Chatty 🤖

Overview

This repository contains a simple Natural Language Processing (NLP) chatbot implemented in Python using Flask, NLTK, and scikit-learn. The chatbot is trained to understand user intents and generate appropriate responses.

Features

• Preprocessing: User messages and intents are preprocessed to ensure consistency and better understanding.
• Multinomial Naive Bayes Classifier: A classifier is trained using the Multinomial Naive Bayes algorithm to predict user intents based on their messages.
• Web Interface: The chatbot is integrated into a web interface using Flask, allowing users to interact with it through a simple web application.

Requirements

• Python 3.x
• Flask
• NLTK
• scikit-learn

Installation

1. Install the required dependencies:

   pip install flask nltk scikit-learn

2. Download NLTK data:

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

Usage

1. Run the app.py file:

   python app.py

2. Open a web browser and navigate to http://localhost:5000 to interact with the chatbot.

Training Data

The chatbot is trained using a dataset stored in intents.json. Customize this file to add more intents and patterns for a richer user experience.

How It Works

1. Preprocessing: User messages are preprocessed to tokenize, stem, and remove stopwords, ensuring consistent input for the classifier.
2. Training: The Multinomial Naive Bayes classifier is trained on the preprocessed data from intents.json.
3. Response Generation: User messages are preprocessed and passed through the trained classifier to predict the intent. A random response associated with the predicted intent is then provided.

Web Interface

• The home route (/) renders the index.html template.
• The /get_response route handles user messages via POST requests and returns a JSON response containing the chatbot's reply.

Feel free to explore and enhance this chatbot by customizing the training data and improving the response generation logic. Enjoy chatting!