https://ai-task-management-ui.vercel.app
https://ai-task-backend-2zjn.onrender.com
https://ai-task-ai-service.onrender.com
https://ai-task-backend-2zjn.onrender.com/swagger-ui/index.html
https://github.com/KunalP1303/AI_TaskManagement_Application
A production-grade AI-powered full-stack task management platform built using Spring Boot, React, FastAPI, PostgreSQL, Docker, Render, and Vercel.
The system enables users to securely manage tasks while leveraging AI-powered task analysis for intelligent prioritization, effort estimation, and task summarization.
This project was designed to simulate real-world product engineering challenges including:
- Full-stack architecture
- JWT-based authentication
- REST API design
- AI microservice integration
- Dockerized deployment
- Cloud-native deployment
- Distributed system communication
- Production debugging
React Frontend (Vercel)
↓
Spring Boot Backend (Render)
↓
FastAPI AI Microservice (Render)
↓
PostgreSQL Database
- JWT-based authentication
- BCrypt password encryption
- Protected APIs using Spring Security
- Token-based authorization
- Secure frontend-backend communication
- Create tasks
- Update tasks
- Delete tasks
- Task filtering
- Pagination support
- Priority management
- Status tracking
Integrated FastAPI AI microservice for:
- Intelligent priority suggestion
- Effort estimation
- AI-generated task summaries
- DTO-based API design
- Global exception handling
- Request validation
- Standardized API responses
- Layered architecture
- Production-grade REST APIs
- React component-based architecture
- Axios API integration
- Loading & error states
- JWT interceptor handling
- Protected API calls
- Dynamic UI rendering
- Dockerized services
- Multi-stage Docker builds
- Cloud deployment using Render & Vercel
- PostgreSQL cloud integration
- Environment variable configuration
- Swagger/OpenAPI integration
| Layer | Technology |
|---|---|
| Frontend | React.js |
| API Integration | Axios |
| Backend | Spring Boot 3.5.x |
| Language | Java 17 |
| Security | Spring Security + JWT |
| ORM | Spring Data JPA / Hibernate |
| AI Service | FastAPI |
| Database | PostgreSQL |
| Containerization | Docker |
| Backend Hosting | Render |
| Frontend Hosting | Vercel |
| API Documentation | Swagger/OpenAPI |
| Version Control | Git + GitHub |
The application follows a distributed architecture where:
- React frontend communicates with secured Spring Boot APIs
- Spring Boot backend communicates with FastAPI AI microservice
- AI service enriches tasks with intelligent analysis
- PostgreSQL persists application data
User Login
↓
JWT Token Generated
↓
Stored in localStorage
↓
Axios Interceptor attaches token
↓
Spring Security validates token
↓
Protected APIs accessed
Task Creation Request
↓
Spring Boot Backend
↓
FastAPI AI Service
↓
AI Analysis Generated
↓
Response returned to Backend
↓
Task saved with AI enrichment
↓
Frontend updated
- CRUD APIs
- Pagination
- Filtering
- DTO Layer
- Global Exception Handling
- Validation
- Protected APIs
- Swagger Documentation
- Standardized API Responses
- Role-based authorization
- Refresh token flow
- Advanced search functionality
- REST API design
- DTO mapping
- Exception handling architecture
- Validation lifecycle
- Spring Security filter chain
- JWT authentication
- BCrypt hashing
- JPA/Hibernate ORM
- Layered architecture
- API contract design
- React hooks
- State management
- Component architecture
- Axios interceptors
- Conditional rendering
- API integration patterns
- Error handling
- Docker containerization
- Multi-stage Docker builds
- Cloud deployment
- Environment variable management
- CI/CD workflow
- Cross-origin configuration
- Microservice communication
- API contract debugging
- Distributed system interaction
- Request/response validation
This project involved solving several production-like engineering issues:
- JWT authentication failures
- CORS configuration issues
- Docker build failures
- PostgreSQL migration problems
- Swagger security conflicts
- 502 Bad Gateway issues
- Cloud deployment debugging
- Inter-service communication issues
- Validation and enum conversion errors
- Async frontend rendering issues
Install:
- Java 17
- Node.js
- Docker
- PostgreSQL
- Maven
- Python 3.10+
git clone https://github.com/KunalP1303/AI_TaskManagement_Application.gitcd backendUpdate:
spring.datasource.url=
spring.datasource.username=
spring.datasource.password=
jwt.secret=mvn spring-boot:runBackend runs on:
http://localhost:8080
cd frontendnpm installnpm startFrontend runs on:
http://localhost:3000
cd ai-servicepip install -r requirements.txtuvicorn main:app --reloadAI service runs on:
http://localhost:8000
docker build -t ai-task-backend .docker run -p 8080:8080 ai-task-backend
Create a folder named:
screenshots
Inside your GitHub repository root and add the screenshots using these filenames:
login-page.png
dashboard-ui.png
task-creation-filtering.png
ai-task-analysis.png
empty-state.png
swagger-ui.png
This project demonstrates:
- Full-stack development capability
- Production-grade backend engineering
- Distributed system understanding
- Secure authentication implementation
- AI microservice integration
- Cloud deployment knowledge
- Dockerization skills
- Real-world debugging ability
Potential improvements:
- Role-based access control
- Redis caching
- Kafka/event-driven architecture
- Search optimization
- WebSocket notifications
- AWS deployment
- CI/CD pipeline automation
- Monitoring & logging integration
Java Full Stack Developer
This project evolved from a basic CRUD application into a distributed AI-powered full-stack platform with:
- Secure authentication
- AI-powered task enrichment
- Dockerized deployment
- Cloud-native architecture
- Production-grade API design
- Distributed service communication
- Frontend-backend integration
The project reflects practical engineering skills aligned with modern full-stack Java developer roles.