Ai Recipe

🍳 About the Project

Our project is a web application that generates a personalized menu based on a photo of the user’s available ingredients. By combining AI image analysis with recipe databases, the app helps users turn the contents of their fridge into creative, practical, and delicious meal suggestions. This solution is perfect for people who want to reduce food waste, save time, and cook without needing to plan ahead or shop for new ingredients.

The platform was developed during a hackathon by two developers, with the help of AI Bolt.new, and was built smoothly and efficiently, without any technical blockers. The result is an AI-enhanced, user-friendly application that makes cooking more accessible and sustainable.

💡 What Inspired Us

We were inspired by a simple but frustrating experience shared by many: standing in front of the fridge, unsure what to cook. Often, people waste time or money because they don’t realize what meals they could make from ingredients they already have. We wanted to create a tool that could analyze those ingredients visually and suggest recipes — instantly and intelligently.

With the rise of AI in everyday tools, we saw an opportunity to bring computer vision and smart recommendation systems into the home kitchen, in a way that’s actually useful.

🛠️ How We Built It

We developed the app as part of hackathon, using AI Bolt.new to generate most of the frontend and backend structure quickly. The platform helped us:

Scaffold the project architecture
Generate UI components for image upload, result pages, and recipe views
Implement clean logic for state management and user flows

Key Features:

Upload a photo of ingredients
AI detects and lists items in the image
Generates a personalized list of meal ideas
Detailed recipe pages with instructions, difficulty, allergens, and ingredients
Links to external recipes for further exploration

🤖 Development Experience

With the power of Bolt.new, we experienced a streamlined development flow. The platform generated components, helped with logic suggestions, and allowed us to focus more on design, UX, and API integration instead of repetitive coding. There were no significant bugs or issues, and we completed all core functionality within the hackathon timeframe.