I Built an AI Recipe Generator for My Sister 🍳[Hacktoberfest Weekend Challenge: Build for a Friend]

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

I built an AI Recipe Generator for my sister, who loves cooking but often has trouble finding recipes that match the ingredients she actually has at home.

Her usual process was to search YouTube for a recipe, open a video that looked good, and then discover that it required ingredients she didn't have. Even when she found a recipe she could make, the instructions were sometimes unclear or the recipe simply wasn't what she was looking for.

So I built something that starts with what she already has, instead of making her search through recipes first.

She can either type in her ingredients or upload a photo of them. The AI then suggests five recipes she could make. She chooses one, and the app generates the complete recipe with ingredients, preparation and step-by-step cooking instructions.

It also lets her specify dietary preferences, cuisine, number of servings, and maximum cooking time.

Demo

🌐 Try the live app

The app is deployed on Render and can be used directly in the browser.

Code

GitHub: AI Recipe Generator

The project is built with Python and Streamlit, with the AI functionality handled through Backboard and Kimi K2.6.

How I Built It

I built the application with Python and Streamlit and used Kimi K2.6, an open-weight model, through Backboard and OpenRouter.

The project has two main AI steps.

First, the user provides ingredients either as text, an uploaded image, or both. When an image is uploaded, Kimi analyzes the image to identify the visible ingredients. The application then asks the model for five recipe suggestions rather than immediately generating a long recipe.

Second, the user selects the recipe they want. The application sends that selection back to the AI and generates the complete recipe, including ingredients, timings, substitutions, cooking tips, and step-by-step instructions.

I deployed the application on Render, and used environment variables to keep the Backboard API key out of the source code.

Why Does Open Innovation Matter?

The open-weight model was important because the project is built around an AI model that can understand both text and images while still being part of an open-weight ecosystem.

That gave me the flexibility to build the application around the model rather than tying the entire project to one closed AI provider. It also makes it easier to experiment with different open-weight models and providers through the same architecture.

For this project, that flexibility was especially useful because image understanding is a core part of the experience: my sister can simply take a photo of what she has instead of manually listing every ingredient.

My Agent Session

I did not use DevRelay for this project.

Prize Categories

• Best Use of Render — The application is deployed and publicly hosted on Render.
• Best Use of Backboard — Backboard is used as the AI interface for the open-weight model powering the application.

Built for my sister ❤️