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")

# 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 check subject-verb agreement
def check_subject_verb_agreement(doc):
    corrected_text = []
    for token in doc:
        if token.dep_ == "nsubj":  # Check if the token is a subject
            subject = token
            verb = token.head  # Find the associated verb
            if verb.tag_ in {"VBZ", "VBP"}:  # Singular/plural verb forms
                if subject.tag_ == "NNS" and verb.tag_ == "VBZ":  # Plural subject with singular verb
                    corrected_text.append(verb.lemma_)  # Convert verb to plural form
                elif subject.tag_ == "NN" and verb.tag_ == "VBP":  # Singular subject with plural verb
                    corrected_text.append(verb.lemma_ + 's')  # Convert verb to singular form
                else:
                    corrected_text.append(verb.text)  # No correction needed
            else:
                corrected_text.append(verb.text)
        corrected_text.append(token.text)
    return ' '.join(corrected_text)

# Function to correct singular/plural errors using dependency parsing
def correct_singular_plural_errors(doc):
    corrected_text = []
    
    for token in doc:
        if token.pos_ == "NOUN":
            if token.tag_ == "NN" and token.head.pos_ == "VERB" and token.head.tag_ == "VBP":
                corrected_text.append(token.lemma_ + 's')  # Singular noun, plural verb
            elif token.tag_ == "NNS" and token.head.pos_ == "VERB" and token.head.tag_ == "VBZ":
                corrected_text.append(token.lemma_)  # Plural noun, singular verb
            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_ in {"NOUN"}:
            pos = wordnet.NOUN
        elif token.pos_ in {"VERB"}:
            pos = wordnet.VERB
        elif token.pos_ in {"ADJ"}:
            pos = wordnet.ADJ
        elif token.pos_ in {"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"} and synonyms[0] != token.text.lower():
            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: Parse the text with spaCy
    doc = nlp(paraphrased_text)
    
    # Step 3: Apply grammatical corrections on the paraphrased text
    corrected_text = correct_article_errors(doc)
    
    corrected_text = capitalize_sentences_and_nouns(corrected_text)
    
    corrected_text = check_subject_verb_agreement(nlp(corrected_text))
    
    corrected_text = correct_singular_plural_errors(nlp(corrected_text))
    
    # Step 4: Capitalize sentences and proper nouns (final correction step)
    final_text = correct_tense_errors(nlp(corrected_text))
    
    return final_text
def predict_en(text):
    prediction = pipeline_en(text)
    label = prediction[0]['label']
    score = prediction[0]['score']
    return label, round(score, 4)

# 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()