We then go to Token generation, and set our Facebook page, grating the permission that it asks for. We click in the Messenger icon, and check the Messenger Platform in the right. This will allow us to subscribe for the messaging configuration of our Chatbot later.Īfter pressing Setup up, we have a new icon in the Product section int the bottom left side. We also need to set up the product ‘Messenger application’ on the Dashboard, as shown below: The app id is easily found in the Facebook developers page, as highlighted below We go to our Facebook page, on Settings -> Messenger Platform, and put the app id in the field ‘ Link Your App to Your Page‘. So, after the we got the page and app, we need to link them together. We then go to the Facebook Developers website and create a new app. Facebook Configurationįor starts, we need to create a Facebook page for our Facebook bot to live on. But here we will focus on learning the basic aspects on a local machine thanks to ngrok. Normally in a productive environment, one would host the bot in some Internet server service, such as heroku, aws, etc. So when in the client side, if we send a message to the Facebook bot, it can contact the server bot that is online via the ngrok and the flask infrastructure will handle the communication tunnels for where our server bot will answer. Now, we know that the client side (Facebook) needs to contact the server over the Internet, and for that to be possible we make use of ngrok, that allows local app communication over the web. For the server side we will use Python to work in the following: to configure the Flask framework and to write the behavior of our bot. We can see this part as the client side of the project. The basic workflow for this tutorial is the following: We will configure the Facebook so it is able to support and contact our bot. It is a good choice of language since there are many developing technology such as TensorFlow, SciKit Learn, and NLTK that we can explore later on for more sophisticated behaviors. We will be using Python for developing the infrastructure and logic of our bot. For example, if someone asks the Chatbot “what is the expected weather for today?” the Chatbot can refer to an API such as OpenWeatherMap and answer the question. Nowadays, many APIs can be used by the Chatbots for answering users’ questions. ChatBots have usually been deployed for automatically answering questions of users, but nowadays they can be programmed for more engaging behaviors, such as suggesting topics based on users profiles and interests. Chatbot Building with Node.JS: DialogFlow & Wit.AI ChatbotsĬhatBots have been a popular technology for many years, but with the recent advances in the fields of Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI), many new and interesting uses for Chatbots have arisen.

Chatbot Building with Python: Rasa NLU, Rasa Core, DialogFlow & WIT.AI Bots.

Use python to make facebook chatbot