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comps/guardrails/src/polite_guard/README.md

@@ -2,13 +2,12 @@
 
 ## Introduction
 
-The Polite Guard Microservice allows AI application developers to ensure that user input and Large Language Model (LLM) outputs remain polite and respectful. By leveraging, [Polite Guard](https://huggingface.co/Intel/polite-guard),  a fine-tuned Transformer model for politeness classification, this lightweight guardrails microservice ensures courteous interactions without significantly sacrificing performance, making it suitable for deployment on both Intel Gaudi and Xeon.
+The Polite Guard Microservice allows AI application developers to ensure that user input and Large Language Model (LLM) outputs remain polite and respectful. By leveraging, [Polite Guard](https://huggingface.co/Intel/polite-guard), a fine-tuned Transformer model for politeness classification, this lightweight guardrails microservice ensures courteous interactions without significantly sacrificing performance, making it suitable for deployment on both Intel Gaudi and Xeon.
 
-Politeness plays a crucial role in creating a positive and respectful environment. The service classifies text into four categories: *polite*, *somewhat polite*, *neutral*, and *impolite*. Any *impolite* text is rejected, along with a score, ensuring that systems maintain a courteous tone.
+Politeness plays a crucial role in creating a positive and respectful environment. The service classifies text into four categories: _polite_, _somewhat polite_, _neutral_, and _impolite_. Any _impolite_ text is rejected, along with a score, ensuring that systems maintain a courteous tone.
 
 More details about the Polite Guard model can be found [here](https://github.com/intel/polite-guard).
 
-
 ## 🚀1. Start Microservice with Python（Option 1）
 
 ### 1.1 Install Requirements

comps/guardrails/src/polite_guard/integrations/politeguard.py

@@ -32,7 +32,10 @@ async def invoke(self, input: str):
         """
         response = await asyncio.to_thread(self.polite_pipeline, input)
         if response[0]["label"] == "impolite":
-            return TextDoc(text=f"Violated policies: Impolite (score: {response[0]['score']:0.2f}), please check your input.", downstream_black_list=[".*"])
+            return TextDoc(
+                text=f"Violated policies: Impolite (score: {response[0]['score']:0.2f}), please check your input.",
+                downstream_black_list=[".*"],
+            )
         else:
             return TextDoc(text=input)