---
title: Course Recommender & Learning Roadmap Generator
emoji: 📚
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.23.1
app_file: app.py
pinned: false
license: mit
---

# Course Recommender & Learning Roadmap Generator

A comprehensive tool to help you find the perfect courses and create personalized learning journeys based on your interests, skill level, and goals.

## Features

- ✨ **Course Recommendations**: Find curated courses that match your topic of interest and skill level.
- 🤖 **AI-Enhanced Personalization**: Get recommendations tailored to your specific learning goals (requires OpenAI API key).
- 📚 **Learning Roadmaps**: Generate detailed, step-by-step learning paths for any subject.
- 📚 **Project Suggestions**: Receive practical project ideas to apply your new skills.
- 📖 **Resource Lists**: Get recommendations for books, communities, and tools.

## Installation

1. Clone this repository:
   ```bash
   git clone https://github.com/yourusername/course-recommender.git
   cd course-recommender
   ```

2. Install the required dependencies:
   ```bash
   pip install -r requirements.txt
   ```

3. Create a `.env` file in the root directory with your OpenAI API key (optional):
   ```bash
   OPENAI_API_KEY=your_api_key_here
   ```

## Usage

Run the main script:
```bash
python app.py
```

Follow the interactive prompts to:
1. Enter your topic of interest.
2. Specify your current skill level.
3. Enable AI-enhanced recommendations (if API key is configured).
4. Describe your learning goals or career objectives.
5. Explore the generated learning roadmap.
6. Browse recommended courses.
7. View detailed information about specific courses.

## Deployment on Hugging Face Spaces

### Using Gradio (Recommended)
If deploying on **Hugging Face Spaces** with Gradio, ensure your repository includes:

- `app.py`
- `requirements.txt`
- `README.md` (this file)

Once uploaded, your Space should automatically launch with Gradio.

### Using Flask (Alternative)
If you prefer **Flask**, update the `README.md` configuration:
```yaml
sdk: docker
```
Then ensure you have a `Dockerfile` for deployment.

## Data Source

The application uses Coursera course data loaded from a CSV file. The default file name is `Coursera.csv`, which should be placed in the same directory as the script.

## Requirements

- Python 3.7+
- Dependencies listed in `requirements.txt`

## Contributing

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

## License

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

## Acknowledgments

- Uses the [Rich](https://github.com/Textualize/rich) library for beautiful terminal output.
- Optionally uses OpenAI's GPT models for enhanced recommendations.
- Deployable on [Hugging Face Spaces](https://huggingface.co/spaces) with **Gradio** or **Flask**.