import os import gradio as gr from transformers import pipeline import spacy import subprocess import nltk from nltk.corpus import wordnet # Initialize the English text classification pipeline for AI detection pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta") # 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'] # Ensure necessary NLTK data is downloaded for Humanifier nltk.download('wordnet') nltk.download('omw-1.4') # Ensure the SpaCy model is installed for Humanifier 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 get synonyms using NLTK WordNet (Humanifier) 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 capitalize the first letter of sentences and proper nouns (Humanifier) 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 correct tense errors in a sentence (Tense Correction) def correct_tense_errors(text): doc = nlp(text) corrected_text = [] for token in doc: # Check for tense correction based on modal verbs if token.pos_ == "VERB" and token.dep_ in {"aux", "auxpass"}: # Replace with appropriate verb form 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 (Singular/Plural Correction) def correct_singular_plural_errors(text): doc = nlp(text) corrected_text = [] for token in doc: if token.pos_ == "NOUN": # Check if the noun is singular or plural if token.tag_ == "NN": # Singular noun # Look for determiners like "many", "several", "few" to correct to plural 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 # Look for determiners like "a", "one" to correct to singular 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 get the correct synonym while maintaining verb form def replace_with_synonym(token): pos = None if token.pos_ == "VERB": pos = wordnet.VERB elif token.pos_ == "NOUN": pos = wordnet.NOUN elif token.pos_ == "ADJ": pos = wordnet.ADJ elif token.pos_ == "ADV": pos = wordnet.ADV synonyms = get_synonyms_nltk(token.lemma_, pos) if synonyms: synonym = synonyms[0] # Ensure the correct grammatical form is maintained 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' return synonym return token.text # Function to check for and avoid double negatives def correct_double_negatives(text): doc = nlp(text) corrected_text = [] for token in doc: if token.text.lower() == "not" and any(child.text.lower() == "never" for child in token.head.children): # Replace the double negative with a positive statement corrected_text.append("always") 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": # Check if the verb agrees with the subject in number if token.tag_ == "NN" and token.head.tag_ != "VBZ": # Singular noun, should use singular verb corrected_text.append(token.head.lemma_ + "s") elif token.tag_ == "NNS" and token.head.tag_ == "VBZ": # Plural noun, should not use singular verb corrected_text.append(token.head.lemma_) corrected_text.append(token.text) return ' '.join(corrected_text) # Function to paraphrase and correct grammar def paraphrase_and_correct(text): # Capitalize first to ensure proper noun capitalization paraphrased_text = capitalize_sentences_and_nouns(text) # Apply grammatical corrections 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) # Replace words with synonyms while maintaining verb form doc = nlp(paraphrased_text) final_text = [] for token in doc: if token.pos_ in {"VERB", "NOUN", "ADJ", "ADV"}: final_text.append(replace_with_synonym(token)) else: final_text.append(token.text) return ' '.join(final_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(predict_en, inputs=[t1], outputs=[label1, score1], api_name='predict_en') with gr.Tab("Humanifier"): text_input = gr.Textbox(lines=5, label="Input Text") paraphrase_button = gr.Button("Paraphrase & Correct") output_text = gr.Textbox(label="Paraphrased Text") # Connect the paraphrasing function to the button paraphrase_button.click(paraphrase_and_correct, inputs=text_input, outputs=output_text) # Launch the app with the remaining functionalities demo.launch()