Update app.py

app.py

@@ -108,22 +108,13 @@ llm = HuggingFaceEndpoint(
 )
 
 
-def ra(user_question):
-    prompt = f"You know things about League of Legends. Please correct the following question for grammar and clarity Do not give explaination:\n{user_question}\nCorrected question:"
-    #prompt = f"You know things about League of Legends. Please ONLY correct the following question for grammar and clarity. Do NOT give explaination:\n{user_question}\nCorrected question:"
-
-    # Pass the prompt to the LLM and get the response
-    rephrased_query = llm.invoke(prompt)  # Replace `llm` with the appropriate LLM function or API call
-    if "\n\n" in rephrased_query:
-        rephrased_query = rephrased_query.split("\n\n")[0]
-    new_query = rephrased_query.strip()
-    return {'context' : retriever(new_query), 'question': new_query}
+
 # chain = RunnablePassthrough() | RunnableLambda(ra) | prompt_template | client.chat_completion() | StrOutputParser() for notebook
 
 """-------------------------------------------------------------------"""
 def get_system_prompt(message):
     """Generate a system message based on the user's input."""
-    context = ra(message)  # Call external function to get context dynamically
+    #context = ra(message)  # Call external function to get context dynamically
     return f"""
     You are an expert in League of Legends (LoL) lore. You will only answer questions related to the champions and their stories within the game.
 
@@ -139,7 +130,7 @@ def respond(
     temperature,
     top_p,
 ):
-    res = ra(message)
+    #res = ra(message)
     # Initialize a list to store messages.
     system_message = f"""
     You are an expert in League of Legends (LoL) lore. You will only answer questions related to the champions and their stories within the game.