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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.