I’m rolling out a Python-based platform that keeps an eye on city roads in real time. The first goal is clear: monitor live video from roadside traffic cameras, spot congestion, accidents or blockages within seconds, and push actionable alerts through a cloud-hosted pipeline. Later phases will weave in IoT sensors, GPS data and public-transport schedules, but for now cameras take centre stage and all processing happens in the cloud. Here’s what I need from you: • A complete stream-to-insight workflow: camera feed ingestion → cloud message bus → analytics micro-service. • Computer-vision models (OpenCV, YOLO, TensorFlow—your call) that flag incidents with >90 % precision/recall on my test clips. • A REST API that surfaces live traffic state and returns diversion routes in real time. • Extension hooks so I can sync bus, train and metro timetables and forward delay alerts to commuters. • Containerised or serverless deployment scripts so I can spin the stack up on AWS or GCP with a single command. I’m committed to Python throughout and prefer a cloud-native architecture, though I’m open to any cost-efficient scaling ideas you may have. I’ll stress-test your solution against simulated rush-hour loads; passing means every alert and route suggestion reaches the demo mobile app in under five seconds. If you’ve built real-time video analytics or smart-city IoT systems before, I’d love to see your work and hear how you’d tackle this challenge.