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🐾 PawSafe: An AI-Powered Food Safety Checker for Dogs

A developer has built PawSafe, an AI-powered web application that helps dog owners determine whether a food is safe for their pets. The app uses Google's Gemini API to analyze text and image inputs, providing a structured safety assessment with explanations and alternatives. The full-stack application, featuring a React frontend and Express backend, keeps the Gemini API integration on the server side to protect the API key.

read1 min views1 publishedAug 17, 2026

This is a submission for Weekend Challenge: Dog Days Edition

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:

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.

PawSafe is a full-stack application built with:

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.

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.

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