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iris-webapp

This is a demo web application, powered by Flask in the backend and AngularJS in the frontend. It demonstrates how to handle file validation and parsing in the frontend, i.e. CSV file parsing and how to use this parsed data in the backend to perform a machine learning task k-means clustering using scikit-learn) on it.

It consists of two parts: the server and the client:

iris-server

The server is just a small Flask application that accepts the parsed CSV data and returns the results from the k-means clustering.

For the installation you need to have a running version of Python (tested with 3.4). Then follow these steps:

$ cd iris-server
$ pip install -r requirements.txt
$ python server.py

Now the server is running on http://localhost:5000.

iris-client

This is the AngularJS app that powers the frontend of the webapp. To install its dependencies you need bower (the installation of bower is out-of-scope for this document). Install the dependencies like this:

$ cd iris-client
$ bower install

To actually serve the frontend you need a webserver. Since you need Python anyway for the iris-server its easiest to use Python's built-in HTTP server:

python -m http.server

Now you have a running web server on http://localhost:8000. Visit this link and start playing around with the app.

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Demo webapp that parses a CSV in the frontend and applies a ML algorithm on the parsed data in the backend.

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