import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import spacy
import subprocess
import nltk
from nltk.corpus import wordnet
import language_check  # Use language-check instead of language-tool-python
from gensim import downloader as api

# 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)