Show HN: PyIng – Ingredient parser

That would be nice, but at the moment the algo is at the mercy of the training dataset. The dataset doesn't distinguish adjectives from the ingredient, so this module doesn't :(

There's a great app for this! It's called Half Lemons (iOS only).

I'd like to have a go at ingredient substitution next, that takes quantity into account (For instance 2 drops of vanilla essence, might be equivalent to 1/2 a vanilla pod)

Yeah? https://www.youtube.com/watch?v=7sn8df97-JU

There was IBM Chef Watson

Great question. Models for generating plausible recipes have existed in some form for about a decade now.

Arguably the first "successful" attempt at this was Chef Watson, which blew my mind when it was first released in 2014 despite its well-documented tendency to suggest all kinds of spectacularly odd combinations of flavors and ingredients, like garlic ice cream and mayonnaise-spiked Bloody Marys[1].

It's worth noting that preprocessing the textual inputs isn't entirely necessary to produce somewhat reasonable, ML-generated recipes. For example GPT-3 is capable of generating fairly interesting zero-shot recipes, despite having been trained on raw text data without any preliminary feature selection to label (e.g.) a recipe's ingredients.[2] Still not exempt from the occasional wacky, whimsical suggestion[3], but I, for one, wouldn't want my ML-generated recipes any other way.

1. https://www.google.com/amp/s/www.newyorker.com/magazine/2016...

2. https://github.com/LARC-CMU-SMU/RecipeGPT-exp

3. https://thenextweb.com/news/ai-generated-recipes-three-cours...

Not yet, wanted to get something out there first. The model could do with a bit of improvement anyway.

You could consider https://traindown.com/ which is "Markdown for training data"

Previously mentioned here [https://news.ycombinator.com/item?id=25884762]

I like the concept, but no pictures is sort of a no go for me. Thanks for sharing, nonetheless.

Not the author but the setup script lists it as MIT: https://github.com/whitew1994WW/PyIng/blob/master/setup.py

To be honest you might well be able to deal with it without ML and it certainly would be easier to transfer to other languages you are right. There are a couple of reasons though:

- I wanted to see if I could, I hadn't done any NLP before :) - Some ingredient strings can be complex or contain multiple quantities/ingredients e.g. "3 1/2 cups icing sugar, plus 1/2 cup for dusting". "3 ripe avocados or guacamole" - I am interested to see what can be done with the ingredient embedding, perhaps for ingredient substitution - Hopefully the ML algorithm can learn to identify the unit and quantity based on context rather than a fixed list of ingredients

I wrote an ingredient parser a few years ago (for a project I never finished/released); it mostly works.

The way this works is by having a static list of units ("cup", "liter", "gram", "large", etc.) and ingredients ("egg", "eggs", "courgette", etc.) and a bit of logic to combine the two and extract reasonable things from a piece of text. From what I remember it assumes the ingredient name is last, and then tries to find a unit or quantity before this (which can just be "a", or "a large", "150 gram", etc.), but it's been 4 years since I worked on this.

It works mostly well, but it's not perfect. Looking at the list of ingredients it contains things like "guinness or other stout" or "rice vermicelli noodles". Adverbs and descriptions like that are an issue; I think I extracted that list from that NYT recipe parser thing (which isn't perfect, as it's only in American English – it doesn't know about courgettes for example). I think there were some other issues as well, but I forgot. It's 128 lines of Go code, and this includes a bit of code to modify the text to mark things. It's not very complex, and I didn't spend that much time fine-tuning it. However, I agree that you probably don't need ML for this, and can probably get equal or better results without it.

I don't know exactly how well it works compared to other solutions; from what I remember I tried zestful a bit with their online demo, and it worked better than that, but I only ever tested it on some of my own recipes. I never got very good results with that NYT thing at all. It doesn't attempt to split out the quantity with the ingredient name.

The thing doesn't run at the moment so I can't compare it with the demos in here right now. I should probably finish this some day...

Parsing natural language is hard. Writing the rules takes ages and then they still aren't perfect. Statistical modeling has been actively researched to overcome this problem since the 1990s. The "obsene complexity" NY parser OP mentions uses Conditional Random Fields instead of conventional symbolic rules. It seems to tag the words in the text (with custom tags, not just "noun", "verb", etc.), after which picking out the ingredients is fairly straight-forward.

If you want to give it a go, grab a ratpack parser in the language of your choice, pick a dozen recipes, write rules to extract the ingredients, and then apply them to other recipes. I'm fairly sure you'll be surprised.

I wrote an ingredient parser that is basically just one giant regular expression: https://github.com/falk-hueffner/metric-cooking/blob/master/...

The use case is a bit different (the task includes finding ingredients mentioned in a longer text, and it'll rather not parse something rather than parsing it wrong), but it works fairly OK and can even parse things like "3/4 cup plus 2 tablespoons packed light-brown sugar".