Hospital Management System

Overview

This is a full-stack hospital management system built using:

• Frontend: React with Tailwind CSS
• Backend: Node.js with Express
• Database: MongoDB cluster (shard, router, and config services)
• AI Model: An LLM used for live chat functionality
• DevOps: Docker, Docker Compose, GitHub Actions for CI

This document explains the setup process, development workflows, Makefile commands, and project conventions.

Table of Contents

• Getting Started
• Makefile Commands
• Running the Project
• Pushing Code and Branching Patterns
• Code Quality
• CI Pipeline
• Development Tips
• AI Services

Getting Started

Prerequisites

Before you can run the project, make sure you have the following installed:

• Docker
• Docker Compose
• Node.js (for local development if needed "backend/frontend teams")
• Git

Installation

1. Clone the repository:

   git clone <git@github.com:Gofies/GHMS.git>
   cd GHMS

2. Rename the partent folder to softeng

   mv GHMS softeng
   cd softeng

3. Make sure you have make installed on your system. If you are using a linux based system you probably have it, but in case run the command below. If you use Windows, google how to install make on windows.

   sudo apt install make

4. To set up the project and start the services, run:

   make all

   This will spin up the Docker containers for MongoDB services, and both the frontend and backend.

5. To stop and clean the services:

   sudo make clean

6. To remove unnecessary, outdated, or temporary files that may have been created during the build or development process:

   make prune

Makefile Commands

The project uses a Makefile to simplify common tasks. Below are the available commands:

Command	Description
make all	Build and start all services (MongoDB cluster, frontend backend)
make build	Build all service images
make up	Start all services
make down	Stop all running services.
make up service=<frontend/backend/etc...> "overloaded"	Start a specific service.
make down service=<frontend/backend/etc...> "overloaded"	Stop a specific service.
make restart	Restart all running services.
sudo make clean	Remove all Docker images, containers, and volumes.
make status	Show the status of all running services.
make logs	View logs for all services.

How to attach to a running container

To attach to one of the running containers, you can use the following commands:

Command	Description
make attach service=<backend/etc...>	Attach to a running container (MongoDB services, frontend, backend)
make attach-mongos	Attach to the MongoDB cluster router container's cli.

When running the commands make sure to check the .env file for the environment variables needed for the services to run. It is used to determine whether the services are running in development or production mode.

Running the Project

1. Start all services: Use make all to start the Docker containers for the database, backend, and frontend.

2. Accessing the services:

   • Frontend: http://localhost:3000
   • Backend: https://localhost:5000
   • MongoDB cluster: mongodb://${MONGO_USERNAME}:${MONGO_PASSWORD}@router01:27117,router02:27118/HospitalDatabase?authMechanism=DEFAULT

3. Stopping services: To stop and remove all running services, run sudo make clean.

Pushing Code and Branching Patterns

We follow the Git Flow branching model with the following conventions:

General Practices:

• Always create a new branch from develop for any feature or bug fix. NEVER push directly to main.

• Merges will be done via pull requests (PRs) after at least one code review.

• Before doing any commits or starting any development, make sure to pull the latest changes from all branches by running the following command:

   git pull --all

Branch Types:

• main: The production-ready code.
• develop: The main branch for integration of all new features.
• <Team>/<feature-name>: Feature branches that implement new functionality.

Pull Request (PR) Process:

• Always create a new branch from develop for any feature or bug fix.

  git checkout develop
  git checkout -b <team>/<feature-name>

• Push the feature branch to the remote repository:

  git push origin <team>/<feature-name>

• Create a pull request (PR) to merge your feature branch into develop.

• Ensure your code passes all tests and linting checks before submitting the PR.

• After approval, the feature will be merged into develop.

• Occasionally, develop will be merged into main for deployment testing.

Commit Message Format:

We follow the Conventional Commits style:

• feat: A new feature
• fix: A bug fix
• docs: Documentation changes
• refactor: Refactoring code
• test: Adding or updating tests
• dev: feature development tasks

Example:

git commit -m "feat: add patient appointment booking feature"

Code Quality

Testing

• All new code should include unit tests where applicable.

CI Pipeline

Our CI pipeline runs on GitHub Actions. Every push or pull to develop branch will trigger:

1. Build: The CI will build the Docker containers for the project.
2. Install Dependencies: Dependencies for both frontend and backend will be installed.
3. Run Tests: Tests for both frontend and backend will be executed.
4. Tear Down: The Docker environment will be cleaned up after testing.

If all checks pass, the changes can be merged into develop after a code review.

Development Tips

• Consistent Environment: Use Docker Compose to ensure your local development environment mirrors the CI environment.
• Makefile: Use the provided Makefile to simplify common tasks and reduce errors.
• Code Review: Always request a code review before merging any changes to develop.
• Documentation: Keep this README.md up-to-date as the project evolves.

AI Services

The AI services consist of two main models one responsible of handling live chat with patients using a language model, and the other one being a cluster of models responsible of analyzing the patient based on the reports and the data provided by the patient.

needs revisiting after a while

Running the AI Service

To run the AI service in isolation, use:

make up-ai

To stop the AI service:

make down-ai

Interacting with the AI Service

The AI service exposes a REST API at http://localhost:8000/chat. You can send a POST request with a patient's query, and it will return the AI-generated response.

Example request payload:

{
  "patientQuery": "How can I book an appointment?"
}