Use uv to quickly build FastAPI applications
The following steps demonstrate how to use the uv tool to quickly create a simple FastAPI application containing GET and POST requests:
-
Initialization project:
uv init uv add fastapi --extra standard
-
Create project directories and files:
Create a folder named
/app
and add two files__init__.py
andmain.py
in it. -
Write FastAPI code (main.py):
Copy the following code into the
main.py
file:from typing import Union from pydantic import BaseModel from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from datetime import datetime app = FastAPI() # 注意:生产环境中不要使用"*",请替换为你的允许域名 origins = [ "*", ] app.add_middleware( CORSMiddleware, allow_origins=origins, allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) class Stuff(BaseModel): content: str @app.get("/") def read_root(): return {"Message": "Hello World! FastAPI is working."} @app.post("/getdata/") async def create_secret(payload: Stuff): with open('output_file.txt', 'a') as f: now = datetime.now() formatted_date = now.strftime("%B %d, %Y at %I:%M %p") f.write(formatted_date + ": " + payload.content) f.write('\n') return payload.content
-
Run FastAPI application:
uv run fastapi dev
This will start the development server. You can test GET requests by accessing
http://127.0.0.1:8000
and send data to the/getdata/
endpoint using POST requests.
For more FastAPI tutorials, please refer to the official documentation: https://www.php.cn/link/b446e7f68f7a79f9de9d9f9ee9b764e8
This example demonstrates a simple GET and POST API. The /getdata/
endpoint will receive the content
field in the POST request and append the content to the output_file.txt
file, recording the timestamp. *Please note: In a production environment, `origins = [""]` is unsafe and must be replaced with your allowed domain name list. **
The above is the detailed content of Python FastAPI quickstart in uv. For more information, please follow other related articles on the PHP Chinese website!

Pythonarrayssupportvariousoperations:1)Slicingextractssubsets,2)Appending/Extendingaddselements,3)Insertingplaceselementsatspecificpositions,4)Removingdeleteselements,5)Sorting/Reversingchangesorder,and6)Listcomprehensionscreatenewlistsbasedonexistin

NumPyarraysareessentialforapplicationsrequiringefficientnumericalcomputationsanddatamanipulation.Theyarecrucialindatascience,machinelearning,physics,engineering,andfinanceduetotheirabilitytohandlelarge-scaledataefficiently.Forexample,infinancialanaly

Useanarray.arrayoveralistinPythonwhendealingwithhomogeneousdata,performance-criticalcode,orinterfacingwithCcode.1)HomogeneousData:Arrayssavememorywithtypedelements.2)Performance-CriticalCode:Arraysofferbetterperformancefornumericaloperations.3)Interf

No,notalllistoperationsaresupportedbyarrays,andviceversa.1)Arraysdonotsupportdynamicoperationslikeappendorinsertwithoutresizing,whichimpactsperformance.2)Listsdonotguaranteeconstanttimecomplexityfordirectaccesslikearraysdo.

ToaccesselementsinaPythonlist,useindexing,negativeindexing,slicing,oriteration.1)Indexingstartsat0.2)Negativeindexingaccessesfromtheend.3)Slicingextractsportions.4)Iterationusesforloopsorenumerate.AlwayschecklistlengthtoavoidIndexError.

ArraysinPython,especiallyviaNumPy,arecrucialinscientificcomputingfortheirefficiencyandversatility.1)Theyareusedfornumericaloperations,dataanalysis,andmachinelearning.2)NumPy'simplementationinCensuresfasteroperationsthanPythonlists.3)Arraysenablequick

You can manage different Python versions by using pyenv, venv and Anaconda. 1) Use pyenv to manage multiple Python versions: install pyenv, set global and local versions. 2) Use venv to create a virtual environment to isolate project dependencies. 3) Use Anaconda to manage Python versions in your data science project. 4) Keep the system Python for system-level tasks. Through these tools and strategies, you can effectively manage different versions of Python to ensure the smooth running of the project.

NumPyarrayshaveseveraladvantagesoverstandardPythonarrays:1)TheyaremuchfasterduetoC-basedimplementation,2)Theyaremorememory-efficient,especiallywithlargedatasets,and3)Theyofferoptimized,vectorizedfunctionsformathematicalandstatisticaloperations,making


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

WebStorm Mac version
Useful JavaScript development tools

mPDF
mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

EditPlus Chinese cracked version
Small size, syntax highlighting, does not support code prompt function

DVWA
Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

SublimeText3 English version
Recommended: Win version, supports code prompts!
