Create app.py

app.py

@@ -0,0 +1,37 @@
+import torch
+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):
+    
+    return classifier(user_input)
+
+
+textbox = gr.Textbox(placeholder="Enter user input presented 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()