Preface
First, we isolate my project using virtualenv. For example, we want to develop a polling application (a poll app).
mkdir poll_app cd poll_app virtualenv . source bin/activate
Commonly used Python libraries
I am developing an application that requires a database. Therefore, I always use flask_script and flask_migrate libraries. I don't like Flask's CLI tooling.
Flask-Script: https://flask-script.readthedocs.io/en/latest/
Flask-Migrate: https://flask-migrate.readthedocs.io/en/latest/
Similarly to Django, I created a file called manage. py Python file, for example:
from MYAPP.data.models import db from MYAPP import app from flask_script import Manager from flask_migrate import Migrate, MigrateCommand db.init_app(app) migrate = Migrate(app, db) manager = Manager(app) manager.add_command('db', MigrateCommand) if __name__ == "__main__": manager.run()
Then, we can perform the following operations on the data:
python manage.py db init # --> init migrations python manage.py db migrate # --> migrate models python manage.py db upgrade # --> apply changes python manage.py db --help # --> :)
Main application file
Create a new project , I create a file app.py in the root folder and then it changes like this.
from MYAPP import app # To do: This place will change later config = { "development": "config.Development" } if __name__ == "__main__": app.config.from_object(config["development"]) app.run()
Configuration File
I also created a configuration file called config.py in the root folder.
class BaseConfig(object): """ Base config class. This fields will use by production and development server """ ORIGINS = ["*"] # for api calls SECRET_KEY = 'YOUR SECRET KEY' class Development(BaseConfig): """ Development config. We use Debug mode """ PORT = 5000 DEBUG = True TESTING = False ENV = 'dev' # Currently we only have development config. # If you have production, you will need to pass it to here. config = { 'development': 'config.Development' } def configure_app(app): """ App configuration will be here. Parameters ---------- app : Flask app instance """ app.config.from_object(config['development'])
Folder structure
I create a folder in the root directory and name it om_core, and then create two new folders in Qi api and data..
api files store application logic and routing. For example, I created a folder called user in the api.
Generate two files named init.py and controllers.py in the user folder, as will our other API layers. controllers.py (controller file) should look like this:
from flask import Blueprint, jsonify, request from MYAPP.data.models import db, User user = Blueprint('user', __name__) @user.route('/', methods=['GET']) def get_users(): return jsonify({ "message": "Hi user :)"}) @user.route('/<int:id>', methods=['GET']) def users(id): return jsonify({ "id": id })
I always use blueprints.
The data folder stores some models. For example, I created a file called models.py:
from flask_sqlalchemy import SQLAlchemy from MYAPP import app # We didn't pass app instance here. db = SQLAlchemy() class User(db.Model): """ Model for user management """ id = db.Column(db.Integer, primary_key=True) email = db.Column(db.String(100), unique=True) password = db.Column(db.String(100)) name = db.Column(db.String(100)) surname = db.Column(db.String(100)) active = db.Column(db.Boolean(), default=True) created_at = db.Column(db.DateTime, default=db.func.now()) updated_at = db.Column(db.DateTime, default=db.func.now()) def __init__(self, email, password, name, surname, active, created_at, updated_at): self.email = email self.password = password self.name = name self.surname = surname self.active = active self.created_at = created_at self.updated_at = updated_at
Let’s go back to the om_core folder. I created a file called init .py to use the Api layer as an endpoint.
from flask import Flask from flask_cors import CORS from config import BaseConfig from config import configure_app app = Flask(__name__) from MYAPP.api.user.controllers import user """ Corst settings will be here. We maybe use this endpoint later. """ cors = CORS(app, resources={ r'/api/*': { 'origins': BaseConfig.ORIGINS } }) configure_app(app) app.url_map.strict_slashes = False app.register_blueprint(user, url_prefix='/api/users')
In the above code, I have used Flask-CORS to allow requests from different origins. This is not required if you do not want to allow requests from different origins.
Screenshots of the overall project structure
The screenshots are as follows:
Recommended tutorial: "Python Tutorial"
The above is the detailed content of Understand the Flask project structure. For more information, please follow other related articles on the PHP Chinese website!

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

Dreamweaver Mac version
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

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

WebStorm Mac version
Useful JavaScript development tools