app.py

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

# 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 get synonyms using NLTK WordNet (Humanifier)
def get_synonyms_nltk(word, pos):
    synsets = wordnet.synsets(word, pos=pos)
    synonyms = set()
    for synset in synsets:
        for lemma in synset.lemmas():
            if lemma.name() != word:
                synonyms.add(lemma.name())
    return list(synonyms)

# 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 correct tense errors in a sentence (Tense Correction)
def correct_tense_errors(text):
    doc = nlp(text)
    corrected_text = []

    for token in doc:
        if token.pos_ == "VERB":
            # Check if verb is in its base form
            if token.tag_ == "VB" and token.text.lower() not in ["be", "have", "do"]:
                # Attempt to correct verb form based on sentence context
                context = " ".join([t.text for t in doc if t.i != token.i])
                corrected_text.append(token.lemma_)
            else:
                corrected_text.append(token.text)
        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 = []

    # Create a context dictionary for singular/plural determination
    context = defaultdict(int)
    for token in doc:
        if token.pos_ == "NOUN":
            # Track context for noun usage
            if token.tag_ == "NNS":
                context['plural'] += 1
            elif token.tag_ == "NN":
                context['singular'] += 1

    for token in doc:
        if token.pos_ == "NOUN":
            if token.tag_ == "NN":  # Singular noun
                if context['plural'] > context['singular']:
                    corrected_text.append(token.lemma_ + 's')
                else:
                    corrected_text.append(token.text)
            elif token.tag_ == "NNS":  # Plural noun
                if context['singular'] > context['plural']:
                    corrected_text.append(token.lemma_)
                else:
                    corrected_text.append(token.text)
            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)

# Paraphrasing function using SpaCy and NLTK (Humanifier)
def paraphrase_with_spacy_nltk(text):
    doc = nlp(text)
    paraphrased_words = []
    
    for token in doc:
        # Map SpaCy POS tags to WordNet POS tags
        pos = None
        if token.pos_ == "NOUN":
            pos = wordnet.NOUN
        elif token.pos_ == "VERB":
            pos = wordnet.VERB
        elif token.pos_ == "ADJ":
            pos = wordnet.ADJ
        elif token.pos_ == "ADV":
            pos = wordnet.ADV
        
        synonyms = get_synonyms_nltk(token.text.lower(), pos) if pos else []
        
        # Replace with a synonym only if it makes sense
        if synonyms and token.pos_ in {"NOUN", "VERB", "ADJ", "ADV"}:
            paraphrased_words.append(synonyms[0])
        else:
            paraphrased_words.append(token.text)
    
    return ' '.join(paraphrased_words)

# Combined function: Paraphrase -> Grammar Correction -> Capitalization (Humanifier)
def paraphrase_and_correct(text):
    # Step 1: Paraphrase the text
    paraphrased_text = paraphrase_with_spacy_nltk(text)
    
    # Step 2: Apply grammatical corrections on the paraphrased text
    corrected_text = correct_article_errors(paraphrased_text)
    corrected_text = capitalize_sentences_and_nouns(corrected_text)
    corrected_text = correct_singular_plural_errors(corrected_text)
    final_text = correct_tense_errors(corrected_text)
    
    return final_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()