Update app.py

app.py

@@ -1,7 +1,7 @@
 # app.py — Spine Coder (Chatbot + Feedback + Session Logs) — Gradio 4.x
 # ------------------------------------------------------------------------------
-# Stable build: FINAL-v2.0 core wired, transitional regions, flags, laterality,
-# structured logs, graceful errors, and a clean UI.
+# Stable build for FINAL-v2.1 core: transitional regions, flags, laterality,
+# case-level modifiers panel, structured logs, graceful errors, and clean UI.
 # ------------------------------------------------------------------------------
 
 import os
@@ -20,13 +20,13 @@ import os as _os, sys as _sys, inspect as _inspect
 _sys.path.insert(0, _os.path.abspath("."))  # ensure repo root is first on sys.path
 
 try:
-    # If your package layout is spine_coder/spine_coder/spine_coder_core.py
+    # Preferred package layout: spine_coder/spine_coder/spine_coder_core.py
     from spine_coder.spine_coder.spine_coder_core import suggest_with_cpt_billing
 except ImportError:
-    # Fallback to flat layout if you later flatten the folder tree
+    # Fallback to flat layout if you flattened the folder tree
     from spine_coder.spine_coder_core import suggest_with_cpt_billing
 
-# Prove which file is actually loaded (shows in Space logs)
+# Prove which file is actually loaded (visible in Space logs)
 try:
     print("[CORE] loaded from:", _inspect.getsourcefile(suggest_with_cpt_billing))
 except Exception:
@@ -64,63 +64,56 @@ def export_session(session_id: str) -> str:
         json.dump(data, f, indent=2, ensure_ascii=False)
     return out_path
 
+
 # ==== UI helpers ==============================================================
 
-TABLE_COLS = [
+SUGG_COLS = [
     "CPT","Modifier","Modifiers","Description","Rationale",
     "Confidence","Primary","Category","Laterality","Units"
 ]
-EMPTY_DF = pd.DataFrame(columns=TABLE_COLS)
+EMPTY_SUGG_DF = pd.DataFrame(columns=SUGG_COLS)
+EMPTY_MODS_DF = pd.DataFrame(columns=["modifier","reason"])
 
-def _coalesce_suggestions(result: Dict[str, Any]) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
-    """
-    Return (rows_for_table, meta_badges) from core result.
-    Robust to minor schema differences (list vs dict for levels/flags).
-    """
+def _coalesce_rows(result: Dict[str, Any]) -> Tuple[pd.DataFrame, Dict[str, Any]]:
+    """Build the suggestions table and meta badges from core result."""
     if not isinstance(result, dict):
-        return [], {}
+        return EMPTY_SUGG_DF, {}
 
-    # ---------- rows ----------
     sugg = result.get("suggestions") or []
     rows: List[Dict[str, Any]] = []
     case_lat = (result.get("laterality") or "").strip().lower()
 
     for s in sugg:
-        if not isinstance(s, dict):
+        if not isinstance(s, dict): 
             continue
-
-        # derive row laterality
-        row_lat = (s.get("laterality") or "").strip().lower()
         mods = s.get("modifiers", []) or []
+        # derive row laterality from LT/RT or case laterality
+        row_lat = (s.get("laterality") or "").strip().lower()
         if not row_lat:
-            if isinstance(mods, list) and "LT" in mods:
-                row_lat = "left"
-            elif isinstance(mods, list) and "RT" in mods:
-                row_lat = "right"
-            elif case_lat in ("left", "right", "bilateral"):
-                row_lat = case_lat
+            if isinstance(mods, list) and "LT" in mods:   row_lat = "left"
+            elif isinstance(mods, list) and "RT" in mods: row_lat = "right"
+            elif case_lat in ("left","right","bilateral"): row_lat = case_lat
 
         conf_val = s.get("confidence")
-        conf_out = round(float(conf_val), 2) if isinstance(conf_val, (int, float)) else (conf_val or "")
+        conf_out = round(float(conf_val), 2) if isinstance(conf_val, (int,float)) else (conf_val or "")
 
         rows.append({
-            "CPT": s.get("cpt", ""),
+            "CPT": s.get("cpt",""),
             "Modifier": s.get("modifier") or "",
             "Modifiers": ", ".join(mods) if isinstance(mods, list) else (s.get("modifiers") or ""),
-            "Description": s.get("desc", ""),
-            "Rationale": s.get("rationale", ""),
+            "Description": s.get("desc",""),
+            "Rationale": s.get("rationale",""),
             "Confidence": conf_out,
             "Primary": "✓" if s.get("primary") else "",
-            "Category": s.get("category", ""),
+            "Category": s.get("category",""),
             "Laterality": row_lat,
-            "Units": s.get("units", 1),
+            "Units": s.get("units",1),
         })
 
-    # ---------- normalize levels ----------
+    # normalize levels
     segs: List[str] = []
     inters_list: List[str] = []
-    lvl_lat: str = ""
-
+    lvl_lat = ""
     levels_obj = result.get("levels")
     if isinstance(levels_obj, dict):
         segs = list(levels_obj.get("segments") or [])
@@ -129,7 +122,7 @@ def _coalesce_suggestions(result: Dict[str, Any]) -> Tuple[List[Dict[str, Any]],
     elif isinstance(levels_obj, list):
         segs = [str(x) for x in levels_obj]
 
-    # ---------- normalize flags ----------
+    # normalize flags
     flags_obj = result.get("flags")
     if isinstance(flags_obj, list):
         flags_list = [str(x) for x in flags_obj]
@@ -138,22 +131,33 @@ def _coalesce_suggestions(result: Dict[str, Any]) -> Tuple[List[Dict[str, Any]],
     else:
         flags_list = []
 
-    # ---------- meta ----------
     meta = {
-        "payer": result.get("payer", ""),
-        "region": result.get("region", ""),
-        "laterality": result.get("laterality", "") or lvl_lat,
+        "payer": result.get("payer",""),
+        "region": result.get("region",""),
+        "laterality": result.get("laterality","") or lvl_lat,
         "levels_segments": ", ".join(segs),
-        # Prefer numeric estimate if present; else join the interspaces list
         "levels_interspaces": (
-            str(result.get("interspaces_est", "")) if "interspaces_est" in result
+            str(result.get("interspaces_est","")) if "interspaces_est" in result
             else ", ".join(inters_list)
         ),
         "flags": ", ".join(sorted(flags_list)),
-        "build": result.get("build", ""),
-        "mode": result.get("mode", ""),
+        "build": result.get("build",""),
+        "mode": result.get("mode",""),
     }
-    return rows, meta
+
+    df = EMPTY_SUGG_DF if not rows else pd.DataFrame(rows)
+    if not df.empty:
+        for col in SUGG_COLS:
+            if col not in df.columns:
+                df[col] = ""
+        df = df[SUGG_COLS]
+    return df, meta
+
+def _case_mods_df(result: Dict[str, Any]) -> pd.DataFrame:
+    mods = result.get("case_modifiers", []) or []
+    if not mods:
+        return pd.DataFrame([{"modifier":"—","reason":"No case-level modifiers"}])
+    return pd.DataFrame([{"modifier": f"-{m.get('modifier','')}", "reason": m.get("reason","")} for m in mods])
 
 def _summary_md(meta: Dict[str, Any]) -> str:
     chips = []
@@ -169,11 +173,16 @@ def _summary_md(meta: Dict[str, Any]) -> str:
 def new_session() -> str:
     return str(uuid.uuid4())[:8]
 
+
 # ==== Core actions ============================================================
 
 def run_inference(note: str, payer: str, top_k: int, session_id: str):
     if not note.strip():
-        return EMPTY_DF, "—", "", "", session_id
+        return (
+            EMPTY_SUGG_DF,                           # suggestions table
+            pd.DataFrame([{"modifier":"—","reason":""}]),  # case mods table
+            "—", "", "", session_id                  # summary, json, warn, session
+        )
 
     _append_log(session_id, {"event": "request", "payer": payer, "top_k": top_k, "note": note})
     warn_text = ""
@@ -189,22 +198,27 @@ def run_inference(note: str, payer: str, top_k: int, session_id: str):
         warn = f"⚠️ Error: {e}"
         if DEBUG:
             warn += f"\n\n```traceback\n{tb}\n```"
-        return EMPTY_DF, "—", "", warn, session_id
-
-    rows, meta = _coalesce_suggestions(result)
-    df = EMPTY_DF if not rows else pd.DataFrame(rows)
-    if not df.empty:
-        # ensure consistent column order
-        for col in TABLE_COLS:
-            if col not in df.columns:
-                df[col] = ""
-        df = df[TABLE_COLS]
-
+        return (
+            EMPTY_SUGG_DF,
+            pd.DataFrame([{"modifier":"—","reason":""}]),
+            "—", "", warn, session_id
+        )
+
+    sugg_df, meta = _coalesce_rows(result)
+    case_mods_df = _case_mods_df(result)
     summary = _summary_md(meta)
     json_pretty = json.dumps(result, indent=2, ensure_ascii=False)
 
-    _append_log(session_id, {"event": "response", "meta": meta, "rows_len": len(rows)})
-    return df, summary, json_pretty, warn_text, session_id
+    _append_log(session_id, {
+        "event": "response",
+        "meta": {
+            **meta,
+            # log case modifiers as a short string for quick scanning
+            "case_modifiers": ", ".join([f"-{m}" for m in [cm.get("modifier","") for cm in (result.get("case_modifiers") or [])] if m]) or ""
+        },
+        "rows_len": int(len(sugg_df) if hasattr(sugg_df, "__len__") else 0)
+    })
+    return sugg_df, case_mods_df, summary, json_pretty, "", session_id
 
 def record_feedback(session_id: str, vote: str, text: str):
     if not vote and not text:
@@ -218,7 +232,14 @@ def do_export(session_id: str):
     return path
 
 def on_clear():
-    return "", "Medicare", 10, EMPTY_DF, "—", "", "", new_session()
+    return (
+        "", "Medicare", 10,
+        EMPTY_SUGG_DF,
+        pd.DataFrame([{"modifier":"—","reason":""}]),
+        "—", "", "",
+        new_session()
+    )
+
 
 # ==== Examples ================================================================
 
@@ -228,6 +249,11 @@ EXAMPLES = [
     ["Posterior cervical foraminotomy right C6–C7; no fusion or instrumentation.", "Medicare", 10],
     ["ALIF L5–S1 with structural allograft; non-segmental instrumentation placed.", "Medicare", 10],
     ["Removal of posterior segmental instrumentation T10–L2; no new hardware placed.", "Medicare", 10],
+    # Case-modifier smoke tests:
+    ["TLIF L4–L5 was initiated but aborted midway due to neuromonitoring changes.", "Medicare", 10],  # -53
+    ["Bilateral decompression and foraminotomy at L4–L5 and L5–S1.", "Medicare", 10],                # -50
+    ["Assistant surgeon present; resident not available.", "Medicare", 10],                           # -82 not -80
+    ["Complex exposure with severe deformity and adhesiolysis.", "Medicare", 10],                     # -22
 ]
 
 # ==== Theme / CSS =============================================================
@@ -266,11 +292,11 @@ CUSTOM_CSS = """
 /* Table container */
 .table-wrap { max-height: 520px; overflow: auto; }
 
-/* Base table styles */
+/* Suggestions table target */
 #suggestions_table .dataframe {
   font-size: 15px;
   width: 100% !important;
-  table-layout: auto !important;  /* allow columns to size naturally */
+  table-layout: auto !important;
   border-collapse: collapse;
 }
 #suggestions_table .dataframe th,
@@ -386,15 +412,28 @@ with gr.Blocks(theme=THEME, css=CUSTOM_CSS, title="Spine Coder — CPT Billing")
         with gr.Column(scale=7):
             gr.Markdown("#### Results")
             summary_md = gr.Markdown("—", elem_classes=["badge-row"])
+
+            # Suggestions table
             table = gr.Dataframe(
-                value=EMPTY_DF,
-                label="Suggestions",
+                value=EMPTY_SUGG_DF,
+                label="CPT Suggestions",
                 interactive=False,
                 row_count=(0, "dynamic"),
                 wrap=True,
                 elem_classes=["table-wrap"],
-                elem_id="suggestions_table",   # important for CSS targeting
+                elem_id="suggestions_table",
             )
+
+            # Case-level modifiers table
+            gr.Markdown("### Case Modifiers (visit-level)")
+            case_mods_table = gr.Dataframe(
+                value=EMPTY_MODS_DF,
+                headers=["modifier","reason"],
+                interactive=False,
+                wrap=True,
+                label="Case Modifiers",
+            )
+
             with gr.Accordion("Raw JSON", open=False):
                 json_out = gr.Code(language="json", value="", interactive=False)
             warn_md = gr.Markdown("")
@@ -407,15 +446,17 @@ with gr.Blocks(theme=THEME, css=CUSTOM_CSS, title="Spine Coder — CPT Billing")
     demo.load(_on_load, outputs=[session_id, sid_show])
 
     run_inputs = [note_in, payer_dd, topk, session_id]
-    run_outputs = [table, summary_md, json_out, warn_md, session_id]
+    run_outputs = [table, case_mods_table, summary_md, json_out, warn_md, session_id]
 
     run_btn.click(run_inference, inputs=run_inputs, outputs=run_outputs)
     note_in.submit(run_inference, inputs=run_inputs, outputs=run_outputs)
 
-    clear_btn.click(on_clear, outputs=[note_in, payer_dd, topk, table, summary_md, json_out, warn_md, session_id])
+    clear_btn.click(
+        on_clear,
+        outputs=[note_in, payer_dd, topk, table, case_mods_table, summary_md, json_out, warn_md, session_id]
+    )
 
     fb_submit.click(record_feedback, inputs=[session_id, fb_choice, fb_text], outputs=fb_status)
-
     export_btn.click(do_export, inputs=[session_id], outputs=[export_file])
 
 if __name__ == "__main__":