app.py

import gradio as gr
import torch
import os
import transformers
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
)
from deployment import preprocess, detect

device = 'cpu'
model_dir = "nealcly/detection-longformer"

# load the Longformer detector
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSequenceClassification.from_pretrained(model_dir).to(device)

def detect(input_text,device,th=-3.08583984375):
    label2decisions = {
        0: "machine-generated",
        1: "human-written",
    }
    tokenize_input = tokenizer(input_text)
    tensor_input = torch.tensor([tokenize_input["input_ids"]]).to(device)
    outputs = model(tensor_input)
    is_machine = -outputs.logits[0][0].item()
    if is_machine < th:
        decision = 0
    else:
        decision = 1

    return label2decisions[decision]

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