app.py

import os
import gradio as grimport 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 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 []

# 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 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:  # First word of the sentence
                sentence.append(token.text.capitalize())
            elif token.pos_ == "PROPN":  # Proper noun
                sentence.append(token.text.capitalize())
            else:
                sentence.append(token.text)
        corrected_text.append(' '.join(sentence))

    return ' '.join(corrected_text)

# 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 correct singular/plural errors
def correct_singular_plural_errors(text):
    doc = nlp(text)
    corrected_text = []
    
    for token in doc:
        if token.pos_ == "NOUN":
            if token.tag_ == "NN":  # Singular noun
                if any(child.text.lower() in ['many', 'several', 'few'] for child in token.head.children):
                    corrected_text.append(token.lemma_ + 's')
                else:
                    corrected_text.append(token.text)
            elif token.tag_ == "NNS":  # Plural noun
                if any(child.text.lower() in ['a', 'one'] for child in token.head.children):
                    corrected_text.append(token.lemma_)
                else:
                    corrected_text.append(token.text)
        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 get the correct synonym while maintaining verb form
def replace_with_synonym(token):
    pos = None
    if token.pos_ == "VERB":
        pos = wordnet.VERB
    elif token.pos_ == "NOUN":
        pos = wordnet.NOUN
    elif token.pos_ == "ADJ":
        pos = wordnet.ADJ
    elif token.pos_ == "ADV":
        pos = wordnet.ADV
    
    synonyms = get_synonyms_nltk(token.lemma_, pos)
    
    if synonyms:
        synonym = synonyms[0]
        if token.tag_ == "VBG":  # Present participle (e.g., running)
            synonym = synonym + 'ing'
        elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
            synonym = synonym + 'ed'
        elif token.tag_ == "VBZ":  # Third-person singular present
            synonym = synonym + 's'
        return synonym
    return token.text

# Function to check for and avoid double negatives
def correct_double_negatives(text):
    doc = nlp(text)
    corrected_text = []
    for token in doc:
        if token.text.lower() == "not" and any(child.text.lower() == "never" for child in token.head.children):
            corrected_text.append("always")
        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":  # Singular noun, should use singular verb
                corrected_text.append(token.head.lemma_ + "s")
            elif token.tag_ == "NNS" and token.head.tag_ == "VBZ":  # Plural noun, should not use singular verb
                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)
        corrected_words.append(corrected_word if corrected_word else word)  # Keep original if correction is None
    return ' '.join(corrected_words)

# Function to correct punctuation issues
def correct_punctuation(text):
    text = re.sub(r'\s+([?.!,";:])', r'\1', text)  # Remove space before punctuation
    text = re.sub(r'([?.!,";:])\s+', r'\1 ', text)  # Ensure a single space after punctuation
    return text

# Function to ensure correct handling of possessive forms
def handle_possessives(text):
    text = re.sub(r"\b(\w+)'s\b", r"\1's", text)  # Preserve possessive forms
    return text

# Function to rephrase text and replace words with their synonyms while maintaining form
def rephrase_with_synonyms(text):
    doc = nlp(text)
    rephrased_text = []

    for token in doc:
        if token.pos_ == "NOUN" and token.text.lower() == "earth":
            rephrased_text.append("Earth")
            continue
        
        pos_tag = None
        if token.pos_ == "NOUN":
            pos_tag = wordnet.NOUN
        elif token.pos_ == "VERB":
            pos_tag = wordnet.VERB
        elif token.pos_ == "ADJ":
            pos_tag = wordnet.ADJ
        elif token.pos_ == "ADV":
            pos_tag = wordnet.ADV
        
        if pos_tag:
            synonyms = get_synonyms_nltk(token.lemma_, pos_tag)
            if synonyms:
                synonym = synonyms[0]  # Just using the first synonym for simplicity
                if token.pos_ == "VERB":
                    if token.tag_ == "VBG":  # Present participle (e.g., running)
                        synonym = synonym + 'ing'
                    elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
                        synonym = synonym + 'ed'
                    elif token.tag_ == "VBZ":  # Third-person singular present
                        synonym = synonym + 's'
                rephrased_text.append(synonym)
            else:
                rephrased_text.append(token.text)
        else:
            rephrased_text.append(token.text)

    return ' '.join(rephrased_text)

# Function to paraphrase and correct grammar with enhanced accuracy
def paraphrase_and_correct(text):
    # Remove meaningless or redundant words first
    cleaned_text = remove_redundant_words(text)
    
    # Capitalize sentences and nouns
    paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)

    # Correct tense and singular/plural errors
    paraphrased_text = correct_tense_errors(paraphrased_text)
    paraphrased_text = correct_singular_plural_errors(paraphrased_text)
    paraphrased_text = correct_article_errors(paraphrased_text)
    paraphrased_text = correct_double_negatives(paraphrased_text)
    paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)

    # Correct spelling and punctuation
    paraphrased_text = correct_spelling(paraphrased_text)
    paraphrased_text = correct_punctuation(paraphrased_text)
    paraphrased_text = handle_possessives(paraphrased_text)  # Handle possessives

    # Rephrase with synonyms
    paraphrased_text = rephrase_with_synonyms(paraphrased_text)

    # Force capitalization of the first letter of each sentence
    final_text = force_first_letter_capital(paraphrased_text)

    return final_text

# Gradio Interface
def process_text(input_text):
    ai_label, ai_score = predict_en(input_text)
    corrected_text = paraphrase_and_correct(input_text)
    return ai_label, ai_score, corrected_text

# Create Gradio interface
iface = gr.Interface(
    fn=process_text,
    inputs="text",
    outputs=["text", "number", "text"],
    title="AI Content Detection and Grammar Correction",
    description="Enter text to detect AI-generated content and correct grammar."
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()

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 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 []

# 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 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:  # First word of the sentence
                sentence.append(token.text.capitalize())
            elif token.pos_ == "PROPN":  # Proper noun
                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
def force_first_letter_capital(text):
    sentences = text.split(". ")  # Split by period to get each sentence
    capitalized_sentences = [sentence[0].capitalize() + sentence[1:] if sentence else "" for sentence in sentences]
    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 correct singular/plural errors
def correct_singular_plural_errors(text):
    doc = nlp(text)
    corrected_text = []
    
    for token in doc:
        if token.pos_ == "NOUN":
            if token.tag_ == "NN":  # Singular noun
                if any(child.text.lower() in ['many', 'several', 'few'] for child in token.head.children):
                    corrected_text.append(token.lemma_ + 's')
                else:
                    corrected_text.append(token.text)
            elif token.tag_ == "NNS":  # Plural noun
                if any(child.text.lower() in ['a', 'one'] for child in token.head.children):
                    corrected_text.append(token.lemma_)
                else:
                    corrected_text.append(token.text)
        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 get the correct synonym while maintaining verb form
def replace_with_synonym(token):
    pos = None
    if token.pos_ == "VERB":
        pos = wordnet.VERB
    elif token.pos_ == "NOUN":
        pos = wordnet.NOUN
    elif token.pos_ == "ADJ":
        pos = wordnet.ADJ
    elif token.pos_ == "ADV":
        pos = wordnet.ADV
    
    synonyms = get_synonyms_nltk(token.lemma_, pos)
    
    if synonyms:
        synonym = synonyms[0]
        if token.tag_ == "VBG":  # Present participle (e.g., running)
            synonym = synonym + 'ing'
        elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
            synonym = synonym + 'ed'
        elif token.tag_ == "VBZ":  # Third-person singular present
            synonym = synonym + 's'
        return synonym
    return token.text

# Function to check for and avoid double negatives
def correct_double_negatives(text):
    doc = nlp(text)
    corrected_text = []
    for token in doc:
        if token.text.lower() == "not" and any(child.text.lower() == "never" for child in token.head.children):
            corrected_text.append("always")
        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":  # Singular noun, should use singular verb
                corrected_text.append(token.head.lemma_ + "s")
            elif token.tag_ == "NNS" and token.head.tag_ == "VBZ":  # Plural noun, should not use singular verb
                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)
        corrected_words.append(corrected_word if corrected_word else word)  # Keep original if correction is None
    return ' '.join(corrected_words)

# Function to correct punctuation issues
def correct_punctuation(text):
    text = re.sub(r'\s+([?.!,";:])', r'\1', text)  # Remove space before punctuation
    text = re.sub(r'([?.!,";:])\s+', r'\1 ', text)  # Ensure a single space after punctuation
    return text

# Function to ensure correct handling of possessive forms
def handle_possessives(text):
    text = re.sub(r"\b(\w+)'s\b", r"\1's", text)  # Preserve possessive forms
    return text

# Function to rephrase text and replace words with their synonyms while maintaining form
def rephrase_with_synonyms(text):
    doc = nlp(text)
    rephrased_text = []

    for token in doc:
        pos_tag = None
        if token.pos_ == "NOUN":
            pos_tag = wordnet.NOUN
        elif token.pos_ == "VERB":
            pos_tag = wordnet.VERB
        elif token.pos_ == "ADJ":
            pos_tag = wordnet.ADJ
        elif token.pos_ == "ADV":
            pos_tag = wordnet.ADV
        
        if pos_tag:
            synonyms = get_synonyms_nltk(token.text, pos_tag)
            if synonyms:
                synonym = synonyms[0]  # Just using the first synonym for simplicity
                if token.pos_ == "VERB":
                    if token.tag_ == "VBG":  # Present participle (e.g., running)
                        synonym = synonym + 'ing'
                    elif token.tag_ == "VBD" or token.tag_ == "VBN":  # Past tense or past participle
                        synonym = synonym + 'ed'
                    elif token.tag_ == "VBZ":  # Third-person singular present
                        synonym = synonym + 's'
                elif token.pos_ == "NOUN" and token.tag_ == "NNS":  # Plural nouns
                    synonym += 's' if not synonym.endswith('s') else ""
                rephrased_text.append(synonym)
            else:
                rephrased_text.append(token.text)
        else:
            rephrased_text.append(token.text)

    return ' '.join(rephrased_text)

# Function to paraphrase and correct grammar with enhanced accuracy
def paraphrase_and_correct(text):
    # Remove meaningless or redundant words first
    cleaned_text = remove_redundant_words(text)
    
    # Capitalize sentences and nouns
    paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)

    # Correct tense and singular/plural errors
    paraphrased_text = correct_tense_errors(paraphrased_text)
    paraphrased_text = correct_singular_plural_errors(paraphrased_text)
    paraphrased_text = correct_article_errors(paraphrased_text)
    paraphrased_text = correct_double_negatives(paraphrased_text)
    paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)

    # Correct spelling and punctuation
    paraphrased_text = correct_spelling(paraphrased_text)
    paraphrased_text = correct_punctuation(paraphrased_text)
    paraphrased_text = handle_possessives(paraphrased_text)  # Handle possessives

    # Rephrase with synonyms
    paraphrased_text = rephrase_with_synonyms(paraphrased_text)

    # Force capitalization of the first letter of each sentence
    final_text = force_first_letter_capital(paraphrased_text)

    return final_text

# Gradio Interface
def process_text(input_text):
    ai_label, ai_score = predict_en(input_text)
    corrected_text = paraphrase_and_correct(input_text)
    return ai_label, ai_score, corrected_text

# Create Gradio interface
iface = gr.Interface(
    fn=process_text,
    inputs="text",
    outputs=["text", "number", "text"],
    title="AI Content Detection and Grammar Correction",
    description="Enter text to detect AI-generated content and correct grammar."
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()