A GET request to returns a random quote out of a pool of 1500 of them in JSON format.įor cat pictures I’m going to use the Cat as a Service API from Kevin Balicot. For famous quotes, I’ve chosen the Quotable API from Luke Peavey. To supply the chatbot with original quotes and cat pictures I’m going to use two publicly available APIs. The responded boolean is useful to track the case where the message does not include any of the keywords we are looking for, and in that case offer a generic response.
With this simple structure we can detect references to quotes and/or cats and configure the Twilio response object accordingly. Don’t worry about copying this code, I will first show you all the different parts of the implementation and then once you understand them I’ll show you how they are all combined into a working application. Below is a skeleton application with a webhook definition. With the Flask framework it is extremely easy to define a webhook. Our chatbot application needs to define an endpoint that is going to be configured as this webhook so that Twilio can communicate with it. The Twilio API for WhatsApp uses a webhook to notify an application when there is an incoming message. If both “quote” and “cat” are present in the message, then the bot will respond with a quote and a cat picture together.
If instead the message has the word “cat”, then a random cat picture will be returned. If the user writes anything that contains the word “quote”, then the chatbot will return a random famous quote. For this tutorial I’m going to build an extremely simple chatbot that recognizes two keywords in messages sent by the user and reacts to them. The type of chatbot that will work best for you is going to be largely dependent on your particular needs.
#Chatbot for website python mac os
If you are using a Unix or Mac OS system, open a terminal and enter the following commands to do the tasks described above: We then are going to install the Python packages that we need for our chatbot on it. Create a Python virtual environmentįollowing Python best practices, we are going to make a separate directory for our chatbot project, and inside it we are going to create a virtual environment. Note that this step needs to be repeated for any additional phones you’d like to have connected to your sandbox. Shortly after you send the message you should receive a reply from Twilio indicating that your mobile number is connected to the sandbox and can start sending and receiving messages. The code is going to begin with the word join, followed by a randomly generated two-word phrase. To enable the WhatsApp sandbox for your smartphone send a WhatsApp message with the given code to the number assigned to your account. The WhatsApp sandbox page will show you the sandbox number assigned to your account, and a join code. From your Twilio Console, select Programmable Messaging, then click on "Try it Out" and finally click on Try WhatsApp. Let’s connect your smartphone to the sandbox. Once your application is complete you can request production access for your Twilio phone number, which requires approval by WhatsApp. Twilio provides a WhatsApp sandbox where you can easily develop and test your application. The following sections will give you all the necessary details to configure and create a WhatsApp chatbot using Python and the Flask framework. If you don’t have ngrok it installed, you can download a copy for Windows, MacOS or Linux This is necessary for the development version of the chatbot because your computer is likely behind a router or firewall, so it isn’t directly reachable on the Internet. We will use this handy utility to connect the Flask application running on your system to a public URL that Twilio can connect to. You can review the features and limitations of a free Twilio account. If you are new to Twilio create a free account now. A smartphone with an active phone number and WhatsApp installed.We will create a web application that responds to incoming WhatsApp messages with it. If your operating system does not provide a Python interpreter, you can go to to download an installer.
To follow this tutorial you need the following components: Below you can see an example interaction I had with this chatbot: In this tutorial I’m going to show you how easy it is to build a chatbot for WhatsApp using the Twilio API for WhatsApp and the Flask framework for Python. While some chatbots have a fairly basic understanding of language, others employ sophisticated artificial intelligence (AI) and machine learning (ML) algorithms to achieve an almost human conversational level. The level of “intelligence” among chatbots varies greatly. A chatbot is a software application that is able to conduct a conversation with a human user through written or spoken language.
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