How to create a simple chatbot

How to create a simple chatbot 

If you are interested in developing a chatbot that can automate your chat responses. You can try to develop a simple one. By using the Python language, the first step is to have an understanding of the principles of writing Python and how it works. Then set up the work structure. How to answer in which direction the chatbot will answer questions? You need to make the chatbot understand your intent. Determine the difference between each question: How should Question A be answered? Question B must be answered.

Essential packages

The required Python packages are as follows.

tensorflow==2.3.1
nltk==3.5
colorama==0.4.3
numpy==1.18.5
scikit_learn==0.23.2
Flask==1.1.2

set intentions

Define simple intents and the message clusters that correspond to those intents.

Data preparation

Import all required packages and extract the required data.

The variable “training_sentences” stores all training data. (which is the sample text in each Intent Category) and the variable “training_labels” stores all target labels corresponding to each training data we vectorize our text corpus using the “ Tokenizer ” class and it helps us to constrain and Keep our vocabulary size at a certain number. When we use this class for text pre-processing tasks. Turn text into a sequence of words separated by spaces. These sequences are then divided into lists of tokens. It is then indexed or vectorized. We can also add "oov_token" which is the value for "out of token" to handle words (tokens) that are out of processing time.

Simulate usage

We define our Neural Network architecture for the proposed model and we use Keras' "Sequential" model class and test it.

Test the actual use

Use the chat function to engage with real users. When a new user message is received The chatbot calculates the similarity between the new text sequence and the training data. When considering the confidence scores received in each category User messages are categorized based on the intent with the highest sentiment score.

Integrating with applications

Integrate tested chatbot models with other chat applications to make dealing with real-world users more efficient.