Prototype ML
Machine Learning For Everyone
Django app to expose interface of scikit-learn through API
Update : Refactored code to dynamically fetch model classes mentioned by the user in API. Theoretically, all models in scikit learn can be tested now.
Features
- Independent login for users
- Dashboard for users to manage models
- Train and save models through API
- Run predictions through API
Installation
git clone https://github.com/ramansah/ml_webapp.git Configure credentials for MySQL client at ~/mysql.cnf
[client]
database = ml_webapp
user = username
password = ****
default-character-set = utf8 Install mysqlclient-python
https://github.com/PyMySQL/mysqlclient-python
Install MongoDB
https://www.digitalocean.com/community/tutorials/how-to-install-mongodb-on-ubuntu-16-04
Create virtual environment and run locally
python -m venv myenv
source myenv/bin/activate
cd ml_webapp
pip install --upgrade pip
pip install -r requirements.txt
python manage.py makemigrations
python manage.py migrate
python manage.py runserver Usage
Visit http://localhost:8000 and register a new user
Fetch the JWT for current user
POST /api/login/
Content-Type: application/json
{
"username": "username",
"password": "password"
}
Response
{
"token": "abcd12345"
}Create a model and save in the DB
Consider the
POST /api/model/
Content-Type: application/json
Accept: application/json
Authorization: JWT abcd12345
{
"model_path": "sklearn.linear_model.LinearRegression",
"action": "new_model",
"name": "Compute Final Score",
"input_x": [[95, 87, 69], [99, 48, 54], [85, 57, 98], [90, 95, 91]],
"input_y": [291, 200, 254, 326]
}
Response
{
"status": "Trained",
"model_id": "randommodelid"
}Use this model to predict your score
POST /api/model/
Content-Type: application/json
Accept: application/json
Authorization: JWT abcd12345
{
"action": "predict",
"model_id": "randommodelid",
"input_x": [[90, 95, 91]]
}
Response
{
"status": "OK",
"prediction": [
326
]
}Check out your trained models at Dashboard
