https://github.com/codelander07/ai-recruiter-agency
AI Recruiter Agency An intelligent system that analyzes resumes and matches them with job requirements using Llama 3.2 model.
https://github.com/codelander07/ai-recruiter-agency
analysis llama3 llm python streamlit
Last synced: about 1 month ago
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AI Recruiter Agency An intelligent system that analyzes resumes and matches them with job requirements using Llama 3.2 model.
- Host: GitHub
- URL: https://github.com/codelander07/ai-recruiter-agency
- Owner: CodeLander07
- Created: 2025-04-10T13:59:45.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-10T14:38:20.000Z (about 1 year ago)
- Last Synced: 2025-06-08T12:19:49.842Z (about 1 year ago)
- Topics: analysis, llama3, llm, python, streamlit
- Language: Python
- Homepage:
- Size: 35.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AI Recruiter Agency
An intelligent system that analyzes resumes and matches them with job requirements using Llama 3.2 model.
## Project Structure
```
AI-Recruiter-Agency/
├── src/ # Source code
│ ├── models/ # AI model integration
│ │ ├── llama/ # Llama model implementation
│ │ └── embeddings/ # Text embedding models
│ ├── utils/ # Utility functions
│ │ ├── file_utils.py # File handling utilities
│ │ └── text_utils.py # Text processing utilities
│ ├── parsers/ # Document parsers
│ │ ├── resume_parser.py # Resume parsing logic
│ │ └── job_parser.py # Job description parsing
│ ├── matching/ # Matching algorithms
│ │ ├── skill_matcher.py # Skill matching logic
│ │ ├── experience_matcher.py # Experience matching
│ │ └── education_matcher.py # Education matching
│ └── ui/ # User interface components
│ ├── components/ # Reusable UI components
│ ├── pages/ # Streamlit pages
│ └── app.py # Main application file
├── data/ # Data storage
│ ├── resumes/ # Sample resumes
│ └── job_descriptions/ # Sample job descriptions
├── tests/ # Test cases
│ ├── unit/ # Unit tests
│ └── integration/ # Integration tests
├── config/ # Configuration files
│ ├── model_config.yaml # Model configuration
│ └── app_config.yaml # Application configuration
├── docs/ # Documentation
│ ├── api/ # API documentation
│ └── user_guide/ # User guide
├── .env.example # Example environment variables
├── .gitignore # Git ignore file
├── requirements.txt # Python dependencies
├── README.md # Project documentation
└── LICENSE # License file
```
## Installation
### Prerequisites
- Python 3.8 or higher
- Git
- Virtual environment (recommended)
### Setup Instructions
1. Clone the repository:
```bash
git clone https://github.com/unstopablesid/AI-Recruiter-Agency.git
cd AI-Recruiter-Agency
```
2. Create and activate a virtual environment:
```bash
# Windows
python -m venv .venv
.venv\Scripts\activate
# Linux/MacOS
python3 -m venv .venv
source .venv/bin/activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Set up environment variables:
```bash
# Copy the example environment file
cp .env.example .env
# Edit .env with your configuration
# Add your API keys and other sensitive information
```
## Project Essentials
### Required Files
- `requirements.txt`: Lists all Python dependencies
- `.env`: Contains environment variables and API keys
- `src/ui/app.py`: Main Streamlit application
- `config/`: Configuration files for the application
### Development Tools
- Code formatting: `black`
- Linting: `flake8`
- Testing: `pytest`
### Running the Application
1. Start the Streamlit app:
```bash
streamlit run src/ui/app.py
```
2. Access the application at `http://localhost:8501`
## Features
- Resume parsing and analysis
- Job requirement extraction
- Skill matching algorithm
- Experience matching
- Education matching
- Overall compatibility score
- Detailed match breakdown
## Development Guidelines
1. Code Formatting:
```bash
black .
```
2. Linting:
```bash
flake8 .
```
3. Testing:
```bash
pytest tests/
```
## GitHub Repository
- Repository: [AI-Recruiter-Agency](https://github.com/unstopablesid/AI-Recruiter-Agency)
- Main Branch: `main`
- License: MIT
## Contributing
1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.