import os
import gradio as gr
import spacy
import subprocess
import nltk
from nltk.corpus import wordnet
from spellchecker import SpellChecker
from ginger import get_ginger_result  # Importing the grammar correction function

# 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()

# 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]
    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 force capitalization of the first letter of every sentence
def force_first_letter_capital(text):
    sentences = text.split(". ")  # Split by period to get each sentence
    capitalized_sentences = [sentence[0].capitalize() + sentence[1:] if sentence else "" for sentence in sentences]
    return ". ".join(capitalized_sentences)

# 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" 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)

# Function to correct singular/plural errors
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(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)

# 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 = None
    if token.pos_ == "VERB":
        pos = wordnet.VERB
    elif token.pos_ == "NOUN":
        pos = wordnet.NOUN
    elif token.pos_ == "ADJ":
        pos = wordnet.ADJ
    elif token.pos_ == "ADV":
        pos = wordnet.ADV
    
    synonyms = get_synonyms_nltk(token.text, pos)
    if synonyms:
        return synonyms[0]
    return token.text

# Function to use Ginger API for grammar correction (NEW)
def correct_grammar_with_ginger(text):
    result = get_ginger_result(text)
    corrected_text = text
    for suggestion in result["LightGingerTheTextResult"]:
        if suggestion["Suggestions"]:
            from_index = suggestion["From"]
            to_index = suggestion["To"] + 1
            suggested_text = suggestion["Suggestions"][0]["Text"]
            corrected_text = corrected_text[:from_index] + suggested_text + corrected_text[to_index:]
    return corrected_text

# Gradio interface
def process_text(text):
    text = correct_article_errors(text)
    text = correct_singular_plural_errors(text)
    text = correct_tense_errors(text)
    text = capitalize_sentences_and_nouns(text)
    text = remove_redundant_words(text)
    text = correct_grammar_with_ginger(text)  # Add grammar correction using Ginger here
    return text

iface = gr.Interface(fn=process_text, inputs="text", outputs="text")
iface.launch()