Establishing the Backend Infrastructure for Edge AI Home Monitoring System

Today, I successfully built the core communication layer for my Edge AI Home Monitoring System. I focused on creating a robust backend using Flask, enabling the system to act as a central hub for my home devices.
Key Achievements:
API Orchestration: Developed Flask endpoints (/api/light and /api/tv) to handle state management and device control requests efficiently.
Hardware Integration: Implemented an abstraction layer for AndroidTV control, allowing the server to issue commands like power toggles, volume adjustment, and channel navigation over the network.
Dynamic Configuration: Built a utility module to handle JSON-based device mapping, ensuring the system can scale easily by reading device IPs and types from a central devices.json configuration file.
Logging System: Integrated a custom logger to monitor command execution in real-time, which is essential for debugging my edge-computing setup.
Next Steps: Now that the server is successfully running and handling commands, the next phase is to add more devices and create the frontend dashboard.