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Learn FastAPI with a Full-Stack Project: Movie Recommendation
Learn how FastAPI serves a front-end Streamlit application.
Introduction
FastAPI is a web application framework created for high performance when building APIs in Python language. It was first released in 2018 and rapidly grew in popularity, now being used by many large corporations.
With online services only growing — even more now with LLMs and agents — web services tend to keep growing and conquering space in the tech world. Therefore, for those who like Python better than other famous web development languages like Java Script, FastAPI shows itself as a great tool to learn.
When browsing a website, clicking buttons or submitting forms sends requests to a backend API. With FastAPI, this process is seamless.
Suppose you have a form that collects user input for a machine-learning model to make predictions. When submitted, FastAPI receives the data, validates it, passes it to the model, and returns the prediction — all in milliseconds.
Think of FastAPI as a smart waiter: it takes your order (request), sends it to the kitchen (ML model), and quickly brings back your meal (prediction).
In this post, I want to show you a full-stack project for a simple movie recommendation system…
