from typing import Annotated

from fastapi import Depends, FastAPI

app = FastAPI()


def get_username():
    try:
        yield "Rick"
    finally:
        print("Cleanup up before response is sent")


@app.get("/users/me")
def get_user_me(username: Annotated[str, Depends(get_username, scope="function")]):

But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

///

## Dataclasses in `response_model` { #dataclasses-in-response-model }

You can also use `dataclasses` in the `response_model` parameter:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

The dataclass will be automatically converted to a Pydantic dataclass.

### Build execution model

Gradle generally does some work in response to a client request. There are several different clients, for example the `gradlew` command or the tooling API client, 
that can send requests to a Gradle daemon.
Each daemon runs one request at a time. Generally speaking, the daemons only act in response to a request from a client and do not take any action on their own.

Cuando necesitas enviar datos desde un cliente (digamos, un navegador) a tu API, los envías como un **request body**.

Un **request** body es un dato enviado por el cliente a tu API. Un **response** body es el dato que tu API envía al cliente.

Tu API casi siempre tiene que enviar un **response** body. Pero los clientes no necesariamente necesitan enviar **request bodies** todo el tiempo, a veces solo solicitan un path, quizás con algunos parámetros de query, pero no envían un body.

            // nothing
        }

        @Override
        public List<String> convert(final String text, final String field, final String... dummy) throws IOException {
            final AnalyzeAction.Response readingResponse = client.admin()
                    .indices()
                    .prepareAnalyze(analyzerSettings.getAnalyzerSettingsIndexName(), text)

    writeOutputFile(importResults)
  }

  private suspend fun updateLocalFile() =
    withContext(Dispatchers.IO) {
      client.newCall(request).executeAsync().use { response ->
        fileSystem.sink(publicSuffixListDotDat).buffer().use { sink ->
          sink.writeAll(response.body.source())
        }
      }
    }

  private suspend fun readImportResults(): ImportResults =
    withContext(Dispatchers.IO) {

Wenn ein eingehender Request nicht korrekt validiert wird, wird eine `400`-<abbr title="Response – Antwort: Daten, die der Server zum anfragenden Client zurücksendet">Response</abbr> gesendet.

## `GZipMiddleware` { #gzipmiddleware }

Verarbeitet GZip-Responses für alle Requests, die `"gzip"` im `Accept-Encoding`-Header enthalten.

)

// writeSTSErrorResponse writes error headers
func writeSTSErrorResponse(ctx context.Context, w http.ResponseWriter, errCode STSErrorCode, err error) {
	stsErr := stsErrCodes.ToSTSErr(errCode)

	// Generate error response.
	stsErrorResponse := STSErrorResponse{}
	stsErrorResponse.Error.Code = stsErr.Code
	stsErrorResponse.RequestID = w.Header().Get(xhttp.AmzRequestID)
	stsErrorResponse.Error.Message = stsErr.Description
	if err != nil {

Beachten Sie, dass die *Pfadoperationen* die Modelle definieren, die sie für die <abbr title="Request – Anfrage: Daten, die der Client zum Server sendet">Request</abbr>- und <abbr title="Response – Antwort: Daten, die der Server zum anfragenden Client zurücksendet">Response</abbr>-<abbr title="Die eigentlichen Nutzdaten, abzüglich der Metadaten">Payload</abbr> verwenden, indem sie die Modelle `Item` und `ResponseMessage` verwenden.

### API-Dokumentation { #api-docs }

    context.close()
    browser.close()


process = subprocess.Popen(
    ["fastapi", "run", "docs_src/sql_databases/tutorial002.py"],
)
try:
    for _ in range(3):
        try:
            response = httpx.get("http://localhost:8000/docs")
        except httpx.ConnectError:
            time.sleep(1)
            break
    with sync_playwright() as playwright:
        run(playwright)
finally: