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A chatbot is an artificial intelligence (AI) program can simulate a conversation with a user in natural language i.e.; through messaging applications, websites, or mobile applications. It interprets the user intent, process their requests, and give prompt relevant answers.
Programmed File Structure:
Import Libraries and cargo the Data: Create a replacement python file and name it as train. chatbot and then import all the required modules. After that, name it as JSON data file in the Python program.
Preprocessing the Data: The model cannot take the data. It has to travel through tons of pre-processing for the machine to simply understand. For textual data, there are many techniques available.
By observing intents file, i.e.; each tag contains a list of patterns and responses. Also, create an inventory of classes and documents to feature all the intents related to patterns.
Create Training and Testing Data: To train the model, need to convert each input pattern into numbers. First, i.e.; each word of the pattern creates a list of zeroes of the same length as the total number of words and set value 1 to those indexes that contain the word in the patterns. In the same way, create output by setting 1 to the input pattern belongs to.
Training the Model: The architecture of the model will be a neural network i.e.; consisting of 3 dense layers. The first layer has 128 neurons, the other has 64 and therefore the last layer will have an equivalent neuron because the number of classes.
It used the SGD optimizer and i.e.; fit the data to start the training of the model. After the training of 200 epochs is completed, then save the trained model using the Keras model. Save function.
Interacting With the Chatbot: Let’s create a pleasant graphical interface for chatbot during a new file and name the file as gui_chatbot.py. In GUI file, i.e.; using the Tkinter module to build the structure of the desktop application and then capture the user message and again perform some preprocessing before input the message into trained model.
It will predict the tag of user’s message, and randomly select response from the list of the intents file.
Run the chatbot: To run the chatbot, it has two main files; train_chatbot.py and chatapp.py. First, i.e.; train model by using command in the terminal. If there is no error during training, it has successfully created the model.
Then run the app, i.e.; of the second file. The program will open up a GUI window within a couple of seconds. With the GUI can easily chat with the bot.
The time savings and efficiency derived from AI chatbots converse and answering reoccurring questions is attractive to companies looking to increase sales productivity. As consumers continue to move from traditional forms of communication, i.e.; chat-based communication methods are expected to rise.
Chatbot virtual assistants are increasingly used to handle simple tasks, i.e.; to specialize in higher-profile service or sales.
Chatbots are expected to continue growing in popularity. Artificial intelligence and machine learning still evolve, i.e.; offering new capabilities to chatbots and introducing a replacement level of text and voice-enabled user experiences which can still transform the customer experience.
These improvements also will impact data collection and can offer deeper customer insights which will cause predictive buyer behaviors. Increased focus is being placed on developing a voice-based chatbot that can act as a conversational agent, i.e.; understand numerous languages and respond in that same language.
In this project, i.e.; show a variety of methods and techniques for creating an intelligent AI chatbot for the business. Also, help to build a chatbot that imitates human. Develop rule-based transactional responses for the most common inputs and build a fully functional, intelligently responding chatbot trained for a specific task.