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Deploying a Text Classification Model in Python Deploying a Text Classification Model in Python
This article is the last of a series in which I cover the whole process of developing a machine learning project. If you have not read... Deploying a Text Classification Model in Python

Considerations before the deployment

The data

Source

Source

The features

The environment

The user experience


Creation of a Dash web application

Deployment with Heroku

# after signing in to Heroku and opening the anaconda prompt
# we create a new folder
$ mkdir dash-app-lnclass
$ cd dash-app-lnclass# initialize the folder with git
$ git init
name: dash_app_lnclass #Environment name
dependencies:
  - python=3.6
  - pip:
    - dash
    - dash-renderer
    - dash-core-components
    - dash-html-components
    - dash-table
    - plotly
    - gunicorn # for app deployment
    - nltk
    - scikit-learn
    - beautifulsoup4
    - requests
    - pandas
    - numpy
    - lxml
$ conda env create
$ activate dash_app_lnclass
# the procfile must contain the following line of code
web: gunicorn app:server

# to create the requirements.txt file, we run the following:
$ pip freeze > requirements.txt
$ heroku create lnclass # change my-dash-app to a unique name
$ git add . # add all files to git
$ git commit -m 'Comment'
$ git push heroku master # deploy code to heroku
$ heroku ps:scale web=1  # run the app with a 1 heroku "dyno"

Final thoughts

Miguel Fernández Zafra

Miguel Fernández Zafra

Passionate about Finance and Data Science, and looking forward to combining these two worlds so as to take advantage of what technology can bring to us.

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