Update app.py

app.py

@@ -1,5 +1,4 @@
 # app.py
-# app.py
 import os, traceback, regex as re2
 import gradio as gr
 import pandas as pd
@@ -18,11 +17,9 @@ from llm_router import generate_narrative, cohere_chat, open_fallback_chat
 
 def _sanitize_text(s: str) -> str:
     if not isinstance(s, str): return s
-    # strip control chars (keep newlines/tabs)
     return re2.sub(r'[\p{C}--[\n\t]]+', '', s)
 
 def _dataset_catalog(results: Dict[str, Any]) -> Dict[str, List[str]]:
-    """Expose available columns per dataset to the planner."""
     cat: Dict[str, List[str]] = {}
     for k, v in results.items():
         if isinstance(v, pd.DataFrame):
@@ -30,14 +27,12 @@ def _dataset_catalog(results: Dict[str, Any]) -> Dict[str, List[str]]:
     return cat
 
 def is_healthcare_scenario(text: str, has_files: bool) -> bool:
-    """Heuristic: scenario mode when user provided files + scenario-ish text."""
     t = (text or "").lower()
     kws = HEALTHCARE_SETTINGS["healthcare_keywords"]
     structured = any(s in t for s in ["background", "situation", "tasks", "deliverables"])
     return has_files and (structured or any(k in t for k in kws))
 
 def _append_msg(history_messages: List[Dict[str, str]], role: str, content: str) -> List[Dict[str, str]]:
-    """Return a new history list with one message appended."""
     return (history_messages or []) + [{"role": role, "content": content}]
 
 def handle(user_msg: str, history_messages: List[Dict[str, str]], files: list) -> Tuple[List[Dict[str, str]], str]:
@@ -49,10 +44,10 @@ def handle(user_msg: str, history_messages: List[Dict[str, str]], files: list) -
             new_hist = _append_msg(new_hist, "assistant", reply)
             return new_hist, ""
 
-        # Normalize files -> paths (safe when files is None)
+        # Normalize files -> paths
         file_paths = [getattr(f, "name", None) or f for f in (files or [])]
 
-        # Register CSVs into the registry
+        # Register CSVs
         registry = DataRegistry()
         for p in file_paths:
             try:
@@ -60,36 +55,34 @@ def handle(user_msg: str, history_messages: List[Dict[str, str]], files: list) -
             except Exception as e:
                 log_event("ingest_error", None, {"file": p, "err": str(e)})
 
-        # RAG ingest (best-effort, text only; safe on empty)
+        # RAG ingest (text only; safe on empty)
         rag = RAGIndex()
         ing = extract_text_from_files(file_paths)
         rag.add(ing.get("chunks", []))
 
-        # Scenario mode: plan -> deterministic execution -> narrative
+        # Scenario flow
         if is_healthcare_scenario(safe_in, bool(file_paths)) and USE_SCENARIO_ENGINE:
             analyzer = HealthcareAnalyzer(registry)
-            datasets = analyzer.comprehensive_analysis(safe_in)  # expose dataframes by filename
+            datasets = analyzer.comprehensive_analysis(safe_in)
             catalog = _dataset_catalog(datasets)
 
-            # 1) LLM parses scenario into a plan (scenario-agnostic, no hardcoding)
+            # LLM → plan (Cohere API)
             plan = parse_to_plan(safe_in, catalog)
 
-            # 2) Deterministic execution of the plan (pandas-based)
+            # Deterministic execution
             structured_md = ScenarioEngine.execute_plan(plan, datasets)
 
-            # 3) Canadian grounding + narrative (Cohere primary, open-model fallback)
+            # Narrative via Cohere API (fallback only if enabled)
             rag_hits = [txt for txt, _ in rag.retrieve(safe_in, k=6)]
             narrative = generate_narrative(safe_in, structured_md, rag_hits)
 
-            final = f"{structured_md}\n\n# Narrative & Recommendations\n\n{narrative}"
-            reply = _sanitize_text(final)
+            reply = _sanitize_text(f"{structured_md}\n\n# Narrative & Recommendations\n\n{narrative}")
         else:
-            # General conversation mode (no scenario/files required)
+            # General chat via Cohere API
             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)
 
-        # Append user then assistant messages to history
         new_hist = _append_msg(history_messages, "user", user_msg)
         new_hist = _append_msg(new_hist, "assistant", reply)
         return new_hist, ""
@@ -103,9 +96,8 @@ def handle(user_msg: str, history_messages: List[Dict[str, str]], files: list) -
 
 # -------- UI --------
 with gr.Blocks(analytics_enabled=False) as demo:
-    gr.Markdown("## Canadian Healthcare AI • Scenario-Agnostic (Cohere primary • Deterministic analytics)")
-    # Use the new 'messages' format to avoid deprecation
-    chat = gr.Chatbot(type="messages", height=520)
+    gr.Markdown("## Canadian Healthcare AI • Cohere API • Scenario-Agnostic • Deterministic analytics")
+    chat = gr.Chatbot(type="messages", height=520)  # OpenAI-style role/content
     files = gr.Files(
         file_count="multiple",
         type="filepath",
@@ -116,7 +108,6 @@ with gr.Blocks(analytics_enabled=False) as demo:
     clear = gr.Button("Clear")
 
     def _on_send(m, h, f):
-        # h is already a list of {'role','content'} dicts with type="messages"
         h2, _ = handle(m, h or [], f or [])
         return h2, ""