README.md

metadata

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:

   git clone https://github.com/yourusername/course-recommender.git
   cd course-recommender
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Create a .env file in the root directory with your OpenAI API key (optional):

   OPENAI_API_KEY=your_api_key_here
   

Usage

Run the main script:

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:

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 library for beautiful terminal output.
• Optionally uses OpenAI's GPT models for enhanced recommendations.
• Deployable on Hugging Face Spaces with Gradio or Flask.