Update app.py

app.py

@@ -104,14 +104,36 @@ def handle(user_msg: str, history_messages: List[Dict[str, str]], files: list) -
                 # Initialize the Cohere Chat LLM for the agent
                 llm = ChatCohere(model=COHERE_MODEL_PRIMARY, temperature=0)
 
-                # Create the pandas DataFrame agent, powered by Cohere
+                # --- PROMPT ENGINEERING FIX ---
+                # This prefix gives the LLM strict instructions to prevent parsing errors.
+                AGENT_PREFIX = """
+                You are working with a pandas dataframe in Python. The name of the dataframe is `df`.
+                You are a data analysis expert providing a structured response.
+                
+                You are given a user query and you must use the python_repl_ast tool to answer.
+
+                RULES:
+                1. You MUST use the python_repl_ast tool to derive your answers.
+                2. You MUST NOT provide a "Final Answer" until you have used the tool and have an observation.
+                3. You MUST work step-by-step. First Think, then use the Action/Action Input, then get an Observation. Do not try to do everything in one step.
+                
+                This is an example of a valid response:
+                Thought: I need to check the column names in the dataframe.
+                Action: python_repl_ast
+                Action Input: print(df.columns)
+
+                Begin!
+                """
+
+                # Create the pandas DataFrame agent with our custom prefix
                 agent = create_pandas_dataframe_agent(
                     llm,
                     df,
                     agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
                     verbose=True,
                     allow_dangerous_code=True,
-                    handle_parsing_errors=True  # <-- THE FINAL FIX IS HERE
+                    handle_parsing_errors=True,
+                    prefix=AGENT_PREFIX  # <-- APPLYING THE PROMPT ENGINEERING FIX
                 )
 
                 # Run the agent with the user's scenario text.