app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained("roberta-large-openai-detector")
model = AutoModelForSequenceClassification.from_pretrained("roberta-large-openai-detector").to(device)

pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device=device)

def predict(text):
    outputs = pipe(text, return_all_scores=True)[0]
    predictions = dict([ (x['label'], x['score']) for x in outputs ])
    return predictions["LABEL_1"]

iface = gr.Interface(fn=predict, inputs="text", outputs="number")
iface.launch()