danivpv/linkedincareeradvice

LinkedIn Career Advisor 🚀

A professional career pathway analysis tool that leverages AI to provide tailored career path recommendations based on your professional background, education, and goals. No telemetry.

✨ Features

• —Comprehensive Career Analysis: Get detailed insights on potential career paths tailored to your unique professional background
• —Multi-dimensional Evaluation: Each path is scored on financial potential, human impact, and opportunity creation
• —Customizable Preferences: Adjust weights for different factors based on your personal priorities
• —Time Horizon Options: Choose between short-term (3 years), mid-term (10 years), or long-term (10+ years) career planning
• —Easy Sharing: Export your analysis to LinkedIn or copy to clipboard with a single click
• —Intuitive Interface: User-friendly Gradio UI for seamless interaction

🔧 Installation

Prerequisites

• —Python 3.12 or higher
• —OpenAI API key

Setup

1. 1.Clone the repository:

git clone https://github.com/danivpv/linkedinadvice.git
cd linkedinadvice

1. 1.Install dependencies using uv (recommended):

uv sync --all-extras

1. 1.Create a .env file in the root directory with your OpenAI API key:

OPENAI_API_KEY=your-api-key-here

🚀 Running Locally

Start the application with:

uv run poe start # alternatively, uv run gradio app/main.py

The Gradio interface will be available at http://localhost:7860 in your browser.

📦 Project Structure

linkedin-career-advisor/
├── app/
│   ├── main.py          # Application entry point and Gradio UI
│   ├── constants.py     # Application constants and example data
│   └── utils.py         # Utility functions for clipboard and sharing
│
├── linkedinadvice/
│   ├── __init__.py
│   ├── career_analysis.py  # Career analysis using LangChain and LLM
│   └── monitoring.py       # API usage monitoring and logging
│
├── requirements.txt     # Project dependencies
├── .env                 # Environment variables (not tracked by git)
└── README.md            # Project documentation

🌐 Deploying to Hugging Face Spaces

1. 1.Create a new Space on Hugging Face Spaces
2. 2.Choose Gradio as the SDK
3. 3.Push this repository to the created Space
4. 4.Add your OPENAI_API_KEY as a secret in the Space settings

💻 Development

Dependencies

• —gradio: For the web interface
• —langchain: For LLM integration and career analysis pipeline
• —openai: For accessing GPT models
• —python-dotenv: For environment variable management

Adding New Features

To extend the application:

1. 1.Modify the analysis logic in linkedinadvice/career_analysis.py
2. 2.Update UI components in app/main.py
3. 3.Add new constants in app/constants.py if needed

📊 Usage Example

1. 1.Input your professional experience:
2. 2.Add current and previous roles
3. 3.Specify years of experience
4. 4.List notable achievements

1. 1.Add educational background:
2. 2.Enter degrees, institutions, and dates
3. 3.Highlight academic achievements

1. 1.Define your goals and preferences:
2. 2.Set career goals
3. 3.Add additional insights
4. 4.Adjust time horizon and factor weights

1. 1.Analyze and review results:
2. 2.Get personalized career path recommendations
3. 3.See detailed scoring across dimensions
4. 4.Export or share your results

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

• —OpenAI for providing the underlying language models
• —Gradio for the easy-to-use UI framework framework

Built with ❤️ for career professionals worldwide