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 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 paraphrase and correct grammar """
    paraphrased_text = capitalize_sentences_and_nouns(text)  # Capitalize first to ensure proper noun capitalization
    
    # 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)

    return paraphrased_text

# Setup Gradio interface
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='Score')

        button1.click(predict_en, inputs=[t1], outputs=[label1, score1])

    with gr.Tab("Humanifier"):
        text_input = gr.Textbox(lines=5, label="Input Text")
        paraphrase_button = gr.Button("Paraphrase & Correct")
        output_text = gr.Textbox(label="Paraphrased Text")

        paraphrase_button.click(paraphrase_and_correct, inputs=[text_input], outputs=[output_text])

# Launch the app
demo.launch()