Update app.py

app.py

@@ -20,18 +20,26 @@ from sklearn.metrics import mean_squared_error, mean_absolute_error
 # Constants (Ts variant)
 # =========================
 APP_NAME = "ST_Sonic_Ts"
-TAGLINE  = "Real-Time Shear Slowness (Ts) Tracking"
-
-# Keep your drilling features the same (match training & Excel headers)
-FEATURES  = ["WOB(klbf)", "TORQUE(kft.lbf)", "SPP(psi)", "RPM(1/min)", "ROP(ft/h)", "Flow Rate, gpm"]
-TARGET    = "Ts"          # <-- actual Ts column in your sheets
-PRED_COL  = "Ts_Pred"     # <-- predicted Ts column added by the app
+TAGLINE  = "Real-Time Shear Slowness (Ts) Prediction"
+
+# Use the exact column headers you have in Excel (from your screenshot)
+FEATURES  = [
+    "WOB, klbf",
+    "Torque(kft.lbf)",
+    "SPP(psi)",
+    "RPM(1/min)",
+    "ROP(ft/h)",
+    "Flow Rate, gpm",
+]
+TARGET    = "Ts,us/ft_Actual"   # actual Ts column in your sheets
+PRED_COL  = "Ts_Pred"           # predicted Ts column we’ll add
 
 MODELS_DIR = Path("models")
-DEFAULT_MODEL = MODELS_DIR / "ts_model.joblib"
+DEFAULT_MODEL = MODELS_DIR / "ts_model.joblib"  # your uploaded model filename
 MODEL_FALLBACKS = [MODELS_DIR / "model.joblib", MODELS_DIR / "model.pkl"]
 COLORS = {"pred": "#1f77b4", "actual": "#f2b702", "ref": "#5a5a5a"}
 
+
 # ---- Plot sizing controls ----
 CROSS_W = 350
 CROSS_H = 350
@@ -216,6 +224,26 @@ def _excel_engine() -> str:
     except Exception:
         return "openpyxl"
 
+def _normalize_columns(df: pd.DataFrame) -> pd.DataFrame:
+    out = df.copy()
+    out.columns = [str(c).strip() for c in out.columns]
+
+    # Map legacy/alternate headers -> current headers (so older files still pass)
+    alias = {
+        "WOB(klbf)": "WOB, klbf",
+        "TORQUE(kft.lbf)": "Torque(kft.lbf)",
+        "Flow Rate, gpm ": "Flow Rate, gpm",
+        "Fow Rate, gpm": "Flow Rate, gpm",
+        "Fow Rate, gpm ": "Flow Rate, gpm",
+        "Ts": "Ts,us/ft_Actual",
+        "Ts_Actual": "Ts,us/ft_Actual",
+        "Ts (us/ft)": "Ts,us/ft_Actual",
+        "Depth": "Depth, ft",
+        "Depth(ft)": "Depth, ft",
+    }
+    out = out.rename(columns={k: v for k, v in alias.items() if k in out.columns})
+    return out
+
 def _excel_safe_name(name: str) -> str:
     bad = '[]:*?/\\'
     safe = ''.join('_' if ch in bad else ch for ch in str(name))
@@ -498,18 +526,19 @@ def track_plot(df, include_actual=True):
 
     # X axis with NO decimals (Ts is in µs/ft; typically integer-like)
     fig.update_xaxes(
-        title_text="Ts (µs/ft)",
-        title_font=dict(size=20, family=BOLD_FONT, color="#000"),
-        tickfont=dict(size=15, family=BOLD_FONT, color="#000"),
-        side="top",
-        range=[xmin, xmax],
-        ticks="outside",
-        tickformat=",.0f",
-        tickmode="auto",
-        tick0=tick0,
-        showline=True, linewidth=1.2, linecolor="#444", mirror=True,
-        showgrid=True, gridcolor="rgba(0,0,0,0.12)", automargin=True
-    )
+    title_text="Ts (μs/ft)",
+    title_font=dict(size=20, family=BOLD_FONT, color="#000"),
+    tickfont=dict(size=15, family=BOLD_FONT, color="#000"),
+    side="top",
+    range=[xmin, xmax],
+    ticks="outside",
+    tickformat=",.0f",   # ← integers; change to ",.1f" if you want one decimal
+    tickmode="auto",
+    tick0=tick0,
+    showline=True, linewidth=1.2, linecolor="#444", mirror=True,
+    showgrid=True, gridcolor="rgba(0,0,0,0.12)", automargin=True
+)
+
     fig.update_yaxes(
         title_text=ylab,
         title_font=dict(size=20, family=BOLD_FONT, color="#000"),