Update app.py

app.py

@@ -120,6 +120,22 @@ add_password_gate()
 # Utilities
 # =========================
 def rmse(y_true, y_pred): return float(np.sqrt(mean_squared_error(y_true, y_pred)))
+
+def render_bo_bd_note():
+    st.markdown(
+        """
+        <div class="st-message-box">
+            <b>What do BO and BD mean?</b><br>
+            <ul style="margin-top:6px;">
+              <li><b>BO (Breakout)</b> — the <i>lower</i> mud-weight bound to avoid compressive/shear failure of the wellbore (breakout). Drilling below BO increases breakout risk.</li>
+              <li><b>BD (Breakdown)</b> — the <i>upper</i> mud-weight bound to avoid tensile fracturing (hydraulic breakdown). Drilling above BD risks induced fractures and losses.</li>
+            </ul>
+            The safe mud-weight window is typically between <b>BO</b> and <b>BD</b>.
+        </div>
+        """,
+        unsafe_allow_html=True,
+    )
+
 def pearson_r(y_true, y_pred):
     a = np.asarray(y_true, dtype=float); p = np.asarray(y_pred, dtype=float)
     if a.size < 2: return float("nan")
@@ -414,6 +430,7 @@ def preview_tracks(df: pd.DataFrame, cols: list[str]):
         if i == 0: ax.set_ylabel(y_label)
         else: ax.tick_params(labelleft=False); ax.set_ylabel("")
     fig.tight_layout(); return fig
+
 # =========================
 # Load models + metas
 # =========================
@@ -424,7 +441,6 @@ def _first_in_dirs(names):  # prefer uploaded over repo copy
             if p.exists() and p.stat().st_size > 0:
                 return p
     return None
-    
 
 def _load_meta(p: Path) -> dict:
     if not p or not p.exists(): return {}
@@ -471,12 +487,8 @@ model_bo, info_bo = _unwrap(payload_bo)
 model_bd, info_bd = _unwrap(payload_bd)
 
 # Resolve features per model (prefer model_info → meta → fallback)
-features_bo = list((info_bo.get("features")
-                    or meta_bo.get("features")
-                    or FEATURES_DEFAULT))
-features_bd = list((info_bd.get("features")
-                    or meta_bd.get("features")
-                    or FEATURES_DEFAULT))
+features_bo = list((info_bo.get("features") or meta_bo.get("features") or FEATURES_DEFAULT))
+features_bd = list((info_bd.get("features") or meta_bd.get("features") or FEATURES_DEFAULT))
 
 # Use the UNION so BD's 'Depth (ft)' is required and both models can run from one input
 # Order: keep BO order, then append BD-only columns in order
@@ -495,7 +507,6 @@ st.session_state["TARGET_BO"]   = TARGET_BO
 st.session_state["TARGET_BD"]   = TARGET_BD
 st.session_state["X_UNITS"]     = X_UNITS
 
-
 # =========================
 # Session state
 # =========================
@@ -539,6 +550,7 @@ def sticky_header(title, message):
 if st.session_state.app_step == "intro":
     st.header("Welcome!")
     st.markdown("This software estimates **Breakout** and **Breakdown** mud-weight limits from drilling data.")
+    render_bo_bd_note()
     st.subheader("How It Works")
     st.markdown("1) **Upload data** and preview.\n2) **Run Model** to compute Train/Test metrics.\n3) Go to **Validation** (with actual BO/BD) or **Prediction** (no actuals).\n4) Use **Combined** tab to see both limits on one track.")
     if st.button("Start Showcase", type="primary"):
@@ -589,6 +601,7 @@ if st.session_state.app_step == "dev":
         sticky_header("Case Building", "📄 **Preview** then click **Run Model**.")
     else:
         sticky_header("Case Building", "**Upload your data** and run the model.")
+    render_bo_bd_note()
 
     if run and st.session_state.dev_file_bytes:
         book = read_book_bytes(st.session_state.dev_file_bytes)
@@ -605,14 +618,12 @@ if st.session_state.app_step == "dev":
         Xtr_bd = _make_X(tr, st.session_state["FEATURES_BD"])
         Xte_bo = _make_X(te, st.session_state["FEATURES_BO"])
         Xte_bd = _make_X(te, st.session_state["FEATURES_BD"])
-        
+
         tr[PRED_BO] = model_bo.predict(Xtr_bo)
         tr[PRED_BD] = model_bd.predict(Xtr_bd)
         te[PRED_BO] = model_bo.predict(Xte_bo)
         te[PRED_BD] = model_bd.predict(Xte_bd)
 
-
-
         st.session_state.results["Train"]=tr; st.session_state.results["Test"]=te
         st.session_state.results["m_train_bo"]={"R": pearson_r(tr[TARGET_BO], tr[PRED_BO]), "RMSE": rmse(tr[TARGET_BO], tr[PRED_BO]), "MAE": mean_absolute_error(tr[TARGET_BO], tr[PRED_BO])}
         st.session_state.results["m_train_bd"]={"R": pearson_r(tr[TARGET_BD], tr[PRED_BD]), "RMSE": rmse(tr[TARGET_BD], tr[PRED_BD]), "MAE": mean_absolute_error(tr[TARGET_BD], tr[PRED_BD])}
@@ -630,14 +641,15 @@ if st.session_state.app_step == "dev":
         _metrics_block("BO", mbo); _metrics_block("BD", mbd)
         st.markdown("<div style='text-align:left;font-size:0.8em;color:#6b7280;margin-top:-16px;margin-bottom:8px;'><strong>R</strong> = Pearson correlation • <strong>RMSE/MAE</strong> in MW (pcf)</div>", unsafe_allow_html=True)
         t1, t2, t3 = st.tabs(["Breakout", "Breakdown", "Combined"])
+        units = st.session_state.get("X_UNITS","MW (pcf)")
         with t1:
             st.plotly_chart(track_plot_single(df, PRED_BO, actual_col=TARGET_BO, title_suffix="Breakout"),
                             use_container_width=False, config={"displayModeBar": False, "scrollZoom": True})
-            st.pyplot(cross_plot_static(df[TARGET_BO], df[PRED_BO], f"Actual {TARGET_BO}", f"Predicted {TARGET_BO}", COLORS["pred_bo"]), use_container_width=False)
+            st.pyplot(cross_plot_static(df[TARGET_BO], df[PRED_BO], f"Actual {TARGET_BO} ({units})", f"Predicted {TARGET_BO} ({units})", COLORS["pred_bo"]), use_container_width=False)
         with t2:
             st.plotly_chart(track_plot_single(df, PRED_BD, actual_col=TARGET_BD, title_suffix="Breakdown"),
                             use_container_width=False, config={"displayModeBar": False, "scrollZoom": True})
-            st.pyplot(cross_plot_static(df[TARGET_BD], df[PRED_BD], f"Actual {TARGET_BD}", f"Predicted {TARGET_BD}", COLORS["pred_bd"]), use_container_width=False)
+            st.pyplot(cross_plot_static(df[TARGET_BD], df[PRED_BD], f"Actual {TARGET_BD} ({units})", f"Predicted {TARGET_BD} ({units})", COLORS["pred_bd"]), use_container_width=False)
         with t3:
             st.plotly_chart(track_plot_combined(df), use_container_width=False, config={"displayModeBar": False, "scrollZoom": True})
 
@@ -667,6 +679,7 @@ if st.session_state.app_step == "validate":
     if st.sidebar.button("Proceed to Prediction ▶", use_container_width=True): st.session_state.app_step="predict"; st.rerun()
 
     sticky_header("Validate the Models", "Upload a dataset with the same **feature** columns and **BO/BD** actuals.")
+    render_bo_bd_note()
 
     if go_btn and up is not None:
         book = read_book_bytes(up.getvalue())
@@ -702,15 +715,16 @@ if st.session_state.app_step == "validate":
         st.markdown("<div style='text-align:left;font-size:0.8em;color:#6b7280;margin-top:-16px;margin-bottom:8px;'>R = Pearson correlation</div>", unsafe_allow_html=True)
 
         t1, t2, t3 = st.tabs(["Breakout", "Breakdown", "Combined"])
+        units = st.session_state.get("X_UNITS","MW (pcf)")
         with t1:
             st.plotly_chart(track_plot_single(df, PRED_BO, actual_col=TARGET_BO, title_suffix="Breakout"),
                             use_container_width=False, config={"displayModeBar": False, "scrollZoom": True})
-            st.pyplot(cross_plot_static(df[TARGET_BO], df[PRED_BO], f"Actual {TARGET_BO}", f"Predicted {TARGET_BO}", COLORS["pred_bo"]),
+            st.pyplot(cross_plot_static(df[TARGET_BO], df[PRED_BO], f"Actual {TARGET_BO} ({units})", f"Predicted {TARGET_BO} ({units})", COLORS["pred_bo"]),
                       use_container_width=False)
         with t2:
             st.plotly_chart(track_plot_single(df, PRED_BD, actual_col=TARGET_BD, title_suffix="Breakdown"),
                             use_container_width=False, config={"displayModeBar": False, "scrollZoom": True})
-            st.pyplot(cross_plot_static(df[TARGET_BD], df[PRED_BD], f"Actual {TARGET_BD}", f"Predicted {TARGET_BD}", COLORS["pred_bd"]),
+            st.pyplot(cross_plot_static(df[TARGET_BD], df[PRED_BD], f"Actual {TARGET_BD} ({units})", f"Predicted {TARGET_BD} ({units})", COLORS["pred_bd"]),
                       use_container_width=False)
         with t3:
             st.plotly_chart(track_plot_combined(df), use_container_width=False, config={"displayModeBar": False, "scrollZoom": True})
@@ -738,6 +752,7 @@ if st.session_state.app_step == "predict":
     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 **feature columns only** (no BO/BD actuals).")
+    render_bo_bd_note()
 
     if go_btn and up is not None:
         book = read_book_bytes(up.getvalue()); name = list(book.keys())[0]