External ML API Integration & Real-time WebSockets

[levelslip]

• Description: Connect to Python ML service and implement Socket.io
• Activities:
  • ML Service HTTP Client:
    • Setup Axios/Fetch client to communicate with the separate Python ML API.
    • POST /api/booking/predict: Receive patient request, fetch current queue/db state, send payload to ML API, return ML estimation to frontend.
    • Handle ML API timeouts gracefully (fallback to historical DB averages).
  • Socket.io Implementation:
    • Attach Socket.io server to Express instance.
    • Setup socket authentication (validate JWT on connection).
    • Create rooms based on userId and departmentId.
  • Broadcast Service:
    • Emit events when queue statuses change (e.g., queue_updated, patient_called).
    • Push live queue position updates to active patient clients.
• Deliverables: Axios ML API client wrapper, fallback logic, Socket.io server, real-time event emitters