NLP-Based Chatbot

Introduction

Welcome to the NLP-Based Chatbot project! This project showcases how Natural Language Processing (NLP) can be used to build a chatbot that understands and responds to user inputs. Using TensorFlow and the NLTK library, this chatbot can interpret and answer queries in a meaningful way.

Table of Contents

• Features
• Tech Stack
• How It Works
• Installation

Features

• NLP Integration: The chatbot uses NLP techniques to tokenize, stem, and classify user inputs.
• Intent Recognition: The chatbot identifies the user's intent and provides an appropriate response.
• Scalable Design: The project is built with a flexible design that allows for easy expansion of intents and responses.
• Interactive Responses: Based on the identified intent, the chatbot selects and returns a random appropriate response.

Tech Stack

• Programming Language: Python
• Libraries: NLTK, TensorFlow, NumPy, Keras
• Tools: Google Colab, JSON

How It Works

The chatbot is built using the following steps:

1. Data Preparation:

   • Load intents from a JSON file containing different user intents and their associated responses.
   • Tokenize and stem the text data to create a bag of words that the model can use.

2. Model Training:

   • A TensorFlow neural network is trained using the processed text data.
   • The model learns to classify user inputs into specific intents based on the training data.

3. Response Generation:

   • When a user input is received, it is processed similarly to the training data.
   • The trained model predicts the most likely intent.
   • A response corresponding to the predicted intent is selected and returned.

Installation

To run this project locally, follow these steps:

Clone the repository:

git clone https://github.com/your-username/Chatbot_using_NLP.git
cd Chatbot_using_NLP