Metadata-Version: 2.1
Name: PyIng
Version: 0.1.11
Summary: Parses ingredient names into Name, Unit and Quantity
Home-page: https://github.com/whitew1994WW/PyIng
Author: Will White
License: MIT
Description: # PyIng - Ingredient parser
        This is a python package for parsing ingredient strings. There is only one function `parse_ingredients`. It takes in a 
        single string or list of strings and returns a dictionary containing the name, quantity and unit in that recipe string.
        ## Get Started
        
        First install the package using pip:
        
        ```commandline
        pip install pying
        ```
        
        Then you can use it like follows:
        
        ```python
        from PyIng import parse_ingredients
        
        ingredients = ["3 large melons", "5 1/2 cups water", "2 cups flour"]
        
        parsed_ingredients = parse_ingredients(ingredients)
        ```
        
        the output `parsed_ingredients` should look like so:
        
        ```python
        parsed_ingredients = [
            {
                "name": "melons",
                "unit": None,
                "qty": 3.0
            },
            {
                "name": "water",
                "unit": "cups",
                "qty": 5.5
            },
            {
                "name": "flour",
                "unit": "cups",
                "qty": 2
            }
        ]
        ```
        
        ## Training the model
        
        The ingredient parser uses a LSTM model written in tensorflow. It is trained on a publically available dataset produced 
        by the new york times:
        
        https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjowpyb9uj2AhWNiFwKHcFQBC8QFnoECAgQAQ&url=https%3A%2F%2Fgithub.com%2Fnytimes%2Fingredient-phrase-tagger&usg=AOvVaw1AHIgZ0BfSe8ddG7E8alYt 
        
        The entire model is trained using the `train_model.ipynb` and the model is saved in tflite format. Please try and improve on my model, it is far from optimal, I wanted to get something that works without spending too long.
        
        THere are more details on training the model in the jupyter notebook.
        
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
