🚀 CareerPathAI - AI-Powered Career Guidance System

CareerPathAI is a comprehensive AI-powered career guidance system that helps job seekers analyze their skills, get personalized career recommendations, and develop learning plans to achieve their career goals.

✨ Features

• 📄 Resume Parsing: Extract skills and information from PDF/DOCX resumes using NLP
• 🎯 Career Recommendations: Get personalized job recommendations based on your skills
• 🔍 Skill Gap Analysis: Identify missing skills for your target roles
• 📚 Learning Plans: Get curated learning resources for skill development
• 💡 Personalized Advice: Receive tailored career guidance and next steps
• 📊 Skill Visualization: Interactive charts and metrics for skill analysis
• 🔧 Manual Skills Input: Don't have a resume? Input skills manually

🛠️ Technology Stack

Backend

• FastAPI: Modern, fast web framework for building APIs
• spaCy: Advanced NLP for text processing and entity extraction
• scikit-learn: Machine learning for clustering and analysis
• Sentence Transformers: State-of-the-art text embeddings
• PDFMiner: PDF text extraction
• python-docx: DOCX file processing

Frontend

• Streamlit: Beautiful, interactive web application
• Plotly: Interactive data visualizations
• Requests: HTTP client for API communication

ML/AI

• NLP: Named Entity Recognition, skill extraction
• Clustering: Career path clustering
• Similarity Matching: Skill-to-job matching algorithms

📋 Prerequisites

• Python 3.8+
• pip (Python package manager)
• Git

🚀 Quick Start

1. Clone the Repository

git clone <repository-url>
cd CareerPathAI

2. Create Virtual Environment

python -m venv venv

3. Activate Virtual Environment

Windows:

venv\Scripts\activate

macOS/Linux:

source venv/bin/activate

4. Install Dependencies

pip install -r requirements.txt

5. Download spaCy Language Model

python -m spacy download en_core_web_sm

6. Start the Backend Server

cd app
uvicorn main:app --reload

The API will be available at http://localhost:8000

7. Start the Frontend Application

cd frontend
streamlit run app.py

The web app will be available at http://localhost:8501

📁 Project Structure

CareerPathAI/
├── app/
│   ├── main.py                 # FastAPI backend
│   ├── models/                 # ML models (future)
│   ├── services/
│   │   ├── resume_parser.py    # Resume parsing service
│   │   └── career_recommender.py # Career recommendation engine
│   └── utils/                  # Helper utilities
├── frontend/
│   └── app.py                  # Streamlit frontend
├── data/
│   ├── resumes/                # Sample resumes
│   └── jobs/                   # Job data
├── notebooks/                  # Jupyter notebooks for development
├── requirements.txt            # Python dependencies
└── README.md                  # This file

🎯 Usage Guide

Resume Analysis

1. Open the web application at http://localhost:8501
2. Navigate to "Upload Resume" in the sidebar
3. Upload your PDF or DOCX resume
4. View the analysis results:
   • Extracted skills by category
   • Career recommendations with match scores
   • Learning plan for missing skills
   • Personalized career advice

Manual Skills Input

1. Navigate to "Manual Skills Input" in the sidebar
2. Select your skills from the dropdown menus
3. Click "Analyze Skills"
4. Get the same comprehensive analysis without a resume

🔧 API Endpoints

Core Endpoints

• GET /: API information and available endpoints
• GET /health: Health check
• POST /upload_resume: Upload and parse resume file
• POST /parse_text: Parse resume from text input
• POST /analyze_skills: Analyze skills without resume

Information Endpoints

• GET /job_profiles: Get available job profiles
• GET /learning_resources: Get learning resources

📊 Sample Output

Resume Analysis Results

{
  "parsed_resume": {
    "name": "John Doe",
    "email": "john.doe@email.com",
    "phone": "(555) 123-4567",
    "skills": {
      "programming": ["python", "javascript", "java"],
      "web_development": ["react", "node.js", "django"],
      "databases": ["mysql", "postgresql", "mongodb"],
      "cloud_platforms": ["aws", "docker", "kubernetes"]
    }
  },
  "career_analysis": {
    "recommendations": [
      {
        "job_title": "Software Engineer",
        "match_score": 85.5,
        "description": "Design, develop, and maintain software applications",
        "missing_skills": ["algorithms", "data structures"]
      }
    ],
    "learning_plan": {
      "algorithms": {
        "courses": ["https://www.coursera.org/learn/algorithms"],
        "books": ["Introduction to Algorithms"],
        "practice": ["LeetCode", "HackerRank"]
      }
    }
  }
}

🎨 Customization

Adding New Skills

Edit app/services/resume_parser.py to add new skill categories:

SKILL_KEYWORDS = {
    'new_category': [
        'skill1', 'skill2', 'skill3'
    ]
}

Adding New Job Profiles

Edit app/services/career_recommender.py to add new job profiles:

JOB_PROFILES = {
    'New Job Title': {
        'required_skills': ['skill1', 'skill2'],
        'preferred_skills': ['skill3', 'skill4'],
        'description': 'Job description here'
    }
}

Adding Learning Resources

Update the LEARNING_RESOURCES dictionary in career_recommender.py:

LEARNING_RESOURCES = {
    'new_skill': {
        'courses': ['course_url'],
        'books': ['book_title'],
        'practice': ['practice_platform']
    }
}

🚀 Deployment

Local Development

1. Follow the Quick Start guide above
2. Both backend and frontend will run on localhost

Production Deployment

1. Backend: Deploy to cloud platforms like:

   • Heroku
   • Railway
   • AWS EC2
   • Google Cloud Run

2. Frontend: Deploy to:

   • Streamlit Cloud
   • Vercel
   • Netlify

Docker Deployment

# Backend Dockerfile
FROM python:3.9
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY app/ .
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

🚀 Streamlit Cloud Deployment

1. Push this repository to a GitHub repo.
2. Go to https://share.streamlit.io and connect your GitHub account.
3. Add an app with the following settings:
   • Repository: your-repo
   • Main file path: streamlit_app.py
4. (Optional) Set the environment variable API_BASE_URL in Streamlit Cloud to your hosted backend (e.g. https://my-backend.example.com). If not set, the app will attempt to use http://localhost:8000 (useful for local development).
5. Ensure the repository has the requirements.txt at the root (already present).
6. Deploy — Streamlit Cloud will install dependencies and run streamlit_app.py automatically.

Notes:

• If you host the FastAPI backend separately, point API_BASE_URL in the Streamlit Cloud app settings to that backend URL.
• If you want both frontend and backend in the same deployment, consider deploying the backend to a separate service (Heroku/Railway/GCP/AWS) and set API_BASE_URL accordingly.

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes
4. Add tests if applicable
5. Commit your changes: git commit -m 'Add feature'
6. Push to the branch: git push origin feature-name
7. Submit a pull request

Made with ❤️ by Dasini Jayakody for job seekers worldwide