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 re
from inflect import engine  # For pluralization

# 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()
inflect_engine = engine()

# 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 get synonyms using NLTK WordNet
def get_synonyms_nltk(word, pos):
    synsets = wordnet.synsets(word, pos=pos)
    if synsets:
        lemmas = synsets[0].lemmas()
        return [lemma.name() for lemma in lemmas if lemma.name() != word]  # Avoid original word
    return []

# 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 capitalize the first letter of sentences 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 correct tense errors in a sentence
def correct_tense_errors(text):
    doc = nlp(text)
    corrected_text = []
    for token in doc:
        if token.pos_ == "VERB":
            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 using inflect
def correct_singular_plural_errors(text):
    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(inflect_engine.plural(token.lemma_))
                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(inflect_engine.singular_noun(token.text) or 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)

# Function to get the correct synonym while maintaining verb form
def replace_with_synonym(token):
    pos = {
        "VERB": wordnet.VERB,
        "NOUN": wordnet.NOUN,
        "ADJ": wordnet.ADJ,
        "ADV": wordnet.ADV
    }.get(token.pos_, None)

    synonyms = get_synonyms_nltk(token.lemma_, pos)

    if synonyms:
        synonym = synonyms[0]
        if token.tag_ == "VBG":  # Present participle
            synonym += 'ing'
        elif token.tag_ in {"VBD", "VBN"}:  # Past tense or past participle
            synonym += 'ed'
        elif token.tag_ == "VBZ":  # Third-person singular present
            synonym += 's'
        return synonym
    return token.text

# Function to check for and avoid double negatives
def correct_double_negatives(text):
    doc = nlp(text)
    corrected_text = []
    for token in doc:
        if token.text.lower() == "not" and any(child.text.lower() == "never" for child in token.head.children):
            corrected_text.append("always")
        else:
            corrected_text.append(token.text)
    return ' '.join(corrected_text)

# Function to ensure subject-verb agreement
def ensure_subject_verb_agreement(text):
    doc = nlp(text)
    corrected_text = []
    for token in doc:
        corrected_text.append(token.text)
        if token.dep_ == "nsubj" and token.head.pos_ == "VERB":
            if token.tag_ == "NN" and token.head.tag_ != "VBZ":  # Singular noun, should use singular verb
                corrected_text[-1] = token.head.lemma_ + "s"
            elif token.tag_ == "NNS" and token.head.tag_ == "VBZ":  # Plural noun, should not use singular verb
                corrected_text[-1] = token.head.lemma_
    return ' '.join(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 if correction is None
    return ' '.join(corrected_words)

# Function to correct punctuation issues
def correct_punctuation(text):
    text = re.sub(r'\s+([?.!,";:])', r'\1', text)  # Remove space before punctuation
    text = re.sub(r'([?.!,";:])\s+', r'\1 ', text)  # Ensure a single space after punctuation
    return text

# Function to ensure correct handling of possessive forms
def handle_possessives(text):
    text = re.sub(r"\b(\w+)'s\b", r"\1's", text)  # Preserve possessive forms
    return text

# 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:
        if token.pos_ == "NOUN" and token.text.lower() == "earth":
            rephrased_text.append("Earth")
            continue
        
        pos_tag = {
            "NOUN": wordnet.NOUN,
            "VERB": wordnet.VERB,
            "ADJ": wordnet.ADJ,
            "ADV": wordnet.ADV
        }.get(token.pos_, None)
        
        if pos_tag:
            synonyms = get_synonyms_nltk(token.lemma_, pos_tag)
            if synonyms:
                synonym = synonyms[0]  # Just using the first synonym for simplicity
                if token.pos_ == "VERB":
                    if token.tag_ == "VBG":  # Present participle
                        synonym += 'ing'
                    elif token.tag_ in {"VBD", "VBN"}:  # Past tense or past participle
                        synonym += 'ed'
                    elif token.tag_ == "VBZ":  # Third-person singular present
                        synonym += 's'
                rephrased_text.append(synonym)
            else:
                rephrased_text.append(token.text)
        else:
            rephrased_text.append(token.text)

    return ' '.join(rephrased_text)

# Function to paraphrase and correct grammar with enhanced accuracy
def paraphrase_and_correct(text):
    # Remove meaningless or redundant words first
    cleaned_text = remove_redundant_words(text)
    
    # Capitalize sentences and nouns
    paraphrased_text = capitalize_sentences_and_nouns(cleaned_text)

    # Correct tense and singular/plural errors
    paraphrased_text = correct_tense_errors(paraphrased_text)
    paraphrased_text = correct_singular_plural_errors(paraphrased_text)
    paraphrased_text = correct_article_errors(paraphrased_text)

    # Correct spelling errors
    paraphrased_text = correct_spelling(paraphrased_text)

    # Correct punctuation issues
    paraphrased_text = correct_punctuation(paraphrased_text)

    # Handle possessives
    paraphrased_text = handle_possessives(paraphrased_text)

    # Ensure subject-verb agreement
    paraphrased_text = ensure_subject_verb_agreement(paraphrased_text)

    # Replace with synonyms
    paraphrased_text = rephrase_with_synonyms(paraphrased_text)

    # Correct for double negatives
    paraphrased_text = correct_double_negatives(paraphrased_text)

    return paraphrased_text

# Function to handle the user interface
def process_text(input_text):
    ai_label, ai_score = predict_en(input_text)
    ai_result = f"AI Detected: {ai_label} (Score: {ai_score:.2f})"

    if ai_label == "HUMAN":
        corrected_text = paraphrase_and_correct(input_text)
        return corrected_text, ai_result
    else:
        return "The text seems to be AI-generated; no correction applied.", ai_result

# Gradio interface
iface = gr.Interface(
    fn=process_text,
    inputs=gr.Textbox(lines=10, placeholder="Enter your text here..."),
    outputs=[gr.Textbox(label="Corrected Text"), gr.Textbox(label="AI Detection Result")],
    title="Text Correction and AI Detection",
    description="This app corrects grammar, spelling, and punctuation while also detecting AI-generated content."
)

# Launch the interface
iface.launch()