Jobfinity - AI-Powered Resume Analyzer

🚀 Welcome to Jobfinity!

Jobfinity is an AI-powered resume analyzer that helps CS students find more accurate job matches based on their interests and resume content. Our platform uses advanced NLP and machine learning techniques to parse resumes and provide tailored job recommendations.

👥 Team Members

• Kristian Vazquez: Team Lead / ML Engineer / Full-Stack Dev
• Miguel Garcia: ML Engineer / Full-Stack Dev
• Raidel Almeida: ML / Full-Stack Dev
• Elijah Chin: Backend & Frontend Developer
• Baire Diaz: Backend & Frontend Developer

🎯 Project Objectives

• Build an ML model that can accurately parse user resumes
• Provide job recommendations based on users' skills, experience, and interests
• Create an intuitive, aesthetically pleasing UI with Streamlit
• Implement advanced NLP techniques for better job matching

🛠️ Tech Stack

• Frontend: Streamlit, HTML, CSS
• Backend: Python
• ML & NLP Libraries:
  • pyresparser, pdfminer3
  • NLTK, spaCy
  • scikit-learn, TensorFlow/PyTorch
  • TF-IDF, Word2Vec, GloVe

🔧 Setup Instructions

Prerequisites

• Python 3.8+
• Git

Installation Steps

1. Clone the repository

   git clone https://github.com/Ceaseless04/Jobfinity.git
   cd Jobfinity

2. Create and activate a virtual environment

   # For Windows
   python -m venv venv
   venv\Scripts\activate
   
   # For macOS/Linux
   python -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Install spaCy language model

   python -m spacy download en_core_web_md

5. Install NLTK data

   # Run this in Python
   import nltk
   nltk.download('punkt')
   nltk.download('stopwords')
   nltk.download('wordnet')

6. Run the application

   cd src
   streamlit run app.py

7. Access the application

   • Open your browser and go to http://localhost:8501

📁 Project Structure

Jobfinity/
├── README.md                 # Project overview
├── requirements.txt          # Dependencies
├── data/                     # Sample data for testing
│   ├── sample_resumes/
│   └── sample_job_descriptions/
├── models/                   # ML models
│   ├── resume_parser.py
│   ├── job_matcher.py
|   └── career_path.py
├── src/                      # Source code
│   ├── app.py                # Main Streamlit application
│   ├── utils/                # Utility functions
│   └── components/           # Streamlit UI components
├── tests/                    # Unit and integration tests
└── test_outputs/             # output of test dataset (PDF Resumes)

⚙️ Development Workflow

1. Pull the latest changes

   git pull origin main

2. Create a new branch for your feature

   git checkout -b feature/your-feature-name

3. Make your changes and commit them

   git add .
   git commit -m "Add your descriptive commit message"

4. Push your changes

   git push origin feature/your-feature-name

5. Create a Pull Request on GitHub

   • Navigate to the repository on GitHub
   • Click on "Pull Requests" and then "New Pull Request"
   • Select your branch and create the PR with a description of your changes

🧪 Testing

Run tests using pytest:

python3 -m unittest discover -s tests -p "test_*.py" -v

📊 Project Milestones

1. Data Collection - Gather sample resumes and job descriptions
2. Repository Setup - Initialize project structure and version control
3. Frontend Prototype - Develop initial Streamlit UI
4. Model Prototype - Implement basic resume parsing and job matching
5. Model Fine-tuning - Improve model accuracy and performance
6. Job Recommendation Integration - Connect with job APIs and implement recommendation logic

📋 Weekly Meetings

• We follow a sprint model with weekly standups
• Each team member reports on:
  • What they accomplished since the last meeting
  • What they plan to work on next
  • Any blockers or challenges they're facing

🔍 Documentation Resources

• ML Model Documentation: Check /docs/ml_models.md for details on our ML pipeline
• API Documentation: Check /docs/api.md for API endpoint details
• UI Components: Check /docs/ui_components.md for UI component documentation

🤝 Contributing Guidelines

1. Follow PEP 8 style guidelines for Python code
2. Write meaningful commit messages
3. Update documentation alongside code changes
4. Add unit tests for new functionality
5. Conduct code reviews for all PRs

🔗 Useful Links

• Video Demo
• Streamlit Documentation
• spaCy Documentation
• Adzuna API Documentation

🆘 Getting Help

If you're stuck or have questions:

1. Check the project documentation
2. Ask in the team Slack channel
3. Reach out to the responsible team member based on the component you're working on
4. Contact Kristian (Team Lead) for overall project questions

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Happy coding! Let's build something amazing together! 🚀