Earlent - A Mental Health Platform

Dhrumil Patel - Full Stack Developer
Anthony Santana - Software Engineer in AI/ML

How we built it

• Database: Azure Cosmos DB (v-core) for its robust, scalable database services to manage dynamic data requirements efficiently.
• LLM: The Azure OpenAI platform was integrated for generating empathetic, context-aware responses through advanced AI models like GPT 3.5.
• AI Services: Azure Speech Services enable users to interact with the platform using their voice, improving accessibility and user experience. Speech-to-text allowed users to input requests through speech, while text-to-speech provided responses audibly using window.speechSynthesis.
• Frontend: React was chosen for its efficiency in building interactive user interfaces, with Vite used to optimize the development experience and MUI (Material-UI) to design a modern, user-friendly interface.
• Backend: Flask was chosen to manage backend operations, including API routing and middleware functionalities, due to its lightweight and unopinionated structure.
• Agentic Framework: LangChain was integrated to enhance the AI's functionality, enabling the AI to keep track of conversation history and use tools for web search, vector search and map services.
• Services & Libraries: pywebpush allowed for real-time communication with users through web push notifications, facilitated by user subscriptions managed through the Subscription model. Flask-Mail was used to manage email communications within the app.
• CI/CD Docker was used to containerize the application, ensuring consistency across different computing environments. Github Actions was used to optimize the deployment process by wiring it directly to Azure's web services.

Export the Application

1. Clone the repo

https://github.com/dhrumilp12/Mental-Health-Companion.git

2. Build the server with Docker

cd ./server
docker build --pull --rm -t mental-health-app:latest .

3. Configure environment variables:
   • Copy the .env.example file to a new file named .env.
   • Update the .env file with your specific configurations.

   cp .env.example .env
   

4. Setup the frontend environment with NPM

cd ./client
npm install

5. Run the backend:

python app.py

6. Build the frontend:

npm run build

7. Execute the frontend:

npm run preview

Install FFmpeg and Add FFmpeg to System PATH

Download and Extract FFmpeg Properly

1. Download FFmpeg Again:

   • Go to the FFmpeg download page.
   • Click on the link for Windows builds provided by Gyan or BtbN (these are popular and reliable sources). This will redirect you to their respective pages where you can download the build.
   • Choose a static build (which includes all necessary files in a single package) and download the zip file.

2. Extract the FFmpeg Zip File:

   • Once downloaded, right-click on the zip file and choose 'Extract All...' or use any preferred extraction tool like 7-Zip or WinRAR.
   • Choose a location where you want to extract the files. You can extract them directly to C:\FFmpeg to keep things organized.

3. Verify the Contents:

   • Navigate to the folder where you extracted the files.
   • You should see a bin folder inside this directory. Inside bin, there will be at least three files: ffmpeg.exe, ffplay.exe, and ffprobe.exe.

Add FFmpeg to System PATH

If you've successfully located the bin folder now:

1. Edit the PATH Environment Variable:

   • Press Windows key + R, type sysdm.cpl, and press Enter.
   • Go to the 'Advanced' tab and click on 'Environment Variables'.
   • Under 'System Variables', scroll down to find the 'Path' variable and click on 'Edit'.
   • Click 'New' and add the full path to the bin folder, e.g., C:\FFmpeg\bin.
   • Click 'OK' to save your changes and close all remaining windows by clicking 'OK'.

2. Verify FFmpeg Installation:

   • Open a new command prompt or PowerShell window (make sure to open it after updating the PATH).
   • Type ffmpeg -version and press Enter. This command should now return the version of FFmpeg, confirming it's installed correctly and recognized by the system.