README.md

CMSC495 Chatbot

A (somewhat) simple Chatbot for Slack backed by a Recurrent Neural Network(RNN).

Dependencies

• Python 3.6
• TensorFlow 0.12
• Numpy
• SciPy
• Six
• Slackbot
• NLTK (punkt, a NLTK library, is also required. See punkt_dl.py script in repo)

The program is run in a Python virtual environment for Python 3.5, which is included in the venv directory.

Running the Program

This section assumes you are using CentOS 7

1. Clone this repo
2. Create a Slack chatbot integration. Add the bot user to whatever Slack channel you would like. Take note of the Bot User OAuth Access Token.
3. Copy the slackbot_settings_template.py to slackbot_settings.py in the same directory. Edit slackbot_settings.py and add your API token, and what channel you would like errors sent to, if any.
4. Gather all dependencies

• To install Python 3.6, see here: https://www.digitalocean.com/community/tutorials/how-to-install-python-3-and-set-up-a-local-programming-environment-on-centos-7
• To install TensorFlow, see here: https://gist.github.com/87170360/0d445ae4100c2686067ee56901b42f9b
• All other dependencies are installed using Pip, Python's package manager

5. Copy the slack_integration directory from the repo to your Python distribution's site-packages directory. Typically /usr/lib/python3.5/site-packages/
6. cd to the repo, and run venv/tensorflow/bin/python3.5 run.py

Using the Docker Image as a production environment

Building the Docker image locally with the Dockerfile

1. Installing Docker is outside the scope of this repository, please see here for more information: https://docs.docker.com/engine/installation/
2. Clone this repository
3. Open a terminal (BASH or PowerShell depending on environment), and navigate to the repository directory
4. Build the container using the following command: docker build -t cmsc495:chatbot .
5. Run the container using the following command: docker run -m 2g -p 80:80 -p 443:443 -e "SLACKBOT_API_TOKEN=<Your token from step 2 here>" cmsc495:chatbot