Python Tkinter Attendance and Random Selection GUI Application Tutorial
This article explains how to build a lightweight Python GUI attendance and random‑selection tool using Tkinter (and optionally PyQt5), covering required libraries, installation steps, interface design, name‑list handling, sequential and random calling modes, and provides the complete source code.
This tutorial demonstrates the creation of a simple classroom attendance and random‑selection program written in Python. The application uses Tkinter for the graphical user interface and Pillow for image handling, with an optional PyQt5 version offering a different visual style.
1. Preparation
Tkinter : Tkinter is Python’s built‑in wrapper for the Tcl/Tk GUI toolkit. It allows rapid development of desktop interfaces and is fully integrated into the Python interpreter.
Pillow (PIL) : Pillow is the actively maintained fork of the original Python Image Library. Install it via pip install pillow . It provides image loading, scaling, cropping, and drawing capabilities, which are used to embed a logo in the GUI.
2. Preview
Start : Double‑click the executable to open the main window. The program automatically reads names.txt from the same directory and loads the names.
Sequential calling : After selecting “Sequential Call”, click Start . Names scroll across the screen with equal probability; clicking Stop halts the scrolling and finalizes the selected name.
Random calling : Selecting “Random Call” makes the program pick names at random, displaying each name for a short interval until the user stops the process.
Manual name loading : Users can manually import a .txt file where each line contains a single name. The program validates that each entry consists of Chinese characters and reports any non‑Chinese entries.
PyQt5 version : An alternative implementation using PyQt5 follows the same logic but offers a different appearance; the file size is larger due to the additional Qt libraries.
3. Design Idea
The overall architecture consists of a main GUI class that creates widgets, binds events, and controls the calling logic. Two calling modes are implemented: a loop that iterates through the name list for sequential mode, and a random selection loop for random mode. Threading is used to keep the UI responsive while the calling loop runs.
4. Source Code
<code>import random
import re
import time
import threading
from tkinter import *
from tkinter import ttk
from base64 import b64decode
from PIL import Image,ImageTk
from tkinter import messagebox
from tkinter.filedialog import askopenfilename
"""
2021-11-10点名/抽奖程序
主要亮点:
1.两种模式:①顺序点名 ②随机点名
2.自动识别人名单
3.支持手动导入人名单
4.人名单导入校验
5.人名显示位置自动矫正
6.最多显示五个大字
"""
imgs=['./point_name.png']
class APP:
def __init__(self):
self.root = Tk()
self.running_flag=False #开始标志
self.time_span=0.05 #名字显示间隔
self.root.title('Point_name-V1.0')
width = 680
height = 350
left = (self.root.winfo_screenwidth() - width) / 2
top = (self.root.winfo_screenheight() - height) / 2
self.root.geometry("%dx%d+%d+%d" % (width, height, left, top))
self.root.resizable(0,0)
self.create_widget()
self.set_widget()
self.place_widget()
self.root.mainloop()
def create_widget(self):
self.label_show_name_var=StringVar()
self.label_show_name=ttk.Label(self.root,textvariable=self.label_show_name_var,font=('Arial', 100,"bold"),foreground = '#1E90FF')
self.btn_start=ttk.Button(self.root,text="开始",)
self.btn_load_names=ttk.Button(self.root,text="手动加载人名单",)
self.lf1=ttk.LabelFrame(self.root,text="点名方式")
self.radioBtn_var=IntVar()
self.radioBtn_var.set(1)
self.radioBtn_sequence=ttk.Radiobutton(self.lf1,text="顺序点名",variable=self.radioBtn_var, value=1)
self.radioBtn_random=ttk.Radiobutton(self.lf1,text="随机点名",variable=self.radioBtn_var, value=2)
self.label_show_name_num=ttk.Label(self.root,font=('Arial', 20),foreground = '#FF7F50')
paned = PanedWindow(self.root)
self.img = imgs
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</code>5. Summary
The program provides six main features: two calling modes (sequential and random), automatic name‑list detection, manual name‑list import with validation, automatic name‑position adjustment, and a maximum display of five large characters. The entire implementation is under 200 lines of Python code.
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