Trail Sense("From Screen to soil")

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

What We Built

TrailSense ("From Screen to Soil") is a beginner garden helper for people who want to grow something but don't know where to start. It is for students and first-time gardeners with a windowsill or balcony.

It is built so the screen is the shortest part of the experience:

• Find Plants: you set your space, sunlight, budget (in rupees) and daily care time. The app scores each plant against your setup and explains why it fits.
• My Garden: save plants, get a small daily task for each one, mark tasks complete, and remove plants. Your garden is saved in a local file.
• AI Assistant: ask a gardening question and get a short answer in simple words. Every answer ends with one 5-minute outdoor task to do away from the screen.
• Today's little challenge: the dashboard nudges you to step outside for five minutes, notice the sunlight, and feel the soil.

Team

• @vyshu1230 : Designed and built the app: plant matching, My Garden, dashboard.
• @himacharan69: Found that the AI Assistant page was missing and built it, tested the app.
• @pashya_rishitha_0610 : Helped to design the app.

Demo

The app runs locally, so there is no hosted link. That is the point: it needs no server and no signal.

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Code

https://github.com/vyshu1230/Trailsense-/invitations

How We Built It

• Model: llama3.2, an open-weight model, run locally with Ollama.
• App: Streamlit and plain Python. Plant data lives in plants.json, and your saved garden in garden_progress.json.
• Plant matching: a scoring function rates sunlight fit, space fit, daily care time and cost. Budget is a hard limit.
• AI Assistant: the model gets a short instruction, plus your sidebar setup and the plants you have saved:

You are TrailSense, a friendly gardening helper for beginners.
Use the garden setup you are given. Answer in simple words, in at most
5 sentences. Do not invent facts about plants; if you are not sure, say
so. End with one short outdoor task the person can do in 5 minutes away
from the screen.

The first version of the app listed an AI Assistant in its menu, but the page was never built. We found this while testing and added it.

Where it falls short:

• Only five plants (Tulsi, Mint, Aloe Vera, Tomato and Spinach) have their own daily task. Every other plant gets the same generic task.
• A small local model can make mistakes, so its advice is a starting point, not expert advice.
• A vague answer or an ignored setting.

Why Does Open Innovation Matter?

A garden helper is most useful where there is no signal: a balcony, an allotment, a trail. A closed API would need internet and an account in exactly those places.

• It works without a connection. The model runs on the laptop. We turned off Wi-Fi and the assistant still answered.

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• Your data stays with you. What you plant and ask never leaves your machine.
• It costs nothing to run. There is no API key and no bill.
• We can swap models. Changing one line (AI_MODEL) switches to any model shown by ollama list.

One honest note: llama3.2 is open-weight, not fully open source. We can download and run it, but its training data is not public.

What's Next

We have not yet tested TrailSense in a real garden. Our plan is to take it outside, ask the assistant about the plants in front of us, do its 5-minute task, and report how it goes.