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Scrape recipe ingredients for pantry tracking

To scrape recipe ingredients for pantry tracking, you need a script or tool that extracts ingredient lists from recipe webpages and organizes them into a structured format (e.g., JSON or CSV). Here’s a Python-based approach using BeautifulSoup and requests, suitable for sites that allow scraping:

python
import requests from bs4 import BeautifulSoup import re import json def scrape_ingredients(url): headers = { "User-Agent": "Mozilla/5.0" } response = requests.get(url, headers=headers) soup = BeautifulSoup(response.content, 'html.parser') # Try common classes used by recipe sites ingredient_elements = soup.find_all( lambda tag: tag.name in ["li", "span", "p"] and ('ingredient' in (tag.get('class') or []) or re.search(r'ingredient', str(tag.get('class')), re.IGNORECASE)) ) # Fallback if no specific class found if not ingredient_elements: ingredient_elements = soup.find_all("li") ingredients = [] for elem in ingredient_elements: text = elem.get_text(strip=True) if text and any(char.isdigit() for char in text): # crude filter for quantities ingredients.append(text) return ingredients # Example usage: url = 'https://www.allrecipes.com/recipe/24074/alysias-basic-meat-lasagna/' ingredients = scrape_ingredients(url) print(json.dumps(ingredients, indent=2))

Key Features

  • Ingredient Extraction: Looks for HTML elements likely to contain ingredients.

  • Regex Filtering: Optionally refines the search using class names like ingredient.

  • Pantry Integration: You can further process ingredients to match against your pantry database (e.g., comparing “1 cup sugar” to inventory).

Optional Enhancements

  • Normalize ingredient names (e.g., “1 cup sugar” → “sugar”).

  • Use NLP for better parsing (e.g., spaCy, langchain).

  • Store results in a database or sync with a pantry app like Grocy.

Let me know if you want this integrated into a pantry tracking system or turned into a web app.

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