Update app.py

app.py

@@ -7,7 +7,7 @@ from langchain_huggingface import HuggingFaceEndpoint
 load_dotenv()
 HF_TOKEN = os.getenv("HF_TOKEN")
 
-# Initialize the primary chatbot model
+# Initialize the Hugging Face endpoint for inference (Primary model)
 llm = HuggingFaceEndpoint(
     repo_id="mistralai/Mistral-7B-Instruct-v0.3",  # Replace with your model repo
     huggingfacehub_api_token=HF_TOKEN.strip(),
@@ -15,32 +15,33 @@ llm = HuggingFaceEndpoint(
     max_new_tokens=100
 )
 
-# Initialize the Meta-Llama Guard model
+# Initialize the Meta-Llama Guard model for content safety
 guard_llm = HuggingFaceEndpoint(
-    repo_id="meta-llama/Meta-Llama-Guard-2-8B",
+    repo_id="meta-llama/Meta-Llama-Guard-2-8B",  # Replace with your guard model repo
     huggingfacehub_api_token=HF_TOKEN.strip(),
     temperature=0.7,
     max_new_tokens=100
 )
 
-# Function for guarded chatbot response
+# Function to handle chatbot response with guardrails
 def guarded_chatbot_response(message):
     try:
-        # Get raw response from primary model
+        # Get raw response from the primary model
         raw_response = llm(message)
 
-        # Validate with Meta-Llama Guard
+        # Guardrail logic: Ensure the response is safe and appropriate
         guard_prompt = f"""
         You are a content safety guard for kids under 16. Analyze the following text and ensure it is friendly and appropriate for children.
         If appropriate, return the text as is. If not, rephrase or respond with "Content not suitable." 
         Text: {raw_response}
         """
+        
+        # Get the guarded response from the Meta-Llama Guard model
         guard_response = guard_llm(guard_prompt)
         return guard_response
     except Exception as e:
         return f"Error: {e}"
 
-
 # Gradio Interface for Chatbot with Guardrails
 with gr.Blocks() as app_with_guardrails:
     gr.Markdown("## Chatbot With Guardrails")