HooHacks + MLC@UVA Demo - Spam Classification App

Built with Flask and Scikit-Learn.

Prerequisites

Make sure that you have the following

• Python 3+ and pip (which comes with Python 3+)
• A Unix command line (for windows users, I personally recommend Git Bash). This isn't strictly necessary to complete the workshop, but if you don't want the packages installed globally, this is required.

Workshop Instructions

If you're going to be participating in the workshop, then download the starter code from the hoohacks-starter branch either by running git clone https://github.com/dylankfernandes/spam-classifier.git -b hoohacks-starter or downloading the branch as a zip file.

Running the Completed Demo

To run the demo, you first have to understand what each file does.

• server.py - runs the server and loads the user interface.
• templates/layout.html - contains the base HTML
• templates/index.html - contains the body of the HTML
• models/saved/ - contains all locally saved models and dataframes for future loading
• models/save_df.py - cleans the dataframe and saves the new dataframe to local file structure.
• models/model.py - builds and saves the logistic regression model.

To actually run the demo, complete the following steps

1. Install the virtualenv package using pip install virtualenv.
2. Create a virtual environment using virtualenv <environment-name>. Start the virtual environment by executing the following, depending on your operating system.
   1. Windows - <environment-name>\Scripts\activate
   2. OS X - source <environment-name>/bin/activate
3. Install the required packages using pip install -r requirements.txt. You can manually install them as you come across them if need be, but this will install them all for you. Note that if you add more packages, run pip freeze > requirements.txt to save them to your requirements file.
4. Navigate into the models directory (cd models) and create a saved directory (mkdir saved or manually creating the folder in your file system). Navigate out of the models directory (cd ..) back into the root directory
5. Run python models/save_df.py. This will save the cleaned dataframe as a csv under the models/saved directory.
6. Run python models/model.py. This will build the model and save it as a .joblib file under the models/saved directory. At this point, make sure that the models/saved/ directory contains both the dataframe.csv and model.joblib files.
7. Run the app using python server.py.
8. After you are finished using your app, deactivate your virtual environment.