# Build for a Friend: a local-first meal planner on open-weight Gemma

> Source: <https://dev.to/sank8/build-for-a-friend-a-local-first-meal-planner-on-open-weight-gemma-59po>
> Published: 2026-10-05 03:33:46+00:00

My roommate Nagaraj needed high-protein vegetarian food on a ₹2000/week budget — while strictly avoiding peanuts and lactose. So for the Build for a Friend theme, I built him a meal planner that runs **entirely on his laptop**: no accounts, no API keys, no cloud.

**Friend Meal Planner** — a web app (vanilla HTML/JS + a zero-dependency Python server, stdlib only) that generates a 7-day Indian meal plan with a grocery list and budget check. You fill in your friend's profile — diet, allergies, dislikes, budget — and the plan streams in, token by token.

`python3 app.py` and open `http://localhost:8000` (needs Ollama + `gemma3:1b` pulled; ~40s per plan on CPU)
Nagaraj, my roommate in Bengaluru. Hostel-style cooking, 30-minute recipes, ingredients from local markets, everything priced in ₹. I handed him the first plan — his verdict: *"now that's cooking"* (he also immediately asked for a recipe mode and non-veg mode, so that's next).

Open-weight AI is what makes this project *possible*, not just cheaper:

Testing caught something important: the 1B model kept suggesting paneer and yogurt to my *lactose-allergic* friend, despite explicit instructions. Rather than hide that, I added a **deterministic allergy guardrail** — every plan is scanned line-by-line against an allergen keyword map (lactose → milk, curd, paneer, ghee…) and an ⚠️ Allergy Check section is appended flagging risky dishes for review. Prompt engineering sets the intent; code enforces the safety property. That's the architecture I'd defend: never let a probabilistic model be the last word on food safety.

This also enters the **Best Use of Gemma** category — the whole app is Gemma-powered, running the open-weight `gemma3:1b` model locally through Ollama.