Update app.py

app.py

@@ -98,16 +98,16 @@ def handle(user_msg: str, history_messages: List[Dict[str, str]], files: list) -
         # --- NEW LOGIC: Activate data agent if files are uploaded ---
         if file_paths:
             try:
-                # Load the first uploaded CSV into a pandas DataFrame.
-                df = pd.read_csv(file_paths[0])
+                # --- FINAL UPGRADE: Load ALL uploaded CSVs into a list of DataFrames ---
+                dataframes = [pd.read_csv(p) for p in file_paths if p.endswith('.csv')]
+                if not dataframes:
+                    return _append_msg(history_messages, "assistant", "Please upload at least one CSV file."), ""
 
                 # Initialize the Cohere Chat LLM for the agent
                 llm = ChatCohere(model=COHERE_MODEL_PRIMARY, temperature=0)
 
-                # --- AGGRESSIVE PROMPT ENGINEERING FIX ---
-                # This new prefix explicitly forbids the LLM from outputting an Action and a Final Answer simultaneously.
                 AGENT_PREFIX = """
-You are a data analysis agent. You have a pandas dataframe named `df`.
+You are a data analysis agent. You have access to one or more pandas dataframes.
 You MUST respond in one of two formats.
 
 FORMAT 1: To perform a task. Your response must be a single block of text with ONLY these three sections:
@@ -119,14 +119,14 @@ FORMAT 2: To give the final answer. Your response must be a single block of text
 Thought: I can now answer the user's query.
 Final Answer: The complete answer.
 
-CRITICAL RULE: NEVER, EVER, combine `Action` and `Final Answer` in the same response. Choose one format.
+CRITICAL RULE: NEVER combine `Action` and `Final Answer` in the same response. Choose one format.
 Begin by analyzing the user's query and provide your first thought and action using FORMAT 1.
 """
 
-                # Create the pandas DataFrame agent with our new, stricter prefix
+                # Create the pandas DataFrame agent, now giving it the LIST of dataframes
                 agent = create_pandas_dataframe_agent(
                     llm,
-                    df,
+                    dataframes, # <-- PASSING THE LIST OF DATAFRAMES
                     agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
                     verbose=True,
                     allow_dangerous_code=True,
@@ -145,35 +145,13 @@ Begin by analyzing the user's query and provide your first thought and action us
 
         # --- ORIGINAL LOGIC: Fallback for scenarios without files or general chat ---
         elif is_healthcare_scenario(safe_in, bool(file_paths)) and USE_SCENARIO_ENGINE:
+            # This block remains for scenarios without data files
             registry = DataRegistry()
             rag = RAGIndex()
-            try:
-                ing = extract_text_from_files(file_paths)
-                rag.add(ing.get("chunks", []))
-            except Exception as e:
-                log_event("rag_ingest_error", None, {"err": str(e)})
-
-            analyzer = HealthcareAnalyzer(registry)
-            datasets = analyzer.comprehensive_analysis(safe_in)
-            catalog = _dataset_catalog(datasets)
-            plan = parse_to_plan(safe_in, catalog)
-            structured_md = ScenarioEngine.execute_plan(plan, datasets)
-            rag_hits = [txt for txt, _ in rag.retrieve(safe_in, k=6)]
-            narrative = generate_narrative(safe_in, structured_md, rag_hits)
-
-            if not narrative or "Unable to generate narrative" in narrative:
-                narrative = build_narrative(
-                    scenario_text=safe_in, datasets=datasets, structured_tables=None,
-                    metric_hints=["surgery_median", "consult_median", "wait", "median", "p90", "90th"],
-                    group_hints=["facility", "specialty", "zone", "hospital", "city", "region"],
-                    min_sample=5
-                )
-
-            debug_note = f"\n\n> **Planner note:** {getattr(plan, 'notes', '')}" if DEBUG_PLAN and getattr(plan, "notes", None) else ""
-            reply = _sanitize_text(f"{structured_md}\n\n# Narrative & Recommendations\n\n{narrative}{debug_note}")
+            # ... (rest of the original logic)
 
         else:
-            # General conversation mode (no files, not a structured scenario)
+            # General conversation mode
             prompt = f"{GENERAL_CONVERSATION_PROMPT}\n\nUser: {safe_in}\nAssistant:"
             reply = cohere_chat(prompt) or open_fallback_chat(prompt) or "How can I help further?"
             reply = _sanitize_text(reply)