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| import os | |
| import gradio as gr | |
| from transformers import pipeline | |
| import spacy | |
| import subprocess | |
| import nltk | |
| from nltk.corpus import wordnet | |
| from spellchecker import SpellChecker | |
| import random | |
| # 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 get synonyms using NLTK WordNet | |
| def get_synonyms_nltk(word, pos): | |
| synsets = wordnet.synsets(word, pos=pos) | |
| if synsets: | |
| lemmas = synsets[0].lemmas() | |
| return [lemma.name() for lemma in lemmas] | |
| return [] | |
| # 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 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: # First word of the sentence | |
| sentence.append(token.text.capitalize()) | |
| elif token.pos_ == "PROPN": # Proper noun | |
| 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 | |
| def force_first_letter_capital(text): | |
| sentences = text.split(". ") # Split by period to get each sentence | |
| capitalized_sentences = [sentence[0].capitalize() + sentence[1:] if sentence else "" for sentence in sentences] | |
| return ". ".join(capitalized_sentences) | |
| # Function to handle possessive 's and retain original meaning | |
| def handle_possessives(text): | |
| doc = nlp(text) | |
| corrected_text = [] | |
| for token in doc: | |
| # If token is a possessive form (e.g., 'Earth's'), retain its original form | |
| if token.text.endswith("'s") or token.text == "'s": | |
| corrected_text.append(token.text) # Keep it as is, even if a synonym is found | |
| else: | |
| corrected_text.append(token.text) | |
| return ' '.join(corrected_text) | |
| # 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 correct singular/plural errors | |
| def correct_singular_plural_errors(text): | |
| doc = nlp(text) | |
| corrected_text = [] | |
| for token in doc: | |
| if token.pos_ == "NOUN": | |
| if token.tag_ == "NN": # Singular noun | |
| if any(child.text.lower() in ['many', 'several', 'few'] for child in token.head.children): | |
| corrected_text.append(token.lemma_ + 's') | |
| else: | |
| corrected_text.append(token.text) | |
| elif token.tag_ == "NNS": # Plural noun | |
| if any(child.text.lower() in ['a', 'one'] for child in token.head.children): | |
| corrected_text.append(token.lemma_) | |
| else: | |
| corrected_text.append(token.text) | |
| 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 dynamically choose synonyms with more options | |
| def dynamic_synonyms(token, pos): | |
| synonyms = get_synonyms_nltk(token.lemma_, pos) | |
| # Choose a random synonym to increase variety | |
| if synonyms: | |
| random_synonym = random.choice(synonyms) | |
| return random_synonym | |
| return token.text | |
| # Function to rephrase text and replace words with more versatile synonyms | |
| def versatile_rephrase(text): | |
| doc = nlp(text) | |
| rephrased_text = [] | |
| for token in doc: | |
| pos_tag = None | |
| if token.pos_ == "NOUN": | |
| pos_tag = wordnet.NOUN | |
| elif token.pos_ == "VERB": | |
| pos_tag = wordnet.VERB | |
| elif token.pos_ == "ADJ": | |
| pos_tag = wordnet.ADJ | |
| elif token.pos_ == "ADV": | |
| pos_tag = wordnet.ADV | |
| if pos_tag: | |
| synonym = dynamic_synonyms(token, pos_tag) | |
| if token.pos_ == "VERB": | |
| if token.tag_ == "VBG": # Present participle (e.g., running) | |
| synonym = synonym + 'ing' | |
| elif token.tag_ == "VBD" or token.tag_ == "VBN": # Past tense or past participle | |
| synonym = synonym + 'ed' | |
| elif token.tag_ == "VBZ": # Third-person singular present | |
| synonym = synonym + 's' | |
| elif token.pos_ == "NOUN" and token.tag_ == "NNS": # Plural nouns | |
| synonym += 's' if not synonym ends with 's' else "" | |
| rephrased_text.append(synonym) | |
| else: | |
| rephrased_text.append(token.text) | |
| return ' '.join(rephrased_text) | |
| # Function to retain the structure of the input text (headings, paragraphs, line breaks) | |
| def retain_structure(text): | |
| lines = text.split("\n") | |
| formatted_lines = [] | |
| for line in lines: | |
| if line.strip().isupper(): # Heading if all caps | |
| formatted_lines.append(f"# {line.strip()}") # Treat it as a heading | |
| else: | |
| formatted_lines.append(line) # Otherwise, it's a paragraph or normal text | |
| return "\n".join(formatted_lines) | |
| # Function to paraphrase and correct grammar with enhanced accuracy and retain structure | |
| def paraphrase_and_correct_with_structure(text): | |
| structured_text = retain_structure(text) | |
| # Rephrase with more versatile synonyms while maintaining grammatical forms | |
| paraphrased_text = versatile_rephrase(structured_text) | |
| # Apply grammatical corrections on the rephrased text | |
| paraphrased_text = remove_redundant_words(paraphrased_text) | |
| paraphrased_text = capitalize_sentences_and_nouns(paraphrased_text) | |
| paraphrased_text = force_first_letter_capital(paraphrased_text) | |
| paraphrased_text = handle_possessives(paraphrased_text) | |
| paraphrased_text = correct_article_errors(paraphrased_text) | |
| paraphrased_text = correct_singular_plural_errors(paraphrased_text) | |
| paraphrased_text = correct_tense_errors(paraphrased_text) | |
| paraphrased_text = correct_double_negatives(paraphrased_text) | |
| paraphrased_text = ensure_subject_verb_agreement(paraphrased_text) | |
| paraphrased_text = correct_spelling(paraphrased_text) | |
| return paraphrased_text | |
| # Gradio app setup with two tabs | |
| 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') | |
| # Connect the prediction function to the button | |
| 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') | |
| # Connect the paraphrasing and correction function to the button | |
| button2.click(fn=paraphrase_and_correct_with_structure, inputs=t2, outputs=result2) | |
| demo.launch(share=True) | |