Scrape recipe sites into shopping lists

Scraping recipe sites into shopping lists can be achieved through a structured workflow that involves web scraping, parsing recipe content, and extracting ingredients for list generation. Below is a comprehensive overview of how to do this:

1. Define the Goal
Convert recipe URLs into organized shopping lists by extracting:

• Ingredients (with quantities and units)

• Grouping by categories (e.g., produce, dairy, spices)

• Optional: combine multiple recipes into one master list

2. Choose Target Recipe Sites
Some commonly scraped recipe sites include:

• AllRecipes

• Food Network

• Epicurious

• BBC Good Food

• Serious Eats

Note: Always check the terms of service. Many sites prohibit scraping, so using their API (if available) is the legal and sustainable option.

3. Tools and Libraries Needed

• Python: Main language

• Libraries:

  • requests: for HTTP requests

  • BeautifulSoup or lxml: for parsing HTML

  • re: for regex processing of ingredient strings

  • pandas: for organizing data

  • spaCy or flashtext: for NLP and keyword extraction (optional)

  • unidecode: to normalize characters

4. Basic Workflow

a. Scrape the Web Page

python
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import requests
from bs4 import BeautifulSoup

url = "https://www.example.com/recipe-link"
headers = {'User-Agent': 'Mozilla/5.0'}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, 'html.parser')

b. Extract Ingredients

python
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ingredients = [li.text.strip() for li in soup.select('.ingredient')]

Adapt the CSS selector based on the site’s structure. You can find it using browser dev tools.

5. Normalize Ingredients
Standardize format:

• Split quantity, unit, ingredient name

• Remove descriptors (e.g., “chopped”, “fresh”)

Example:

python
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import re

def parse_ingredient(ingredient):
    match = re.match(r"(d+/?d*)?s*([a-zA-Z]+)?s*(.*)", ingredient)
    if match:
        quantity, unit, item = match.groups()
        return {'quantity': quantity or '', 'unit': unit or '', 'item': item.strip()}
    return {'quantity': '', 'unit': '', 'item': ingredient}

6. Categorize Ingredients
Use predefined keyword groups:

python
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categories = {
    'Produce': ['onion', 'garlic', 'carrot'],
    'Dairy': ['milk', 'cheese'],
    'Spices': ['salt', 'pepper', 'cumin']
}

def categorize(item):
    for category, keywords in categories.items():
        if any(keyword in item['item'].lower() for keyword in keywords):
            return category
    return 'Misc'

7. Combine and Output Shopping List

python
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from collections import defaultdict

shopping_list = defaultdict(list)

for ing in ingredients:
    parsed = parse_ingredient(ing)
    category = categorize(parsed)
    shopping_list[category].append(parsed)

for cat, items in shopping_list.items():
    print(f"n{cat.upper()}")
    for item in items:
        print(f"- {item['quantity']} {item['unit']} {item['item']}")

8. (Optional) Merge Lists for Multiple Recipes
Create a list of recipe URLs and loop through the same process. Aggregate ingredients intelligently (e.g., summing quantities for the same item).

9. Optional Features

• Export to CSV or PDF

• Grocery store mapping

• Nutritional analysis using USDA API

• Progressive Web App for mobile usage

10. Alternative Tools

• Spoonacular API (for structured recipe data)

• Edamam API

• OpenAI GPT models to extract ingredients from raw text if scraping is not viable

Let me know if you’d like a working script or a simple app interface to automate this process.