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'.

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Are you here for GSSoC 2020?

Check out all related information here

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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.

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Setting Up

How to set me up on CLI?

• 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 paths stanford-corenlp-x.x.x.jar and stanford-corenlp-x.x.x-models.jar handy.
• Run git update-index --assume-unchanged ENV/.env
• Fill the existing template in ENV/.env with the corresponding values following the KEY=VALUE format
• 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

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How to set me up with an UI?

• 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 paths stanford-corenlp-x.x.x.jar and stanford-corenlp-x.x.x-models.jar handy.
• Run git update-index --assume-unchanged ENV/.env
• Fill the existing template in ENV/.env with the corresponding values following the KEY=VALUE format
• Install dependencies from requirements.txt file. Run pip install -r requirements.txt
• You're all set up. Execute python webapp.py to start up the server.
• Visit http://127.0.0.1:5000/ to interact with your MapBot.
• 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

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How to deploy on Docker?

What are some pre-requisites? (with Docker)

• 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

How to set me up Docker style?

• Clone the repository
• Fill up the GCLOUD_API_KEY in ENV/docker.env
• Run docker-compose up
• Visit localhost:5000 to interact with the deployment

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How to set me up on Telegram?

• 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 paths stanford-corenlp-x.x.x.jar and stanford-corenlp-x.x.x-models.jar handy.
• Run git update-index --assume-unchanged ENV/.env
• Fill the existing template in ENV/.env with the corresponding values following the KEY=VALUE format
• For TELEGRAM_BOT_TOKEN=<YOUR_API_KEY_HERE>, open your Telegram app and follow this tutorial on how to create a new bot on Telegram and get your own bot token. Once your token is generated, update the .env file in /ENV with it.
• Find your bot on Telegram using @bot_username that you chose, and send the first text to your new bot. Nothing is supposed to happen for now. No worries.
• Install dependencies from requirements.txt file. Run pip install -r requirements.txt
• You're all set up, run the telegram.py file. python telegram.py and converse with your bot in real time.

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How to set me up on Slack?

• 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 paths stanford-corenlp-x.x.x.jar and stanford-corenlp-x.x.x-models.jar handy.
• Run git update-index --assume-unchanged ENV/.env
• Fill the existing template in ENV/.env with the corresponding values following the KEY=VALUE format
• Follow the steps prompted here to create a classic slack app. Navigate to Basic Information section of the slack app. Under the Add features and functionality subheading click on Bots. Click on Add Legacy Bot User and enter the display name and default username of your bot. Navigate to Basic Information section of the slack app on the sidebar and copy the Client ID and Client Secret and then paste these to the ENV/.env file as: SLACK_CLIENT_ID=<Your Client ID> and SLACK_CLIENT_SECRET=<Your Client Secret>. Navigate to the OAuth & Permissions section. Under the Redirect URLs subheading add http://localhost:5000/post_auth.
• Install dependencies from requirements.txt file. Run pip install -r requirements.txt
• Run python app.py. The server will start at your localhost. Navigate to http://localhost:5000/begin_auth. Click Add to Slack button. Select the workspace from the top right and hit Allow. Successfully completing this step would automate the creation of SLACK_BOT_TOKEN in the ENV/.env file.
• In another terminal, run python slackbot.py.
• Open the workspace in Slack and invite the bot to the channel: @YOUR_BOT_DEFAULT_USERNAME message in the channel. Click on Invite to Channel.

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How to set me up on Facebook Messenger?

• 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 paths stanford-corenlp-x.x.x.jar and stanford-corenlp-x.x.x-models.jar handy.
• Run git update-index --assume-unchanged ENV/.env
• Fill the existing template in ENV/.env with the corresponding values following the KEY=VALUE format
• Create Facebook app from here. Fill out basic information.
• Set Up Messenger option under Add a Product.
• Generate the Access Token by creating a facebook page for your bot by clicking on Create New Page button.
• Click on Add or Remove Pages and add your facebook page just created.
• Click on Generate Token and copy the token to the ENV/.env file as ACCESS_TOKEN=<YOUR_ACCESS_TOKEN>.
• Install ngrok.
• Open a terminal window, type ngrok http 5000. Once you do this, a screen will appear with a link after the “Forwarding” section — make sure to copy the link that begins with “https”.
• Click on Add Callback URL under Webhooks section. Paste the above link in the Callback URL. Add Verify Token of your choice. Copy Verify Token you added in the ENV/.env file as VERIFY_TOKEN=YOUR_VERIFY_TOKEN. Hit Verify and Save
• Click on Add Subscriptions. Check messages, messaging_postbacks, message_deliveries, messaging_pre_checkouts boxes.
• Run python app.py
• You can interact with bot on Facebook Messenger.

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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.

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Want to see me in action?

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