import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.stem import PorterStemmer

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

def preprocess_text(text):
  words = word_tokenize(text)
  stop_words = set(stopwords.words('english'))
  filtered_words = [word for word in words if word.lower() not in stop_words and word.isalnum()]

  stemmer = PorterStemmer()
  stemmer_words=[stemmer.stem(word) for word in filtered_words]

  return stemmer_words


def generate_summary(text,num_sentences=3):
  sentences = sent_tokenize(text)

  preprocessed_text = preprocess_text(text)

  word_frequencies = nltk.FreqDist(preprocessed_text)

  sentence_scores = {}

  for sentence in sentences:
    for word, freq in word_frequencies.items():
      if word in sentence.lower():
        if sentence not in sentence_scores:
          sentence_scores[sentence] = freq
        else:
          sentence_scores[sentence] +=freq

  summary_sentences = sorted(sentence_scores, key=sentence_scores.get, reverse =True)[:num_sentences]
  return ' '.join(summary_sentences)

if __name__ == "__main__":
  input_text = "Natural language processing (NLP) is a subfield of artificial intelligence.
	It involves the development of algorithms and models that enact NLP.
	NLP is used in various applications, including chatbots, language Understanding, and language generation.
	This program demonstrates a simple text summarization using NLP"
  summary = generate_summary(input_text)
  print("Original Text :")
  print(input_text)

  print("\nSummary")
  print(summary)