Shoe Search Engine

A Python-based desktop application that allows users to search and browse shoes from StockX, with various filtering options and direct links to purchase pages.

Description

The Shoe Search Engine provides a user-friendly interface for searching and discovering shoes available on StockX. The application features real-time price updates, high-quality product images, and seamless integration with StockX's marketplace.

Key Features

• Interactive search functionality
• Real-time price information
• High-quality product images
• Direct StockX purchase links
• Comprehensive filtering system
• Brand-specific searches
• Size availability checking
• Price range filtering

Usage

Basic Search

Simply enter your search term in the main search bar and click "Search":

def on_search():
    search_term = searchbar.get()
    size = size_var.get()
    min_price = min_price_var.get()
    max_price = max_price_var.get()
    sort_by = sort_var.get()
    brands = [brand.strip().lower() for brand in brand_var.get().split(',')]

Brand Filtering

Enter brands in the brand entry field, separated by commas:

nike, adidas, reebok

Size and Price Filtering

The application supports various filtering options:

def get_shoes(search, size=None, min_price=None, max_price=None, sort_by=None, brands=None):
    # Filter by size
    if size:
        price = next((variant["price"] for variant in item["variants"] if variant["size"] == size), None)
        
    # Filter by price range
    if min_price:
        if price < min_price:
            continue
            
    if max_price:
        if price > max_price:
            continue

Sorting Options

The following sorting methods are available:

• Price: Low to High
• Price: High to Low
• Most Relevant
• Brand

Features

Search Results Display

Each search result includes:

• Product image
• Product name
• Price
• Clickable link to StockX

Price Filtering

Users can set:

• Minimum price threshold
• Maximum price threshold
• Sort by price (ascending or descending)

Size Availability

• Filter products by specific sizes
• Only shows products available in selected size
• Displays accurate pricing for selected size

Brand Filtering

• Search for specific brands
• Multiple brand selection supported
• Case-insensitive brand matching

Future Enhancements

1. Additional Filters

   • Color options
   • Release date
   • Condition rating

2. Extended Options

   • Saved searches
   • Price alerts
   • Wishlist functionality

3. Expanded Product Coverage

   • More StockX categories
   • Additional marketplace integrations
   • Historical price data

Technical Implementation

The application uses threading for smooth performance:

def on_search():
    # ... search parameter setup ...
    threading.Thread(target=load_shoes, args=(search_term, size, min_price, max_price, sort_by, brands)).start()

Image handling is optimized for performance:

def display_results(results):
    for result in results:
        if result['image']:
            image = Image.open(io.BytesIO(image_data))
            image.thumbnail((600, 600), Image.Resampling.LANCZOS)
            photo = ImageTk.PhotoImage(image)

Security

The application implements secure API key handling using environment variables:

load_dotenv(dotenv_path='token.env')
API_KEY = os.environ.get('API_KEY')

API Integration

The application integrates with the SneakersAPI:

• API Endpoint: https://api.sneakersapi.dev/api/v2/products
• Authentication: Bearer token
• Response format: JSON
• Rate limiting: Implemented through threading

Installation and Setup

1. Clone the Repository

git clone github.com:raghavrat/ShoeSearchEngine.git
cd shoe-search-engine

2. Install Required Libraries

pip install tkinter pillow requests python-dotenv validators

3. API Key Setup

1. Visit SneakersAPI and create an account
2. Generate an API key from your dashboard
3. Create a file named token.env in the project root directory
4. Add your API key to the file:

API_KEY=your_api_key_here

4. Run the Application

python main.py