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Update app.py
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app.py
CHANGED
@@ -5,6 +5,19 @@ import spacy
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import subprocess
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import nltk
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from nltk.corpus import wordnet
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# Initialize the English text classification pipeline for AI detection
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pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
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@@ -52,18 +65,14 @@ def capitalize_sentences_and_nouns(text):
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return ' '.join(corrected_text)
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# Function to correct tense errors in a sentence (Tense Correction)
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def correct_tense_errors(text):
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doc = nlp(text)
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corrected_text.append(lemma)
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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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# Function to correct singular/plural errors (Singular/Plural Correction)
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def correct_singular_plural_errors(text):
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@@ -141,18 +150,17 @@ def paraphrase_with_spacy_nltk(text):
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# Combined function: Paraphrase -> Grammar Correction -> Capitalization (Humanifier)
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def paraphrase_and_correct(text):
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paraphrased_text = paraphrase_with_spacy_nltk(text)
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# Step 2: Apply grammatical corrections on the paraphrased text
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corrected_text = correct_article_errors(paraphrased_text)
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corrected_text = correct_singular_plural_errors(corrected_text)
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# Step 3: Capitalize sentences and proper nouns (final correction step)
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final_text = correct_tense_errors(corrected_text)
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return final_text
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import subprocess
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import nltk
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from nltk.corpus import wordnet
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import language_tool_python
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tool = language_tool_python.LanguageTool('en-US')
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# Function to correct tense errors using LanguageTool
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def correct_tense_errors(text):
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# Check for grammar mistakes, which includes tense errors
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matches = tool.check(text)
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corrected_text = language_tool_python.utils.correct(text, matches)
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return corrected_text
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# Initialize the English text classification pipeline for AI detection
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pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
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return ' '.join(corrected_text)
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# Function to correct tense errors in a sentence (Tense Correction)
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#def correct_tense_errors(text):
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# doc = nlp(text)
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### if token.pos_ == "VERB" and token.dep_ in {"aux", "auxpass"}:
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## lemma = wordnet.morphy(token.text, wordnet.VERB) or token.text
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# corrected_text.append(lemma)
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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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# Function to correct singular/plural errors (Singular/Plural Correction)
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def correct_singular_plural_errors(text):
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# Combined function: Paraphrase -> Grammar Correction -> Capitalization (Humanifier)
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def paraphrase_and_correct(text):
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paraphrased_text = paraphrase_with_spacy_nltk(text)
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# Step 2: Apply grammatical corrections on the paraphrased text
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corrected_text = correct_article_errors(paraphrased_text)
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# Use the new stronger tense correction method
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corrected_text = correct_tense_errors(corrected_text)
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# Step 3: Correct singular/plural issues and capitalization
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corrected_text = correct_singular_plural_errors(corrected_text)
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final_text = capitalize_sentences_and_nouns(corrected_text)
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return final_text
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