This blog will help you create a working chatbot with in 20 minutes.
For creating chatbot we need following libraries to be installed-
>> Python3
>> Pip3
>> Rasa
Lets start installing all libraries & dependencies which are need for creating chatbot.
Note: I have used MAC, therefore sharing commands related to it. You can install it on Windows, Linux or any other operating system using respective commands.
1. Install Python3
> brew install python3
> python --version #make sure you have python3 installed
2. Install Pip3
> curl -O https://bootstrap.pypa.io/get-pip.py
> sudo python3 get-pip.py
If you get issue related to Frameoworks while installing pip, follow below steps -
> cd /usr/local/lib
> mkdir Frameworks
> sudo chown -R $(whoami) $(brew --prefix)/*
Once installed check pip3 version
> pip3 --version
After python3 and pip3 is succeffully installed, proceed to next steps.
> pip3 install rasa
After completion, rasa should be installed on your machine.
> rasa --version #check rasa version to confirm rasa latest version is installed
Now you are all set to proceed and work on you chatbot.
4. Create a new project
> rasa -init --no-prompt
This will create all required files for chatbot with sample trained data.
Next step is to train your model with sample data.
> rasa train
You are all set to go. Run the rasa chatbot(in shell mode) by following command,
> rasa shell
This may take few seconds, so have some patience. Output will look something like this -
> /stop
5. Expose it as Rest API
Goto directly where you have rasa project installed
> cd rasa_project
> ls #look for credentials.yml file
> vi credentials.yml
look for "rest:" and uncomment the line. Run below command to expose it as Rest API-
> rasa run -m models --enable-api --cors "*" --log-file out.log
by default, endpoint will be exposed on port 5005 and API will be http://localhost:5005. Run it in browser and you will see 'Hello from rasa 1.9.6' as response.
6. Test the chatbot with Postman
a. Open Postman and create a new POST request using URL:
http://localhost:5005/webhooks/rest/webhook
b. Enter raw body text in JSON format as input
{"sender":"Sumit B", "message":"who are you?"}
c. you should receive response in return, as shown in below example -
7. Integrate it with your website
You have to create a nice User Interface to call your rasa API and start interacting with your end users. One example of such a file is given below.
a. Create an index.html file and past the following code into it -
<html>
<head>
<link rel="stylesheet" href="https://npm-scalableminds.s3.eu-central-1.amazonaws.com/@scalableminds/chatroom@master/dist/Chatroom.css" />
</head>
<body>
<div class="chat-container"></div>
<script src="https://npm-scalableminds.s3.eu-central-1.amazonaws.com/@scalableminds/chatroom@master/dist/Chatroom.js"/></script>
<script type="text/javascript">
var chatroom = new window.Chatroom({
host: "http://localhost:5005",
title: "Chat with Sumit B",
container: document.querySelector(".chat-container"),
welcomeMessage: "Hi, I am Sumit B. How may I help you?",
speechRecognition: "en-US",
voiceLang: "en-US"
});
chatroom.openChat();
</script>
</body>
</html>
b. Now run this index.html file using below command -
> python3 -m http.server 8080
c. Open browser and check http://localhost:8080 and you should see your chatbot with example UI
You can install Rasa X for better UI and easy training of model which I have covered in next blog 'Know the rasa ecosystem and train your model effectively'.
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