inquire-boulder-chatbot

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inquire-boulder-chatbot

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A chatbot that uses NLP on the Inquire Boulder FAQ text to provide answers to user queries.

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inquire-boulder-chatbot
==============================
A chatbot for the Inquire Boulder FAQ 
Interactive Web Demo: https://willtscott.github.io/inquire-boulder-chatbot/

Keywords: Python, scikit-learn, NLTK, spaCy, GenSim, Doc2Vec, Flask, Docker, Google Cloud Products, Dialogflow

This project is written in Python using Jupyter Notebooks for exploration and prototyping and Flask and Docker for deployment on Google Cloud Products. I used NLTK and spaCy for NLP (including tokenizing and lemmatization) and scikit-learn for vectorization and similarity matching with Bag-of-Words (BOW), Term Frequency-Inverse Document Frequency (TFIDF), and cosine similarity. The cloud service connects with a Dialogflow agent via webhooks, which provides the public interface.

The goal is to provide a working, better-than-baseline chatbot that is publicly accessible and can be flexibly scaled up and/or iterated with different datasets.

The process is explained in further detail by my article:
https://towardsdatascience.com/faq-chatbot-mvp-871ab7db94cc

The text dataset for the FAQ was obtained from a webmaster at the City of Boulder. Thanks goes to Nicolia Eldred-Skemp for help acquiring this data.

Other Links:

Inquire Boulder: 
https://user.govoutreach.com/boulder/faq.php

Boulder Open Data Portal: 
https://bouldercolorado.gov/open-data/areas-of-inquiry

Related Project by Doster Esh: 
https://github.com/doc1000/email_sorting

Project Organization
------------

├── LICENSE
 ├── Makefile <- Makefile with commands like `make data`
 ├── README.md <- The top-level README for developers using this project.
 ├── data
 │   ├── test <- Handmade dataset for testing accuracy.
 │   ├── interim <- Intermediate data that has been transformed.
 │   ├── processed <- The final, ca nonical data sets for modeling.
 │   └── raw <- The original, immutable data dump.
 |
 ├── evaluation <- Scripts to automate model evaluation
 |
 ├── index.html <- Github.io project page 
 |
 ├── lib <- External tools library
 │
 ├── models <- Trained and serialized models, model predictions, or model summaries
 │
 ├── notebooks <- Jupyter notebooks for EDA and prototyping
 | 
 ├── planning <- Project goals and journal
 │
 ├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
 │ generated with `pip freeze > requirements.txt`
 │
 ├── src <- Source code for use in this project.
 │   ├── __init__.py <- Makes src a Python module
 │ │
 │   ├── data <- Scripts to download or generate data
 │
 ├── scripts <- Scripts to automate text cleaning and test set creation
 │   ├── deployment <- Scripts for containerization and cloud deployment 
 │ 
 ├── vizualization <- Images for EDA and masks to generate images
 │
 └── test_environment.py <- tests correct version of python

--------

Project organization based on the cookiecutter data science project template.
https://drivendata.github.io/cookiecutter-data-science/

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