Create your first app on Linux using Python and Flask
Whether you play or work on Linux, this is a great opportunity for you to program in python. Back in college I wish they had taught me Python instead of Java, it was fun to learn and useful in practical applications like the yum package manager.
In this tutorial, I will take you to use python and a micro-framework called flask to build a simple application to display useful information such as the memory usage of each process and CPU percentage.
Prerequisites
Python basics, lists, classes, functions, and modules. HTML/CSS (basic).
You don’t have to be an advanced python developer to follow this tutorial
Installing Python 3 on Linux
Python is installed by default on most Linux distributions. The following command will let you see the installed version.
[root@linux-vps ~]# python -V Python 2.7.5
We will use version 3.x to build our app. According to Python.org, improvements are only being made to this version now and are not backwards compatible with Python 2.
Note: Before starting, I strongly recommend you to try this tutorial in a virtual machine, because Python is a core component of many Linux distributions and any accidents may damage your system .
The following steps are based on Red Hat versions such as CentOS (6 and 7). Debian-based versions such as UbuntuMint and Resbian can skip this step. Pythonn 3 should be installed by default. If it is not installed, please use apt-get instead of yum to install the corresponding package below.
[leo@linux-vps] yum groupinstall 'Development Tools' [leo@linux-vps] yum install -y zlib-dev openssl-devel sqlite-devel bzip2-devel [leo@linux-vps] wget https://www.python.org/ftp/python/3.4.2/Python-3.4.2.tgz [leo@linux-vps] tar -xvzf Python-3.4.2.tgz [leo@linux-vps] cd Python-3.4.2 [leo@linux-vps] ./configure [leo@linux-vps] make # 推荐使用 make altinstall 以覆盖当前的 python 库 [leo@linux-vps] make altinstall
After successful installation, you should be able to enter the Python3.4 shell using the following command.
[leo@linux-vps]# python3.4 Python 3.4.2 (default, Dec 12 2014, 08:01:15) [GCC 4.8.2 20140120 (Red Hat 4.8.2-16)] on linux Type "help", "copyright", "credits" or "license" for more information. >>> exit ()
Use pip to install packages
Python has its own package management, similar to yum and apt-get. You will need it to download, install and uninstall packages.
[leo@linux-vps] pip3.4 install "packagename" [leo@linux-vps] pip3.4 list [leo@linux-vps] pip3.4 uninstall "packagename"
Python virtual environment
In Python, the virtual environment is a directory where the dependent environments of your project are placed. This is a good way to isolate projects with different dependencies. It allows you to install packages without sudo commands.
[leo@linux-vps] mkdir python3.4-flask [leo@linux-vps] cd python3.4-flask [leo@linux-vps python3.4-flask] pyvenv-3.4 venv
To create a virtual environment you need to use the "pyvenv-3.4" command. The above command will create a directory named lib inside the venv folder, where the packages that the project depends on will be installed. A bin folder will also be created here to contain the pip and python executable files in this environment.
Activate a virtual environment for our Linux system information project
[leo@linux-vps python3.4-flask] source venv/bin/activate [leo@linux-vps python3.4-flask] which pip3.4 ~/python3.4-flask/venv/bin/pip3.4 [leo@linux-vps python3.4-flask] which python3.4 ~/python3.4-flask/venv/bin/python3.4
Install flask using pip
Let us continue to install the first module flask framework, which can handle access routing and Rendering displays our app’s template.
[leo@linux-vps python3.4-flask]pip3.4 install flask
The above is the detailed content of Create your first app on Linux using Python and Flask. For more information, please follow other related articles on the PHP Chinese website!

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

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

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

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

Hot Topics



How to use Docker Desktop? Docker Desktop is a tool for running Docker containers on local machines. The steps to use include: 1. Install Docker Desktop; 2. Start Docker Desktop; 3. Create Docker image (using Dockerfile); 4. Build Docker image (using docker build); 5. Run Docker container (using docker run).

The key differences between CentOS and Ubuntu are: origin (CentOS originates from Red Hat, for enterprises; Ubuntu originates from Debian, for individuals), package management (CentOS uses yum, focusing on stability; Ubuntu uses apt, for high update frequency), support cycle (CentOS provides 10 years of support, Ubuntu provides 5 years of LTS support), community support (CentOS focuses on stability, Ubuntu provides a wide range of tutorials and documents), uses (CentOS is biased towards servers, Ubuntu is suitable for servers and desktops), other differences include installation simplicity (CentOS is thin)

Troubleshooting steps for failed Docker image build: Check Dockerfile syntax and dependency version. Check if the build context contains the required source code and dependencies. View the build log for error details. Use the --target option to build a hierarchical phase to identify failure points. Make sure to use the latest version of Docker engine. Build the image with --t [image-name]:debug mode to debug the problem. Check disk space and make sure it is sufficient. Disable SELinux to prevent interference with the build process. Ask community platforms for help, provide Dockerfiles and build log descriptions for more specific suggestions.

Docker process viewing method: 1. Docker CLI command: docker ps; 2. Systemd CLI command: systemctl status docker; 3. Docker Compose CLI command: docker-compose ps; 4. Process Explorer (Windows); 5. /proc directory (Linux).

VS Code system requirements: Operating system: Windows 10 and above, macOS 10.12 and above, Linux distribution processor: minimum 1.6 GHz, recommended 2.0 GHz and above memory: minimum 512 MB, recommended 4 GB and above storage space: minimum 250 MB, recommended 1 GB and above other requirements: stable network connection, Xorg/Wayland (Linux)

Docker uses Linux kernel features to provide an efficient and isolated application running environment. Its working principle is as follows: 1. The mirror is used as a read-only template, which contains everything you need to run the application; 2. The Union File System (UnionFS) stacks multiple file systems, only storing the differences, saving space and speeding up; 3. The daemon manages the mirrors and containers, and the client uses them for interaction; 4. Namespaces and cgroups implement container isolation and resource limitations; 5. Multiple network modes support container interconnection. Only by understanding these core concepts can you better utilize Docker.

VS Code is the full name Visual Studio Code, which is a free and open source cross-platform code editor and development environment developed by Microsoft. It supports a wide range of programming languages and provides syntax highlighting, code automatic completion, code snippets and smart prompts to improve development efficiency. Through a rich extension ecosystem, users can add extensions to specific needs and languages, such as debuggers, code formatting tools, and Git integrations. VS Code also includes an intuitive debugger that helps quickly find and resolve bugs in your code.

The reasons for the installation of VS Code extensions may be: network instability, insufficient permissions, system compatibility issues, VS Code version is too old, antivirus software or firewall interference. By checking network connections, permissions, log files, updating VS Code, disabling security software, and restarting VS Code or computers, you can gradually troubleshoot and resolve issues.
