This project implements a chatbot using natural language processing (NLP) techniques and a trained deep learning model. The chatbot can understand and respond to user queries based on predefined intents and patterns.

• Requirements
• Files
• Training the Model
• Running the Chatbot
• Usage
• Notes

Ensure you have the following libraries installed:

pip install numpy
pip install nltk
pip install tensorflow
pip install pickle-mixin

Additionally, download the required NLTK data:

import nltk
nltk.download('punkt')
nltk.download('wordnet')

• intents.json: Contains the training data with predefined intents and their corresponding patterns and responses.
• words.pkl: A pickle file that stores the unique lemmatized words from the training data.
• classes.pkl: A pickle file that stores the unique intents from the training data.
• chatbotmodel.h5: The trained deep learning model for intent classification.

To train the chatbot model, run the train_chatbot.py script:

python train_chatbot.py

This script will preprocess the data, train the model, and save the trained model as chatbotmodel.h5.

To start the chatbot, run the chatbot.py script:

python chatbot.py

This script will load the trained model and start a loop to interact with the user.

1. Training the Model:

   • Ensure intents.json is in the same directory as your script.
   • Run the training script to preprocess the data and train the model.
   • The trained model will be saved as chatbotmodel.h5.

2. Running the Chatbot:

   • Ensure the trained model (chatbotmodel.h5), words.pkl, classes.pkl, and intents.json are in the same directory as your script.
   • Run the chatbot script to start interacting with the bot.
   • Type your message and press enter. To quit, type "quit".

• Customize the intents.json file to add or modify intents, patterns, and responses according to your requirements.
• Adjust the model architecture and training parameters as needed for improved performance.
• The chatbot uses a simple bag-of-words model for intent classification. Consider exploring more advanced NLP techniques for better accuracy.