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import torch
import json
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("ProtectAI/deberta-v3-base-prompt-injection")
model = AutoModelForSequenceClassification.from_pretrained("ProtectAI/deberta-v3-base-prompt-injection")
classifier = pipeline(
"text-classification",
model=model,
tokenizer=tokenizer,
truncation=True,
max_length=512,
device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
)
def predict(user_input: str):
prediction = classifier(user_input)[0]
return f"Label: {prediction['label']}\nProbability: {round(prediction['score'], 3)}"
textbox = gr.Textbox(placeholder="Enter user input for injection attack classification", lines=12)
interface = gr.Interface(
inputs=textbox, fn=predict, outputs="text",
title="Injection Attack Classifier",
description="This web API flags if the text presented as input to an LLM qualifies to be an injection attack",
allow_flagging="manual", flagging_options=["Useful", "Not Useful"]
)
with gr.Blocks() as demo:
interface.launch()
demo.queue(concurrency_count=4)
demo.launch() |