


How to implement user authentication and authorization in FastAPI
FastAPI is a high-performance web framework based on Python that provides many powerful features such as asynchronous support, automatic document generation and type hints. In modern web applications, user authentication and authorization are very important functions that can protect the security of the application. In this article, we will explore how to implement user authentication and authorization in FastAPI.
- Install the required libraries
Before we begin, we must first install the required libraries. In FastAPI, the PyJWT library is typically used to handle JSON Web Tokens, and the Passlib library is used for password hashing and verification. We can install these libraries using the following command:
pip install fastapi pyjwt passlib
- Create User Model
Before we start implementing authentication and authorization, we need to define a user model. User models usually contain fields such as username and password. The following is the definition of a sample user model:
from pydantic import BaseModel class User(BaseModel): username: str password: str
- Implementing the user registration and login interface
Next, we need to implement the user registration and login interface. In the registration interface, we will obtain the username and password, hash the password and save it to the database. In the login interface we will verify that the username and password provided by the user match those in the database. The following is an example implementation:
from fastapi import FastAPI from passlib.hash import bcrypt app = FastAPI() DATABASE = [] @app.post("/register") def register_user(user: User): # Hash password hashed_password = bcrypt.hash(user.password) # Save user to database DATABASE.append({"username": user.username, "password": hashed_password}) return {"message": "User registered successfully"} @app.post("/login") def login_user(user: User): # Find user in database for data in DATABASE: if data["username"] == user.username: # Check password if bcrypt.verify(user.password, data["password"]): return {"message": "User logged in successfully"} return {"message": "Invalid username or password"}
- Implementing authentication and authorization middleware
Now that we have implemented the user registration and login interface, next we need to implement the identity Authentication and authorization middleware. This will ensure that users can only access protected routes if a valid token is provided.
The following is an example implementation of authentication and authorization middleware:
from fastapi import FastAPI, Depends, HTTPException, status from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials from passlib.hash import bcrypt from jose import jwt, JWTError app = FastAPI() SECRET_KEY = "your-secret-key" security = HTTPBearer() @app.post("/register") def register_user(user: User): # ... @app.post("/login") def login_user(user: User): # ... def get_current_user(credentials: HTTPAuthorizationCredentials = Depends(security)): try: token = credentials.credentials payload = jwt.decode(token, SECRET_KEY, algorithms=["HS256"]) user = payload.get("username") return user except JWTError: raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid token", headers={"WWW-Authenticate": "Bearer"}, ) @app.get("/protected") def protected_route(current_user: str = Depends(get_current_user)): return {"message": f"Hello, {current_user}"}
- Generate and verify tokens
Finally, we need to implement a method to generate tokens. A token is a security credential used for authentication and authorization. After the user successfully logs in, we can use this method to generate a token and return it to the client.
The following is an implementation of a sample method to generate and verify tokens:
from fastapi import FastAPI, Depends, HTTPException, status from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials from passlib.hash import bcrypt from jose import jwt, JWTError, ExpiredSignatureError from datetime import datetime, timedelta app = FastAPI() SECRET_KEY = "your-secret-key" ALGORITHM = "HS256" ACCESS_TOKEN_EXPIRE_MINUTES = 30 security = HTTPBearer() @app.post("/register") def register_user(user: User): # ... @app.post("/login") def login_user(user: User): # ... def get_current_user(credentials: HTTPAuthorizationCredentials = Depends(security)): try: token = credentials.credentials payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM]) user = payload.get("username") return user except JWTError: raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid token", headers={"WWW-Authenticate": "Bearer"}, ) def create_access_token(username: str): expires = datetime.utcnow() + timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES) payload = {"username": username, "exp": expires} token = jwt.encode(payload, SECRET_KEY, algorithm=ALGORITHM) return token @app.get("/protected") def protected_route(current_user: str = Depends(get_current_user)): return {"message": f"Hello, {current_user}"} @app.post("/token") def get_access_token(user: User): # Check username and password for data in DATABASE: if data["username"] == user.username: if bcrypt.verify(user.password, data["password"]): # Generate access token access_token = create_access_token(user.username) return {"access_token": access_token} raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid username or password", headers={"WWW-Authenticate": "Bearer"}, )
In summary, we have learned how to implement user authentication and authorization in FastAPI. By using the PyJWT library and Passlib library, we are able to securely handle user credentials and protect the security of our application. These sample codes serve as a starting point that you can further customize and extend to suit your needs. Hope this article helps you!
The above is the detailed content of How to implement user authentication and authorization in FastAPI. For more information, please follow other related articles on the PHP Chinese website!

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

Dealing with noisy images is a common problem, especially with mobile phone or low-resolution camera photos. This tutorial explores image filtering techniques in Python using OpenCV to tackle this issue. Image Filtering: A Powerful Tool Image filter

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Python, a favorite for data science and processing, offers a rich ecosystem for high-performance computing. However, parallel programming in Python presents unique challenges. This tutorial explores these challenges, focusing on the Global Interprete

This tutorial demonstrates creating a custom pipeline data structure in Python 3, leveraging classes and operator overloading for enhanced functionality. The pipeline's flexibility lies in its ability to apply a series of functions to a data set, ge

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti


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

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot 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),

SublimeText3 Linux new version
SublimeText3 Linux latest version

Notepad++7.3.1
Easy-to-use and free code editor

PhpStorm Mac version
The latest (2018.2.1) professional PHP integrated development tool

Dreamweaver CS6
Visual web development tools
