SlothPlate 🦥: Let Open-Weight AI Handle Your Meal Planning

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.

What I Built

Meet SlothPlate 🦥 — a low-effort meal-planning assistant that helps you spend less time deciding what to cook.

We've all opened the fridge, stared at a few random ingredients, and still had no idea what to make. SlothPlate tackles that everyday problem by turning the ingredients you already have into simple meal ideas and a consolidated grocery list.

Here's how it works:

• Scan your fridge: Upload a fridge or pantry photo, or enter ingredients manually. AI helps identify the ingredients, which you can review and correct.
• Get easy meal ideas: Generate three meal suggestions based on your available ingredients, cooking time, and effort preferences.
• Generate your grocery list: Select the meals you like and combine their missing ingredients into one organised shopping list.
• Save your plans: Keep meal plans and grocery lists in your browser so you can return to them later.

The goal is simple: fewer repetitive decisions, less unnecessary shopping, and more time for everything else.

Demo

🌿 Try SlothPlate — Live Demo

Code

💻 View SlothPlate on GitHub

How I Built It

I built SlothPlate using Google AI Studio Build mode, React, TypeScript, and a Node.js backend.

The application uses Google's open-weight Gemma 4 model through a server-side API integration. Gemma powers the ingredient-analysis and meal-generation workflow, helping transform a list of available ingredients and user preferences into structured meal suggestions.

The shopping-list logic runs in application code, consolidating ingredients from the selected recipes rather than relying on the AI to perform every calculation.

I also implemented browser-based storage for saved plans, keeping the first version simple without requiring accounts or a separate cloud database.

Why Does Open Innovation Matter?

I wanted to build something where open-weight AI performs a useful task rather than simply adding a chatbot to an application.

Gemma gives SlothPlate an open-weight model foundation that developers can inspect, adapt, and explore in alternative inference environments. It also keeps the model integration separate from the main application logic, making future experimentation with other compatible models possible.

The current version uses hosted inference, so AI generation requires an internet connection and submitted images or ingredient information are sent to the model provider for processing. Saved plans are stored in the browser's local storage; the application does not currently provide fully local AI inference or cloud synchronisation.

That distinction matters: open-weight models create opportunities for more control and adaptability, but privacy and offline operation depend on how the model is deployed.

For this version, I focused on making the experience practical and easy to use while leaving room to explore local inference and further improvements later.

SlothPlate is my attempt to make an everyday task a little less tedious. Sometimes, the smartest productivity tool is simply the one that saves you from having to think about dinner again. 🌿