import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import spacy
import subprocess
import nltk
from nltk.corpus import wordnet
from gensim import downloader as api
from textblob import TextBlob  # Import TextBlob for grammar correction

# 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")

# Load a smaller Word2Vec model from Gensim's pre-trained models
word_vectors = api.load("glove-wiki-gigaword-50")

# Check for GPU and set the device accordingly
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load AI Detector model and tokenizer from Hugging Face (DistilBERT)
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english").to(device)

# Function to correct grammar using TextBlob
def correct_grammar_with_textblob(text):
    blob = TextBlob(text)
    corrected_text = str(blob.correct())
    return corrected_text

# AI detection function using DistilBERT
def detect_ai_generated(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(device)
    with torch.no_grad():
        outputs = model(**inputs)
        probabilities = torch.softmax(outputs.logits, dim=1)
    ai_probability = probabilities[0][1].item()  # Probability of being AI-generated
    return f"AI-Generated Content Probability: {ai_probability:.2f}%"

# 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 []

# Paraphrasing function using spaCy and NLTK with TextBlob grammar correction
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)
    
    # Join the words back into a sentence
    paraphrased_sentence = ' '.join(paraphrased_words)
    
    # Correct the grammar of the paraphrased sentence using TextBlob
    corrected_sentence = correct_grammar_with_textblob(paraphrased_sentence)
    
    return corrected_sentence

# Gradio interface definition
with gr.Blocks() as interface:
    with gr.Row():
        with gr.Column():
            text_input = gr.Textbox(lines=5, label="Input Text")
            detect_button = gr.Button("AI Detection")
            paraphrase_button = gr.Button("Paraphrase with spaCy & NLTK (Grammar Corrected with TextBlob)")
        with gr.Column():
            output_text = gr.Textbox(label="Output")

    detect_button.click(detect_ai_generated, inputs=text_input, outputs=output_text)
    paraphrase_button.click(paraphrase_with_spacy_nltk, inputs=text_input, outputs=output_text)

# Launch the Gradio app
interface.launch(debug=False)