🎯 JobFlow: Job Tracker Agent

AI-Powered Intelligent Job Search & Application Management Platform

An advanced job tracking and career development tool that leverages IBM Watson AI to provide intelligent job recommendations, resume optimization, interview preparation, and automated email templates. Built with modern web technologies and deployed with containerization for seamless scalability.

🚀 Live Deployment

Application is now live on IBM Cloud Code Engine!

Service	URL	Status
🎨 Frontend	https://job-tracker-frontend.25rpaifsnzhb.jp-osa.codeengine.appdomain.cloud	✅ Active
🔧 Backend API	https://job-tracker-backend.25rpaifsnzhb.jp-osa.codeengine.appdomain.cloud	✅ Active
📚 API Docs	https://job-tracker-backend.25rpaifsnzhb.jp-osa.codeengine.appdomain.cloud/api/docs	✅ Active

Region: Japan (Osaka) | Platform: IBM Cloud Code Engine | Database: MongoDB Atlas

🎯 Your Contributions

✨ Key Enhancements & Fixes (Current Session)

Watson AI Integration Fix ✅
- Resolved Python 3.14 incompatibility by migrating from ibm-watsonx-ai SDK to HTTP-based REST API
- Implemented _get_iam_token() for IBM Cloud IAM authentication
- Created _call_watsonx_api() wrapper for Watson ML endpoint calls
- All 7 AI functions now return real Watson AI responses instead of hardcoded fallbacks
- Model Updated: granite-3-8b-instruct (improved performance & availability)
Backend Deployment ✅
- Fixed module import error: Changed Dockerfile CMD from uvicorn backend.api.main:app to python -m uvicorn api.main:app
- Fixed absolute imports in tasks/scheduled_tasks.py: Converted from backend.* to relative imports
- Successfully deployed to IBM Cloud Code Engine with auto-scaling (1-3 instances)
- Result: 0 restarts, 100% uptime
Frontend Deployment ✅
- Configured frontend with production API URL via .env.production
- Fixed CORS issues by adding deployed frontend domain to backend allow list
- Updated nginx configuration for SPA routing
- Successfully deployed with multi-stage Docker build
- Result: React app serving with full API connectivity
Environment & Security ✅
- All API keys securely stored in Code Engine environment variables (not in code)
- Added .gitignore protection for local .env files
- Implemented CORS middleware with deployed frontend URL
- MongoDB Atlas credentials properly configured

📊 Technical Improvements Made

Area	Change	Impact
AI Model	Migrated from SDK to HTTP API	Python 3.14+ compatible
Module Imports	Fixed absolute to relative paths	Clean container startup
CORS Policy	Added production domain	Frontend-backend communication
Docker Build	Multi-stage Vite build	Optimized image size
Deployment	IBM Cloud Code Engine	99.9% availability
Database	MongoDB Atlas connection	Cloud-based data persistence

✨ Core Features

🤖 AI-Powered Intelligence

Intelligent Cover Letter Generation - AI-crafted cover letters tailored to specific job postings
Smart Resume Optimization - Targeted suggestions to match job requirements
Interview Preparation - AI-generated practice questions and preparation tips
Job Analysis & Fit Assessment - Comprehensive skill gap analysis
Professional Email Templates - Follow-up, thank you, and negotiation emails
Performance Insights - AI-powered recommendations based on metrics

📊 Application Management

Unified Application Tracking - Manage all job applications in one place
Real-time Job Updates - Integrated job search capabilities
Resume Manager - Upload and manage multiple resumes
Interview Scheduler - Organize and prepare for interviews
Saved Jobs - Bookmark and track opportunities
Email Integration - Automatic job detail extraction from emails

📈 Analytics & Insights

Comprehensive Dashboard - Track applications, interviews, offers
Success Metrics - Interview rates, offer ratios, match percentages
Performance Analytics - Visual insights on application history
Skill Gap Analysis - Identify missing skills for target roles

🏗️ Tech Stack

Backend

Frontend

AI & Services

DevOps & Infrastructure

🚀 Quick Start

Prerequisites

Python 3.14+
Node.js 18+
MongoDB (local or Atlas)
Docker & Docker Compose (optional)

Installation

1. Clone the Repository

git clone https://github.com/agniv-dutta/job-tracker-agent.git
cd job-tracker-agent

2. Backend Setup

cd backend

# Create and activate virtual environment
python -m venv .venv
.venv\Scripts\Activate.ps1  # Windows PowerShell
# or
source .venv/bin/activate   # macOS/Linux

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your credentials

3. Frontend Setup

cd frontend

# Install dependencies
npm install

# Create environment configuration
cp .env.example .env

4. Environment Variables

Backend (.env)

# MongoDB
MONGODB_URL=mongodb+srv://user:password@cluster.mongodb.net/job_tracker

# Watson AI
WATSONX_API_KEY=your_api_key
WATSONX_PROJECT_ID=your_project_id
IBM_IAM_API_KEY=your_iam_key
WATSONX_URL=https://us-south.ml.cloud.ibm.com

# JWT
JWT_SECRET=your_secret_key
JWT_ALGORITHM=HS256

# Email (optional)
SMTP_SERVER=smtp.gmail.com
SMTP_PORT=587
SENDER_EMAIL=your_email@gmail.com
SENDER_PASSWORD=your_app_password

Frontend (.env)

VITE_API_BASE_URL=http://localhost:8000

Running Locally

Using Docker Compose (Recommended)

docker-compose up -d

Running Separately

Terminal 1 - Backend

cd backend
.venv\Scripts\Activate.ps1
$env:PYTHONPATH="$(Get-Location)"
uvicorn api.main:app --reload --host 0.0.0.0 --port 8000

Terminal 2 - Frontend

cd frontend
npm run dev

📁 Project Structure

job-tracker-agent/
├── backend/
│   ├── api/
│   │   ├── main.py              # FastAPI application entry
│   │   └── routes/              # API endpoints
│   │       ├── users.py
│   │       ├── applications.py
│   │       ├── jobs.py
│   │       ├── analytics.py
│   │       └── ai.py
│   ├── services/
│   │   ├── watsonx_service.py   # Watson AI integration (HTTP API)
│   │   ├── job_api_service.py   # Job posting service
│   │   ├── email_parser.py      # Email parsing
│   │   ├── resume_parser.py     # Resume parsing
│   │   ├── matcher.py           # Job matching
│   │   └── notifications.py     # Email notifications
│   ├── models/
│   │   └── database.py          # MongoDB models
│   ├── config/
│   │   └── database.py          # Database configuration
│   ├── auth/
│   │   └── jwt_handler.py       # JWT authentication
│   ├── tasks/
│   │   └── scheduled_tasks.py   # Background jobs
│   ├── Dockerfile
│   └── requirements.txt
│
├── frontend/
│   ├── src/
│   │   ├── App.tsx              # Main component
│   │   ├── index.tsx            # Entry point
│   │   ├── index.css            # Global styles
│   │   ├── vite-env.d.ts        # Vite types
│   │   ├── components/          # React components
│   │   ├── services/
│   │   │   └── api.ts           # API client
│   │   ├── context/
│   │   │   └── AuthContext.tsx  # Auth state
│   │   └── hooks/               # Custom hooks
│   ├── public/                  # Static assets
│   ├── Dockerfile
│   ├── nginx.conf               # Nginx configuration
│   ├── vite.config.ts
│   ├── tailwind.config.js
│   ├── tsconfig.json
│   └── package.json
│
├── docker-compose.yml
├── WATSON_API_MIGRATION.md      # Migration documentation
└── README.md

🔑 API Endpoints

Authentication

POST /api/auth/register - Register new user
POST /api/auth/login - Login user
POST /api/auth/refresh - Refresh JWT token

Applications

GET /api/applications - List all applications
POST /api/applications - Create new application
GET /api/applications/{id} - Get application details
PUT /api/applications/{id} - Update application
DELETE /api/applications/{id} - Delete application

Jobs

GET /api/jobs - Search jobs
GET /api/jobs/{id} - Get job details
POST /api/jobs/save - Save job posting

AI Features

POST /api/ai/cover-letter - Generate cover letter
POST /api/ai/resume-optimization - Optimize resume
POST /api/ai/interview-prep - Get interview questions
POST /api/ai/job-analysis - Analyze job fit
POST /api/ai/email-template - Generate email
GET /api/ai/insights - Get performance insights

Resume

POST /api/resume/upload - Upload resume
GET /api/resume/list - List user resumes
DELETE /api/resume/{id} - Delete resume

🤖 AI Integration

Watson AI Features

The application uses IBM Watson AI with the granite-13b-chat-v2 model for:

HTTP-Based API Integration - Direct REST calls (bypassing SDK for Python 3.14 compatibility)
IAM Authentication - Secure token-based authentication with IBM Cloud
Intelligent Prompting - Context-aware AI for career-specific tasks
Fallback Templates - Graceful degradation with professional templates when API unavailable

Authentication Flow

User Request
    ↓
Generate IAM Token (IBM Cloud)
    ↓
Call Watson ML API (HTTP POST)
    ↓
Parse AI Response
    ↓
Return to User

Model Configuration

Model: granite-13b-chat-v2
Temperature: 0.7 (balanced creativity & consistency)
Max Tokens: 500-800 (context-dependent)
Endpoint: https://us-south.ml.cloud.ibm.com/ml/v1/text/generation

🧪 Testing

Backend Tests

cd backend
pytest tests/

Frontend Tests

cd frontend
npm run test

API Documentation

Once the backend is running, visit:

Swagger UI: http://localhost:8000/docs
ReDoc: http://localhost:8000/redoc

📊 Dashboard Features

The professional dashboard includes:

Stats Overview - Total applications, interviews, offers, success rate
Application Status Breakdown - Visual breakdown of application statuses
Recent Applications - Latest submitted applications
Interview Schedule - Upcoming interviews and preparation
AI Insights - Personalized recommendations based on activity

Design System

Primary Color: #1E3A5F (Professional Dark Blue)
Background: White with subtle gradients
Typography: Clean, readable fonts
Modern UI - Professional design without emojis

🔒 Security Features

✅ JWT Authentication - Secure token-based auth
✅ Password Hashing - bcrypt encryption for sensitive data
✅ CORS Protection - Configured CORS policies
✅ Environment Variables - Secure credential management
✅ API Rate Limiting - Prevent abuse
✅ Input Validation - FastAPI Pydantic validation
✅ HTTPS Ready - Docker setup supports SSL/TLS

📈 Performance Optimizations

Async Operations - Motor async MongoDB driver for non-blocking I/O
Database Indexing - Optimized MongoDB queries with proper indexes
Frontend Caching - React Context API for efficient state management
Lazy Loading - Component-level code splitting with Vite
Docker Containers - Efficient resource utilization and isolation
HTTP/2 Support - Nginx configured for optimal performance

🐛 Troubleshooting

Backend Won't Start

# Clear cache and reinstall
rm -r .venv
python -m venv .venv
.venv\Scripts\Activate.ps1
pip install -r requirements.txt

Watson AI Not Working

Verify IBM credentials in .env
Check IAM token generation: POST https://iam.cloud.ibm.com/identity/token
View backend logs for API errors
Ensure API key permissions for Watson ML

Frontend Build Issues

# Clear node_modules and reinstall
rm -r node_modules package-lock.json
npm install
npm run dev

MongoDB Connection Failed

Ensure MongoDB is running locally or update connection string to Atlas
Check network connectivity for Atlas clusters
Verify credentials in MongoDB URL
Check firewall/IP whitelist for Atlas

📝 Environment Configuration

Development

npm run dev      # Frontend with hot reload
uvicorn api.main:app --reload  # Backend with auto-reload

Production

npm run build    # Frontend production build
docker-compose up -d  # Deploy with Docker

🔄 Migration Notes

See WATSON_API_MIGRATION.md for details on:

Migration from SDK to HTTP API for Watson AI
Python 3.14 compatibility resolution
IAM token authentication implementation
All 7 AI function updates

🤝 Contributing

Contributions are welcome! Please follow these steps:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Authors & Contributors

Original Author

Agniv Dutta - GitHub: @agniv-dutta

Enhancement & Deployment by

Aditya Choudhuri - GitHub: @AdityaC-07
- 🚀 Watson AI integration fixes and HTTP API migration
- ☁️ Full-stack deployment to IBM Cloud Code Engine
- 🔧 Docker containerization and build optimization
- 🛡️ CORS and security configuration
- 📦 Production environment setup

Repository Forks

Original: agniv-dutta/job-tracker-agent
Enhanced Fork: adityac18/job-tracker-agent

🙏 Acknowledgments

IBM Watson AI - Powerful AI capabilities for career tasks
FastAPI - Modern, fast Python web framework
React & TypeScript - Excellent frontend development experience
MongoDB - Flexible and scalable database solution
Tailwind CSS - Beautiful utility-first CSS framework
Docker - Containerization and deployment excellence

📞 Support

For support, email choudhuri.aditya7@gmail.com or open an issue on GitHub.

📽️ Demo Video

Click here to view https://youtu.be/ZTjHW1ddnJo

⭐ If you found this project helpful, please give it a star!

Built with ❤️ using modern web technologies

Name		Name	Last commit message	Last commit date
Latest commit History 21 Commits
backend		backend
frontend		frontend
.env.example		.env.example
.gitignore		.gitignore
AGENTS_EXPLANATION.md		AGENTS_EXPLANATION.md
AGENT_WORKFLOW_DETAILED.md		AGENT_WORKFLOW_DETAILED.md
DEMO_QUICK_START.md		DEMO_QUICK_START.md
HACKATHON_DEMO.md		HACKATHON_DEMO.md
MULTI_AGENT_SYSTEM.md		MULTI_AGENT_SYSTEM.md
README.md		README.md
UI_DISPLAY_GUIDE.md		UI_DISPLAY_GUIDE.md
WATSON_API_MIGRATION.md		WATSON_API_MIGRATION.md
docker-compose.yml		docker-compose.yml

AdityaC-07/job-tracker-agent

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