Setting up an API using FastAPI#

We’ve so far extracted data from an API, performed EDA on said data, and created a recommender system. Let’s put everything into action by serving our recommender system using an easy to use tool called FastAPI.

FastAPI is fast. Serving our recommender system using this tool can be done in just one .py script. Let’s first go through a brief overview of FastAPI before implementing it into our project. If you’re already familiar with FastAPI, feel free to skip to the “Creating the Recommender App” section.

What is FastAPI?#

FastAPI is an extremely easy to use web framework to build APIs using just only Python. To quickly summarize its capabilities for our purposes, let’s run through an example script called

from fastapi import FastAPI

app = FastAPI()

def read_root():
    return {"Hello": "World"}

The code above is all you need to create a simple API that, when accessed at its index, returns a JSON object containing “Hello World.” Now imagine utilizing a recommender function we’ve created in the last section. We can utilize a POST function that returns recommended movies after inputting an initial movie. We’re getting a little ahead of ourselves by introducing a POST method, but don’t worry, we’ll cover it soon.

For now, let’s demonstrate how easy it is to actual start the app. With our example above, we would start the app by running this command in the terminal:

uvicorn app:app

Running the above command would start the server using Uvicorn and create an output in your terminal that includes a link to said server. Uvicorn is an AGSI web server implementation tool for Python. This is a little out of scope, but if you’re interested in learning more, navigate to the provided hyperlinks.

Now that you understand that Uvicorn creates a server for our application, let’s go through how we can have the server communicate.

HTTP Request Methods#

HTTP request methods are used when communicating with servers. There are several methods, but for our case, we’ll go through two of the most common methods: GET and POST.

GET Method#

The GET method is used to request data from a specified resource. In FastAPI, we use the @app.get() decorator to define a function that will handle GET requests. In a GET request, we usually don’t send a payload (data) to the server but rather request information based on the URL or URL parameters.

For example, let’s create a simple GET endpoint that returns movie details based on an ID:

def get_movie(movie_id: int):
    movie = get_movie_details(movie_id)  # An arbitrary function for demonstration purposes
    if movie:
        return {"movie": movie}
    return {"error": "Movie not found"}

Here, {movie_id} in the URL will be replaced by the actual movie ID when making a request, and FastAPI will automatically pass it as an argument to your get_movie() function.

POST Method#

The POST method is used to send data to the server to create a new resource. In FastAPI, you use the decorator to define a function that will handle POST requests. The data you want to send to the server is typically included in the request body.

For example, let’s create an endpoint to handle recommendations. The user will send a movie ID via a POST request, and the server will return a list of recommended movies:

from pydantic import BaseModel

class Movie(BaseModel):
    id: int"/recommend")
def recommend_movie(movie: Movie):
    recommendations = get_recommendations(  # Assuming this is a function you've defined elsewhere
    return {"recommendations": recommendations}

In this example, FastAPI will automatically validate that the incoming request body conforms to the Movie Pydantic model and parse it into the movie parameter. Pydantic is a tool used to validate data types, we’ll get into how it’s used for our recommender soon.

There are several types of HTTP request methods. This project only requires the use of the GET and POST method. Here’s a comprehensive list of HTTP methods if you’d like to learn more about the differing methods.

Creating the Recommender App#

Let’s now break down the file used in this project.

We start with importing the necessary dependencies. Notice how we’re utilizing the get_recommender function we mentioned in the previous section.

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, field_validator
from .recommender import get_recommendation
from fastapi.responses import JSONResponse
import json

app = FastAPI()

Then, we initialize FastAPI by creating an instance of the FastAPI class and storing it in a variable named app. This instance will serve as the main entry point for our API.

The GET Method: Welcome Message#

Moving forward, we use a GET method to provide a welcome message to the users. This is useful as an initial landing point to test if the API is up and running.

async def root():
    return {
        "message": "Welcome! You can use this API to get movie recommendations based on viewers' votes. Visit /docs for more information and to try it out!"  # noqa E501

The POST Method: Movie Recommendations#

Then, we use a POST method for the main functionality of the app: generating movie recommendations based on the user’s input.

First, let’s define our RecommendationRequest model to validate incoming data.

class RecommendationRequest(BaseModel):
    movie: str
    num_rec: int = 10

    def format_movie_name(cls, movie_name):
        """Ensure the movie name is formatted with the
        first letter capitalized."""
        return movie_name.title()  # Convert to title case

What the above code is essentially doing is utilizing Pydantic to ensure the user’s input is in the correct format. Let’s briefly sidetrack to break down what’s going on here.

The BaseModel Inheritance#

The class RecommendationRequest inherits from Pydantic’s BaseModel. This allows it to automatically validate any data that’s passed to it against the type hints and any additional validation you define. This is extremely useful for ensuring that the API receives formatted, valid data before proceeding with any logic.

In the class, we’ve defined two fields: movie and num_rec.

  • movie: str: This specifies that the movie field must be a string. When a request comes in, if movie is not a string, Pydantic will automatically return a validation error.

  • num_rec: int = 10: This specifies that num_rec should be an integer, and if it’s not provided, it will default to 10. Again, Pydantic will validate the type for you.

We’ve also included a field validator for the movie field using Pydantic’s @field_validator decorator. This allows us to add custom validation logic for this field.

  • def format_movie_name(cls, movie_name): This is the actual validator function. It takes the class (cls) and the value of the movie field (movie_name) as arguments.

Inside this function, the .title() method on the string is to ensure that the first letter of each word is capitalized. If the user submits a movie name in all lower-case or all upper-case letters, this method would convert it into title case (e.g., “star wars” would become “Star Wars”).

This ensures a consistent format for the movie names, which is especially useful for our underlying recommendation algorithm.

Now that we understand how we validate data before sending it to our API via a POST method, let’s define our POST endpoint."/recommendations/")
def get_movie_recommendations(recommendation_request: RecommendationRequest):
    Get movie recommendations for a given movie.

    - movie: The name of the movie for which you want recommendations.
    - num_rec: The number of movie recommendations you want. Default is 10.

    JSON containing recommended movies and metrics.
    recommendations = get_recommendation(,

    if isinstance(recommendations, str):
        recommendations = json.loads(recommendations)

    if not recommendations:
        raise HTTPException(
            detail="Movie not found or no recommendations available",  # noqa E501

    return JSONResponse(content=recommendations)

This POST method expects the RecommendationRequest data type, which we defined earlier using Pydantic. FastAPI automatically validates the incoming request using the validation rules we defined in the Pydantic BaseModel. Then, we pass our data into the get_recommendation function we defined in the previous section, essentially getting the recommendations for a given movie. We validate what get_recommendation returns: if it’s a string type, we convert it into JSON so we can appropriately send the response to our API using FastAPI’s JSONResponse. If what get_recommendation returns isn’t a valid data type, we raise an HTTPException and notify that either the movie isn’t found in our data or if there are no recommendations.

Essentially, what we’re doing is:

  1. Validating incoming requests to ensure they’re well-formatted.

  2. Use the validated data to fetch movie recommendations.

  3. Handle different edge cases, such as when a movie doesn’t have any recommendations.

  4. Format and return the output as a JSON response.

Running the App#

Navigate to the mini-projects/movie-rec-system/movie_rec_system/ folder and run the app using the following command:


You should see an output like the below: Uvicorn-Output

In a browser, go to https://localhost:8000 to access the API. This is the index page, where you should see the GET endpoint with the welcome message we defined earlier. To get a more comprehensive overview of our app, FastAPI has a built-in interface using Swagger UI which allows you to test the API directly from your browser. You can access this interface by adding /docs to the url. So instead of https://localhost:8000, navigate to https://localhost:8000/docs to view the interface.


You can follow the instructions on-screen to test out your GET and POST endpoints. Simply enter a movie name and optionally, the number of recommendations you want, and click the “Execute” button. Swagger will then show you the request, the server’s response, as well as any additional information like response codes and headers.

And there you have it! You’ve successfully created a movie recommendation API using FastAPI.