- It has now become unsafe to go to the hospital every time we feel unwell since there is a risk of getting affected by COVID-19. We believe that everyone should have easy access to great health care. Thus, there is a need to connect patients virtually with doctors.
- So, our project aims to not only effectively connect doctors and patients virtually but also if a patient recognizes the symptoms, then he/she can know what disease he/she is likely to be infected with and what precautionary measures can be taken.
The application mainly consists of three features:
- First, we have designed a computer-aided diagnosis system (or disease prediction system) where users can get to know whether they are infected with a particular disease or not using machine/deep learning. For this, they are required to enter their medical details on the form or upload an X-ray/MRI image.
- Secondly, there is a feature to enter the symptoms (either simply type the symptoms or record the audio in the browser) they are experiencing and the patients will get to know what possible diseases they might have along with the precautions that they must take. The third feature is the doctor appointment system wherein patients can not only search for doctors based on region or specialization but also connect virtually with doctors around the globe.
Step 1. Clone the repository into a new folder and then switch to the code directory
git clone https://github.com/himanshubohra13/Health-Checkup.git
cd Health-Checkup
Step 2. Create a Virtual Environment to install dependencies.
pip install virtualenv
Create a new Virtual Environment for the project and activate the environment to install the libraries.
virtualenv env
env\Scripts\activate
Once the virtual environment is activated, the name of your virtual environment will appear on left side of terminal.
Next, we need to install the project dependencies in this virtual environment, which are listed in requirements.txt.
pip install -r requirements.txt
Step3 . Download the trained models and include them in the models directory of the application.
The trained deep learning models can be downloaded from here.
Step 4. Set up Amazon Transcribe API for speech to text conversion
- Create an AWS free tier account.
- Sign in to your Amazon console, create a S3 bucket and give it a unique name. Note your AWS region as it will be required later.
- Go to IAM dashboard, add a new User. Then click on add permissions and grant the following two permissions - AmazonTranscribeFullAccess and AmazonS3FullAccess.
- Then under Security Credentials, click on Create access key to get your credentials i.e, 'aws_access_key_id' and 'aws_secret_access_key'.
Step 5. Update environment variables.
To run the project, you need to configure the application to run locally. This will require updating a set of environment variables specific to your environment.
In the same directory, create a local environment file, named - .env.
Now simply duplicate the variables in .env.sample file and just insert your credentials into local environment file - .env.
Step 6. Run Django Project.
- Make migrations to create/apply changes to the models into the database schema.
python manage.py makemigrations
python manage.py migrate
- Create a superuser for django admin panel.
python manage.py createsuperuser
- Run the server code.
python manage.py runserver
