NLP/chatbot-nlp at main · abeer10-coder/NLP · GitHub

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# 1. Text Preprocessing

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import string

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

def preprocess_text(text):
    # Convert to lowercase
    text = text.lower()

    # Remove punctuation
    text = text.translate(str.maketrans('', '', string.punctuation))

    # Tokenize
    words = word_tokenize(text)

    # Remove stop words
    stop_words = set(stopwords.words('english'))
    filtered_words = [word for word in words if word not in stop_words]

    return filtered_words

text = "Natural Language Processing with Python is fun!"
processed_text = preprocess_text(text)
print(processed_text)


#2. Tokenization and Named Entity Recognition (NER) using spaCy

import spacy

# Load a pre-trained spaCy model
nlp = spacy.load('en_core_web_sm')

# Process the text
doc = nlp("Apple is looking at buying U.K. startup for $1 billion")

# Tokenization
tokens = [token.text for token in doc]
print("Tokens:", tokens)

# Named Entity Recognition (NER)
for ent in doc.ents:
    print(ent.text, ent.label_)


#3. Sentiment Analysis using transformers

from transformers import pipeline

# Initialize sentiment analysis pipeline
sentiment_analysis = pipeline('sentiment-analysis')

# Analyze sentiment
result = sentiment_analysis("I love programming with Python!")
print(result)


#4. Text Classification using Pre-trained Models


from transformers import pipeline

# Initialize text classification pipeline
classifier = pipeline('zero-shot-classification')

# Text to classify
text = "The company's quarterly earnings have increased significantly."

# Possible labels
labels = ["economy", "business", "technology"]

# Classify text
result = classifier(text, candidate_labels=labels)
print(result)


#5. Building a Basic NLP Chatbot


import random

def respond_to_query(query):
    responses = {
        'hello': 'Hi there! How can I assist you today?',
        'bye': 'Goodbye! Have a great day!',
        'thanks': 'You’re welcome! Let me know if you need anything else.'
    }

    for key in responses:
        if key in query.lower():
            return responses[key]
    return "I'm sorry, I don't understand your question."

# Example conversation
query = "Hello"
response = respond_to_query(query)
print("Chatbot:", response)

query = "Thanks for your help!"
response = respond_to_query(query)
print("Chatbot:", response)