Update app.py

change the prompt to follow the example

app.py

@@ -251,13 +251,49 @@ if st.session_state.get('is_key_valid', False):
 
     tools = [get_credit_score, get_account_status, check_pr_status, consult_policy_doc]
 
-    prompt = ChatPromptTemplate.from_messages([
-        ("system", "You are a Loan Risk Officer. Query SQL DB for customer info. Consult Policy Docs for rules."),
-        ("human", "{input}"),
-        MessagesPlaceholder(variable_name="agent_scratchpad"),
-    ])
-    
-    agent = create_tool_calling_agent(llm, tools, prompt)
+    # --- PROMPT TEMPLATE (Steps Enforcement) ---
+    template = """You are a Loan Risk Officer. You must execute the loan assessment process in exactly 4 sequential steps.
+
+    TOOLS AVAILABLE:
+    {tools}
+
+    INSTRUCTIONS:
+    For every request, you MUST follow this reasoning process and output format:
+
+    Step 1. Retrieve information for customer information
+    - Call the SQL tools (get_credit_score, get_account_status).
+    - If Nationality is NOT Singaporean, you MUST call check_pr_status.
+    - Output the gathered data.
+
+    Step 2. Check Overall Risk
+    - Use 'consult_policy_doc' to find the "Risk Classification Matrix" in the PDF.
+    - Compare the Credit Score and Account Status against the matrix.
+    - Determine the Risk Level (Low, Medium, or High).
+
+    Step 3. Check Interest Rate
+    - Use 'consult_policy_doc' to find the "Interest Rate Table".
+    - Find the rate corresponding to the Risk Level from Step 2.
+
+    Step 4. Final Report
+    - Generate the final recommendation.
+
+    EXAMPLE OUTPUT FORMAT (Follow this style exactly):
+    **Customer Information:** [Name], [ID]
+    **Step 1. Retrieve information**
+    Credit Score: [Score], Account Status: [Status], Nationality: [Nationality]
+    **Step 2. Check Overall Risk**
+    Credit Score: [Score] + Account Status: [Status] -> Overall Risk: [Level]
+    **Step 3. Check Interest Rate**
+    Overall Risk: [Level] -> [Rate]%
+    **Step 4. Report**
+    Recommend the loan at [Rate]% interest.
+
+    Begin!
+
+    Question: {input}
+    Thought:{agent_scratchpad}"""
+
+    prompt = PromptTemplate.from_template(template)
     agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, return_intermediate_steps=True)
 
     col1, col2 = st.columns([1, 2])