import os import gradio as gr from transformers import pipeline import spacy import subprocess import nltk from nltk.corpus import wordnet from nltk.corpus import stopwords from spellchecker import SpellChecker import re nltk.download('punkt') nltk.download('stopwords') nltk.download('averaged_perceptron_tagger') nltk.download('wordnet') nltk.download('stopwords') top_words = set(stopwords.words("english")) def plagiarism_removal(text): def plagiarism_remover(word): # Handle stopwords, punctuation, and excluded words if word.lower() in stop_words or word.lower() in exclude_words or word in string.punctuation: return word # Find synonyms synonyms = set() for syn in wordnet.synsets(word): for lemma in syn.lemmas(): # Exclude overly technical synonyms or words with underscores if "_" not in lemma.name() and lemma.name().isalpha() and lemma.name().lower() != word.lower(): synonyms.add(lemma.name()) # Get part of speech for word and filter synonyms with the same POS pos_tag_word = nltk.pos_tag([word])[0] # Avoid replacing certain parts of speech if pos_tag_word[1] in exclude_tags: return word filtered_synonyms = [syn for syn in synonyms if nltk.pos_tag([syn])[0][1] == pos_tag_word[1]] # Return original word if no appropriate synonyms found if not filtered_synonyms: return word # Select a random synonym from the filtered list synonym_choice = random.choice(filtered_synonyms) # Retain original capitalization if word.istitle(): return synonym_choice.title() return synonym_choice # Tokenize, replace words, and join them back para_split = word_tokenize(text) final_text = [plagiarism_remover(word) for word in para_split] # Handle spacing around punctuation correctly corrected_text = [] for i in range(len(final_text)): if final_text[i] in string.punctuation and i > 0: corrected_text[-1] += final_text[i] # Append punctuation to previous word else: corrected_text.append(final_text[i]) return " ".join(corrected_text) # Words we don't want to replace exclude_tags = {'PRP', 'PRP$', 'MD', 'VBZ', 'VBP', 'VBD', 'VBG', 'VBN', 'TO', 'IN', 'DT', 'CC'} exclude_words = {'is', 'am', 'are', 'was', 'were', 'have', 'has', 'do', 'does', 'did', 'will', 'shall', 'should', 'would', 'could', 'can', 'may', 'might'} # Initialize the English text classification pipeline for AI detection pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta") # Initialize the spell checker spell = SpellChecker() # Ensure necessary NLTK data is downloaded nltk.download('wordnet') nltk.download('omw-1.4') # Ensure the SpaCy model is installed try: nlp = spacy.load("en_core_web_sm") except OSError: subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"]) nlp = spacy.load("en_core_web_sm") # Function to predict the label and score for English text (AI Detection) def predict_en(text): res = pipeline_en(text)[0] return res['label'], res['score'] # Function to remove redundant and meaningless words def remove_redundant_words(text): doc = nlp(text) meaningless_words = {"actually", "basically", "literally", "really", "very", "just"} filtered_text = [token.text for token in doc if token.text.lower() not in meaningless_words] return ' '.join(filtered_text) # Function to fix spacing before punctuation def fix_punctuation_spacing(text): # Split the text into words and punctuation words = text.split(' ') cleaned_words = [] punctuation_marks = {',', '.', "'", '!', '?', ':'} for word in words: if cleaned_words and word and word[0] in punctuation_marks: cleaned_words[-1] += word else: cleaned_words.append(word) return ' '.join(cleaned_words).replace(' ,', ',').replace(' .', '.').replace(" '", "'") \ .replace(' !', '!').replace(' ?', '?').replace(' :', ':') # Function to fix possessives like "Earth's" def fix_possessives(text): text = re.sub(r'(\w)\s\'\s?s', r"\1's", text) return text # Function to capitalize the first letter of sentences and proper nouns def capitalize_sentences_and_nouns(text): doc = nlp(text) corrected_text = [] for sent in doc.sents: sentence = [] for token in sent: if token.i == sent.start: sentence.append(token.text.capitalize()) elif token.pos_ == "PROPN": sentence.append(token.text.capitalize()) else: sentence.append(token.text) corrected_text.append(' '.join(sentence)) return ' '.join(corrected_text) # Function to force capitalization of the first letter of every sentence and ensure full stops def force_first_letter_capital(text): sentences = re.split(r'(?<=\w[.!?])\s+', text) capitalized_sentences = [] for sentence in sentences: if sentence: capitalized_sentence = sentence[0].capitalize() + sentence[1:] if not re.search(r'[.!?]$', capitalized_sentence): capitalized_sentence += '.' capitalized_sentences.append(capitalized_sentence) return " ".join(capitalized_sentences) # Function to correct tense errors in a sentence def correct_tense_errors(text): doc = nlp(text) corrected_text = [] for token in doc: if token.pos_ == "VERB" and token.dep_ in {"aux", "auxpass"}: lemma = wordnet.morphy(token.text, wordnet.VERB) or token.text corrected_text.append(lemma) else: corrected_text.append(token.text) return ' '.join(corrected_text) # Function to check and correct article errors def correct_article_errors(text): doc = nlp(text) corrected_text = [] for token in doc: if token.text in ['a', 'an']: next_token = token.nbor(1) if token.text == "a" and next_token.text[0].lower() in "aeiou": corrected_text.append("an") elif token.text == "an" and next_token.text[0].lower() not in "aeiou": corrected_text.append("a") else: corrected_text.append(token.text) else: corrected_text.append(token.text) return ' '.join(corrected_text) # Function to ensure subject-verb agreement def ensure_subject_verb_agreement(text): doc = nlp(text) corrected_text = [] for token in doc: if token.dep_ == "nsubj" and token.head.pos_ == "VERB": if token.tag_ == "NN" and token.head.tag_ != "VBZ": corrected_text.append(token.head.lemma_ + "s") elif token.tag_ == "NNS" and token.head.tag_ == "VBZ": corrected_text.append(token.head.lemma_) corrected_text.append(token.text) return ' '.join(corrected_text) # Function to correct spelling errors def correct_spelling(text): words = text.split() corrected_words = [] for word in words: corrected_word = spell.correction(word) if corrected_word is not None: corrected_words.append(corrected_word) else: corrected_words.append(word) return ' '.join(corrected_words) # Main function for paraphrasing and grammar correction def paraphrase_and_correct(text): # Add synonym replacement here cleaned_text = remove_redundant_words(text) plag_removed=plagiarism_removal(cleaned_text) paraphrased_text = capitalize_sentences_and_nouns(plag_removed) paraphrased_text = force_first_letter_capital(paraphrased_text) paraphrased_text = correct_article_errors(paraphrased_text) paraphrased_text = correct_tense_errors(paraphrased_text) paraphrased_text = ensure_subject_verb_agreement(paraphrased_text) paraphrased_text = fix_possessives(paraphrased_text) paraphrased_text = correct_spelling(paraphrased_text) paraphrased_text = fix_punctuation_spacing(paraphrased_text) return paraphrased_text # Gradio app setup with gr.Blocks() as demo: with gr.Tab("AI Detection"): t1 = gr.Textbox(lines=5, label='Text') button1 = gr.Button("🤖 Predict!") label1 = gr.Textbox(lines=1, label='Predicted Label 🎃') score1 = gr.Textbox(lines=1, label='Prob') button1.click(fn=predict_en, inputs=t1, outputs=[label1, score1]) with gr.Tab("Paraphrasing & Grammar Correction"): t2 = gr.Textbox(lines=5, label='Enter text for paraphrasing and grammar correction') button2 = gr.Button("🔄 Paraphrase and Correct") result2 = gr.Textbox(lines=5, label='Corrected Text') button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2) demo.launch(share=True)