How to build a AI chatbot using NLTK and Deep Learning

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Considering starting a new IT project or improving existing software? Whatever industry you work in, Apriorit experts are ready to answer your tech questions and deliver top-notch IT solutions for your business. Customers’ interests can be piqued at the right time by using chatbots. Follow the steps below to build a conversational interface for our chatbot successfully. Before deciding on the chatbot software you want to invest time and money in, you should understand how you plan on using it and what are the functionalities required for that.

  • After the get_weather() function in your file, create a chatbot() function representing the chatbot that will accept a user’s statement and return a response.
  • The bot uses pattern matching to classify the text and produce a response for the customers.
  • Moving forward, you’ll work through the steps of converting chat data from a WhatsApp conversation into a format that you can use to train your chatbot.
  • The design of ChatterBot is such that it allows the bot to be trained in multiple languages.
  • This bot framework offers great privacy and security measures for your chatbots, including visual recognition security.
  • Here, we will create a function that the bot will use to acquire the current weather in a city. was acquired by Facebook in 2015 which made deploying python chatbot librarys on Facebook Messenger seamless. It also offers integrations with other channels, including websites, mobile apps, wearable devices, and home automation. The SDK is available in multiple coding languages like Ruby, Node.js, and iOS. But if you need to hire a developer to do this for you, be prepared to pay a hefty amount for this job.

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Thus, we can also specify a subset of a corpus in a language we would prefer. In the above snippet of code, we have defined a variable that is an instance of the class “ChatBot”. The first parameter, ‘name’, represents the name of the Python chatbot.

  • It might be very challenging for you to start creating bots if you jump head-first into this task.
  • But tools are not everything, here are our best tips to take advantage of a Python API to build chatbots.
  • An open-source chatbot is a software that has its original code available to everyone.
  • Before we start with the tutorial, we need to understand the different types of chatbots and how they work.
  • Known as NLP, this technology focuses on understanding how humans communicate with each other and how we can get a computer to understand and replicate that behavior.
  • We will also initialize different variables that we want to use in it.

You can add as many keywords/phrases/sentences and intents as you want to make sure your chatbot is robust when talking to an actual human. Now that we have the back-end of the chatbot completed, we’ll move on to taking input from the user and searching the input string for our keywords. Once our keywords list is complete, we need to build up a dictionary that matches our keywords to intents. We also need to reformat the keywords in a special syntax that makes them visible to Regular Expression’s search function. Once we have imported our libraries, we’ll need to build up a list of keywords that our chatbot will look for.


‍Rasa is an open-source bot-building framework that focuses on a story approach to building chatbots. Rasa is a pioneer in open-source natural language understanding engines and a well-established framework. Moreover, from the last statement, we can observe that the ChatterBot library provides this functionality in multiple languages.


The bot uses pattern matching to classify the text and produce a response for the customers. ChatterBot provides a way to install the library as a Django app. As a next step, you could integrate ChatterBot in your Django project and deploy it as a web app. You now collect the return value of the first function call in the variable message_corpus, then use it as an argument to remove_non_message_text().

Python MongoDB

To avoid this problem, you’ll clean the chat export data before using it to train your chatbot. In this step, you’ll set up a virtual environment and install the necessary dependencies. You’ll also create a working command-line chatbot that can reply to you—but it won’t have very interesting replies for you yet. After data cleaning, you’ll retrain your chatbot and give it another spin to experience the improved performance. TensorFlow is an end-to-end open source platform for machine learning. Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora.


Run the following command in the terminal or in the command prompt to install ChatterBot in python. On the next line, you extract just the weather description into a weather variable and then ensure that the status code of the API response is 200 . Introduction In synchronous programming, tasks are executed sequentially, which means that the lower statement… The chatbot market is anticipated to grow at a CAGR of 23.5% reaching USD 10.5 billion by end of 2026. After creating your cleaning module, you can now head back over to and integrate the code into your pipeline.

SVM Kernels: Polynomial Kernel – From Scratch Using Python.

Your chatbot has increased its range of responses based on the training data that you fed to it. As you might notice when you interact with your chatbot, the responses don’t always make a lot of sense. That way, messages sent within a certain time period could be considered a single conversation. You refactor your code by moving the function calls from the name-main idiom into a dedicated function, clean_corpus(), that you define toward the top of the file. In line 6, you replace “chat.txt” with the parameter chat_export_file to make it more general.

Which IDE is the best for Python AI?

  • IDLE. IDLE (Integrated Development and Learning Environment) is a default editor that accompanies Python.
  • PyCharm. PyCharm is a widely used Python IDE created by JetBrains.
  • Visual Studio Code. Visual Studio Code is an open-source (and free) IDE created by Microsoft.
  • Sublime Text 3.
  • Atom.
  • Jupyter.
  • Spyder.
  • PyDev.

NLTK is a leading platform for building NLP programs to work with human language data. This library provides a practical introduction to programming for language processing. In the above snippet of code, we have imported the ChatterBotCorpusTrainer class from the chatterbot.trainers module. We created an instance of the class for the chatbot and set the training language to English. ChatterBot is a Python-based bot flow that is automated through machine learning technology.

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The function is very simple which first greet the user, and ask for any help. And the conversation starts from here by calling a Chat class and passing pairs and reflections to it. You’ll write a chatbot() function that compares the user’s statement with a statement that represents checking the weather in a city. To make this comparison, you will use the spaCy similarity() method. This method computes the semantic similarity of two statements, that is, how similar they are in meaning. This will help you determine if the user is trying to check the weather or not.

  • But while you’re developing the script, it’s helpful to inspect intermediate outputs, for example with a print() call, as shown in line 18.
  • The possibilities with a chatbot are endless with the technological advancements in the domain of artificial intelligence.
  • Then it generates a pickle file in order to store the objects of Python that are utilized to predict the responses of the bot.
  • And according to Statista, the number of online shoppers are only going to keep growing.
  • But, we have to set a minimum value for the similarity to make the chatbot decide that the user wants to know about the temperature of the city through the input statement.
  • However, if you bump into any issues, then you can try to install Python 3.7.9, for example using pyenv.

Following is a sample python program which takes name as input and print your name with hello. Preprocessors are simple functions for input preprocessing, such as for removing consecutive whitespace characters from statement text. To demonstrate how to create a chatbot in Python using a ready-to-use library, we decided to apply the ChatterBot library. In the first example, we make the chatbot model choose the response with the highest probability at each step. Let’s start with the first method by leveraging the transformer model for creating our chatbot.

Nobody likes to be alone always, but sometimes loneliness could be a better medicine to hunch the thirst for a peaceful environment. Even during such lonely quarantines, we may ignore humans but not humanoids. Yes, if you have guessed this article for a chatbot, then you have cracked it right. We won’t require 6000 lines of code to create a chatbot but just a six-letter word “Python” is enough.

types of chatbots

To simulate a real-world process that you might go through to create an industry-relevant chatbot, you’ll learn how to customize the chatbot’s responses. You’ll do this by preparing WhatsApp chat data to train the chatbot. You can apply a similar process to train your bot from different conversational data in any domain-specific topic. Chatbots can provide real-time customer support and are therefore a valuable asset in many industries. When you understand the basics of the ChatterBot library, you can build and train a self-learning chatbot with just a few lines of Python code. Thanks to its extensive capabilities, artificial intelligence helps businesses automate their communication with customers while still providing relevant and contextual information.

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OpenDialog is a no-code platform written in PHP and works on Linux, Windows, macOS. OpenDialog is licensed under the Apache License, Version 2.0. You can manage and future-proof your conversational AI strategy. The SDK for is available in multiple languages such as Python, Ruby, and NodeJS. It has a large number of plugins for different chat platforms including Webex, Slack, Facebook Messenger, and Google Hangout.

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Vincent Kimanzi is a driven and innovative engineer pursuing a Bachelor of Science in Computer Science. He is passionate about developing technology products that inspire and allow for the flourishing of human creativity. He is passionate about programming and is searching for opportunities to cooperate in software development. He demonstrates exceptional abilities and the capacity to expand knowledge in technology. He loves engaging with other Android Developers and enjoys working and contributing to Open Source Projects. While some companies have listed different use cases for their platform, it’s not always the case.


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