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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| import spacy | |
| import subprocess | |
| import nltk | |
| from nltk.corpus import wordnet | |
| import language_tool_python | |
| from gensim import downloader as api | |
| # 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") | |
| # Load a smaller Word2Vec model from Gensim's pre-trained models | |
| word_vectors = api.load("glove-wiki-gigaword-50") | |
| # Check for GPU and set the device accordingly | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # Load AI Detector model and tokenizer from Hugging Face (DistilBERT) | |
| tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english") | |
| model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english").to(device) | |
| # Initialize LanguageTool for grammar correction | |
| tool = language_tool_python.LanguageTool('en-US') | |
| # AI detection function using DistilBERT | |
| def detect_ai_generated(text): | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(device) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| probabilities = torch.softmax(outputs.logits, dim=1) | |
| ai_probability = probabilities[0][1].item() # Probability of being AI-generated | |
| return f"AI-Generated Content Probability: {ai_probability:.2f}%" | |
| # 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 [] | |
| # Paraphrasing function using spaCy and NLTK | |
| 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) | |
| # Correct the grammar of the paraphrased sentence | |
| corrected_sentence = tool.correct(paraphrased_sentence) | |
| return corrected_sentence | |
| # Gradio interface definition | |
| with gr.Blocks() as interface: | |
| with gr.Row(): | |
| with gr.Column(): | |
| text_input = gr.Textbox(lines=5, label="Input Text") | |
| detect_button = gr.Button("AI Detection") | |
| paraphrase_button = gr.Button("Paraphrase with spaCy & NLTK (Grammar Corrected)") | |
| with gr.Column(): | |
| output_text = gr.Textbox(label="Output") | |
| detect_button.click(detect_ai_generated, inputs=text_input, outputs=output_text) | |
| paraphrase_button.click(paraphrase_with_spacy_nltk, inputs=text_input, outputs=output_text) | |
| # Launch the Gradio app | |
| interface.launch(debug=False) | |