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| 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**. |