Add form verification (WTForms available)<\/li>\n<\/ul>\n
\n This example is enough to let you understand the basic process of Flask login. Not complicated but it is easy to ignore details, such as the necessity of secret_key<\/code> and @login_manager.user_loader<\/code> . <\/p>\n
Basically that's it.<\/p>"}
python flask login example
Aug 01, 2025 am 06:39 AM
python
flask
This is a simple login example based on Flask-Login, including user login, session management, and login protection routing. 1. Install flask and flask-login dependencies; 2. Create an app.py file and configure Flask-Login, simulate user data and login callbacks; 3. Implement login, logout and protected dashboard routing; 4. Use the template files login.html and dashboard.html for page rendering; 5. Log in with the user name admin and password password123 after running the application. The complete process covers flash messages, form processing and session retention, which is suitable for beginners to quickly master the Flask login mechanism. It is recommended to introduce database and password encryption to improve security. This example has fully implemented basic functions and can be run directly.

If you are looking for a simple, practical example of Flask login, here is a complete entry-level implementation that includes user login, session management (using Flask-Login), and base template rendering. Suitable for beginners to get started quickly.

? Basic functions
- User login (simulate users, no database involved)
- Login status remains (Flask-Login)
- Login to protect routes
- Simple HTML page
1. Installation dependencies
pip install flask flask-login
2. Project structure
flask_login_example/
│
├── app.py
└── templates/
├── login.html
└── dashboard.html
3. app.py
- main program
from flask import Flask, render_template, request, redirect, url_for, flash
from flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user
app = Flask(____)
# Configure the secret key (for session)
app.secret_key = 'your-secret-key-here'
# Initialize Flask-Login
login_manager = LoginManager()
login_manager.init_app(app)
login_manager.login_view = 'login' # Page that jumps when not logged in # Simulate users (the database should be used in actual projects)
class User(UserMixin):
def __init__(self, user_id, username, password):
self.id = user_id
self.username = username
self.password = password
# Simulate database users = {'user1': User('1', 'admin', 'password123')}
# Load user callback @login_manager.user_loader
def load_user(user_id):
for user in users.values():
if user.id == user_id:
return user
return None
# Login page @app.route('/login', methods=['GET', 'POST'])
def login():
if request.method == 'POST':
username = request.form['username']
password = request.form['password']
user = users.get(username)
if user and user.password == password:
login_user(user)
flash('Login successfully!')
return redirect(url_for('dashboard'))
else:
flash('Username or password is incorrect.')
return render_template('login.html')
# Dashboard (login required)
@app.route('/dashboard')
@login_required
def dashboard():
return render_template('dashboard.html', name=current_user.username)
# Log out @app.route('/logout')
@login_required
def logout():
logout_user()
flash('You're logged out.')
return redirect(url_for('login'))
# Home page redirect to login @app.route('/')
def index():
return redirect(url_for('login'))
if __name__ == '__main__':
app.run(debug=True)
4. Template file
templates/login.html
<!doctype html>
<html>
<head><title>Login</title></head>
<body>
<h2>Login</h2>
{% with messages = get_flashed_messages() %}
{% if messages %}
<ul>
{% for message in messages %}
<li style="color:red;">{{ message }}</li>
{% endfor %}
</ul>
{% endif %}
{% endwith %}
<form method="post">
<label>Username: <input type="text" name="username" required></label><br><br>
<label>Password: <input type="password" name="password" required></label><br><br>
<button type="submit">Login</button>
</form>
</body>
</html>
templates/dashboard.html
<!doctype html>
<html>
<head><title>Dashboard</title></head>
<body>
<h2>Welcome, {{ name }}! </h2>
<p>You have successfully logged in. </p>
<a href="/logout">Login</a>
</body>
</html>
5. Run the application
python app.py
Visit: http://ipnx.cn/link/2dc78e6026928309d3d26dc6dbd0276e
- Username:
admin
- Password:
password123
? Follow-up suggestions
- Use databases (such as SQLite SQLAlchemy) to store users
- Password encrypted storage (using
werkzeug.security
's generate_password_hash
/ check_password_hash
)
- Add registration function
- Add form verification (WTForms available)
This example is enough to let you understand the basic process of Flask login. Not complicated but it is easy to ignore details, such as the necessity of secret_key
and @login_manager.user_loader
.

Basically that's it.
The above is the detailed content of python flask login example. For more information, please follow other related articles on the PHP Chinese website!
Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
How to use PHP combined with AI to achieve text error correction PHP syntax detection and optimization
Jul 25, 2025 pm 08:57 PM
To realize text error correction and syntax optimization with AI, you need to follow the following steps: 1. Select a suitable AI model or API, such as Baidu, Tencent API or open source NLP library; 2. Call the API through PHP's curl or Guzzle and process the return results; 3. Display error correction information in the application and allow users to choose whether to adopt it; 4. Use php-l and PHP_CodeSniffer for syntax detection and code optimization; 5. Continuously collect feedback and update the model or rules to improve the effect. When choosing AIAPI, focus on evaluating accuracy, response speed, price and support for PHP. Code optimization should follow PSR specifications, use cache reasonably, avoid circular queries, review code regularly, and use X
PHP calls AI intelligent voice assistant PHP voice interaction system construction
Jul 25, 2025 pm 08:45 PM
User voice input is captured and sent to the PHP backend through the MediaRecorder API of the front-end JavaScript; 2. PHP saves the audio as a temporary file and calls STTAPI (such as Google or Baidu voice recognition) to convert it into text; 3. PHP sends the text to an AI service (such as OpenAIGPT) to obtain intelligent reply; 4. PHP then calls TTSAPI (such as Baidu or Google voice synthesis) to convert the reply to a voice file; 5. PHP streams the voice file back to the front-end to play, completing interaction. The entire process is dominated by PHP to ensure seamless connection between all links.
How to develop AI intelligent form system with PHP PHP intelligent form design and analysis
Jul 25, 2025 pm 05:54 PM
When choosing a suitable PHP framework, you need to consider comprehensively according to project needs: Laravel is suitable for rapid development and provides EloquentORM and Blade template engines, which are convenient for database operation and dynamic form rendering; Symfony is more flexible and suitable for complex systems; CodeIgniter is lightweight and suitable for simple applications with high performance requirements. 2. To ensure the accuracy of AI models, we need to start with high-quality data training, reasonable selection of evaluation indicators (such as accuracy, recall, F1 value), regular performance evaluation and model tuning, and ensure code quality through unit testing and integration testing, while continuously monitoring the input data to prevent data drift. 3. Many measures are required to protect user privacy: encrypt and store sensitive data (such as AES
python seaborn jointplot example
Jul 26, 2025 am 08:11 AM
Use Seaborn's jointplot to quickly visualize the relationship and distribution between two variables; 2. The basic scatter plot is implemented by sns.jointplot(data=tips,x="total_bill",y="tip",kind="scatter"), the center is a scatter plot, and the histogram is displayed on the upper and lower and right sides; 3. Add regression lines and density information to a kind="reg", and combine marginal_kws to set the edge plot style; 4. When the data volume is large, it is recommended to use "hex"
How to use PHP combined with AI to analyze video content PHP intelligent video tag generation
Jul 25, 2025 pm 06:15 PM
The core idea of PHP combining AI for video content analysis is to let PHP serve as the backend "glue", first upload video to cloud storage, and then call AI services (such as Google CloudVideoAI, etc.) for asynchronous analysis; 2. PHP parses the JSON results, extract people, objects, scenes, voice and other information to generate intelligent tags and store them in the database; 3. The advantage is to use PHP's mature web ecosystem to quickly integrate AI capabilities, which is suitable for projects with existing PHP systems to efficiently implement; 4. Common challenges include large file processing (directly transmitted to cloud storage with pre-signed URLs), asynchronous tasks (introducing message queues), cost control (on-demand analysis, budget monitoring) and result optimization (label standardization); 5. Smart tags significantly improve visual
PHP integrated AI emotional computing technology PHP user feedback intelligent analysis
Jul 25, 2025 pm 06:54 PM
To integrate AI sentiment computing technology into PHP applications, the core is to use cloud services AIAPI (such as Google, AWS, and Azure) for sentiment analysis, send text through HTTP requests and parse returned JSON results, and store emotional data into the database, thereby realizing automated processing and data insights of user feedback. The specific steps include: 1. Select a suitable AI sentiment analysis API, considering accuracy, cost, language support and integration complexity; 2. Use Guzzle or curl to send requests, store sentiment scores, labels, and intensity information; 3. Build a visual dashboard to support priority sorting, trend analysis, product iteration direction and user segmentation; 4. Respond to technical challenges, such as API call restrictions and numbers
How to develop AI-based text summary with PHP Quick Refining Technology
Jul 25, 2025 pm 05:57 PM
The core of PHP's development of AI text summary is to call external AI service APIs (such as OpenAI, HuggingFace) as a coordinator to realize text preprocessing, API requests, response analysis and result display; 2. The limitation is that the computing performance is weak and the AI ecosystem is weak. The response strategy is to leverage APIs, service decoupling and asynchronous processing; 3. Model selection needs to weigh summary quality, cost, delay, concurrency, data privacy, and abstract models such as GPT or BART/T5 are recommended; 4. Performance optimization includes cache, asynchronous queues, batch processing and nearby area selection. Error processing needs to cover current limit retry, network timeout, key security, input verification and logging to ensure the stable and efficient operation of the system.
python list to string conversion example
Jul 26, 2025 am 08:00 AM
String lists can be merged with join() method, such as ''.join(words) to get "HelloworldfromPython"; 2. Number lists must be converted to strings with map(str, numbers) or [str(x)forxinnumbers] before joining; 3. Any type list can be directly converted to strings with brackets and quotes, suitable for debugging; 4. Custom formats can be implemented by generator expressions combined with join(), such as '|'.join(f"[{item}]"foriteminitems) output"[a]|[
See all articles