A simple application that enables a user to train a support vector machine (svm) model on the classical Iris Flower Dataset and use it to predict the classification of a flower based on sepal length, sepal width, petal length, and petal width.
UI: Angular 6
Backend: Python Flask
ORM: PonyORM
Database: Postgres
You can watch a demo of the app running with:
demo.mov
First fork or clone this repo:
i.e. git clone https://github.com/cvkumar/flask-ml-app.git
To run the app locally:
docker-compose up --build app
and
cd frontend/my-app
npm install
npm start
Go to http://localhost:4200.
Using brew:
brew install node
brew install angular-cli
Also install:
-Docker
-Docker-compose
- Run Angular application paired with docker-compose using NGINX
- Expose API for inputting training/test data
- Create user inputs for SVM model hyperparameters and training/test data split
- Improve UI with Angular Material Components
- Caleb Kumar - cvkumar
This project is licensed under the MIT License - see the LICENSE.md file for details