Update app.py

app.py

@@ -5,30 +5,39 @@ from langchain_core.messages import AIMessage, HumanMessage
 from azure_openai import qt, get_response
 from retriver import search_and_reconstruct
 
-placeHolderPersonaQTE = "{assetGlossary}"
-placeHolderPersonaRAGSystem = "{assetGlossary}"
-placeHolderPersonaRAGUser = "{query}, {knowledge}"
+def read_file(file):
+    """
+    Reads the content of a text file and returns it as a string.
 
+    :param approver: The type of approver.
+    :return: The content of the file as a string.
+    """
+    fp = f"assets/{file}.md"
+    try:
+        with open(fp, 'r', encoding='utf-8') as file:
+            content = file.read()
+        return content
+    except FileNotFoundError:
+        print(f"The file at {fp} was not found.")
+    except IOError:
+        print(f"An error occurred while reading the file at {fp}.")
+
+
+QTESystemMessage = read_file("QTESystemMessage")
+RAGSystemMessage = read_file("RAGSystemMessage")
+RAGUserMessage = read_file("RAGUserMessage")
+k = 5
+pagesReturned = 3
+temp1 = 0.5
+tokens1 = 200
+temp2 = 0.5
+tokens2 = 1000
+asset = "GSKGlossary"
 
 # app config
 st.set_page_config(page_title="Reg Intel Chatbot", page_icon="🤖")
 st.title("Reg Intel Toolbox :toolbox:")
 
-# Sidebar for inputting personas
-st.sidebar.title("RAG System Designer")
-st.sidebar.header("Query Designer Config")
-persona1SystemMessage = st.sidebar.text_area("Query Designer System Message", value=placeHolderPersonaQTE, height=300)
-temp1 = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, step=0.1, value=0.6, key='persona1_temp')
-tokens1 = st.sidebar.slider("Tokens", min_value=0, max_value=4000, step=100, value=500, key='persona1_tokens')
-st.sidebar.subheader("Number of Search Results")
-k = st.sidebar.slider("Returned Docs", min_value=1, max_value=10, step=1, value=3, key='k')
-pagesReturned = st.sidebar.slider("Number of Returned Pages", min_value=1, max_value=10, step=1, value=1, key='pagesReturned')
-st.sidebar.header("Engineered Prompt Config")
-persona2SystemMessage = st.sidebar.text_area("Answer Creation System Message", value=placeHolderPersonaRAGSystem, height=300)
-persona2UserMessage = st.sidebar.text_area("Answer Creation User Message", value=placeHolderPersonaRAGUser, height=300)
-temp2 = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, step=0.1, value=0.6, key='persona2_temp')
-tokens2 = st.sidebar.slider("Tokens", min_value=0, max_value=4000, step=100, value=500, key='persona2_tokens')
-
 # session state
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = [
@@ -53,10 +62,9 @@ if user_query is not None and user_query != "":
         st.markdown(user_query)
 
    
-    qte = qt(persona1SystemMessage, st.session_state.chat_history, temp1, tokens1, "GSKGlossary")
-    st.sidebar.header("QTE and Knowledge Results")
-    st.sidebar.header("QTE")
-    st.sidebar.text(qte)
+    qte = qt(QTESystemMessage, st.session_state.chat_history, temp1, tokens1, asset)
+    st.text("Contextualised Query")
+    st.caption(qte)
     knowledge = search_and_reconstruct(qte, k, pagesReturned)
     
     if knowledge:
@@ -74,11 +82,11 @@ if user_query is not None and user_query != "":
         df = pd.DataFrame(table_data)
 
         # Display the table in the sidebar
-        st.sidebar.write("### Knowledge Base Results")
-        st.sidebar.dataframe(df)  # Adjust height as needed
+        st.text("Knowledge Base Results")
+        st.dataframe(df)  # Adjust height as needed
     else:
-        st.sidebar.write("No relevant knowledge base results found.")
+        st.write("No relevant knowledge base results found.")
     
     with st.chat_message("AI"):
-        response = st.write_stream(get_response(st.session_state.chat_history, qte, knowledge, temp1, temp2, tokens1, tokens2, persona2SystemMessage, persona2UserMessage, "GSKGlossary"))
+        response = st.write_stream(get_response(st.session_state.chat_history, qte, knowledge, temp2, tokens2, RAGSystemMessage, RAGUserMessage, asset))
         st.session_state.chat_history.append(AIMessage(content=response))