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 random  # Import random for versatile synonym replacement

# 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']

# Enhanced 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 []

# Retain the structure of the input text (headings, paragraphs, line breaks)
def retain_structure(text):
    lines = text.split("\n")
    formatted_lines = []
    
    for line in lines:
        if line.strip().isupper():  # Heading if all caps
            formatted_lines.append(f"# {line.strip()}")  # Treat it as a heading
        else:
            formatted_lines.append(line)  # Otherwise, it's a paragraph or normal text
    
    return "\n".join(formatted_lines)

# Dynamic and versatile synonym replacement
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:
        # Randomly choose a synonym to add more versatility
        synonym = random.choice(synonyms)
        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 rephrase text and replace words with versatile synonyms
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:
                # Use the dynamic synonym replacement for versatility
                synonym = replace_with_synonym(token)
                rephrased_text.append(synonym)
            else:
                rephrased_text.append(token.text)
        else:
            rephrased_text.append(token.text)

    return ' '.join(rephrased_text)

# 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 handle possessive 's and retain original meaning
def handle_possessives(text):
    doc = nlp(text)
    corrected_text = []

    for token in doc:
        # If token is a possessive form (e.g., 'Earth's'), retain its original form
        if token.text.endswith("'s") or token.text == "'s":
            corrected_text.append(token.text)  # Keep it as is, even if a synonym is found
        else:
            corrected_text.append(token.text)

    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 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 and handle None cases
def correct_spelling(text):
    words = text.split()
    corrected_words = []
    for word in words:
        corrected_word = spell.correction(word)
        # If spell.correction returns None, use the original word
        if corrected_word is None:
            corrected_word = word
        corrected_words.append(corrected_word)
    return ' '.join(corrected_words)

# Function to paraphrase and correct grammar with enhanced accuracy and retain structure
def paraphrase_and_correct(text):
    # Retain the structure (headings, paragraphs, line breaks)
    structured_text = retain_structure(text)
    
    # Remove meaningless or redundant words first
    cleaned_text = remove_redundant_words(structured_text)
    
    # Capitalize sentences and nouns
    paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)
    
    # Ensure first letter of each sentence is capitalized
    paraphrased_text = force_first_letter_capital(paraphrased_text)
    
    # Handle possessives properly
    paraphrased_text = handle_possessives(paraphrased_text)
    
    # Apply grammatical corrections
    paraphrased_text = correct_article_errors(paraphrased_text)
    paraphrased_text = correct_singular_plural_errors(paraphrased_text)
    paraphrased_text = correct_tense_errors(paraphrased_text)
    paraphrased_text = correct_double_negatives(paraphrased_text)
    paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)
    
    # Rephrase with versatile synonyms while maintaining grammatical forms
    paraphrased_text = rephrase_with_synonyms(paraphrased_text)
    
    # Correct spelling errors
    paraphrased_text = correct_spelling(paraphrased_text)
    
    return paraphrased_text

# Gradio app setup with two 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(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')

        # Connect the paraphrasing and correction function to the button
        button2.click(fn=paraphrase_and_correct, inputs=t2, outputs=result2)

demo.launch(share=True)  # Share=True to create a public link