Update app.py

app.py

@@ -43,10 +43,9 @@ qa = ConversationalRetrievalChain.from_llm(
 agent=create_csv_agent(llm,['data/Gretel_Data.csv','data/RAN_Data _T.csv'],verbose=True)
 
 def echo(message, history):
-  context_prompt = """<s> [INST] Assume the role of Clara, a seasoned senior telecom network engineer with access to troubleshooting tickets data and various technical and product documentation.
+  context_prompt = """Assume the role of Clara, a seasoned senior telecom network engineer with access to troubleshooting tickets data and various technical and product documentation.
                       Rely on information derived from these tickets to address queries. Feel free to seek clarification through relevant questions."""
-  # message = "Your name is Clara. You are a helpful senior telecom network engineer having access to troubleshooting tickets data and other technical and product documentation.Stick to the knowledge from these tickets. Ask clarification questions if needed. "+ message
-  message= context_prompt + "User Query: "+ message + "If asked about Root cause analysis (rca), give only one possible awnser and then give the steps to resolve in the exact format which is :- The steps to resolve could be - [/INST]"
+  message= context_prompt + "User Query: "+ message + "If asked about Root cause analysis (rca), give only one possible awnser and then give the steps to resolve in the exact format which is :- The steps to resolve could be:"
   result=qa({"question":message})
   bold_answer= "<b>" + result['answer'] + "</b>"
   return bold_answer + "<br></br>" +'1. ' + str(result["source_documents"][0]) +"<br>" + '2. ' + str(result["source_documents"][1]) +  "<br>" + "3. " + str(result["source_documents"][2])