and draft this post. I tested the app myself, deployed it, and edited the post.
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