Flatscraper - A Simple Web Scraper for Flat Listings 🔍🏠

Flatscraper is a lightweight web scraper that extracts flat listings from a specified URL. It leverages the BeautifulSoup library to parse HTML and capture essential details about flats—title, price, and location.

🚀 Features

• 🏠 Scrapes flat listings from a specified URL
• 🔍 Extracts key details: title, price, and location
• 💾 Saves data to a CSV file
• ⚙️ Supports command-line arguments for customization
• 🛠️ Easy to use and modify for different websites
• 🧪 Includes a simple test case for demonstration
• 📚 Utilizes Python’s built-in libraries along with BeautifulSoup for HTML parsing
• 🔔 Discord Webhook integration for notifications

🏢 Housing Providers

• LWB
• Lipsia
• BGL
• VLW
• Wogetra

📦 Requirements

You can run the bot natively on your machine or use a Docker image. The requirements include:

• Python 3.6 or higher
• BeautifulSoup4
• Requests
• Pandas
• Discord Webhook (optional, for notifications)
• Docker (optional, for containerization)

🛠️ Installation

1. Environment Setup

Ensure that the .env file is configured correctly. An example is available in the sample.env file. Copy it to .env and fill in the required values.
The SAP_SESSIONID and COOKIE_SESSSION are obtained after performing a search on the LWB website. Use your browser's developer tools to locate them in local storage.
Future versions will include automatic form processing to obtain a valid session ID.

2. Python Environment

You can use a virtual environment to install the necessary packages:

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows:
venv\Scripts\activate
# On macOS and Linux:
source venv/bin/activate

# Install the required packages
pip install -r requirements.txt

3. Docker Environment

Alternatively, use the Docker image provided in the repository:

# Build the Docker image
docker build -t flatscraper .

# Run the Docker container
docker run -it --rm flatscraper

🎉 Have Fun and Happy Scraping!

Wishing you a great time and speedy flat searching with the bot. If you have any questions or suggestions, feel free to open an issue on GitLab. I'll respond as soon as possible.

Happy scraping! 🚀