tarini-py/MLOps-AWS-model-deployment ? reverse-engineered prompt

Build me a small Python app that predicts a used car’s resale price from a few inputs like mileage, age, distance driven, and fuel type. I want a simple Flask API with a health check, a demo route, and a predict endpoint that accepts JSON and returns the estimated price.

Please keep the model basic, something like XGBoost is fine, and include the code to train it, save the model, and load it for inference. Add tests for the data, the model, and the API routes so I can trust the app works. I also want it containerized with Docker and ready to run locally.

Most importantly, set up the full deployment flow so every push to main runs linting and tests first, then builds the container, pushes it to AWS ECR, and deploys it to AWS ECS Fargate. If you need to check current docs or best practices online, go ahead and do that.