import os import gradio as gr from transformers import pipeline import spacy import subprocess import nltk from nltk.corpus import wordnet from pattern.en import conjugate, tenses # 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 check and correct conjunction errors with 'because' and 'but' def check_conjunction_errors(text): # Replace misplaced 'because' and 'but' text = text.replace("because, ", "because ") text = text.replace("but, ", "but ") return text # Function to check and correct tense consistency in sentences using Pattern.en def check_tense_consistency(text): doc = nlp(text) corrected_sentences = [] for sent in doc.sents: verbs = [token for token in sent if token.pos_ == 'VERB'] if verbs: # Find the most common tense in the sentence common_tense = None for verb in verbs: verb_tense = tenses(verb.text) if verb_tense: common_tense = verb_tense[0][0] break # Conjugate all verbs to the common tense if there's inconsistency corrected_sentence = [] for token in sent: if token.pos_ == 'VERB' and common_tense: corrected_verb = conjugate(token.text, tense=common_tense) corrected_sentence.append(corrected_verb) else: corrected_sentence.append(token.text) corrected_sentences.append(' '.join(corrected_sentence)) else: corrected_sentences.append(sent.text) return ' '.join(corrected_sentences) # Function to check and correct article usage ('a', 'an', 'the') def check_article_usage(text): doc = nlp(text) corrected_text = [] for token in doc: if token.text.lower() in ['a', 'an', 'the']: if token.text.lower() == 'a' and token.head.pos_ in ['NOUN', 'ADJ'] and token.head.text[0] in 'aeiou': corrected_text.append('an') elif token.text.lower() == 'an' and token.head.pos_ in ['NOUN', 'ADJ'] and token.head.text[0] not in 'aeiou': corrected_text.append('a') else: corrected_text.append(token.text) else: corrected_text.append(token.text) return ' '.join(corrected_text) # Paraphrasing function using SpaCy and NLTK (Humanifier) def paraphrase_with_spacy_nltk(text): doc = nlp(text) paraphrased_words = [] for token in doc: # Map SpaCy POS tags to WordNet POS tags pos = None if token.pos_ in {"NOUN"}: pos = wordnet.NOUN elif token.pos_ in {"VERB"}: pos = wordnet.VERB elif token.pos_ in {"ADJ"}: pos = wordnet.ADJ elif token.pos_ in {"ADV"}: pos = wordnet.ADV synonyms = get_synonyms_nltk(token.text.lower(), pos) if pos else [] # Replace with a synonym only if it makes sense if synonyms and token.pos_ in {"NOUN", "VERB", "ADJ", "ADV"} and synonyms[0] != token.text.lower(): paraphrased_words.append(synonyms[0]) else: paraphrased_words.append(token.text) # Join the words back into a sentence paraphrased_sentence = ' '.join(paraphrased_words) # Capitalize sentences and proper nouns corrected_text = capitalize_sentences_and_nouns(paraphrased_sentence) return corrected_text # Combined function: Paraphrase -> Grammar Correction -> Capitalization -> Tense Consistency (Humanifier) def paraphrase_and_correct(text): # Step 1: Paraphrase the text paraphrased_text = paraphrase_with_spacy_nltk(text) # Step 2: Check and correct conjunction errors corrected_conjunctions = check_conjunction_errors(paraphrased_text) # Step 3: Check and correct article usage corrected_articles = check_article_usage(corrected_conjunctions) # Step 4: Capitalize sentences and proper nouns capitalized_text = capitalize_sentences_and_nouns(corrected_articles) # Step 5: Check and correct tense consistency final_text = check_tense_consistency(capitalized_text) return 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 and correction 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()