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Image gallery with ML tagging in cloud

Why Choose This Project?

Traditional image galleries only store and display photos, but finding specific images becomes difficult when the collection grows. By integrating Machine Learning (ML)-based image tagging in the cloud, this project can automatically detect objects, people, and scenes in uploaded images and assign relevant tags. This makes searching, filtering, and organizing images effortless.

This project helps students learn about:

Cloud ML APIs (AWS Rekognition, Google Vision, Azure Cognitive Services)
Scalable image storage (S3, Firebase Storage, Azure Blob)
Search & filtering with ML-powered metadata
Web-based gallery management

What You Get

Cloud-hosted image gallery with upload & display
Automatic image tagging using ML APIs
Search & filter functionality by tags
User authentication for secure gallery access
Option to manually edit/add tags
Cloud-based image storage with CDN delivery
Responsive UI for mobile and desktop

Key Features

Feature	Description
Image Upload	Users upload photos to cloud storage
Auto ML Tagging	ML APIs analyze images and generate tags
Manual Tag Editing	Users can add/remove tags for accuracy
Search & Filter	Find images by keywords or tags
Gallery View	Grid/lightbox view of stored images
Authentication	Secure access for registered users
Cloud Sync	Images and tags stored and synced in real time
Scalability	Auto-scale storage & ML calls as gallery grows
Sharing	Share gallery links with other users

Technology Stack

Layer	Tools/Technologies
Frontend	React / Angular / Vue, Bootstrap/Tailwind, Lightbox.js
Backend	Node.js + Express / Spring Boot
Cloud Storage	AWS S3 / Firebase Storage / Azure Blob
ML Image Tagging	AWS Rekognition / Google Vision API / Azure Computer Vision
Database	Firestore / DynamoDB / MySQL with JPA / Cosmos DB
Authentication	Firebase Auth / AWS Cognito / OAuth2
Hosting	Firebase Hosting / AWS Amplify / Azure App Service
Deployment	GitHub Actions / Jenkins / CI-CD pipeline

Cloud Services Used

Service	Purpose
AWS S3 / Firebase Storage / Azure Blob	Store and serve images
AWS Rekognition / Google Vision / Azure Computer Vision	Auto-tagging of images
DynamoDB / Firestore / Cosmos DB	Store image metadata and tags
AWS Cognito / Firebase Auth	User authentication
Lambda / Cloud Functions	Process uploads and call ML APIs
CloudFront / Firebase CDN	Fast image delivery
CloudWatch / Firebase Monitoring	Monitor system performance

Working Flow

User Authentication
- Users sign in using Firebase Auth / AWS Cognito.
- Secure session created for each user.
Image Upload
- User uploads an image via frontend.
- Image is stored in cloud storage (S3/Blob/Firestore Storage).
ML Tagging Process
- A serverless function (Lambda/Cloud Function) is triggered.
- Image is sent to ML Vision API (AWS Rekognition/Google Vision).
- Tags (objects, colors, landmarks, text, etc.) are extracted.
Metadata Storage
- Tags and metadata are stored in Firestore/DynamoDB linked to the image ID.
Gallery Display
- UI fetches images + tags from DB.
- Grid or lightbox view shows images with auto-generated tags.
Search & Filter
- Users type keywords in search bar.
- Backend queries DB for images with matching tags.
Tag Management
- Users can add/remove/edit tags manually.
- Updated metadata is synced to DB.
Scalability & Monitoring
- Storage auto-scales.
- ML API usage monitored.
- CDN delivers images globally with low latency.