MIT Smart Confessions API

MIT confessions is a Facebook page where MIT students posts anonymously and get reactions from other MIT students. These posts are called "confessions".

MIT Smart Confessions is a platform that uses machine learning to predict the number of reactions one would get from their confession and to generate confessions in such a way that it maximizes the number of reactions that confession would get.

The website for this application can be found at mit-smart-confessions.herokuapp.com, and the Github repository for the website can be found on github.com/robertvunabandi/mit-smart-confessions-website.

Installation

Python Version: 3.6.x

Make sure you are using Python 3.6.x (3.6.7 preferably). Tensorflow will not run correctly with python 3.7.0 because python 3.7.0 has some additional reserved keywords that tensorflow used. See this link in which you can download python 3.6.7 (scroll to the bottom of the page).

Python Virtual Environment

When running, use a python virtual environment. To create the virtual environment (more instructions here):

python3.6 -m venv {virtualenv-name} # ensures a python 3.6.x virtualenv

To activate, run:

source {virtualenv-name}/bin/activate

For instance, if you name your virtual env myvenv, then you would run:

# sets up the virual env
python3.6 -m venv myvenv
# activates the virtual env
source myvenv/bin/activate

To deactivate the virtualenv, run:

deactivate

You would want to deactivate the environment so that your terminal instance goes back to using the normal, system-wide installed packages.

Package Installations

After activating your virtualenv, run the following to install all the packages used in this application (make sure you have activated the virtualenv before running this):

pip3 install -r requirements.txt

That will install all the packages listed in the file requirements.txt.

Adding New Packages

If you install a new package by running pip3 install {package-name}, make sure to update the requirements.txt file. To do so, just run (make sure you have activated the virtualenv before running this and when installing a new package):

pip3 freeze > requirements.txt

• pip3 freeze lists all the packages currently installed in the environment
• > requirements.txt dumps that list into the requirements.txt file.

Running main.py

Use the following command to run the main server file:

FLASK_APP=main.py flask run

In addition, if you want to specify a specific host or port, you can run:

FLASK_APP=main.py flask run --host={host} --port={port}

Running Other Files

It is recommended that you use JetBrains' PyCharm IDE. If you prefer using your terminal, make sure that you set PYTHONPATH to be the root directory (i.e. where the main.py is located).

For instance, if running the bucket_classification.py model, you should run:

PYTHONPATH=.. python3 bucket_classification.py

Note that we used .. (2 dots in total) because the root directory is one parent directory above the /models directory (in which the bucket_classification.py resides). In case you are two levels deep, then you wanna do ../... For more levels, follow the patttern accordingly.

Data Collection

We collected data using the Facebook API. This is not needed anymore, but if curious, we have some details below.

The /data/scripts should have the code used to collect data.

We used the facebook-sdk package to install data. See this link for details on how to use the facebook package to collect data.

In order to keep this app up to date, however, we will need to collect more data in the future.