ปลายทาง /predict แรกของคุณ

A model in a notebook helps only you. To serve real users, you wrap it in an API that anyone can call over the network. 🚀

FastAPI is a modern Python web framework that is fast to write, validates input for you, and auto-generates interactive docs.

You need two pieces: FastAPI itself and a server to run it. Install both with pip in one line.

Everything starts from one app object. You create a FastAPI instance and attach routes to it.

An endpoint is a URL your app responds to, like /predict. Each one runs a Python function when someone calls it.

Before predicting, you load the trained model from disk. Here joblib reads a saved scikit-learn model into memory.

You mark a function as a POST endpoint with a decorator. POST fits prediction because you send data in the request body.

Inside the function you call the model. predict expects a 2D input, so you wrap the row in a list before passing it.

Whatever your function returns, FastAPI serializes to JSON automatically. Return a plain dict and the client gets clean JSON.

You launch the app with uvicorn, pointing it at the module and the app object. The reload flag restarts on code changes.

Visit /docs in your browser and FastAPI shows an interactive page where you can test /predict without writing any client code. 🎉

You want clients to send feature data to your model. Which HTTP method fits a /predict endpoint?

You wrapped a model in FastAPI: create the app, load the model, expose a POST /predict that returns JSON, and run it with uvicorn. That is a live API! 🙌