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import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import string
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
def lemmatize_and_clean(text):
# Tokenize the text into words
words = nltk.word_tokenize(text)
# Remove punctuation and convert to lowercase
words = [word.lower() for word in words if word.isalpha()]
# Remove stopwords
stop_words = set(stopwords.words('english'))
words = [word for word in words if word not in stop_words]
# Lemmatize the words
lemmatizer = WordNetLemmatizer()
words = [lemmatizer.lemmatize(word) for word in words]
# Join the words back into a cleaned text
cleaned_text = ' '.join(words)
return cleaned_text
# Example usage
input_text = "kushir cover. kushir cover benson and hezes nih unique capsule of our janum benson and hesses breeze aprajanara kushiha benjay a capsule roche egg thorne refreshing taste and smell arapnajudiya trial kotachan tahal ajinita parnakti trial kit donnabat."
cleaned_text = lemmatize_and_clean(input_text)
print("Original Text:")
print(input_text)
print("\nCleaned Text:")
print(cleaned_text)
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