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| import os | |
| import subprocess | |
| import sys | |
| import gradio as gr | |
| from transformers import pipeline | |
| import spacy | |
| import nltk | |
| from nltk.corpus import wordnet | |
| # Function to install GECToR | |
| def install_gector(): | |
| if not os.path.exists('gector'): | |
| print("Cloning GECToR repository...") | |
| subprocess.run(["git", "clone", "https://github.com/grammarly/gector.git"], check=True) | |
| # Install dependencies from GECToR requirements | |
| subprocess.run([sys.executable, "-m", "pip", "install", "-r", "gector/requirements.txt"], check=True) | |
| # Manually add GECToR to the Python path | |
| sys.path.append(os.path.abspath('gector')) | |
| # Install and import GECToR | |
| install_gector() | |
| from gector.gec_model import GecBERTModel | |
| # Initialize GECToR model for grammar correction | |
| gector_model = GecBERTModel(vocab_path='gector/data/output_vocabulary', | |
| model_paths=['https://grammarly-nlp-data.s3.amazonaws.com/gector/roberta_1_gector.th'], | |
| is_ensemble=False) | |
| # 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 correct grammar using GECToR | |
| def correct_grammar_with_gector(text): | |
| corrected_sentences = [] | |
| sentences = [text] | |
| for sentence in sentences: | |
| preds = gector_model.handle_batch([sentence]) | |
| corrected_sentences.append(preds[0]) | |
| return ' '.join(corrected_sentences) | |
| # Gradio app setup with three 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(correct_grammar_with_gector, inputs=text_input, outputs=output_text) | |
| with gr.Tab("Grammar Correction"): | |
| grammar_input = gr.Textbox(lines=5, label="Input Text") | |
| grammar_button = gr.Button("Correct Grammar") | |
| grammar_output = gr.Textbox(label="Corrected Text") | |
| # Connect the GECToR grammar correction function to the button | |
| grammar_button.click(correct_grammar_with_gector, inputs=grammar_input, outputs=grammar_output) | |
| # Launch the app with all functionalities | |
| demo.launch() | |