Resume Match - AI-Powered Resume Analysis & Job Matching

An intelligent resume analysis and job matching system that leverages AI/LLM technology to provide real-time feedback and suggestions for resume optimization.

🚀 Features

• Multi-format Support: Parse resumes in PDF, DOCX, and TXT formats
• AI-Powered Analysis: Leverage Ollama with local LLM inference for privacy
• Real-time Streaming: Get instant feedback with Server-Sent Events (SSE)
• Skill Extraction: Automatically identify and extract skills from resumes
• Job Description Matching: Compare resume content against job requirements
• Modern Web Interface: Beautiful React-based UI with Material-UI components
• Docker Ready: Easy deployment with Docker Compose

🏗️ Architecture

The project consists of four main services:

┌─────────────┐    ┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│  Frontend  │    │   Backend   │    │ ML Service  │    │   Ollama    │
│   (React)  │◄──►│    (Go)     │◄──►│  (Python)   │◄──►│  (LLM)      │
│   Port 3000│    │  Port 8080  │    │  Port 8000  │    │  Port 11434 │
└─────────────┘    └─────────────┘    └─────────────┘    └─────────────┘

Service Details

• Frontend: React 18 + Vite + Material-UI, responsive web interface
• Backend: Go + Gin framework, handles file uploads and API routing
• ML Service: Python + FastAPI, processes resumes and integrates with LLM
• Ollama: Local LLM inference engine with GPU acceleration support

📋 Prerequisites

• Docker & Docker Compose: For containerized deployment
• Git: For cloning the repository
• Modern Web Browser: Chrome, Firefox, Safari, or Edge

🛠️ Installation

1. Clone the Repository

git clone https://github.com/chenjunlin110/resume-match.git
cd resume-match

2. Start Services with Docker Compose

# Navigate to infrastructure directory
cd infra

# Start all services
docker compose -f docker-compose.mac.yml up -d

3. Verify Services

# Check service status
docker compose ps

# View logs
docker compose logs -f

🚀 Usage

Web Interface

1. Open your browser and navigate to http://localhost:3000
2. Upload a resume file (PDF, DOCX, or TXT format)
3. Enter job description text in the provided field
4. Submit and watch real-time AI analysis
5. Review results including skill extraction and matching suggestions

API Endpoints

Traditional Response

POST /api/upload
Content-Type: multipart/form-data

Parameters:
- jd_text: Job description text
- resume_file: Resume file upload

Streaming Response (Recommended)

POST /api/upload_stream
Content-Type: multipart/form-data

Parameters:
- jd_text: Job description text
- resume_file: Resume file upload

Response: Server-Sent Events (SSE) stream

Sample Files

Test the system with sample files in the docs/samples/ directory:

• resume_sample.txt - Sample resume content
• jd_sample.txt - Sample job description

🔧 Configuration

Environment Variables

Backend Service

ML_URL=http://ml:8000/api/score_file_stream  # ML service endpoint

ML Service

OLLAMA_BASE_URL=http://ollama:11434  # Ollama service URL

Ollama Service

OLLAMA_GPU_LAYERS=35              # GPU acceleration layers
OLLAMA_FLASH_ATTENTION=true       # Flash attention optimization
OLLAMA_METAL=1                    # Apple Metal GPU support (macOS)

Docker Compose Configuration

The docker-compose.mac.yml file includes:

• Port mappings for all services
• Volume mounts for persistent data
• Environment variable configurations
• Service dependencies and health checks

🧪 Testing

Test Streaming API

# Test the streaming endpoint directly
curl -v -X POST http://localhost:8080/api/upload_stream \
  -F "jd_text=Go programming" \
  -F "resume_file=@../docs/samples/resume_sample.txt" \
  --no-buffer

Test Individual Services

# Test ML service
curl http://localhost:8000/health

# Test backend
curl http://localhost:8080/health

# Test Ollama
curl http://localhost:11434/api/tags

🚀 Development

Frontend Development

cd frontend
npm install
npm run dev

Backend Development

cd backend
go mod tidy
go run main.go

ML Service Development

cd ml_service
pip install -r requirements.txt
uvicorn app:app --reload --host 0.0.0.0 --port 8000

📁 Project Structure

resume-match/
├── frontend/                 # React frontend application
│   ├── src/
│   │   ├── components/      # React components
│   │   └── config.ts        # API configuration
│   ├── Dockerfile           # Frontend container
│   └── package.json
├── backend/                  # Go backend service
│   ├── main.go              # Main application
│   └── Dockerfile           # Backend container
├── ml_service/              # Python ML service
│   ├── app.py               # FastAPI application
│   ├── requirements.txt     # Python dependencies
│   └── Dockerfile           # ML service container
├── infra/                   # Infrastructure configuration
│   ├── docker-compose.mac.yml  # Docker Compose for macOS
│   └── docker-compose.yml      # Standard Docker Compose
├── docs/                    # Documentation and samples
│   └── samples/             # Sample files for testing
└── README.md                # This file

🔒 Security & Privacy

• Local LLM: All AI processing happens locally via Ollama
• No External APIs: No data sent to third-party services
• File Handling: Secure file upload and processing
• CORS Configuration: Proper cross-origin resource sharing setup

🤝 Contributing

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

📝 License

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

🙏 Acknowledgments

• Ollama - Local LLM inference
• FastAPI - Python web framework
• Gin - Go web framework
• React - Frontend framework
• Material-UI - UI component library

📞 Support

• Issues: GitHub Issues
• Connect: Gmail

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Made with ❤️ by Junlin Chen

Transform your resume with AI-powered insights and real-time feedback.