This repo demonstrates how our semantic nutrition API works. We submitted this process as a clinical abtract to MLHC 2020 (poster, abstract).
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 to build a dataset.
pip install food2vec
from food2vec.semantic_nutrition import Estimator
estimator = Estimator()
# Search database for nutrition estimates
match = estimator.natural_search("I ate an apple")
# Embed words or phrases with food content (limited to our food-related vocabulary)
# Here, embedding1 will be equal to embedding2
embedding1 = estimator.embed('apple')
embedding2 = estimator.embed('I ate an apple')
# See the relationship between embeddings
embedding1 = estimator.embed('orange')
embedding2 = estimator.embed('apple')
relationship = estimator.cosine(embedding1, embedding2)
# Use your own nutrition dataset
my_estimator = Estimator(food_data_filepath = 'my_nutrition_data.csv')
# Use your own embeddings
my_estimator = Estimator(food_embeddings_filepath = 'my_embeddings.csv')
For testing purposes, we used Siri Shortcuts (iOS) and Google Assistant / IFTTT webhooks (Android) to send voice transcriptions to Google Firebase. Once received, the incoming data would trigger a Firebase Cloud Function. The Firebase Function relayed the voice transcription to our Semantic Nutrition API(hosted online with Flask and Google App Engine), 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