#TelegramBot #Python #SQLite #DataExport #CSV #API
Lesson: Creating a Telegram Bot to Export Channel Members to a CSV File
This tutorial guides you through building a Telegram bot from scratch. When added as an administrator to a channel, the bot will respond to an
IMPORTANT NOTE: Due to Telegram's privacy policy, bots CANNOT access users' phone numbers. This field will be marked as "N/A".
---
First, create a bot via the @BotFather on Telegram. Send it the
Next, set up your Python environment. Install the necessary library:
Create a new Python file named
---
To fulfill the requirement of using a database, we will log every export request. Create a new file named
---
Now, we'll write the core function in
Lesson: Creating a Telegram Bot to Export Channel Members to a CSV File
This tutorial guides you through building a Telegram bot from scratch. When added as an administrator to a channel, the bot will respond to an
/export command from the channel owner, exporting the list of members (Username, User ID, etc.) to a CSV file and storing a log of the action in a SQLite database.IMPORTANT NOTE: Due to Telegram's privacy policy, bots CANNOT access users' phone numbers. This field will be marked as "N/A".
---
#Step 1: Bot Creation and Project SetupFirst, create a bot via the @BotFather on Telegram. Send it the
/newbot command, follow the instructions, and save the HTTP API token it gives you.Next, set up your Python environment. Install the necessary library:
python-telegram-bot.pip install python-telegram-bot
Create a new Python file named
bot.py and add the basic structure. Replace 'YOUR_TELEGRAM_API_TOKEN' with the token you got from BotFather.import logging
from telegram import Update
from telegram.ext import Application, CommandHandler, ContextTypes
# Enable logging
logging.basicConfig(format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO)
TOKEN = 'YOUR_TELEGRAM_API_TOKEN'
def main() -> None:
"""Start the bot."""
application = Application.builder().token(TOKEN).build()
# We will add command handlers here in the next steps
# application.add_handler(CommandHandler("export", export_members))
print("Bot is running...")
application.run_polling()
if __name__ == "__main__":
main()
# Hashtags: #Setup #TelegramAPI #PythonBot #BotFather
---
#Step 2: Database Setup for Logging (database.py)To fulfill the requirement of using a database, we will log every export request. Create a new file named
database.py. This separates our data logic from the bot logic.import sqlite3
from datetime import datetime
DB_NAME = 'bot_logs.db'
def setup_database():
"""Creates the database table if it doesn't exist."""
conn = sqlite3.connect(DB_NAME)
cursor = conn.cursor()
cursor.execute('''
CREATE TABLE IF NOT EXISTS export_logs (
id INTEGER PRIMARY KEY AUTOINCREMENT,
chat_id INTEGER NOT NULL,
chat_title TEXT,
requested_by_id INTEGER NOT NULL,
requested_by_username TEXT,
timestamp TEXT NOT NULL
)
''')
conn.commit()
conn.close()
def log_export_action(chat_id, chat_title, user_id, username):
"""Logs a successful export action to the database."""
conn = sqlite3.connect(DB_NAME)
cursor = conn.cursor()
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
cursor.execute(
"INSERT INTO export_logs (chat_id, chat_title, requested_by_id, requested_by_username, timestamp) VALUES (?, ?, ?, ?, ?)",
(chat_id, chat_title, user_id, username, timestamp)
)
conn.commit()
conn.close()
# Hashtags: #SQLite #DatabaseDesign #Logging #DataPersistence
---
#Step 3: Implementing the Export Command and Permission CheckNow, we'll write the core function in
bot.py. This function will handle the /export command. The most crucial part is to check if the user who sent the command is the creator of the channel. This prevents any admin from exporting the data.❤4
#PDF #EPUB #TelegramBot #Python #SQLite #Project
Lesson: Building a PDF <> EPUB Telegram Converter Bot
This lesson walks you through creating a fully functional Telegram bot from scratch. The bot will accept PDF or EPUB files, convert them to the other format, and log each transaction in an SQLite database.
---
Part 1: Prerequisites & Setup
First, we need to install the necessary Python library for the Telegram Bot API. We will also rely on Calibre's command-line tools for conversion.
Important: You must install Calibre on the system where the bot will run and ensure its
#Setup #Prerequisites
---
Part 2: Database Initialization
We'll use SQLite to log every successful conversion. Create a file named
#Database #SQLite #Initialization
---
Part 3: The Main Bot Script - Imports & Basic Commands
Now, let's create our main bot file,
#TelegramBot #Python #Boilerplate
---
Part 4: The Core Conversion Logic
This function will be the heart of our bot. It uses the
Lesson: Building a PDF <> EPUB Telegram Converter Bot
This lesson walks you through creating a fully functional Telegram bot from scratch. The bot will accept PDF or EPUB files, convert them to the other format, and log each transaction in an SQLite database.
---
Part 1: Prerequisites & Setup
First, we need to install the necessary Python library for the Telegram Bot API. We will also rely on Calibre's command-line tools for conversion.
Important: You must install Calibre on the system where the bot will run and ensure its
ebook-convert tool is in your system's PATH.pip install python-telegram-bot==20.3#Setup #Prerequisites
---
Part 2: Database Initialization
We'll use SQLite to log every successful conversion. Create a file named
database_setup.py and run it once to create the database file and the table.# database_setup.py
import sqlite3
def setup_database():
conn = sqlite3.connect('conversions.db')
cursor = conn.cursor()
# Create table to store conversion logs
cursor.execute('''
CREATE TABLE IF NOT EXISTS conversions (
id INTEGER PRIMARY KEY AUTOINCREMENT,
user_id INTEGER NOT NULL,
original_filename TEXT NOT NULL,
converted_filename TEXT NOT NULL,
conversion_type TEXT NOT NULL,
timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
)
''')
conn.commit()
conn.close()
print("Database setup complete. 'conversions.db' is ready.")
if __name__ == '__main__':
setup_database()
#Database #SQLite #Initialization
---
Part 3: The Main Bot Script - Imports & Basic Commands
Now, let's create our main bot file,
converter_bot.py. We'll start with imports and the initial /start and /help commands.# converter_bot.py
import logging
import os
import sqlite3
import subprocess
from telegram import Update
from telegram.ext import Application, CommandHandler, MessageHandler, filters, ContextTypes
# Enable logging
logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO)
# --- Bot Token ---
TELEGRAM_TOKEN = "YOUR_TELEGRAM_BOT_TOKEN"
# --- Command Handlers ---
async def start(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
user = update.effective_user
await update.message.reply_html(
rf"Hi {user.mention_html()}! Send me a PDF or EPUB file to convert.",
)
async def help_command(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
await update.message.reply_text("Simply send a .pdf file to get an .epub, or send an .epub file to get a .pdf. Note: Conversion quality depends on the source file's structure.")
#TelegramBot #Python #Boilerplate
---
Part 4: The Core Conversion Logic
This function will be the heart of our bot. It uses the
ebook-convert command-line tool (from Calibre) to perform the conversion. It's crucial that Calibre is installed correctly for this to work.❤1
• Apply a simple blur filter.
• Apply a box blur with a given radius.
• Apply a Gaussian blur.
• Sharpen the image.
• Find edges.
• Enhance edges.
• Emboss the image.
• Find contours.
VII. Image Enhancement (ImageEnhance)
• Adjust color saturation.
• Adjust brightness.
• Adjust contrast.
• Adjust sharpness.
VIII. Drawing (ImageDraw & ImageFont)
• Draw text on an image.
• Draw a line.
• Draw a rectangle (outline).
• Draw a filled ellipse.
• Draw a polygon.
#Python #Pillow #ImageProcessing #PIL #CheatSheet
━━━━━━━━━━━━━━━
By: @CodeProgrammer ✨
from PIL import ImageFilter
blurred_img = img.filter(ImageFilter.BLUR)
• Apply a box blur with a given radius.
box_blur = img.filter(ImageFilter.BoxBlur(5))
• Apply a Gaussian blur.
gaussian_blur = img.filter(ImageFilter.GaussianBlur(radius=2))
• Sharpen the image.
sharpened = img.filter(ImageFilter.SHARPEN)
• Find edges.
edges = img.filter(ImageFilter.FIND_EDGES)
• Enhance edges.
edge_enhanced = img.filter(ImageFilter.EDGE_ENHANCE)
• Emboss the image.
embossed = img.filter(ImageFilter.EMBOSS)
• Find contours.
contours = img.filter(ImageFilter.CONTOUR)
VII. Image Enhancement (ImageEnhance)
• Adjust color saturation.
from PIL import ImageEnhance
enhancer = ImageEnhance.Color(img)
vibrant_img = enhancer.enhance(2.0)
• Adjust brightness.
enhancer = ImageEnhance.Brightness(img)
bright_img = enhancer.enhance(1.5)
• Adjust contrast.
enhancer = ImageEnhance.Contrast(img)
contrast_img = enhancer.enhance(1.5)
• Adjust sharpness.
enhancer = ImageEnhance.Sharpness(img)
sharp_img = enhancer.enhance(2.0)
VIII. Drawing (ImageDraw & ImageFont)
• Draw text on an image.
from PIL import ImageDraw, ImageFont
draw = ImageDraw.Draw(img)
font = ImageFont.truetype("arial.ttf", 36)
draw.text((10, 10), "Hello", font=font, fill="red")
• Draw a line.
draw.line((0, 0, 100, 200), fill="blue", width=3)
• Draw a rectangle (outline).
draw.rectangle([10, 10, 90, 60], outline="green", width=2)
• Draw a filled ellipse.
draw.ellipse([100, 100, 180, 150], fill="yellow")
• Draw a polygon.
draw.polygon([(10,10), (20,50), (60,10)], fill="purple")
#Python #Pillow #ImageProcessing #PIL #CheatSheet
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By: @CodeProgrammer ✨
❤14🔥6👍2🎉2
python-interview-questions.pdf
1.2 MB
100 Python Interview Questions and Answers
This book is a practical guide to mastering Python interview preparation. It contains 100 carefully curated questions with clear, concise answers designed in a quick-reference style.
#Python #PythonTips #PythonProgramming
https://t.iss.one/CodeProgrammer
This book is a practical guide to mastering Python interview preparation. It contains 100 carefully curated questions with clear, concise answers designed in a quick-reference style.
#Python #PythonTips #PythonProgramming
https://t.iss.one/CodeProgrammer
❤6🔥1
Tip for clean code in Python:
Use Dataclasses for classes that primarily store data. The
#Python #CleanCode #ProgrammingTips #SoftwareDevelopment #Dataclasses #CodeQuality
━━━━━━━━━━━━━━━
By: @CodeProgrammer ✨
Use Dataclasses for classes that primarily store data. The
@dataclass decorator automatically generates special methods like __init__(), __repr__(), and __eq__(), reducing boilerplate code and making your intent clearer.from dataclasses import dataclass
# --- BEFORE: Using a standard class ---
# A lot of boilerplate code is needed for basic functionality.
class ProductOld:
def __init__(self, name: str, price: float, sku: str):
self.name = name
self.price = price
self.sku = sku
def __repr__(self):
return f"ProductOld(name='{self.name}', price={self.price}, sku='{self.sku}')"
def __eq__(self, other):
if not isinstance(other, ProductOld):
return NotImplemented
return (self.name, self.price, self.sku) == (other.name, other.price, other.sku)
# Example Usage
product_a = ProductOld("Laptop", 1200.00, "LP-123")
product_b = ProductOld("Laptop", 1200.00, "LP-123")
print(product_a) # Output: ProductOld(name='Laptop', price=1200.0, sku='LP-123')
print(product_a == product_b) # Output: True
# --- AFTER: Using a dataclass ---
# The code is concise, readable, and less error-prone.
@dataclass(frozen=True) # frozen=True makes instances immutable
class Product:
name: str
price: float
sku: str
# Example Usage
product_c = Product("Laptop", 1200.00, "LP-123")
product_d = Product("Laptop", 1200.00, "LP-123")
print(product_c) # Output: Product(name='Laptop', price=1200.0, sku='LP-123')
print(product_c == product_d) # Output: True
#Python #CleanCode #ProgrammingTips #SoftwareDevelopment #Dataclasses #CodeQuality
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By: @CodeProgrammer ✨
❤4🎉1
Forwarded from Data Science Machine Learning Data Analysis
📌 PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch
🗂 Category: DEEP LEARNING
🕒 Date: 2025-11-19 | ⏱️ Read time: 14 min read
Dive into PyTorch with this hands-on tutorial for beginners. Learn to build a multiple regression model from the ground up using a 3-layer neural network. This guide provides a practical, step-by-step approach to machine learning with PyTorch, ideal for those new to the framework.
#PyTorch #MachineLearning #NeuralNetwork #Regression #Python
🗂 Category: DEEP LEARNING
🕒 Date: 2025-11-19 | ⏱️ Read time: 14 min read
Dive into PyTorch with this hands-on tutorial for beginners. Learn to build a multiple regression model from the ground up using a 3-layer neural network. This guide provides a practical, step-by-step approach to machine learning with PyTorch, ideal for those new to the framework.
#PyTorch #MachineLearning #NeuralNetwork #Regression #Python
❤2
Comprehensive Python Cheatsheet.pdf
6.3 MB
Comprehensive Python Cheatsheet
This Comprehensive #Python Cheatsheet brings together core syntax, data structures, functions, #OOP, decorators, regular expressions, libraries, and more — neatly organized for quick reference and deep understanding.
This Comprehensive #Python Cheatsheet brings together core syntax, data structures, functions, #OOP, decorators, regular expressions, libraries, and more — neatly organized for quick reference and deep understanding.
https://t.iss.one/CodeProgrammer
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