COPY ./requirements.txt /code/requirements.txt

RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

COPY ./app /code/app

CMD ["fastapi", "run", "app/main.py", "--port", "80"]

# Если запускаете за прокси, например Nginx или Traefik, добавьте --proxy-headers
# CMD ["fastapi", "run", "app/main.py", "--port", "80", "--proxy-headers"]
```

</details>

## Что такое контейнер { #what-is-a-container }

    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):
    result = ml_models["answer_to_everything"](x)

from typing import Annotated

from fastapi import FastAPI, File, UploadFile

app = FastAPI()


@app.post("/files/")
async def create_file(file: Annotated[bytes | None, File()] = None):
    if not file:
        return {"message": "No file sent"}
    else:
        return {"file_size": len(file)}


@app.post("/uploadfile/")
async def create_upload_file(file: UploadFile | None = None):
    if not file:

    separate_input_output_schemas = request.param
    app = FastAPI(separate_input_output_schemas=separate_input_output_schemas)

    from pydantic import BaseModel, computed_field

    class Rectangle(BaseModel):
        width: int
        length: int

        @computed_field
        @property
        def area(self) -> int:
            return self.width * self.length

    @app.get("/")
    def read_root() -> Rectangle:

from fastapi import FastAPI
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class Items(BaseModel):
    items: dict[str, int]


@app.post("/foo")
def foo(items: Items):
    return items.items


client = TestClient(app)


def test_additional_properties_post():
    response = client.post("/foo", json={"items": {"foo": 1, "bar": 2}})

from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()

oid = OpenIdConnect(
    openIdConnectUrl="/openid", description="OpenIdConnect security scheme"
)


class User(BaseModel):
    username: str


def get_current_user(oauth_header: str = Security(oid)):
    user = User(username=oauth_header)
    return user


@app.get("/users/me")

        if lang_path.is_dir() and lang_path.name.startswith(incomplete):
            yield lang_path.name


@app.callback()
def callback() -> None:
    # For MacOS with Cairo
    os.environ["DYLD_FALLBACK_LIBRARY_PATH"] = "/opt/homebrew/lib"


@app.command()
def new_lang(lang: str = typer.Argument(..., callback=lang_callback)):
    """

router_a.include_router(
    router_a_b_override, prefix="/b", default_response_class=PlainTextResponse
)
app.include_router(router_a, prefix="/a")
app.include_router(
    router_b_override, prefix="/b", default_response_class=PlainTextResponse
)


client = TestClient(app)

json_type = "application/json"
text_type = "text/plain; charset=utf-8"
html_type = "text/html; charset=utf-8"

///

### Pydantic v1 and v2 on the same app { #pydantic-v1-and-v2-on-the-same-app }

It's **not supported** by Pydantic to have a model of Pydantic v2 with its own fields defined as Pydantic v1 models or vice versa.

```mermaid
graph TB
    subgraph "❌ Not Supported"

             the following code:

             <u style="text-decoration-style:solid">from </u><u style="text-decoration-style:solid"><b>main</b></u><u style="text-decoration-style:solid"> import </u><u style="text-decoration-style:solid"><b>app</b></u>