#importing packages
! pip install nltk
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import wordnet
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')

#reading content from file
f = open("/content/samplefile.txt", "r")
sentences = f.readlines()
f.close()

verbs = [[] for _ in sentences]
i=0
for sentence in sentences:
  print("Sentence",i+1,":", sentence)

  # Tokenize the sentence into words
  words = word_tokenize(sentence)

  # Identify the parts of speech for each word
  pos_tags = nltk.pos_tag(words)

  # Print the parts of speech
  for word,tag in pos_tags:
    print(word,"->",tag)

    # Save verbs
    if tag.startswith('VB'):
      verbs[i].append(word)
  i+=1
  print("\n\n")

# Identify synonyms and antonyms for each word
print("Synonyms and Antonymns for verbs in each sentence:\n")
i=0
for sentence in sentences:
  print("Sentence",i+1,":", sentence)
  pos_tags = nltk.pos_tag(verbs[i])
  for word,tag in pos_tags:
    print(word,"->",tag)
    synonyms = []
    antonyms = []
    for syn in wordnet.synsets(word):
      for lemma in syn.lemmas():
        synonyms.append(lemma.name())
        if lemma.antonyms():
          for antonym in lemma.antonyms():
            antonyms.append(antonym.name())

    # Print the synonyms and antonyms
    print("Synonyms:",set(synonyms))
    print("Antonyms:", set(antonyms) if antonyms else "None")
    print()
  print("\n\n")
  i+=1