<project-name>/src/<project-module-name>/main.py

​

from fastapi import FastAPI

import uvicorn

​

app = FastAPI()

​

@app.get("/")

def read_root():

    return {"Hello": "World"}

​

def start():

    uvicorn.run("src.<project-module-name>.main:app", host="127.0.0.1", port=8000)

​

def dev():

    uvicorn.run("src.<project-module-name>.main:app", host="127.0.0.1", port=8000, reload=True)

​

[tool.poetry]

name = "<project-name>"

version = "0.1.0"

description = ""

authors = ["FirstName <email@gmail.com>"]

readme = "README.md"

packages = [{ include = "<project-module-name>", from = "src" }]

​

[tool.poetry.dependencies]

python = "^3.12"

fastapi = "^0.109.0"

uvicorn = { extras = ["standard"], version = "^0.25.0" }

​

[build-system]

requires = ["poetry-core"]

build-backend = "poetry.core.masonry.api"

​

# script

[tool.poetry.scripts]

dev = "src.<project-module-name>.main:dev"

start = "src.<project-module-name>.main:app"

​

pip install fastapi

pip install uvicorn # ASGI server

pip install starlette # lightweight ASGI framework/toolkit

pip install pydantic # Data validation and type annotations

# OR

pip install fastapi uvicorn starlette pydantic

import uvicorn

from fastapi import FastAPI

​

app = FastAPI()

​

@app.get("/")

async def root():

    return {"message": "Hello World"}

​

if __name__ == '__main__':

    uvicorn.run(app, port=8080, host="0.0.0.0")

from fastapi import FastAPI

​

app = FastAPI()

​

​

@app.get("/")

async def root():

    return {"message": "Hello World"}

from fastapi import FastAPI

from fastapi.testclient import TestClient

​

app = FastAPI()

​

​

@app.get("/")

async def read_main():

    return {"msg": "Hello World"}

​

​

client = TestClient(app)

​

​

def test_read_main():

    response = client.get("/")

    assert response.status_code == 200

    assert response.json() == {"msg": "Hello World"}

​

from typing import Union

​

from fastapi import FastAPI

​

app = FastAPI()

​

​

@app.get("/")

async def read_root():

    return {"Hello": "World"}

​

​

@app.get("/items/{item_id}")

async def read_item(item_id: int, q: Union[str, None] = None):

    return {"item_id": item_id, "q": q}

from fastapi import FastAPI

import uvicorn

from sklearn.datasets import load_iris

from sklearn.naive_bayes import GaussianNB

from pydantic import BaseModel

# Creating FastAPI instance

app = FastAPI()

# Creating class to define the request body

# and the type hints of each attribute

class request_body(BaseModel):

    sepal_length : float

    sepal_width : float

    petal_length : float

    petal_width : float

# Loading Iris Dataset

iris = load_iris()

# Getting our Features and Targets

X = iris.data

Y = iris.target

# Creating and Fitting our Model

clf = GaussianNB()

clf.fit(X,Y)

# Creating an Endpoint to receive the data

# to make prediction on.

@app.post('/predict')

def predict(data : request_body):

    # Making the data in a form suitable for prediction

    test_data = [[

            data.sepal_length,

            data.sepal_width,

            data.petal_length,

            data.petal_width

    ]]

    # Predicting the Class

    class_idx = clf.predict(test_data)[0]

    # Return the Result

    return { 'class' : iris.target_names[class_idx]}

Best framework