app.py

import streamlit as st
from transformers import (GPT2Tokenizer, GPT2ForSequenceClassification)

model = GPT2ForSequenceClassification.from_pretrained(pretrained_model_name_or_path='ErnestBeckham/gpt-2-finetuned-ai-content')
tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model_name_or_path='ErnestBeckham/gpt2-tokenizer-ai-content')

text = st.text_area("Paste your Content")

if text:
    tokenized_input = tokenizer(sentence, return_tensors='pt')
    with torch.no_grad():
    outputs = model(**tokenized_input)

    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=-1).item()
    st.json(predicted_class)