* upgrade libraries

* put disclaimer
* store prompt by default

README.md

@@ -4,7 +4,7 @@ emoji: 📝
 colorFrom: yellow
 colorTo: gray
 sdk: gradio
-sdk_version: 4.7.1
+sdk_version: 4.8.0
 pinned: true
 license: apache-2.0
 ---

app.py

@@ -156,7 +156,7 @@ def is_detected(provider: str, prompt: str) -> (str, bool, bool, float):
     return provider, request_result, is_injection, convert_elapsed_time(end_time - start_time)
 
 
-def execute(prompt: str, store_to_dataset: bool = True) -> List[Union[str, bool, float]]:
+def execute(prompt: str) -> List[Union[str, bool, float]]:
     results = []
 
     with mp.Pool(processes=num_processes) as pool:
@@ -166,19 +166,18 @@ def execute(prompt: str, store_to_dataset: bool = True) -> List[Union[str, bool,
             results.append(result)
 
     # Save image and result
-    if store_to_dataset:
-        fileobj = json.dumps(
-            {"prompt": prompt, "results": results}, indent=2, ensure_ascii=False
-        ).encode("utf-8")
-        result_path = f"/prompts/train/{str(uuid.uuid4())}.json"
-
-        hf_api.upload_file(
-            path_or_fileobj=fileobj,
-            path_in_repo=result_path,
-            repo_id="laiyer/prompt-injection-benchmark",
-            repo_type="dataset",
-        )
-        logger.info(f"Stored prompt: {prompt}")
+    fileobj = json.dumps(
+        {"prompt": prompt, "results": results}, indent=2, ensure_ascii=False
+    ).encode("utf-8")
+    result_path = f"/prompts/train/{str(uuid.uuid4())}.json"
+
+    hf_api.upload_file(
+        path_or_fileobj=fileobj,
+        path_in_repo=result_path,
+        repo_id="laiyer/prompt-injection-benchmark",
+        repo_type="dataset",
+    )
+    logger.info(f"Stored prompt: {prompt}")
 
     return results
 
@@ -196,16 +195,12 @@ if __name__ == "__main__":
         fn=execute,
         inputs=[
             gr.Textbox(label="Prompt"),
-            gr.Checkbox(
-                label="Store prompt and results to the public dataset `laiyer/prompt-injection-benchmark`",
-                value=True,
-            ),
         ],
         outputs=[
             gr.Dataframe(
                 headers=[
                     "Provider",
-                    "Is request successful?",
+                    "Is processed successfully?",
                     "Is prompt injection?",
                     "Latency (seconds)",
                 ],
@@ -214,7 +209,12 @@ if __name__ == "__main__":
             ),
         ],
         title="Prompt Injection Benchmark",
-        description="This interface aims to benchmark the prompt injection detection providers. The results are stored in the public dataset for fairness of all sides.",
+        description="This interface aims to benchmark the prompt injection detection providers. "
+        "The results are <strong>stored in the public dataset</strong> "
+        '<a href="https://huggingface.co/datasets/laiyer/prompt-injection-benchmark" target="_blank">laiyer/prompt-injection-benchmark</a> '
+        "for fairness of all sides.<br /><br />"
+        "HuggingFace (HF) models are hosted on Spaces while other providers are called as APIs.<br /><br />"
+        "<b>Disclaimer</b>: This interface is for research purposes only.",
         examples=[
             [
                 example,

requirements.txt

@@ -1,9 +1,8 @@
 git+https://github.com/automorphic-ai/aegis.git
-gradio==4.4.1
+gradio==4.8.0
 huggingface_hub==0.19.4
 onnxruntime==1.16.3
-optimum[onnxruntime]==1.14.1
-rapidocr_onnxruntime==1.3.8
+optimum[onnxruntime]==1.15.0
 rebuff==0.0.5
 requests==2.31.0
 transformers==4.35.2