Python_internship_task4

NLTK-Based Chatbot

This project implements a rule-based chatbot using NLTK (Natural Language Toolkit). It classifies user input into predefined categories and gives an appropriate response using Decision Tree or Naive Bayes classifiers.

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🧠 Features

• Tokenization, stemming, lemmatization
• POS (Part-of-Speech) tagging
• Feature extraction
• Decision Tree and Naive Bayes classification
• Input/output chatbot interface
• Customizable dataset (leaves.txt)

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📁 Files

File Name	Purpose
second.py	Main chatbot program
leaves.txt	Input training data (user must add)
training_data.npy	Saved processed training data
test_data.npy	Saved processed testing data

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🛠️ Requirements

Install the following Python libraries:

pip install nltk pandas numpy

Also, download required NLTK resources:

import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
nltk.download('tagsets')

📄 Format for leaves.txt

The chatbot learns from a .txt file with 3 columns:

[input sentence] | [category] | [reply/answer]

Example content:

hi there|greeting|Hello! How can I help you?
what is your name|identity|I am a chatbot powered by NLTK.
bye|exit|Goodbye! Take care.

Make sure it uses the pipe symbol (|) to separate fields.

🚀 Running the Bot

python chatbot.py

🔍 How It Works

1.Reads leaves.txt data. 2Processes sentences: tokenize, remove stopwords, lemmatize, POS tag. 3Extracts features and trains 2 classifiers: Decision Tree Naive Bayes 4.Classifies new input and responds using learned replies.

📌 Notes

If leaves.txt file is missing or not formatted correctly, the chatbot won't work. The project saves and loads training data as .npy using NumPy for faster loading.

Sample output:

It starts a chat loop. Type your messages. Example:

you: hi there
Bot: Hello! How can I help you?

you: what is your name
Bot: I am a chatbot powered by NLTK.

you: bye
Bot: Goodbye! Take care.