Metadata-Version: 2.1
Name: food2vec
Version: 0.0.3
Summary: Tools for semantic nutrition
Home-page: https://github.com/pypa/sampleproject
Author: Joshua D'Arcy
Author-email: joshuadrc@gmail.com
License: UNKNOWN
Description: # Semantic Nutrition
        This repo demonstrates how our semantic nutrition API works. We submitted this process as a clinical abtract to MLHC 2020 ([poster](Poster_Semantic_Nutrition.pdf), [abstract](Abstract_Semantic_Nutrition.pdf)). 
        
        This is a base implementation of what was presented at the MLHC, and operates on a limited dataset. The dataset we used for training and publication is not ours to share. If you're interested in using your own nutrition dataset with this API, you can designate a different (local or hosted) csv when you initialize the class. The USDA provides an excellent [starting point](https://fdc.nal.usda.gov) to build a dataset.
        
        ## Installation
        
        ```
        pip install food2vec
        ```
        
        ## Usage
        
        ```
        from food2vec.semantic_nutrition import Estimator
        
        estimator = Estimator() 
        
        # Search database for nutrition estimates
        match = estimator.natural_search("I ate an apple") 
        
        # Search for embeddings in our database
        embedding = estimator.embed('apple')
        
        # See the relationship between embeddings
        embedding1 = estimator.embed('orange')
        embedding2 = estimator.embed('apple')
        relationship = estimator.cosine(embedding1, embedding2)
        
        # Use your own dataset
        my_estimator = Estimator(food_data_filepath = 'my_nutrition_data.csv')
        
        # Use your own embeddings
        my_estimator = Estimator(food_embeddings_filepath = 'my_embeddings.csv')
        ```
        
        ## Connecting to mobile assistants, placing the API in an online server, and managing a database
        
        For testing purposes, we used [Siri Shortcuts](https://support.apple.com/en-us/HT209055) (iOS) and [Google Assistant / IFTTT webhooks](https://ifttt.com/google_assistant) (Android) to send voice transcriptions to [Google Firebase](https://firebase.google.com/docs/storage/web/start). Once received, the incoming data would trigger a [Firebase Cloud Function](https://firebase.google.com/docs/functions). The Firebase Function relayed the voice transcription to our [Semantic Nutrition API](semantic.py)(hosted online with [Flask](https://flask.palletsprojects.com/en/1.1.x/quickstart/) and [Google App Engine](https://cloud.google.com/appengine/docs/standard/python3/building-app)), which responded with estimates for nutrition data. The nutrition data was then logged in Google Firebase.
        
        While useful for testing and demonstrations, this method has been depecrated in favor of a native mobile application and private server / database. Please stay tuned for future developments, and feel free to use our API locally in the meantime!
        
        *updated on 7/27/2020*
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
