Glucofy-Backend

1. Cloud Architecture

The app architecture in the cloud will be like this :

We use Cloud Run, Cloud Firestore, and Vertex AI services to support our applications. The frontend utilizes APIs from two servers: the backend and machine learning models. The backend is directly connected to Cloud Firestore as its database and to Vertex AI for the food recommendation feature. We deploy these services in the Asia-Southeast2 region and the Asia-Southeast2-B zone.

2. Database

Our application's database utilizes Firestore, which contains a primary `User` collection. Each document within this *User* collection represents an individual user and includes a tracker subcollection that stores related tracking information specific to that user. Additionally, there is a `Food` subcollection related to the user's tracking data. This subcollection stores details about various foods

3. API Endpoints

- Express-API Endpoints :

Endpoint	Method	Body	Description
/	GET	-	Accessing our root endpoints
/auth/register	POST	firstName, lastName, phoneNumber, email, password, gender, weight, height, age	Register account for new user
/auth/login	POST	email, password	Login to access the feature in application
/user/profile	GET	-	Show the detail data from user
/user/update	PUT	Anything you want to edit from firstName, lastName, phoneNumber, email, password, gender, weight, height, age	Edit profile from user
/user/delete	DELETE	-	Delete profile from user
/tracker	GET	-	Show all glucose trackers from user
/tracker/add	POST	glucose, condition, notes, datetime	Create new glucose tracker
/tracker/delete/{{trackerId}}	DELETE	-	Delete glucose tracker from user
/food	GET	name -> Query for search food by name	Show all food from user or Search food
/food/today	GET	-	Show all today's food from users
/food/detail/{{foodId}}	GET	-	Show the detail food data
/food/add	POST	foodName, gIndex, gLoad, giCategory, glCategory, carbs, calories, fats, proteins, category	Save food to firestore DB
/food/delete/{{foodId}}	DELETE	-	Delete food from user
/dataset	GET	-	Show all data from dataset
/dataset/detail/{{dataId}}	GET	-	Show the detail data from dataset
/ai/recommend	POST	foodName	Food recommendation by Vertex AI

- Flask-API Endpoints :

Endpoint	Method	Body	Description
/predict_new_data	POST	foodName, category, calories, proteins, carbs, fats	Returns new meal's GI & GL

4. Deploy API to Cloud Run

• First, make sure the Artifact Registry, Cloud Run, and Cloud Build APIs are active by running the following command (click Authorize if the popup appears):

gcloud services enable artifactregistry.googleapis.com cloudbuild.googleapis.com run.googleapis.com

• Create an Artifact Registry repository by running the following command:

gcloud artifacts repositories create backend --repository-format=docker --location=asia-southeast2 --async

• Create a new container image by running the following command:

gcloud builds submit --tag asia-southeast2-docker.pkg.dev/${GOOGLE_CLOUD_PROJECT}/backend/glucofy-api:1.0.0

• Deploy a new container image to Google Cloud Run:

gcloud run deploy --image asia-southeast2-docker.pkg.dev/${GOOGLE_CLOUD_PROJECT}/backend/glucofy-api:1.0.0