In this Python web-based project, we are going to build a chatbot using deep learning and flask techniques. The chatbot will be trained on the dataset which contains categories (intents), pattern and responses. We use a special artificial neural network (ANN) to classify which category the user’s message belongs to and then we will give a random response from the list of responses.
chatbot using Flask, NLTK, Keras, Python, etc.
The project requires you to have good knowledge of Python, Keras, and Natural language processing (NLTK). Along with them, we will use some helping modules which you can download using the python-pip command.
pip install tensorflow pip install keras pip install pickle pip install nltk pip install flask
Now we are going to build the chatbot using Flask framework but first, let us see the file structure and the type of files we will be creating:
.data.json – The data file which has predefined patterns and responses. .trainning.py – In this Python file, we wrote a script to build the model and train our chatbot. .Texts.pkl – This is a pickle file in which we store the words Python object using Nltk that contains a list of our vocabulary. .Labels.pkl – The classes pickle file contains the list of categories(Labels). .model.h5 – This is the trained model that contains information about the model and has weights of the neurons. .app.py – This is the flask Python script in which we implemented web-based GUI for our chatbot. Users can easily interact with the bot.
1.Import and load the data file 2.Preprocess data 3.split the data into training and test 4.Build the ANN model using keras 5.Predict the outcomes
Deploy the model in the Flask app