app.py

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

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

# 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']

# Ensure necessary NLTK data is downloaded for Humanifier
nltk.download('wordnet')
nltk.download('omw-1.4')

# Ensure the SpaCy model is installed for Humanifier
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 capitalize the first letter of sentences and proper nouns (Humanifier)
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 get synonyms using NLTK WordNet and keep the same grammatical form
def get_synonym(word, pos_tag):
    synsets = wordnet.synsets(word)
    if not synsets:
        return word
    
    for synset in synsets:
        if synset.pos() == pos_tag:  # Match the part of speech
            synonym = synset.lemmas()[0].name()  # Get the first lemma
            # Check if the original word and synonym are in the same form (singular/plural, tense, etc.)
            if word.islower():
                return synonym.lower()
            else:
                return synonym.capitalize()
    return word

# 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:
        # Get the correct POS tag for WordNet
        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:
            synonym = get_synonym(token.text, pos_tag)
            # Ensure that the verb/noun/plural/singular is kept intact
            if token.pos_ == "VERB":
                synonym = token.lemma_ if token.morph.get("Tense") == "Past" else synonym
            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)

    return ' '.join(rephrased_text)

# Function to paraphrase and correct grammar
def paraphrase_and_correct(text):
    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)
    
    # Rephrase with synonyms while maintaining grammatical forms
    paraphrased_text = rephrase_with_synonyms(paraphrased_text)
    
    return paraphrased_text

# Function to correct tense errors in a sentence (Tense Correction)
def correct_tense_errors(text):
    doc = nlp(text)
    corrected_text = []
    for token in doc:
        # Check for tense correction based on modal verbs
        if token.pos_ == "VERB" and token.dep_ in {"aux", "auxpass"}:
            # Replace with appropriate verb form
            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 (Singular/Plural Correction)
def correct_singular_plural_errors(text):
    doc = nlp(text)
    corrected_text = []
    
    for token in doc:
        if token.pos_ == "NOUN":
            # Check if the noun is singular or plural
            if token.tag_ == "NN":  # Singular noun
                # Look for determiners like "many", "several", "few" to correct to plural
                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
                # Look for determiners like "a", "one" to correct to singular
                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)

# 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(predict_en, inputs=[t1], outputs=[label1, score1], api_name='predict_en')
    
    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")

        # Connect the paraphrasing function to the button
        paraphrase_button.click(paraphrase_and_correct, inputs=text_input, outputs=output_text)

# Launch the app with the remaining functionalities
demo.launch(share=True)