Update app.py

app.py

@@ -1,7 +1,6 @@
 # ---------- Host/port ----------
 HOST, PORT, SHARE = "0.0.0.0", 7860, True
 
-# ---------- Env hygiene ----------
 import os
 os.environ["NO_PROXY"] = "127.0.0.1,localhost,::1"
 os.environ["no_proxy"] = "127.0.0.1,localhost,::1"
@@ -10,7 +9,6 @@ for _k in ("HTTP_PROXY","http_proxy","HTTPS_PROXY","https_proxy"):
 os.environ.setdefault("GRADIO_OPEN_BROWSER", "false")
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
 
-# --- FORCE NON-INTERACTIVE MATPLOTLIB BACKEND (for SHAP plots) ---
 os.environ["MPLBACKEND"] = "Agg"
 import matplotlib
 matplotlib.use("Agg", force=True)
@@ -118,7 +116,7 @@ def get_global_importance_table(model) -> Optional[pd.DataFrame]:
 
     return None
 
-# ---------- Load model (strip .pkl because PyCaret appends) ----------
+# ---------- Load model  ----------
 local_path = hf_hub_download(repo_id=REPO, filename=FNAME, token=TOKEN)
 MODEL = load_model(str(Path(local_path).with_suffix("")))
 
@@ -190,11 +188,9 @@ def build_sample_choices(df: pd.DataFrame, tgt: str, flt: str = "All") -> List[s
 # ---------- SHAP background / explainer ----------
 def _prepare_background(df_samples: pd.DataFrame | None, max_rows: int = 200) -> pd.DataFrame:
     if df_samples is None or df_samples.empty:
-        # tiny synthetic background of zeros
         bg = pd.DataFrame([{k: 0.0 for k in FEATURES} for _ in range(50)])
     else:
         bg = df_samples[FEATURES].copy()
-    # numeric coercion + boolean to {0,1} + median impute
     for c in FEATURES:
         if c not in bg.columns:
             bg[c] = np.nan