sashtech commited on
Commit
b0aa6fd
·
verified ·
1 Parent(s): e00f367

Update app.py

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Files changed (1) hide show
  1. app.py +5 -8
app.py CHANGED
@@ -61,7 +61,6 @@ def correct_tense_errors(text):
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  for token in doc:
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  if token.pos_ == "VERB":
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- # Check if verb is in its base form
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  if token.tag_ == "VB" and token.text.lower() not in ["be", "have", "do"]:
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  # Attempt to correct verb form based on sentence context
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  context = " ".join([t.text for t in doc if t.i != token.i])
@@ -114,12 +113,10 @@ def correct_article_errors(text):
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  for token in doc:
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  if token.text in ['a', 'an']:
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  next_token = token.nbor(1)
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- if token.text == "a" and next_token.text[0].lower() in "aeiou":
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- corrected_text.append("an")
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- elif token.text == "an" and next_token.text[0].lower() not in "aeiou":
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- corrected_text.append("a")
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  else:
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- corrected_text.append(token.text)
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  else:
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  corrected_text.append(token.text)
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  return ' '.join(corrected_text)
@@ -128,7 +125,7 @@ def correct_article_errors(text):
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  def paraphrase_with_spacy_nltk(text):
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  doc = nlp(text)
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  paraphrased_words = []
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-
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  for token in doc:
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  # Map SpaCy POS tags to WordNet POS tags
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  pos = None
@@ -140,7 +137,7 @@ def paraphrase_with_spacy_nltk(text):
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  pos = wordnet.ADJ
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  elif token.pos_ == "ADV":
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  pos = wordnet.ADV
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-
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  synonyms = get_synonyms_nltk(token.text.lower(), pos) if pos else []
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  # Replace with a synonym only if it makes sense
 
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  for token in doc:
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  if token.pos_ == "VERB":
 
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  if token.tag_ == "VB" and token.text.lower() not in ["be", "have", "do"]:
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  # Attempt to correct verb form based on sentence context
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  context = " ".join([t.text for t in doc if t.i != token.i])
 
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  for token in doc:
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  if token.text in ['a', 'an']:
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  next_token = token.nbor(1)
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+ if next_token.text[0].lower() in "aeiou":
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+ corrected_text.append("an" if token.text == "a" else token.text)
 
 
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  else:
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+ corrected_text.append("a" if token.text == "an" else token.text)
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  else:
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  corrected_text.append(token.text)
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  return ' '.join(corrected_text)
 
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  def paraphrase_with_spacy_nltk(text):
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  doc = nlp(text)
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  paraphrased_words = []
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+
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  for token in doc:
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  # Map SpaCy POS tags to WordNet POS tags
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  pos = None
 
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  pos = wordnet.ADJ
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  elif token.pos_ == "ADV":
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  pos = wordnet.ADV
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+
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  synonyms = get_synonyms_nltk(token.text.lower(), pos) if pos else []
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  # Replace with a synonym only if it makes sense