!!! tip
    If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

### Declare a *path parameter*

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

Here's a little bit of that history.

## Alternatives

I have been creating APIs with complex requirements for several years (Machine Learning, distributed systems, asynchronous jobs, NoSQL databases, etc), leading several teams of developers.

As part of that, I needed to investigate, test and use many alternatives.

The history of **FastAPI** is in great part the history of its predecessors.

# Reference - Code API

Here's the reference or code API, the classes, functions, parameters, attributes, and
all the FastAPI parts you can use in your applications.

If you want to **learn FastAPI** you are much better off reading the

## Dependencies for groups of *path operations*

And you want to have a way for the frontend to authenticate with the backend, using a **username** and **password**.

We can use **OAuth2** to build that with **FastAPI**.

But let's save you the time of reading the full long specification just to find those little pieces of information you need.

Let's use the tools provided by **FastAPI** to handle security.

## How it looks

## 🔑 👨‍💼

### ⚫️❔ "🔑 👨‍💼"

"🔑 👨‍💼" 🙆 👈 🐍 🎚 👈 👆 💪 ⚙️ `with` 📄.

🖼, <a href="https://docs.python.org/3/tutorial/inputoutput.html#reading-and-writing-files" class="external-link" target="_blank">👆 💪 ⚙️ `with` ✍ 📁</a>:

```Python
with open("./somefile.txt") as f:
    contents = f.read()
    print(contents)
```

## Recap

You have been reading here some of the main concepts that you would probably need to keep in mind when deciding how to deploy your application:

* Security - HTTPS
* Running on startup
* Restarts

    * PR [#593](https://github.com/tiangolo/fastapi/pull/593).
* Improve handling of custom classes for `Request`s and `APIRoute`s.
    * This helps to more easily solve use cases like:
        * Reading a body before and/or after a request (equivalent to a middleware).
        * Run middleware-like code only for a subset of *path operations*.

### Create Pydantic *models* / schemas for reading / returning

Now create Pydantic *models* (schemas) that will be used when reading data, when returning it from the API.

For example, before creating an item, we don't know what will be the ID assigned to it, but when reading it (when returning it from the API) we will already know its ID.

### Creating the `Settings` only once with `lru_cache`

Reading a file from disk is normally a costly (slow) operation, so you probably want to do it only once and then re-use the same settings object, instead of reading it for each request.

But every time we do:

```Python
Settings()
```