Here's a prompt you can use to generate a sleek and efficient UI in Next.js that integrates with your Flask image classification API:
Prompt:
Generate a modern, responsive UI using Next.js 14 with TypeScript that allows users to upload or scan an image (via webcam or mobile camera) and sends it to a Flask API for classification. The API determines whether the image is of a cat, dog, or human and returns the result.
UI Requirements:
- Use Tailwind CSS for styling.
- Use a clean and minimal layout with a header: “🐾 Pet Classifier”.
- Have a toggle option to either Upload an Image or Scan with Camera.
- When uploading, show a file input and a preview of the selected image.
- When scanning, access the webcam and allow capturing an image frame.
- On submission, make a
POSTrequest to the/predictendpoint of the Flask API with the image as form-data (filefield).- Display loading animation while waiting for a response.
- Once prediction is received, show a result card with:
- Image preview
- Label (
cat,dog, orhuman)- Confidence score (optional if not returned from API).
- Provide error handling if the image is not uploaded or the API fails.
- Include a button to Reset and try again.
Bonus:
- Use
react-webcamfor the camera scan option.- Encapsulate image upload and camera logic into reusable components.
- Add test button that hits the
/testroute of the Flask API and shows a toast/snackbar with the result.
Let me know if you want the actual implementation or components scaffolded out for this!
I'll create a modern, responsive UI for your pet classifier that integrates with your Flask API. This implementation includes both image upload and camera capture functionality.
This Pet Classifier UI provides a clean, user-friendly interface for classifying images as cat, dog, or human. Here's what it includes:
Main Components:
PetClassifier: The main component that manages state and API interactionsImageUploader: Handles image file uploads with drag-and-drop supportCameraCapture: Uses react-webcam to access the camera and capture photosResultCard: Displays classification results with appropriate stylingKey Features:
API Integration:
/predict endpoint/test endpointInstall the required dependencies:
npm install react-webcamMake sure your Flask API is running at http://localhost:5000 with the following endpoints:
/predict - POST endpoint that accepts an image file and returns a prediction/test - GET endpoint that returns a success messagePetClassifier component if your Flask API is running on a different URL.use env forr the api url and make the look of website more good and pofessional
I'll improve the design to make it more professional and use environment variables for the API URL.
I've enhanced the Pet Classifier UI to make it more professional and configurable:
env.ts file to centralize API URL configurationNEXT_PUBLIC_API_URL environment variable for client-side access.env.local.example file for documentationLayout & Structure:
Visual Design:
UI Components:
Animations & Transitions:
Install the required dependencies:
npm install react-webcam framer-motionCreate a .env.local file in your project root with:
NEXT_PUBLIC_API_URL=http://localhost:5000(Replace with your actual Flask API URL)
Make sure your Flask API is running with the following endpoints:
/predict - POST endpoint that accepts an image file/test - GET endpoint for testing connectivityThe UI will now use the environment variable for API calls and has a much more professional appearance with smooth animations and a cohesive design.
Please make sure to add the following environment variable to your project:
great just make that test api connection part working and animated