Mental Health Tracking Application (MindTrack)

It is a web application designed to facilitate easy and convenient tracking of daily mood and mental health symptoms. Utilizing AI and a user-friendly interface, MindTrack aims to provide comprehensive mental health monitoring for users with Depression.

Purpose

The main objectives behind developing MindTrack are as follows:

1. Simplify Mental Health Tracking: Provide a clean and intuitive interface to make tracking mental health symptoms accessible for all users.
2. Leverage AI for Insights: Utilize AI to analyze user inputs and provide meaningful insights and trends in mental health data.
3. Promote User Engagement: Send daily survey reminders via text message to ensure consistent tracking and engagement.
4. Track Progress: Allow users to visualize their mental health trends and review data over time.
   

Key Features

• Daily Surveys: Receive a survey link via text message each day to track mood, symptoms, weight, hours of sleep, and thoughts.
• Sentiment Analysis: Uses a TensorFlow model to analyze and provide feedback on user input.
• Future Features: Home page, account creation/login, medication tracking, and a user dashboard for visualizing trends.
• User-Friendly Design: Designed to be easy to use, even for individuals with Depression or any other mental health issues.
  

Technical Details

• Backend: Django (Python), MySQL
• Frontend: React.js (JavaScript)
• AI: TensorFlow for sentiment analysis
• Deployment: Docker, Azure Kubernetes Service (AKS)

Azure Deployment

1. Create Azure Resources:

   • Container Registry: Create an Azure Container Registry (ACR) to store your Docker images.
   • Kubernetes Cluster: Create an Azure Kubernetes Service (AKS) cluster to host and manage your containerized application.

2. Push Docker Images to ACR:

   • Build your Docker images locally and push them to your ACR:

     docker build -t your-acr-name.azurecr.io/mindtrack-backend ./mindtrack-backend
     docker push your-acr-name.azurecr.io/mindtrack-backend
     
     docker build -t your-acr-name.azurecr.io/mindtrack-frontend ./mindtrack-frontend
     docker push your-acr-name.azurecr.io/mindtrack-frontend

3. Deploy to AKS:

   • Deploy your application to AKS using Kubernetes manifests (deployment.yaml, service.yaml). Ensure to update these files with your ACR details.
   • Example deployment.yaml for backend:

     apiVersion: apps/v1
     kind: Deployment
     metadata:
       name: mindtrack-backend
     spec:
       replicas: 1
       selector:
         matchLabels:
           app: mindtrack-backend
       template:
         metadata:
           labels:
             app: mindtrack-backend
         spec:
           containers:
             - name: mindtrack-backend
               image: your-acr-name.azurecr.io/mindtrack-backend:latest
               ports:
                 - containerPort: 8000

   • Example service.yaml for backend:

     apiVersion: v1
     kind: Service
     metadata:
       name: mindtrack-backend-service
     spec:
       selector:
         app: mindtrack-backend
       ports:
         - protocol: TCP
           port: 80
           targetPort: 8000
       type: LoadBalancer

   • Apply these manifests:

     kubectl apply -f deployment.yaml
     kubectl apply -f service.yaml

How to Use

1. Clone the Repository:

   git clone https://github.com/yourusername/mindtrack.git
   

2. Navigate to the Project Directory:

   cd mindtrack
   

3. Backend Setup:

   • Create a virtual environment and activate it:

     python -m venv venv
     source venv/bin/activate  # On Windows use `venv\Scripts\activate`

   • Install Python dependencies:

     pip install -r requirements.txt

   • Setup MySQL database and configure database settings in mindtrack-backend/mindtrack_backend/settings.py.
   • Apply migrations and start the Django server:

     python manage.py makemigrations
     python manage.py migrate
     python manage.py runserver

4. Frontend Setup:

   • Navigate to the frontend directory:

     cd ../mindtrack-frontend

   • Install npm packages:

     npm install

   • Start the React development server:

     npm start

   • Access the application at http://localhost:3000 in your web browser.

5. TensorFlow Setup:

   • Train and save a TensorFlow model for sentiment analysis (model.h5).
   • Save the tokenizer used for preprocessing (tokenizer.json).
   • Place these files in appropriate directories and update paths in mindtrack-backend/api/views.py.

Contributing

If you'd like to contribute to the MindTrack project, please follow these steps:

1. Fork the repository.
2. Create a new branch: git checkout -b feature/new-feature.
3. Make your changes and commit them: git commit -m "Add new feature".
4. Push to the branch: git push origin feature/new-feature.
5. Create a pull request.