A Python-based chatbot that utilizes Natural Language Processing (NLP) techniques to process user inputs and provide appropriate responses. This project demonstrates key steps in building a chatbot, including preprocessing text, training a neural network model, and designing interactive conversations.

• Interactive and responsive chatbot for text-based inputs.
• Utilizes Bag of Words (BoW) for feature extraction.
• Customizable intents with responses provided in intents.json.
• Neural network model built with TensorFlow/Keras.
• Designed to be lightweight and easy to modify.

• intents.json contains predefined patterns, tags, and responses for the chatbot.
• The .ipynb notebook file has the Python code implementing the chatbot with detailed explanations.
• README.md has the documentation for the project (this file).

To run this project, ensure the following dependencies are installed:

• tensorflow
• numpy
• nltk

You can install them using pip:

pip install tensorflow numpy nltk

1. Mount Google Drive

Update the data_root variable to the correct path where intents.json is located in your Google Drive.

2. Load Required Libraries

The necessary libraries such as nltk, numpy, and tensorflow are imported.

3. Load the Dataset

The intents.json file is parsed to extract patterns, responses, and corresponding tags.

4. Preprocess the Data

• Tokenize and lemmatize text.

• Create a Bag of Words (BoW) representation.

• One-hot encode class labels.

5. Train the Neural Network

• A feedforward neural network with dropout is used.

• Train the model on the preprocessed data.

6. Interact with the Chatbot

Use the chatbot in an interactive session. Type your queries, and the bot will respond based on the trained model.

To modify or extend the chatbot:

• Update intents.json with new patterns and responses.
• Retrain the model to include the changes.

1. Text Preprocessing

• Tokenizes user inputs and patterns from intents.json.
• Converts text into numerical features using BoW.

2. Neural Network Training

• Trains a model on feature-label pairs.
• Predicts the most relevant tag for a given user query.

3. Response Generation

• Matches the predicted tag to the appropriate response from intents.json.

For a detailed explanation of each step and the fundamentals, read the complete blog: Python Chatbot Project-Learn to build a chatbot from Scratch

Feel free to fork this repository, raise issues, or submit pull requests to enhance the project!