I aim to give users a new way to interact with Google Maps through engaging text-based conversational interfaces.
I'm only a baby bot right now, I need you to feed me with logic, data and inspiration.
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.
- I want to help users in the most comprehensive way.
- I want to give 'geeks' a platform to try out all things 'cool'.
Check out all related information here
- 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 -hlocalhostshould open the mySQL monitor. (Enter the root password when prompted)
- StanfordCoreNLP
- 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.jarandstanford-corenlp-x.x.x-models.jarhandy. - Add config.py file to .gitignore to avoid pushing changes made to config
- Run
git rm --cached config.py - Edit the
config.pyfile 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.txtfile. Runpip install -r requirements.txt - You're all set up, run the
init.pyfile.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 -
- StanfordCoreNLP
- Docker
- Take a look at this for detailed installation instructions for Docker on Windows, Linux and Mac systems.
- Verify the installations by
docker --versionanddocker-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.
- 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.pyfile with the corresponding values:- key = <your_Google_Cloud_API_key>
- You're all set up, kick off with
start.shfile by runningbash start.sh.
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.
Here's a Medium article with the some superficial explanations, there are some video links too!