import os import gradio as gr from transformers import pipeline import spacy import subprocess import nltk from nltk.corpus import wordnet from collections import defaultdict # 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) synonyms = set() for synset in synsets: for lemma in synset.lemmas(): if lemma.name() != word: synonyms.add(lemma.name()) return list(synonyms) # 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: if token.pos_ == "VERB": # Check if verb is in its base form if token.tag_ == "VB" and token.text.lower() not in ["be", "have", "do"]: # Attempt to correct verb form based on sentence context context = " ".join([t.text for t in doc if t.i != token.i]) corrected_text.append(token.lemma_) else: corrected_text.append(token.text) 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 = [] # Create a context dictionary for singular/plural determination context = defaultdict(int) for token in doc: if token.pos_ == "NOUN": # Track context for noun usage if token.tag_ == "NNS": context['plural'] += 1 elif token.tag_ == "NN": context['singular'] += 1 for token in doc: if token.pos_ == "NOUN": if token.tag_ == "NN": # Singular noun if context['plural'] > context['singular']: corrected_text.append(token.lemma_ + 's') else: corrected_text.append(token.text) elif token.tag_ == "NNS": # Plural noun if context['singular'] > context['plural']: corrected_text.append(token.lemma_) else: corrected_text.append(token.text) 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) # 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_ == "NOUN": pos = wordnet.NOUN elif token.pos_ == "VERB": pos = wordnet.VERB elif token.pos_ == "ADJ": pos = wordnet.ADJ elif token.pos_ == "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"}: paraphrased_words.append(synonyms[0]) else: paraphrased_words.append(token.text) return ' '.join(paraphrased_words) # Combined function: Paraphrase -> Grammar Correction -> Capitalization (Humanifier) def paraphrase_and_correct(text): # Step 1: Paraphrase the text paraphrased_text = paraphrase_with_spacy_nltk(text) # Step 2: Apply grammatical corrections on the paraphrased text corrected_text = correct_article_errors(paraphrased_text) corrected_text = capitalize_sentences_and_nouns(corrected_text) corrected_text = correct_singular_plural_errors(corrected_text) final_text = correct_tense_errors(corrected_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 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()