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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()
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