app.py

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

# 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")

# Initialize the English text classification pipeline for AI detection
pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")

def predict_en(text):
    """Function to predict the label and score for English text (AI Detection)"""
    res = pipeline_en(text)[0]
    return res['label'], res['score']

def get_synonyms_nltk(word, pos):
    """Function to get synonyms using NLTK WordNet"""
    synsets = wordnet.synsets(word, pos=pos)
    if synsets:
        lemmas = synsets[0].lemmas()
        return [lemma.name() for lemma in lemmas]
    return []

def rephrase_text(text):
    """Function to rephrase text by replacing words with synonyms"""
    doc = nlp(text)
    rephrased_text = []

    for token in doc:
        if token.pos_ in ["NOUN", "VERB", "ADJ"]:
            synonyms = get_synonyms_nltk(token.text, pos=token.pos_.lower())
            if synonyms:
                rephrased_text.append(synonyms[0])  # Replace with first synonym found
            else:
                rephrased_text.append(token.text)
        else:
            rephrased_text.append(token.text)
    
    return ' '.join(rephrased_text)

def capitalize_sentences_and_nouns(text):
    """Function to capitalize the first letter of sentences and proper nouns"""
    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)

def correct_tense_errors(text):
    """Function to correct tense errors in a sentence"""
    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)

def correct_singular_plural_errors(text):
    """Function to correct singular/plural errors"""
    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)

def correct_article_errors(text):
    """Function to check and correct article errors"""
    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)

def paraphrase_and_correct(text):
    """Function to rephrase and correct grammar"""
    rephrased_text = rephrase_text(text)
    rephrased_text = capitalize_sentences_and_nouns(rephrased_text)  # Capitalize first to ensure proper noun capitalization
    rephrased_text = correct_article_errors(rephrased_text)
    rephrased_text = correct_tense_errors(rephrased_text)
    rephrased_text = correct_singular_plural_errors(rephrased_text)
    return rephrased_text

# Define Gradio interface
with gr.Blocks() as demo:
    with gr.Row():
        t1 = gr.Textbox(label="Input Text", lines=5)
        button1 = gr.Button("Process")
    with gr.Row():
        output_text = gr.Textbox(label="Processed Text", lines=5)
        label1 = gr.Label(label="AI Detection Label")
        score1 = gr.Label(label="AI Detection Score")
    
    button1.click(
        fn=lambda text: (paraphrase_and_correct(text), *predict_en(text)),
        inputs=[t1],
        outputs=[output_text, label1, score1]
    )

demo.launch()