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 language_tool_python

# 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()

# Initialize the LanguageTool for grammar correction
tool = language_tool_python.LanguageTool('en-US')

# 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 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 apply grammatical corrections using LanguageTool
def correct_grammar(text):
    corrected_text = tool.correct(text)
    return 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 word if no correction
    return ' '.join(corrected_words)

# Function to capitalize the first letter of each sentence 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 rephrase with contextually appropriate 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 = wordnet.synsets(token.text, pos=pos_tag)
            if synonyms:
                synonym = synonyms[0].lemmas()[0].name()  # Choose the first synonym
                rephrased_text.append(synonym)
            else:
                rephrased_text.append(token.text)
        else:
            rephrased_text.append(token.text)

    return ' '.join(rephrased_text)

# Comprehensive function for paraphrasing and grammar correction
def paraphrase_and_correct(text):
    # Step 1: Remove meaningless or redundant words
    cleaned_text = remove_redundant_words(text)
    
    # Step 2: Capitalize sentences and proper nouns
    paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)
    
    # Step 3: Correct grammar using LanguageTool
    paraphrased_text = correct_grammar(paraphrased_text)
    
    # Step 4: Rephrase with contextually appropriate synonyms
    paraphrased_text = rephrase_with_synonyms(paraphrased_text)
    
    # Step 5: Correct spelling errors
    paraphrased_text = correct_spelling(paraphrased_text)
    
    # Step 6: Correct any remaining grammar issues after rephrasing
    paraphrased_text = correct_grammar(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)