Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py — ST_GeoMech_YM (mirrors your UCS GUI, adapted for Young's Modulus)
+# app.py — ST_Sonic_Ts (mirrors your Ym GUI, adapted for Shear Slowness Ts)
 import io, json, os, base64, math
 from pathlib import Path
 import streamlit as st
@@ -17,17 +17,18 @@ import plotly.graph_objects as go
 from sklearn.metrics import mean_squared_error, mean_absolute_error
 
 # =========================
-# Constants (Ym variant)
+# Constants (Ts variant)
 # =========================
-APP_NAME = "ST_GeoMech_YM"
-TAGLINE  = "Real-Time Young's Modulus Tracking"
+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    = "Actual Ym"
-PRED_COL  = "Ym_Pred"
+TARGET    = "Ts"          # <-- actual Ts column in your sheets
+PRED_COL  = "Ts_Pred"     # <-- predicted Ts column added by the app
 
 MODELS_DIR = Path("models")
-DEFAULT_MODEL = MODELS_DIR / "ym_rf.joblib"
+DEFAULT_MODEL = MODELS_DIR / "ts_model.joblib"
 MODEL_FALLBACKS = [MODELS_DIR / "model.joblib", MODELS_DIR / "model.pkl"]
 COLORS = {"pred": "#1f77b4", "actual": "#f2b702", "ref": "#5a5a5a"}
 
@@ -193,7 +194,7 @@ def find_sheet(book, names):
     return None
 
 def _nice_tick0(xmin: float, step: float = 0.1) -> float:
-    # Rounded start tick for continuous Ym scales (unit-agnostic)
+    # Rounded start tick for continuous Ts scales (unit-agnostic)
     return step * math.floor(xmin / step) if np.isfinite(xmin) else xmin
 
 def df_centered_rounded(df: pd.DataFrame, hide_index=True):
@@ -349,7 +350,7 @@ def build_export_workbook(selected: list[str], ndigits: int = 3, do_autofit: boo
                 _excel_autofit(writer, sheet, df)
     bio.seek(0)
 
-    fname = f"YM_Export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
+    fname = f"TS_Export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
     return bio.getvalue(), fname, order
 
 # --------- SIMPLE export UI (dropdown checklist, starts empty) ----------
@@ -380,7 +381,7 @@ def render_export_button(phase_key: str) -> None:
         st.download_button(
             label="⬇️ Export Excel",
             data=b"",
-            file_name="YM_Export.xlsx",
+            file_name="TS_Export.xlsx",
             mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
             disabled=True,
             key=f"download_{phase_key}",
@@ -394,16 +395,16 @@ def render_export_button(phase_key: str) -> None:
     st.download_button(
         "⬇️ Export Excel",
         data=(data or b""),
-        file_name=(fname or "YM_Export.xlsx"),
+        file_name=(fname or "TS_Export.xlsx"),
         mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
         disabled=(data is None),
         key=f"download_{phase_key}",
     )
 
 # =========================
-# Cross plot (Matplotlib) — auto-scaled for Ym
+# Cross plot (Matplotlib) — auto-scaled for Ts
 # =========================
-def cross_plot_static(actual, pred, xlabel="Actual Ym", ylabel="Predicted Ym"):
+def cross_plot_static(actual, pred, xlabel="Actual Ts (µs/ft)", ylabel="Predicted Ts (µs/ft)"):
     a = pd.Series(actual, dtype=float)
     p = pd.Series(pred, dtype=float)
 
@@ -426,7 +427,7 @@ def cross_plot_static(actual, pred, xlabel="Actual Ym", ylabel="Predicted Ym"):
     ax.set_yticks(ticks)
     ax.set_aspect("equal", adjustable="box")
 
-    # Generic numeric formatting (2 decimals)
+    # Generic numeric formatting (2 decimals) in plot; export uses 3 decimals
     fmt = FuncFormatter(lambda x, _: f"{x:.2f}")
     ax.xaxis.set_major_formatter(fmt)
     ax.yaxis.set_major_formatter(fmt)
@@ -495,15 +496,15 @@ def track_plot(df, include_actual=True):
         legend_title_text=""
     )
 
-    # X axis with NO decimals
+    # X axis with NO decimals (Ts is in µs/ft; typically integer-like)
     fig.update_xaxes(
-        title_text="Ym",
+        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, thousands separated
+        tickformat=",.0f",
         tickmode="auto",
         tick0=tick0,
         showline=True, linewidth=1.2, linecolor="#444", mirror=True,
@@ -598,7 +599,7 @@ def ensure_model() -> Path|None:
 
 mpath = ensure_model()
 if not mpath:
-    st.error("Model not found. Upload models/ym_rf.joblib (or set MODEL_URL).")
+    st.error("Model not found. Upload models/ts_model.joblib (or set MODEL_URL).")
     st.stop()
 try:
     model = load_model(str(mpath))
@@ -608,13 +609,16 @@ except Exception as e:
 
 # ---------- Load meta (optional) ----------
 meta = {}
-meta_candidates = [MODELS_DIR / "meta.json", MODELS_DIR / "ym_meta.json"]
+# Prefer a Ts-specific meta, fall back to a generic one if present
+meta_candidates = [MODELS_DIR / "ts_meta.json", MODELS_DIR / "meta.json"]
 meta_path = next((p for p in meta_candidates if p.exists()), None)
 if meta_path:
     try:
         meta = json.loads(meta_path.read_text(encoding="utf-8"))
         FEATURES = meta.get("features", FEATURES)
         TARGET   = meta.get("target", TARGET)
+        # If meta provides a custom pred column, respect it
+        PRED_COL = meta.get("pred_col", PRED_COL)
     except Exception as e:
         st.warning(f"Could not parse meta file ({meta_path.name}): {e}")
 
@@ -679,12 +683,12 @@ def sticky_header(title, message):
 # =========================
 if st.session_state.app_step == "intro":
     st.header("Welcome!")
-    st.markdown("This software is developed by *Smart Thinking AI-Solutions Team* to estimate Young's Modulus (Ym) from drilling data.")
+    st.markdown("This software is developed by *Smart Thinking AI-Solutions Team* to estimate **Shear Slowness (Ts)** from drilling data.")
     st.subheader("How It Works")
     st.markdown(
         "1) **Upload your data to build the case and preview the model performance.** \n"
         "2) Click **Run Model** to compute metrics and plots. \n"
-        "3) **Proceed to Validation** (with actual Ym) or **Proceed to Prediction** (no Ym)."
+        "3) **Proceed to Validation** (with actual Ts) or **Proceed to Prediction** (no Ts)."
     )
     if st.button("Start Showcase", type="primary"):
         st.session_state.app_step = "dev"; st.rerun()
@@ -791,7 +795,7 @@ if st.session_state.app_step == "dev":
         render_export_button(phase_key="dev")
 
 # =========================
-# VALIDATION (with actual Ym)
+# VALIDATION (with actual Ts)
 # =========================
 if st.session_state.app_step == "validate":
     st.sidebar.header("Validate the Model")
@@ -807,7 +811,7 @@ if st.session_state.app_step == "validate":
     if st.sidebar.button("⬅ Back to Case Building", use_container_width=True): st.session_state.app_step="dev"; st.rerun()
     if st.sidebar.button("Proceed to Prediction ▶", use_container_width=True): st.session_state.app_step="predict"; st.rerun()
 
-    sticky_header("Validate the Model", "Upload a dataset with the same **features** and **Actual Ym** to evaluate performance.")
+    sticky_header("Validate the Model", "Upload a dataset with the same **features** and **Ts** to evaluate performance.")
 
     if go_btn and up is not None:
         book = read_book_bytes(up.getvalue())
@@ -874,10 +878,10 @@ if st.session_state.app_step == "validate":
             df_centered_rounded(st.session_state.results["oor_tbl"])
 
 # =========================
-# PREDICTION (no actual Ym)
+# PREDICTION (no actual Ts)
 # =========================
 if st.session_state.app_step == "predict":
-    st.sidebar.header("Prediction (No Actual Ym)")
+    st.sidebar.header("Prediction (No Actual Ts)")
     up = st.sidebar.file_uploader("Upload Prediction Excel", type=["xlsx","xls"])
     if up is not None:
         book = read_book_bytes(up.getvalue())
@@ -889,7 +893,7 @@ if st.session_state.app_step == "predict":
     go_btn = st.sidebar.button("Predict", type="primary", use_container_width=True)
     if st.sidebar.button("⬅ Back to Case Building", use_container_width=True): st.session_state.app_step="dev"; st.rerun()
 
-    sticky_header("Prediction", "Upload a dataset with the feature columns (no **Actual Ym**).")
+    sticky_header("Prediction", "Upload a dataset with the feature columns (no **Ts**).")
 
     if go_btn and up is not None:
         book = read_book_bytes(up.getvalue()); name = list(book.keys())[0]