CareerCraft — Resume Evaluator & Job Match Assistant

CareerCraft is an AI-powered career enhancement platform designed to help job seekers optimize their resumes, evaluate job-fit, and generate tailored cover letters.
It uses modern AI, NLP, and web technologies to simulate real-world Applicant Tracking Systems (ATS) and hiring workflows.

The platform provides actionable insights that improve resume quality, skill alignment, and overall job readiness.

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🚀 Key Features

📝 Resume Analyzer (ATS Optimization)

• Upload PDF / DOCX resumes
• ATS-style scoring based on:
  • Keyword alignment
  • Resume structure & formatting
  • Readability
  • Section completeness
• Detailed improvement suggestions

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🎯 Skill Match & Job Fit Scoring

• Paste or select a target job description
• Get:
  • Job match percentage
  • Identified missing skills
  • Role-specific improvement tips
• Personalized upskilling recommendations with learning resources

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✍️ AI Cover Letter Generator

• Generates professionally tailored cover letters
• Uses:
  • Resume content
  • Job description insights
• Supports tone customization:
  • Formal
  • Confident
  • Friendly
• One-click PDF export

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📊 User Dashboard & History Tracking

• Secure user authentication
• Store:
  • Resumes
  • Generated cover letters
  • Job match results
• Track progress across multiple applications
• Manage multiple resume versions

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🧠 System Architecture

CareerCraft follows a microservice-based architecture:

CareerCraft/ ├── frontend/ # User Interface (Next.js)
├── backend/ # API & Authentication (Node.js / Express)
└── ml-service/ # AI & NLP Engine (FastAPI)

Architecture Flow

Frontend → Backend → ML Service → Backend → Frontend

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🛠️ Tech Stack

Frontend

• Next.js
• Tailwind CSS
• Axios

Backend

• Node.js
• Express.js
• MongoDB
• JWT Authentication

ML Service

• Python
• FastAPI
• NLP-based keyword & role matching
• Rule-based + AI-expandable models

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⚙️ Setup & Installation

🐳 Docker Setup (Recommended)

Prerequisites

• Docker and Docker Compose installed on your system
• Git

Quick Start with Docker

1. Clone the Repository

git clone https://github.com/your-username/CareerCraft.git
cd CareerCraft

2. Configure Environment Variables

# Copy the example environment file
cp .env.example .env

# Edit the .env file with your configuration
# Required: Cloudinary credentials, JWT secret
# Optional: Google/GitHub OAuth credentials

3. Start All Services

# Build and start all services
docker-compose up --build

# Or run in detached mode
docker-compose up --build -d

4. Access the Application

• Frontend: http://localhost:3000
• Backend API: http://localhost:5000
• ML Service: http://localhost:8001
• MongoDB: localhost:27017

5. Stop Services

docker-compose down

Docker Commands

# View logs for all services
docker-compose logs -f

# View logs for specific service
docker-compose logs -f frontend
docker-compose logs -f backend
docker-compose logs -f ml-service

# Rebuild a specific service
docker-compose up --build backend

# Stop and remove all containers
docker-compose down -v

🛠️ Manual Setup (Without Docker)

Prerequisites

• Node.js (v18+)
• Python (v3.11+)
• MongoDB
• Git

1️⃣ Clone the Repository

git clone https://github.com/your-username/CareerCraft.git
cd CareerCraft

2️⃣ Start ML Service

cd ml-service
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8001

3️⃣ Start Backend

cd backend
npm install
cp .env.production .env  # Configure with your credentials
npm run dev

4️⃣ Start Frontend

cd frontend
npm install
npm run dev

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🔧 Environment Variables

Required Variables

• MONGODB_URI: MongoDB connection string
• CLOUDINARY_CLOUD_NAME: Cloudinary cloud name
• CLOUDINARY_API_KEY: Cloudinary API key
• CLOUDINARY_API_SECRET: Cloudinary API secret
• JWT_SECRET: Secret for JWT token signing

Optional Variables

• GOOGLE_CLIENT_ID & GOOGLE_CLIENT_SECRET: Google OAuth
• GITHUB_CLIENT_ID & GITHUB_CLIENT_SECRET: GitHub OAuth
• OLLAMA_BASE_URL: Ollama service URL (default: http://localhost:11434)

Service URLs

• Frontend: http://localhost:3000
• Backend: http://localhost:5000
• ML Service: http://localhost:8001
• MongoDB: localhost:27017

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📝 Docker Development Notes

Service Architecture

• frontend: Next.js application (Port 3000)
• backend: Node.js/Express API (Port 5000)
• ml-service: Python/FastAPI ML service (Port 8001)
• mongodb: MongoDB database (Port 27017)

Volume Mounts

• Source code is mounted for live development
• Node modules are isolated to prevent conflicts
• MongoDB data persists in Docker volume

Health Checks

All services include health checks for monitoring:

• Backend: HTTP check on /api/health
• ML Service: HTTP check on /
• Frontend: Process monitoring

Production Deployment

For production deployment:

1. Update environment variables with production values
2. Remove volume mounts for source code
3. Use proper secrets management
4. Configure reverse proxy (nginx/traefik)
5. Set up SSL certificates

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👨‍💻 Contributors

See CONTRIBUTORS for the full list of contributors.

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📄 License

This project is licensed under the MIT License.

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⭐ Acknowledgements

Inspired by real-world ATS systems and modern hiring workflows.
Built for learning, experimentation, and real-world impact.