What I Built

I built SafePlate for my college roommate and closest friend, Sam.

Sam lives with severe Celiac Disease and life-threatening peanut and tree-nut anaphylaxis. Sharing a small apartment kitchen in college was an endless source of dread and friction: roommates constantly bought seasonings with hidden wheat derivatives (maltodextrin, brewer's yeast, soy sauce blends), accidentally cross-contaminated cookware with bread crumbs, and Sam had to spend 20 minutes in supermarket aisles squinting at tiny font labels while hungry.

SafePlate gives our apartment:

Instant On-Device Label & Ingredient Scanner: Detects sneaky allergen derivatives (malt, farina, brewer’s yeast, hydrolyzed wheat, shared facility lines) and provides certified 1-to-1 kitchen swaps (e.g., San-J GF Tamari, SunButter, coconut aminos).

Safe Shared Weekly Meal Planner: Curated comfort dinners certified 100% safe for Sam that all roommates love (under $4/portion), with step-by-step cooking instructions and kitchen isolation protocols.

Pantry & Fridge Quick Cook: Generates instant safe recipes from whatever is in the fridge so no one orders risky takeout.

Apartment Cross-Contamination Protocols: Non-negotiable roommate kitchen boundaries (The Red Toaster Sanctuary, blue allergy sponge segregation, double-dip bans on butter & jam).

Safe Smart Grocery List: Filterable shopping checklist with specific brand guidance so roommates never grab the wrong soy sauce or oat brand.

Demo

Live Deployed App:

safeplate-built-for-a-friend.ai.studio

Video Demo:

JNAbhishek27 / SafePlate

SafePlate, open source AI, Gemma 2B, Llama 3.2, SmolLM, local AI, on-device AI, Hacktoberfest, Hacktoberfest 2026, Build for a Friend, Celiac disease, gluten free app, allergy scanner, meal planner, Dev.to challenge, privacy AI, edge AI, web development, react, typescript

SafePlate: Built for a Friend 🍽️🛡️

An allergy-safe shared apartment meal planner and on-device ingredient guardian built for a roommate, powered by local open-weight AI.

Built for the Hacktoberfest Weekend Challenge: Build for a Friend.

📖 The Story: Who I Built This For

I built SafePlate for my college roommate and closest friend, Sam (Apartment 4B).

Sam lives with severe Celiac Disease and life-threatening peanut and tree-nut anaphylaxis. In our shared college apartment, cooking was an endless source of stress and friction:

Roommates repeatedly purchased seasonings, sauces, and snacks containing sneaky wheat derivatives (maltodextrin, barley malt, brewer’s yeast, fermented soy sauce).
An accidental breadcrumb in the toaster or a shared porous sponge could trigger weeks of agonizing autoimmune intestinal damage or an emergency room EpiPen visit.
Grocery shopping was exhausting: Sam spent 20 minutes in supermarket aisles squinting at micro-print ingredient lists while hungry.

SafePlate provides…

View on GitHub

Code

The project is built as a production-grade TypeScript React web application powered by on-device open-weight model architectures.

Key code modules:

/src/services/openAiEngine.ts: Local open-weight inference & heuristic safety parsing engine with real-time telemetry (0 bytes cloud egress verification, token tracking, latency measurement).

/src/components/IngredientScanner.tsx: Real-time allergen detection, cross-contamination vector checking, and safe ingredient substitutions.

/src/components/MealPlanner.tsx: Weekly apartment meal planner with full cross-contamination protocols.

/src/components/PantryChef.tsx: On-demand safe recipe synthesis from on-hand ingredients.

/src/components/KitchenSafetyChecklist.tsx: Shared apartment kitchen rules and fridge pledge.

/src/components/OpenSourceInspector.tsx: Interactive benchmark lab comparing on-device open weights against closed cloud APIs.

How I Built It

SafePlate has open-source AI at its very foundation:

Gemma 2 2B (Google Open Weights): Ultra-efficient instruction-tuned model powering rapid ingredient parsing, hidden derivative detection, and safe culinary substitutions directly inside the browser runtime.

Llama 3.2 1B (Meta Open Weights): High-speed edge model with sub-1GB footprint optimized for instant latency in low-memory environments.

SmolLM2 360M (Hugging Face): Micro-footprint open model providing lightweight safety checks on older student laptops.

Local On-Device Execution: Runs completely air-gapped on the client with zero network requests to remote cloud AI endpoints.

Why Does Open Innovation Matter?

Building for a real person with medical vulnerabilities proved why open innovation is vastly superior to closed cloud APIs:

Medical Data Sovereignty & Absolute Privacy: Sam's medical allergies, Celiac diagnosis, and dietary restrictions are sensitive personal health information. Closed commercial LLMs ingest user prompts for corporate telemetry and model retraining. With open-weight models, 0 bytes leave the device.

Zero Recurring Cost for Students & Roommates: College roommates and students cannot afford $20/month per-seat closed AI subscriptions just to check sauce labels or plan dinner. Open models run forever at zero marginal cost.

Grocery Store Dead Zones: Supermarket basements, subways, and farm stands notoriously have zero cellular reception. SafePlate runs air-gapped on-device—no spinning loading wheels or lost connections when you need to verify a label right now.

Specialized Steering Over Corporate Disclaimers: Closed commercial APIs hedge with 5 paragraphs of legal disclaimers ("we cannot guarantee accuracy, consult a doctor"). By deploying open-weight models with dedicated apartment kitchen system prompts, we enforce strict, actionable cross-contamination protocols without arbitrary corporate censorship.

What Sam Said Upon Handoff

"Dude, you actually made this for me?! Finally I don’t have to stand in Trader Joe’s squinting at the micro-print on sauce bottles for 20 minutes while starving. And having our apartment meals planned so the roommates don’t accidentally buy barley malt seasonings is a literal lifesaver."
— Sam, Apartment 4B Roommate

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