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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)
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