Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py — ST_Sonic_Ts (Shear Slowness Ts)
+# app_tc.py — ST_Sonic_Tc (Compressional Slowness Tc)
 
 import io, json, os, base64, math
 from pathlib import Path
@@ -18,12 +18,12 @@ import plotly.graph_objects as go
 from sklearn.metrics import mean_squared_error, mean_absolute_error
 
 # =========================
-# Constants (Ts variant)
+# Constants (Tc variant)
 # =========================
-APP_NAME = "ST_Log_Sonic (Ts)"
-TAGLINE  = "Real-Time Shear Slowness (Ts) Prediction"
+APP_NAME = "ST_Log_Sonic (Tc)"
+TAGLINE  = "Real-Time Compressional Slowness (Tc) Prediction"
 
-# Defaults (overridden by ts_meta.json if present)
+# Defaults (overridden by tc_meta.json if present)
 FEATURES  = [
     "WOB (klbf)",
     "Torque (kft.lbf)",
@@ -32,11 +32,11 @@ FEATURES  = [
     "ROP (ft/h)",
     "Flow Rate (gpm)",
 ]
-TARGET    = "Ts (us/ft_Actual)"
-PRED_COL  = "Ts_Pred"
+TARGET    = "Tc (us/ft_Actual)"
+PRED_COL  = "Tc_Pred"
 
 MODELS_DIR = Path("models")
-DEFAULT_MODEL = MODELS_DIR / "ts_model.joblib"
+DEFAULT_MODEL = MODELS_DIR / "tc_model.joblib"
 MODEL_FALLBACKS = [MODELS_DIR / "model.joblib", MODELS_DIR / "model.pkl"]
 COLORS = {"pred": "#1f77b4", "actual": "#f2b702", "ref": "#5a5a5a"}
 
@@ -63,8 +63,8 @@ st.markdown("""
  .st-message-box.st-success { background-color: #d4edda; color: #155724; border-color: #c3e6cb; }
  .st-message-box.st-warning { background-color: #fff3cd; color: #856404; border-color: #ffeeba; }
  .st-message-box.st-error   { background-color: #f8d7da; color: #721c24; border-color: #f5c6cb; }
- .main .block-container { overflow: unset !important; }
- div[data-testid="stVerticalBlock"] { overflow: unset !important; }
+ .main .block-container { overflow: unset !IMPORTANT; }
+ div[data-testid="stVerticalBlock"] { overflow: unset !IMPORTANT; }
  div[data-testid="stExpander"] > details > summary {
    position: sticky; top: 0; z-index: 10; background: #fff; border-bottom: 1px solid #eee;
  }
@@ -152,7 +152,7 @@ def read_book_bytes(b: bytes):
 def _build_alias_map(canonical_features: list[str], target_name: str) -> dict:
     """
     Map common header variants -> the *canonical* names in canonical_features.
-    Whatever appears in canonical_features (from ts_meta.json) wins.
+    Whatever appears in canonical_features (from tc_meta.json) wins.
     """
     def pick(expected_list, variants):
         for v in variants:
@@ -181,13 +181,13 @@ def _build_alias_map(canonical_features: list[str], target_name: str) -> dict:
         # Depth (plot only)
         "Depth (ft)": can_DEPTH, "Depth, ft": can_DEPTH, "Depth(ft)": can_DEPTH, "DEPTH, ft": can_DEPTH,
 
-        # Target family
-        "Ts (us/ft_Actual)": target_name,
-        "Ts,us/ft_Actual":   target_name,
-        "Ts, us/ft_Actual":  target_name,
-        "Ts":                target_name,
-        "TS_Actual":         target_name,
-        "Ts (us/ft)_Actual": target_name,
+        # Target family (Tc)
+        "Tc (us/ft_Actual)": target_name,
+        "Tc,us/ft_Actual":   target_name,
+        "Tc, us/ft_Actual":  target_name,
+        "Tc":                target_name,
+        "TC_Actual":         target_name,
+        "Tc (us/ft)_Actual": target_name,
     }
     return alias
 
@@ -365,7 +365,7 @@ def build_export_workbook(selected: list[str], ndigits: int = 3, do_autofit: boo
             if do_autofit:
                 _excel_autofit(writer, sheet, df)
     bio.seek(0)
-    fname = f"TS_Export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
+    fname = f"TC_Export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
     return bio.getvalue(), fname, order
 
 # --------- SIMPLE export UI ----------
@@ -390,7 +390,7 @@ def render_export_button(phase_key: str) -> None:
         st.download_button(
             label="⬇️ Export Excel",
             data=b"",
-            file_name="TS_Export.xlsx",
+            file_name="TC_Export.xlsx",
             mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
             disabled=True,
             key=f"download_{phase_key}",
@@ -403,7 +403,7 @@ def render_export_button(phase_key: str) -> None:
     st.download_button(
         "⬇️ Export Excel",
         data=(data or b""),
-        file_name=(fname or "TS_Export.xlsx"),
+        file_name=(fname or "TC_Export.xlsx"),
         mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
         disabled=(data is None),
         key=f"download_{phase_key}",
@@ -412,7 +412,7 @@ def render_export_button(phase_key: str) -> None:
 # =========================
 # Cross plot (Matplotlib)
 # =========================
-def cross_plot_static(actual, pred, xlabel="Actual Ts (µs/ft)", ylabel="Predicted Ts (µs/ft)"):
+def cross_plot_static(actual, pred, xlabel="Actual Tc (µs/ft)", ylabel="Predicted Tc (µs/ft)"):
     a = pd.Series(actual, dtype=float)
     p = pd.Series(pred, dtype=float)
 
@@ -494,7 +494,7 @@ def track_plot(df, include_actual=True):
         legend_title_text=""
     )
     fig.update_xaxes(
-        title_text="Ts (μs/ft)",
+        title_text="Tc (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],
@@ -587,7 +587,7 @@ def ensure_model() -> Path|None:
 
 mpath = ensure_model()
 if not mpath:
-    st.error("Model not found. Upload models/ts_model.joblib (or set MODEL_URL).")
+    st.error("Model not found. Upload models/tc_model.joblib (or set MODEL_URL).")
     st.stop()
 try:
     model = load_model(str(mpath))
@@ -595,9 +595,9 @@ except Exception as e:
     st.error(f"Failed to load model: {e}")
     st.stop()
 
-# Load meta (prefer Ts-specific)
+# Load meta (prefer Tc-specific)
 meta = {}
-meta_candidates = [MODELS_DIR / "ts_meta.json", MODELS_DIR / "meta.json", MODELS_DIR / "ym_meta.json"]
+meta_candidates = [MODELS_DIR / "tc_meta.json", MODELS_DIR / "meta.json"]
 meta_path = next((p for p in meta_candidates if p.exists()), None)
 if meta_path:
     try:
@@ -665,12 +665,12 @@ 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 **Shear Slowness (Ts)** from drilling data.")
+    st.markdown("This software is developed by *Smart Thinking AI-Solutions Team* to estimate **Compressional Slowness (Tc)** 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 Ts) or **Proceed to Prediction** (no Ts)."
+        "3) **Proceed to Validation** (with actual Tc) or **Proceed to Prediction** (no Tc)."
     )
     if st.button("Start Showcase", type="primary"):
         st.session_state.app_step = "dev"; st.rerun()
@@ -767,7 +767,7 @@ if st.session_state.app_step == "dev":
         render_export_button(phase_key="dev")
 
 # =========================
-# VALIDATION (with actual Ts)
+# VALIDATION (with actual Tc)
 # =========================
 if st.session_state.app_step == "validate":
     st.sidebar.header("Validate the Model")
@@ -783,7 +783,7 @@ 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 **Ts** to evaluate performance.")
+    sticky_header("Validate the Model", "Upload a dataset with the same **features** and **Tc** to evaluate performance.")
 
     if go_btn and up is not None:
         book = read_book_bytes(up.getvalue())
@@ -843,10 +843,10 @@ if st.session_state.app_step == "validate":
             df_centered_rounded(st.session_state.results["oor_tbl"])
 
 # =========================
-# PREDICTION (no actual Ts)
+# PREDICTION (no actual Tc)
 # =========================
 if st.session_state.app_step == "predict":
-    st.sidebar.header("Prediction (No Actual Ts)")
+    st.sidebar.header("Prediction (No Actual Tc)")
     up = st.sidebar.file_uploader("Upload Prediction Excel", type=["xlsx","xls"])
     if up is not None:
         book = read_book_bytes(up.getvalue())
@@ -858,7 +858,7 @@ 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 **Ts**).")
+    sticky_header("Prediction", "Upload a dataset with the feature columns (no **Tc**).")
 
     if go_btn and up is not None:
         book = read_book_bytes(up.getvalue()); name = list(book.keys())[0]