-- kubenetes
-- data # data to train the model
-- kube config # cluster config (e.g. the images location)
-- deployment.yaml
-- sercvice.yaml
train.py # scripts to train the model
predict.py # a flask server to do the predict, comunication with MongoDB and SQLite
Dockerfile # containernized the predict.py and the corresponding env
-- frontend
...
-- menu-items # home page index and the correspond child info (url)
-- routes # url -> resource on the host
-- views # resource on the host
-- ai-model
-- record.js # Front-end(React.js), back-end(Node.js) communication, to access the
...
-- components
-- database.js # api that communicate with sqlite DB
-- context
-- UsersContext.js # call database.js to get users, add users
-- hooks
-- useSqlite.js # setup databse using hook
-- database
-- sqlite.sql
-- backend
-- db
-- conn.js # connection to the database
-- routes
-- record.js # handle different record
-- server.js # Website Node.Js server, comunication with MongoDB and SQLite
SQLite
Account_Info
| Email (varchar 30) | Password (varchar 30) | API_Key(uuid) | Account_Balance |
|---|---|---|---|
| Adam@usc.edu | "Aa123456" | b6f97a69-428b-4e74-862d-4ad22d7e0de8 | default 0 |
| ... | |||
| The constraint | need to be | further considered |
CREATE TABLE Account_Info (Email varchar(30) primary key, Password varchar(30) NOT NULL, Account_Balance real DEFAULT 0);
insert into Account_Info(Email, Password) values("Adam@usc.edu", "Aa123456");
MongoDB:
model_request
| Request_id | API_Key | Request_Time | Request_Type | Request_Status |
|---|---|---|---|---|
| "63d9c7447fb0edda4d6b0172" | b6f97a69-428b-4e74-862d-4ad22d7e0de8 | "31/01/2023 21:58:58" | Model1 | Pending/Done |
model_result
| Request_id | Result |
|---|---|
| "63d9c7447fb0edda4d6b0172" | Candidate features have been evaluated and output is {'Placement': True, 'Placement_Probability': 0.90234375} |
> use ml_model
switched to db ml_model
> db.model_request.insert({"UserName":"Adam", "Request_Type": "Model1"})
WriteResult({ "nInserted" : 1 }
> db.model_request.find()
{ "_id" : ObjectId("63d9c7447fb0edda4d6b0172"), "UserName" : "Adam", "Request_Type" : "Model1" }Python usage of MongoDB
from pymongo import MongoClient
client = MongoClient('localhost', 27017)
# print(client.list_database_names())
db = client.ml_model
collections = db.model_request
for x in collections.find():
print(x)