Project Healix is a compassionate and empowering initiative committed to fostering emotional well-being and mental health awareness. Leveraging the power of FastAPI, this Python server delivers crucial support and knowledge to users seeking guidance and understanding.
Emotional Support: Provides users with access to various tools and resources tailored to address emotional challenges, fostering a sense of comfort and connection. Mental Health Knowledge: Offers comprehensive information on a range of mental health topics, presented in a clear, accessible, and destigmatizing manner
To set up the Project Healix backend locally, follow these steps:
Make sure you have the following installed on your system:
- Python 3.11.x
- pip (Python package installer)
-
Clone the repository:
git clone https://github.com/DeepeshKalura/HealixServer
-
Navigate into the project directory:
cd HealixServer
-
Install dependencies using pip:
pip install -r requirements.txt
Once you have installed the dependencies, you can start the FastAPI server by running the following command:
uvicorn main:app --reload
This command will start the server, and it will automatically reload whenever you make changes to the code.
You can then access the server at http://localhost:8000
.
We welcome contributions from the community to make Project Healix even better! If you'd like to contribute, please follow these guidelines:
- Fork the repository.
- Create a new branch for your feature or bug fix:
git checkout -b feature-name
. - Make your changes and commit them with descriptive commit messages.
- Push your branch to your fork:
git push origin feature-name
. - Submit a pull request to the
production
branch of the original repository.
If you encounter any bugs or have ideas for new features, please open an issue on GitHub.
Please follow the PEP 8 style guide for Python code. Additionally, make sure to write clear and concise commit messages.
Before submitting a pull request, make sure to test your changes locally and ensure they do not introduce any regressions.
This project is licensed under the MIT License. See the LICENSE file for details.