add app.py

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+import gradio.components as grc
+# from wmdetection.models import get_watermarks_detection_model
+# from wmdetection.pipelines.predictor import WatermarksPredictor
+import os, glob
+import spaces
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+model_name = 'https://huggingface.co/hyunseoki/ReMoDetect-deberta'
+
+THESHOLD=4.0
+predictor = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+@spaces.GPU
+def predict(text):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    predictor.to(device)
+    tokenized = tokenizer(text, return_tensors='pt', truncation=True, max_length=512).to(device)
+    result = predictor(**tokenized).logits[0].cpu().detach().item()
+    AI_score = round(torch.sigmoid(torch.tensor(result-THESHOLD)*2).item(),2)
+    return f'{AI_score*100} %', f'{round(result,2)}'
+
+iface = gr.Interface(
+    fn=predict, 
+    title="ReMoDetect: Reward Model for LLM Generated Text Detection",
+    description="The continuously finetuned reward model so that can classify LLM generated text from human writen text.",
+    inputs=grc.Textbox(label='INPUT', placeholder="Type here..."), 
+    # examples=examples, 
+    outputs=[grc.Textbox(label="AI likelihood"), grc.Textbox(label="Raw score")],
+)
+iface.launch(share=True)