IKEA Stock API

A Python web application for tracking and displaying IKEA product stock levels across Hong Kong and Macau stores.

Features

• Real-time stock checking for IKEA products
• Web interface for easy access to stock information
• Product information stored in CSV format
• Automatic product image management
• 4-hour cache for stock data to reduce API load

Project Structure

.
├── web_app.py                    # Flask web application
├── stock_checker.py              # Stock checking functionality
├── config.py                     # Configuration settings
├── ikea_products.csv            # Product database
├── ikea_products_with_images.csv # Product database with image information
├── templates/                   # HTML templates
│   └── index.html              # Main web interface
├── static/                     # Static assets
│   └── product_images/        # Product images
└── requirements.txt            # Project dependencies

Installation

1. Clone the repository
2. Install dependencies:

pip install -r requirements.txt

3. Set up your Firecrawl API key in config.py

Usage

Start the web application:

python web_app.py

The web interface will be available at http://localhost:5000

Stock Information

Stock levels are tracked for the following locations:

• Warehouse
• Causeway Bay
• Kowloon Bay
• Macau Taipa
• Shatin
• Tsuen Wan

Features

Stock Caching

• Stock data is cached for 4 hours to reduce API load
• Cache is automatically refreshed when expired

Price Formatting

• Prices are displayed in HKD with proper formatting
• Handles various price formats including those with commas

Image Management

• Product images are stored locally
• Automatic symbolic link creation for serving images through Flask

Technical Details

• Built with Flask web framework
• Uses Firecrawl API for stock checking
• CSV-based product database for simplicity and portability
• Implements error handling and rate limiting
• Asynchronous stock checking capabilities

Error Handling

The application includes:

• Graceful handling of API failures
• Cache management for reliability
• Proper error messages for various scenarios
• Fallback mechanisms for missing data