Welcome to the Nutrics project. We think that choosing nutritious, local food should be easy, and we're building an application that does it for you.
git clone 'https://github.com/Big-Ideas-Lab/nutrics.git'
cd nutrics
pip install -r requirements.txt
FLASK_APP=run.py FLASK_DEBUG=1 flask run
Updated by Joshua D'Arcy on 5.8.2020
This file contains a Flask application and RESTful API logic. Think of it as our central hub that reroutes users to their chosen endpoints. The API constructs and manages a database named "app.db". app.db is defined in models, controlled by resources, and connected by endpoints in run.py.
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UserModel.py
UserModel coordinates with the 'users' table in our app.db. It handles user access data and login / registration / session management / tokens.
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PreferenceModel.py
PreferenceModel coordinates with the 'user_preferences' table in our app.db. It handles user preference data (historical items the user has enjoyed).
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FoodModel.py
FoodModel coordinates with the 'food' table in our app.db. FoodModel builds and adds convenience functions to edit our database of local items to offer.
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RevokedTokenModel.py
RevokedTokenModel coordinates with the 'revoked_tokens' table in our app.db. It allows us to blacklist tokens and adds another layer of security to our application.
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UserResources.py
UserResources defines resources accessible by any user (including admins). Classes (tied to endpoints in run.py) are defined in this table:
| Class | Authorization Header | Endpoint | Required Params |
|---|---|---|---|
| UserRegistration | Open | ~/registration | username (string), password (string), email (string), age (int), gender_identity (int), activity_level (int) |
| UserLogin | JWT required | ~/login | username (string), password (string) |
| UserLogoutAccess | JWT required | ~/logout/access | None |
| UserLogoutRefresh | JWT required | ~/logout/refresh | None |
| TokenRefresh | JWT required | ~/token/refresh | None |
| GetUserPreference | JWT required | ~/prefs | None |
| EditPreference | JWT required | ~/edit | preference (string), preference_action ('add' or 'remove') |
| EmailVerification | JWT required | ~/verification | clickable URL |
| Recommender | JWT required | ~/recommendation | latitude (float), longitude (float), distance (m) (float) |
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AdminResources.py
AdminResources defines resources accessible by only those with admin privelages. Classes (tied to endpoints in run.py) are defined in this table:
| Class | Authorization Header | Endpoint | Required Params |
|---|---|---|---|
| AdminUserInfo | JWT required | ~/admin/user/info | None |
| AdminUserPreferences | JWT required | ~/admin/user/preferences | None |
| AdminAccess | JWT required | ~/admin/access | new_admin (string) |
| AdminFoodDump | JWT required | ~/admin/food/dump | None |
| AdminFoodAdd | JWT required | ~/admin/food/add | item_name (string), latitude (float), longitude (float), restaurant_name (string), item_description (string), price (float), nutrition (JSON string) |
| AdminFoodEdit | JWT required | ~/admin/food/edit | item_name (string), latitude (float), longitude (float), restaurant_name (string), item_description (string), price (float), nutrition (JSON string) |
| AdminFoodRemove | JWT required | ~/admin/food/remove | item_name (string), latitude (float), longitude (float) |
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natural_language.py
natural_language contains two classes to assist with our natural language processing -- Distance and Embed. Distance is a simple numpy implementation for cosine similarity (or difference if you choose) between two vectors. Embed is an implementation of our palate reduction technique, which is a dimensionality reduction method we developed to compress 300 dim food representations to just 30 dim vectors. The smaller vector size allows for non-abritrary optimizations in our recommendation model.
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nutrition_scoring.py
nutrition_scoring contains one class (Nutrition_Score) that scores a food item's nutritional value (from FoodModel) based on age, gender identity, and activity level. The model was developed by Sabrina Qi with guidance from Duke Nutrition. The more positive (less negative) a score is, the more nutritional value the food has for you.
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request_parsers.py
request_parsers were abstracted out from our resources file for readability.
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flavors.pickle
flavors.pickle is a Python list of ~30 palate features (umami, sweet, salty, etc) that we use for our dimensionality reduction technique.
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unique_foods.pickle
unique_foods is a Python dictionary of several thousand food items with a 300 dim vector representation from the Google News Corpus. They are organized as key:value pairs (food:vector). They were put in a python dictionary to limit the number of vectors from the Google News Corpus, and thus can load much more quickly (file size 1.5GB to 3.5 MB)
This is a jupyter notebook that uses each of the above described modules and endpoints for demonstration purposes. NOTE: Change the administrator password prior to production, since it is included in this demo.
Our frontend was maticulously and thoughtfully designed by @Ryan_Bolick, with guidance from behavioral psychologists at Duke University. We follow AI design guidelines that have been inspired by Google.
You can find Ryan's most up to date work here with his permission.
We considered multiple platforms to build on, and tested iOS and React Native prototypes. We decided Flutter was the most appropriate platform to suit our needs.
| Platform | Pros | Cons |
|---|---|---|
| iOS (Swift) | Fast, native | Single platform use |
| Android (Kotlin) | Fast, native | Single platform use |
| React Native (Javascript) | Multiplatform design, easy to learn, supported by Facebook | Compiles through javascript bridge (slow) |
| Flutter (Dart) | Multiplatform design, easy to learn, compiles to native code (fast), supported by Google | Still a developing language and online community. |
With the powerful backend API we are building, we can interface with different frontend products. One example was created by Aman Ibrahim and can be found at this repo.