How to create an AI Chatbot in Python and Flask DEV Community

There are a lot of undertones dialects and complicated wording that makes it difficult to create a perfect chatbot or virtual assistant that can understand and respond to every human. In this article, we will create an AI chatbot using Natural Language Processing (NLP) in Python. First, we’ll explain NLP, which helps computers understand human language. Then, we’ll show you how to use AI to make a chatbot to have real conversations with people.

The above function will call the following functions which clean up sentences and return a bag of words based on the user input. Now that we are done with training let’s create the Flask interface to initialize the chat functionalities. We shall be using ReLu activation function as it’s easier to train and achieves good perfomance.

Simple ChatBot build by using Python

Another parameter called ‘read_only’ accepts a Boolean value that disables (TRUE) or enables (FALSE) the ability of the bot to learn after the training. We have also included another parameter named ‘logic_adapters’ that specifies the adapters utilized to train the chatbot. Next, we await new messages from the message_channel by calling our consume_stream method. If we have a message in the queue, we extract the message_id, token, and message.

ChatterBot is a library in python which generates responses to user input. It uses a number of machine learning algorithms to produce a variety of responses. It becomes easier for the users to make chatbots using the ChatterBot library with more accurate responses. Chatbots have revolutionized the way businesses interact with customers and users. In this blog post, we will embark on an exciting journey to create our very own chatbot using the OpenAI library in Python. Nobody likes to be alone always, but sometimes loneliness could be a better medicine to hunch the thirst for a peaceful environment.

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In the last step, we have created a function called ‘start_chat’ which will be used to start the chatbot. Data preprocessing can refer to the manipulation or dropping of data before it is used in order to ensure or enhance performance, and it is an important step in the data mining process. It takes the maximum time of any model-building exercise which is almost 70%.

If Chainlit piqued your interest, there are a few more projects with code that you can look at. There’s also a GitHub cookbook repository with over a dozen more projects. The Generative AI section on the Streamlit website features several sample LLM projects, including file Q&A with the Anthropic API (if you have access) and searching with LangChain. If you want to try another relatively new Python front-end for LLMs, check out Shiny for Python’s chatstream module.

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Unless you’ve made the app private by making your GitHub repository private—so each account gets one private application—you’ll want to ask users to provide their own API key. We need all of the patterns and which class/tag they belong to. We also want a list of all of the unique words in our patterns (we will talk about why later), so lets setup some blank lists to store these values. Before starting to work on our chatbot we need to download a few python packages. Please note as of writing this these packages will ONLY WORK IN PYTHON 3.6.

The client listening to the response_channel immediately sends the response to the client once it receives a response with its token. We are sending a hard-coded message to the cache, and getting the chat history from the cache. When you run python main.py in the terminal within the worker directory, you should get something like this printed in the terminal, with the message added to the message array. Update worker.src.redis.config.py to include the create_rejson_connection method. Also, update the .env file with the authentication data, and ensure rejson is installed. The GPT class is initialized with the Huggingface model url, authentication header, and predefined payload.

To do this, you’re using spaCy’s named entity recognition feature. A named entity is a real-world noun that has a name, like a person, or in our case, a city. You want to extract the name of the city from the user’s statement. Learn how to use HuggingFace transformers library to fine tune BERT and other transformer models for text classification task in Python.

These libraries contain packages to perform tasks from basic text processing to more complex language understanding tasks. Python AI chatbots are essentially programs designed to simulate human-like conversation using Natural Language Processing (NLP) and Machine Learning. 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.

The AI Chatbot Handbook – How to Build an AI Chatbot with Redis, Python, and GPT

Natural language Processing (NLP) is a necessary part of artificial intelligence that employs natural language interaction. Sometimes the questions added are not related to available questions, and sometimes some letters are forgotten to write in the chat. At that time, the bot will not answer any questions, but another function is forward. In the below image, I have used the Tkinter in python to create a GUI. Please note that if you are using Google Colab then Tkinter will not work.

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