This is a submission for Weekend Challenge: Dog Days Edition
What I Built
PawSafe is an AI-powered web application that helps dog owners answer a simple but important question:
"Can my dog eat this?"
Users can enter the name of a food, upload a photo, or provide both. PawSafe then analyzes the information using Google's Gemini API and provides a simple safety assessment.
The result is categorized into four levels:
- 🟢 Generally Safe
- 🟡 Use Caution
- 🔴 Not Safe
- ⚪ Unable to Determine
Along with the result, PawSafe provides explanations, potential warnings, and safer alternatives when appropriate.
My goal was to build something that was useful, simple to understand, and approachable for dog owners rather than making users search through multiple sources every time they encounter an unfamiliar food.
Demo
Code
How I Built It
PawSafe is a full-stack application built with:
Frontend
- React
- Vite
- Tailwind CSS
- Lucide React
Backend
- Node.js
- Express
- Multer
- CORS
- Google Gemini API
Deployment
- Render
- GitHub
The basic flow looks like this:
User
↓
Food name / Image / Both
↓
React Frontend
↓
Express API
↓
Google Gemini
↓
Structured Analysis
↓
PawSafe Result Card
One of the main technical decisions I made was to keep the Gemini API integration on the backend rather than exposing the API key in the frontend.
The frontend sends the user's food information to the Express API. The backend then communicates with Gemini and returns the structured analysis to the frontend.
I also wanted the application to support both text and images independently, while still allowing users to provide both when additional context is useful.
Prize Categories
Best Use of Google AI
PawSafe is submitted for the Best Use of Google AI prize category.
Google's Gemini API is the core intelligence behind the application. It is used to analyze both text-based and image-based food information and generate a structured safety assessment.
The AI response is then presented through PawSafe's interface as an easy-to-understand result rather than exposing users to a raw AI response.
