app.py

import os
import gradio as gr
from transformers import pipeline
import spacy
import subprocess
import nltk
from nltk.corpus import wordnet
from spellchecker import SpellChecker
import re

# 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 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")

# 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']

# Function to remove redundant and meaningless words
def remove_redundant_words(text):
    doc = nlp(text)
    meaningless_words = {"actually", "basically", "literally", "really", "very", "just"}
    filtered_text = [token.text for token in doc if token.text.lower() not in meaningless_words]
    return ' '.join(filtered_text)

# Function to fix spacing before punctuation
def fix_punctuation_spacing(text):
    # Split the text into words and punctuation
    words = text.split(' ')
    cleaned_words = []
    punctuation_marks = {',', '.', "'", '!', '?', ':'}

    for word in words:
        if cleaned_words and word and word[0] in punctuation_marks:
            cleaned_words[-1] += word
        else:
            cleaned_words.append(word)

    return ' '.join(cleaned_words).replace(' ,', ',').replace(' .', '.').replace(" '", "'") \
                                    .replace(' !', '!').replace(' ?', '?').replace(' :', ':')

# Function to fix possessives like "Earth's"
def fix_possessives(text):
    text = re.sub(r'(\w)\s\'\s?s', r"\1's", text)
    return text

# Function to capitalize the first letter of sentences and proper nouns
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:
                sentence.append(token.text.capitalize())
            elif token.pos_ == "PROPN":
                sentence.append(token.text.capitalize())
            else:
                sentence.append(token.text)
        corrected_text.append(' '.join(sentence))

    return ' '.join(corrected_text)

# Function to force capitalization of the first letter of every sentence and ensure full stops
def force_first_letter_capital(text):
    sentences = re.split(r'(?<=\w[.!?])\s+', text)
    capitalized_sentences = []
    
    for sentence in sentences:
        if sentence:
            capitalized_sentence = sentence[0].capitalize() + sentence[1:]
            if not re.search(r'[.!?]$', capitalized_sentence):
                capitalized_sentence += '.'
            capitalized_sentences.append(capitalized_sentence)
    
    return " ".join(capitalized_sentences)

# Function to correct tense errors in a sentence
def correct_tense_errors(text):
    doc = nlp(text)
    corrected_text = []
    for token in doc:
        if token.pos_ == "VERB" and token.dep_ in {"aux", "auxpass"}:
            lemma = wordnet.morphy(token.text, wordnet.VERB) or token.text
            corrected_text.append(lemma)
        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)

# Function to ensure subject-verb agreement
def ensure_subject_verb_agreement(text):
    doc = nlp(text)
    corrected_text = []
    for token in doc:
        if token.dep_ == "nsubj" and token.head.pos_ == "VERB":
            if token.tag_ == "NN" and token.head.tag_ != "VBZ":
                corrected_text.append(token.head.lemma_ + "s")
            elif token.tag_ == "NNS" and token.head.tag_ == "VBZ":
                corrected_text.append(token.head.lemma_)
        corrected_text.append(token.text)
    return ' '.join(corrected_text)

# Function to correct spelling errors
def correct_spelling(text):
    words = text.split()
    corrected_words = []
    for word in words:
        corrected_word = spell.correction(word)
        if corrected_word is not None:
            corrected_words.append(corrected_word)
        else:
            corrected_words.append(word)
    return ' '.join(corrected_words)

# Function to replace a word with its synonym
def replace_with_synonyms(text):
    words = text.split()
    replaced_words = []

    for word in words:
        synonyms = wordnet.synsets(word)
        if synonyms:
            # Take the first synonym if available
            synonym = synonyms[0].lemmas()[0].name()
            # Replace the word with its synonym if it's different
            if synonym.lower() != word.lower():
                replaced_words.append(synonym.replace('_', ' '))
            else:
                replaced_words.append(word)
        else:
            replaced_words.append(word)

    return ' '.join(replaced_words)

# Main function for paraphrasing and grammar correction
def paraphrase_and_correct(text):
    cleaned_text = remove_redundant_words(text)
    paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)
    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)
    paraphrased_text = replace_with_synonyms(paraphrased_text)  # Add synonym replacement here

    return paraphrased_text

# 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)