SkillSync - Resume Analyzer

A comprehensive resume analysis tool built with Streamlit that helps job seekers optimize their resumes and identify skill gaps for their target roles.

Features

• Resume Parsing: Extract and analyze text from PDF resumes
• Formatting Check: Evaluate resume formatting and structure
• Scoring Engine: Provide comprehensive scoring based on various criteria
• Skill Gap Analysis: Identify missing skills for target job roles
• AI-Powered Insights: Generate summaries and explanations using Google's Gemini AI
• Cover Letter Generation: Create personalized cover letters
• Learning Recommendations: Get YouTube tutorial suggestions for skill improvement

Technologies Used

• Frontend: Streamlit
• AI/ML: Google Gemini AI
• Data Processing: Pandas, Python
• Visualization: Plotly
• PDF Processing: Resume parsing capabilities
• Data Sources: O*NET occupational data

Installation

1. Clone this repository:

git clone https://github.com/yourusername/resume_analyzer.git
cd resume_analyzer

2. Install required dependencies:

pip install -r requirements.txt

3. Set up your API keys:
   • Copy .streamlit/secrets.toml.template to .streamlit/secrets.toml
   • Get a Google Gemini AI API key from Google AI Studio
   • Get a YouTube Data API key from Google Cloud Console
   • Add your API keys to the .streamlit/secrets.toml file

Usage

1. Run the Streamlit application:

streamlit run app.py

2. Upload your resume (PDF format)
3. Select your target job role
4. Get comprehensive analysis including:
   • Resume score and feedback
   • Skill gap analysis
   • Improvement recommendations
   • Cover letter generation

Project Structure

resume_analyzer/
├── app.py                      # Main Streamlit application
├── resume_parser.py            # Resume text extraction
├── formatter_checker.py       # Resume formatting analysis
├── scoring_engine.py          # Resume scoring logic
├── recommendations.py         # Skill recommendations and matching
├── gemini_ai.py               # AI integration for insights
├── requirements.txt           # Python dependencies
├── json/                      # O*NET data files
│   ├── Knowledge.txt
│   ├── Skills.txt
│   ├── Task Statements.txt
│   └── Work Activities.txt
└── *.json                     # Processed job role data

Contributing

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

License

This project is open source and available under the MIT License.

Acknowledgments

• O*NET for occupational data
• Google Gemini AI for intelligent insights
• Streamlit for the web framework