/MapBot

A chatbot developed in Python

Primary LanguagePython

MapBot

Hey! I'm your friendly navigator bot! Try me out, not to brag but I'm FUN!

What I do?

I aim to give users a new way to interact with Google Maps through engaging text-based conversational interfaces.

How old am I?

I'm only a baby bot right now, I need you to feed me with logic, data and inspiration.

What is the motivation behind building me?

The primary motivation of the developers of MapBot is to provide a playground to tech enthusiasts, both beginners and advanced to try algorithms, approaches and ideas while contributing to a real-life project.

What I aspire to be one day?

  • I want to help users in the most comprehensive way.
  • I want to give 'geeks' a platform to try out all things 'cool'.

Are you here for GSSoC 2020?

Check out all related information here

What are some pre-requisites?

  • MySQL
    • Install the community version of mySQL from the official mySQL documentation page.
    • Create root user credentials during installation.
    • Verify the installation, running the command mysql -uroot -p -hlocalhost should open the mySQL monitor. (Enter the root password when prompted)
  • StanfordCoreNLP
    • StanfordCoreNLP has a dependency on Java 8. java -version should complete successfully with version 1.8 or higher.
    • Windows- Download as a .zip file from here.
    • Linux and MacOS- Follow the instructions to download the file from here.

How to set me up?

  • Clone the repository
  • Create the mapbot database in mySQL
    • mysql -uroot -p -hlocalhost
    • Enter root password when prompted
    • create database mapbot;
    • Verify creation of the database show databases;
  • Unzip the StanfordCoreNLP package in the repository and keep the file names stanford-corenlp-x.x.x.jar and stanford-corenlp-x.x.x-models.jar handy.
  • Add config.py file to .gitignore to avoid pushing changes made to config
  • Run git rm --cached config.py
  • Edit the config.py file with the corresponding values
    • user = "root"
    • password = <your_root_password>
    • host = "localhost"
    • database = "mapbot"
    • key = <your_Google_Cloud_API_key>
    • stanford_path_to_jar = <your_path_to_stanford-corenlp-x.x.x.jar>
    • stanford_path_to_models_jar = <your_path_to_stanford-corenlp-x.x.x-models.jar>
    • javahome = <your_path_to_jdk_bin_java.exe>
  • Install dependencies from requirements.txt file. Run pip install -r requirements.txt
  • You're all set up, run the init.py file. python init.py
  • It is recommended that you set this project up in a virtual environment to keep the dependencies separated and for easier debugging. Here's how you can do that -
    1. Python
    2. Conda

What are some pre-requisites? (with Docker)

  • StanfordCoreNLP
    • StanfordCoreNLP has a dependency on Java 8. java -version should complete successfully with version 1.8 or higher.
    • Windows- Download as a .zip file from here.
    • Linux and MacOS- Follow the instructions to download the file from here.
  • Docker
    • Take a look at this for detailed installation instructions for Docker on Windows, Linux and Mac systems.
    • Verify the installations by docker --version and docker-compose --version
  • You won't need to download MySQL locally to make Docker work, but it's recommended as a pre-requisite to be able to debug programs outside Docker.

How to set me up Docker style?

  • Clone the repository
  • Unzip the StanfordCoreNLP package in the repository. Make sure the StanfordCoreNLP folder you downloaded in the prerequisite steps are in the cloned repository folder.
  • Add config.py file to .gitignore to avoid pushing changes made to config
  • Run git rm --cached config.py
  • Modify only the following fields in config.py file with the corresponding values:
    • key = <your_Google_Cloud_API_key>
  • You're all set up, kick off with start.sh file by running bash start.sh.

How do I work?

The analysis folder contains data files for the project. The sentences.csv contains the base training dataset which is used to classify the user's input into three classes - Statement, Question, and Chat. Going through some examples would clarify the difference between statement and chat. The featuresDump.csv is the result of text pre-processing done using the code in features.py and featuresDump.py.

Want to see me in action?

Here's a Medium article with the some superficial explanations, there are some video links too!