Swarogya - The Hospital Management System

Android App Installation Guide

• First start with installing Android Studio on your pc.
• Clone this repository.
• Add the app to a Firebase project on the Firebase console by using the applicationId value specified in the app/build.gradle file of the app as the Android package name.
• Download the generated google-services.json file, and copy it to the app/ directory of the app.
• Enable Phone Authentication, Firestore and Storage on the Firebase console.
• Use the gradlew build command to build the project directly or use the IDE to run the project to your phone or the emulator.

Angular App Installation Guide

• First start with installing node and the angular cli on your pc.
• Start the terminal and run commands in the following order.

git clone https://github.com/akshaaatt/Swarogya.git
cd angular
npm install
npm start

• Now browse to the app at localhost:8000/index.html

Django App Installation Guide

• Login to test the web app at https://swarogya-web.herokuapp.com/staff_login/ with credentials - Email: [email protected] , Password: dummy123
• First start with installing python on your pc.
• Then run the command $ pip install virtualenv.
• Clone this repository.
• Update the valid credentials for the files:

1. firebase-sdk.json
2. Swarogya-36675b238292.json
3. config_file.json
4. firebase-messaging-sw.js

• Start the terminal in django directory and run commands in the following order.

$ source env/bin/activate on linux/mac
       OR
$ env/scripts/activate  on windows
(env) $ pip install -r requirements.txt
(env) $ python manage.py migrate
(env) $ python manage.py createsuperuser
(env) $ python manage.py runserver

Cloud Functions Guide

• Install the Firebase CLI to your device.
• Then run the command firebase init and create a function.
• Replace the code in index.js with the one present in cloud-functions.js
• Run the command firebase deploy to upload the cloud function to firebase.

Training the ML models

• Folder jupyter-notebooks contains all the .ipynb notebooks required for training the various ML models.
• These ML models can be saved after training and used later.
• The notebooks are Google Colab compatible and can be run directly via colab.

Datasets used for Machine Learning Models

The datasets used for training the ML models are taken from https://www.kaggle.com

1. Blood Cell Images: https://www.kaggle.com/paultimothymooney/blood-cells
2. Ocular Disease Recognition: https://www.kaggle.com/andrewmvd/ocular-disease-recognition-odir5k
3. CoronaHack -Chest X-Ray-Dataset: https://www.kaggle.com/praveengovi/coronahack-chest-xraydataset
4. Leukemia Classification: https://www.kaggle.com/andrewmvd/leukemia-classification
5. Chest X-Ray Images (Pneumonia): https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia
6. Head CT - hemorrhage: https://www.kaggle.com/felipekitamura/head-ct-hemorrhage