AIGDSCMeet is a project that utilizes technologies such as FastAPI, Socket.IO, Gunicorn, PyTorch, CUDA, and Transformers to process and extract information from audio in meetings using GPU acceleration.

• Docker
• NVIDIA Docker Toolkit
• CUDA Toolkit
• NVIDIA Container Toolkit

1. Clone the project from GitHub:

   git clone https://github.com/TrMTu4898/services-ai-gdscmeet.git
   cd services-ai-gdscmeet

2. Run Docker Compose:

   docker-compose up --build

3. Access the application at ws://127.0.0.1:5000/

• APIAI/main.py: Contains the main source code for the FastAPI, Gunicorn, Uvicorn for AI function.
• AIGDSCMeet/main.py: Container the main source code for SocketIO, FastAPI, Uvicorn connect to the client at the gateway [ws://127.0.0.1:5000/].
• APIAI/Dockerfile: Dockerfile to build the Docker image for the api alication.
• AIGDSCMeet/Dockerfile: Dockerfile to build the Docker image for socketIO application.
• docker-compose.yml: Docker Compose configuration file.
• APIAI/requirements.txt: List of Python dependencies for APIAI.
• AIGDSCMeet/requirements.txt: List of Python dependencies for AIDSCMeet.

1. After installation and running, access ws://127.0.0.1:5000/ in your web browser.
2. Instructions for use
3. Perform actions on the interface to interact with the application.

• //Initialize socket connection
  const socket = io.connect('ws://127.0.0.1:5000/', {
      auth: user_info
  });
  
  //Connect to the server
  socket.on('connect', () => {
      console.log('Connected to the server!');
  });
  
  //Disconnect from the server
  socket.on('disconnect', () => {
      console.log('Disconnected from the server!');
  });
  
  //Send data to the server
  socket.emit('speech_to_text_result', interimResults)
  
  
  //Receive results from the server
  socket.on('speechToKeywords', (result) => {
      console.log('Speech to Keywords Result:', result);
  });
  

• Ensure your computer has a GPU compatible with CUDA for optimal performance of the project.

• To optimize GPU performance, make sure to install the NVIDIA Docker Toolkit and CUDA Toolkit correctly.

• If you encounter issues, check the logs of the containers for detailed information.