Update app.py

app.py

@@ -26,25 +26,22 @@ llm = ChatGroq(
 # ... (Keep your imports and LLM initialization the same)
 
 # 4. Load Math Tools
-tools = load_tools(["llm-math"], llm=llm)
+# Define the strict instructions as a SystemMessage
+from langchain_core.messages import SystemMessage
 
-# --- ADD THE NEW BLOCK HERE ---
-system_message = (
-    "You are a strict Mathematics Assistant. "
-    "Your ONLY task is to solve mathematical problems using your tools. "
-    "If a user asks about anything other than math (such as history, general knowledge, "
-    "coding, or personal opinions), you must politely refuse and explain that you "
-    "only provide assistance with mathematics."
+system_message = SystemMessage(
+    content=(
+        "You are a strict Mathematics Assistant. "
+        "Your ONLY task is to solve mathematical problems using your tools. "
+        "If a user asks about anything other than math (such as history, general knowledge, "
+        "coding, or personal opinions), you must politely refuse and explain that you "
+        "only provide assistance with mathematics."
+    )
 )
 
-agent_executor = create_react_agent(
-    llm, 
-    tools, 
-    state_modifier=system_message
-)
-# --- END OF NEW BLOCK ---
-
-# ... (Keep the Chat Interface logic below this)
+# Re-create the agent WITHOUT the state_modifier keyword
+# We will pass the system message in the actual chat logic instead
+agent_executor = create_react_agent(llm, tools)
 
 # 5. Chat Interface Setup
 if "messages" not in st.session_state:
@@ -66,9 +63,14 @@ if prompt := st.chat_input("Ask me a complex math question!"):
     with st.chat_message("assistant"):
         with st.spinner("Thinking..."):
             try:
-                # Invoke the agent
-                response = agent_executor.invoke({"messages": [("user", prompt)]})
-                
+               # Change this line:
+# response = agent_executor.invoke({"messages": [("user", prompt)]})
+
+# To this:
+response = agent_executor.invoke({
+    "messages": [system_message, ("user", prompt)]
+})
+
                 # Get the final response from the message list
                 final_answer = response["messages"][-1].content