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import os
import gradio as gr
from transformers import pipeline
import spacy
import subprocess
import nltk
from nltk.corpus import wordnet
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from spellchecker import SpellChecker
import re
import string
import random
# Download necessary NLTK data
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('averaged_perceptron_tagger')
nltk.download('averaged_perceptron_tagger_eng')
nltk.download('wordnet')
nltk.download('omw-1.4')
nltk.download('punkt_tab')
# Initialize stopwords
stop_words = set(stopwords.words("english"))
# Words we don't want to replace
exclude_tags = {'PRP', 'PRP$', 'MD', 'VBZ', 'VBP', 'VBD', 'VBG', 'VBN', 'TO', 'IN', 'DT', 'CC'}
exclude_words = {'is', 'am', 'are', 'was', 'were', 'have', 'has', 'do', 'does', 'did', 'will', 'shall', 'should', 'would', 'could', 'can', 'may', 'might'}
# Initialize the English text classification pipeline for AI detection
pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
# Initialize the spell checker
spell = SpellChecker()
# 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")
def plagiarism_removal(text):
def plagiarism_remover(word):
if word.lower() in stop_words or word.lower() in exclude_words or word in string.punctuation:
return word
# Find synonyms
synonyms = set()
for syn in wordnet.synsets(word):
for lemma in syn.lemmas():
if "_" not in lemma.name() and lemma.name().isalpha() and lemma.name().lower() != word.lower():
synonyms.add(lemma.name())
pos_tag_word = nltk.pos_tag([word])[0]
if pos_tag_word[1] in exclude_tags:
return word
filtered_synonyms = [syn for syn in synonyms if nltk.pos_tag([syn])[0][1] == pos_tag_word[1]]
if not filtered_synonyms:
return word
synonym_choice = random.choice(filtered_synonyms)
if word.istitle():
return synonym_choice.title()
return synonym_choice
para_split = word_tokenize(text)
final_text = [plagiarism_remover(word) for word in para_split]
corrected_text = []
for i in range(len(final_text)):
if final_text[i] in string.punctuation and i > 0:
corrected_text[-1] += final_text[i]
else:
corrected_text.append(final_text[i])
return " ".join(corrected_text)
def paraphrase_and_correct(text):
paragraphs = text.split("\n\n") # Split by paragraphs
# Process each paragraph separately
processed_paragraphs = []
for paragraph in paragraphs:
cleaned_text = remove_redundant_words(paragraph)
plag_removed = plagiarism_removal(cleaned_text)
paraphrased_text = capitalize_sentences_and_nouns(plag_removed)
paraphrased_text = force_first_letter_capital(paraphrased_text)
paraphrased_text = correct_article_errors(paraphrased_text)
paraphrased_text = correct_tense_errors(paraphrased_text)
paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)
paraphrased_text = fix_possessives(paraphrased_text)
paraphrased_text = correct_spelling(paraphrased_text)
paraphrased_text = fix_punctuation_spacing(paraphrased_text)
processed_paragraphs.append(paraphrased_text)
return "\n\n".join(processed_paragraphs) # Reassemble the text with paragraphs
# Gradio app setup
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')
button1.click(fn=predict_en, inputs=t1, outputs=[label1, score1])
with gr.Tab("Paraphrasing & Grammar Correction"):
t2 = gr.Textbox(lines=5, label='Enter text for paraphrasing and grammar correction')
button2 = gr.Button("🔄 Paraphrase and Correct")
result2 = gr.Textbox(lines=5, label='Corrected Text')
button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)
demo.launch(share=True)
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