Update app.py

app.py

@@ -194,11 +194,11 @@ def cross_plot(actual, pred):
         margin=dict(l=64, r=18, t=8, b=48), hovermode="closest",
         font=dict(size=FONT_SZ)
     )
-    fig.update_xaxes(title_text="<b>Actual UCS, psi</b>", range=[x0, x1],
+    fig.update_xaxes(title_text="<b>Actual UCS</b>", range=[x0, x1],
                      ticks="outside", tickformat=",.0f",
                      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="<b>Predicted UCS, psi</b>", range=[x0, x1],
+    fig.update_yaxes(title_text="<b>Predicted UCS</b>", range=[x0, x1],
                      ticks="outside", tickformat=",.0f",
                      showline=True, linewidth=1.2, linecolor="#444", mirror=True, showgrid=True, gridcolor="rgba(0,0,0,0.12)",
                      scaleanchor="x", scaleratio=1, automargin=True)
@@ -213,17 +213,17 @@ def track_plot(df, include_actual=True):
 
     fig = go.Figure()
     fig.add_trace(go.Scatter(
-        x=df["UCS_Pred, psi"], y=y, mode="lines",
+        x=df["UCS_Pred"], y=y, mode="lines",
         line=dict(color=COLORS["pred"], width=1.8),
-        name="UCS_Pred, psi",
-        hovertemplate="UCS_Pred, psi: %{x:.0f}<br>"+ylab+": %{y}<extra></extra>"
+        name="UCS_Pred",
+        hovertemplate="UCS_Pred: %{x:.0f}<br>"+ylab+": %{y}<extra></extra>"
     ))
     if include_actual and TARGET in df.columns:
         fig.add_trace(go.Scatter(
             x=df[TARGET], y=y, mode="lines",
             line=dict(color=COLORS["actual"], width=2.0, dash="dot"),
-            name="UCS (actual), psi",
-            hovertemplate="UCS (actual), psi: %{x:.0f}<br>"+ylab+": %{y}<extra></extra>"
+            name="UCS (actual)",
+            hovertemplate="UCS (actual): %{x:.0f}<br>"+ylab+": %{y}<extra></extra>"
         ))
 
     fig.update_layout(
@@ -236,7 +236,7 @@ def track_plot(df, include_actual=True):
         ),
         legend_title_text=""
     )
-    fig.update_xaxes(title_text="<b>UCS, psi</b>", side="top",
+    fig.update_xaxes(title_text="<b>UCS</b>", side="top",
                      ticks="outside", tickformat=",.0f",
                      showline=True, linewidth=1.2, linecolor="#444", mirror=True, showgrid=True, gridcolor="rgba(0,0,0,0.12)",
                      automargin=True)
@@ -416,12 +416,12 @@ if st.session_state.app_step == "dev":
             st.error("Workbook must include Train/Training/training2 and Test/Testing/testing2 sheets."); st.stop()
         tr = book[sh_train].copy(); te = book[sh_test].copy()
         if not (ensure_cols(tr, FEATURES+[TARGET]) and ensure_cols(te, FEATURES+[TARGET])): st.stop()
-        tr["UCS_Pred, psi"] = model.predict(tr[FEATURES])
-        te["UCS_Pred, psi"] = model.predict(te[FEATURES])
+        tr["UCS_Pred"] = model.predict(tr[FEATURES])
+        te["UCS_Pred"] = model.predict(te[FEATURES])
 
         st.session_state.results["Train"]=tr; st.session_state.results["Test"]=te
-        st.session_state.results["m_train"]={"R2":r2_score(tr[TARGET],tr["UCS_Pred, psi"]), "RMSE":rmse(tr[TARGET],tr["UCS_Pred, psi"]), "MAE":mean_absolute_error(tr[TARGET],tr["UCS_Pred"])}
-        st.session_state.results["m_test"] ={"R2":r2_score(te[TARGET],te["UCS_Pred, psi"]), "RMSE":rmse(te[TARGET],te["UCS_Pred, psi"]), "MAE":mean_absolute_error(te[TARGET],te["UCS_Pred"])}
+        st.session_state.results["m_train"]={"R2":r2_score(tr[TARGET],tr["UCS_Pred"]), "RMSE":rmse(tr[TARGET],tr["UCS_Pred"]), "MAE":mean_absolute_error(tr[TARGET],tr["UCS_Pred"])}
+        st.session_state.results["m_test"] ={"R2":r2_score(te[TARGET],te["UCS_Pred"]), "RMSE":rmse(te[TARGET],te["UCS_Pred"]), "MAE":mean_absolute_error(te[TARGET],te["UCS_Pred"])}
 
         tr_min = tr[FEATURES].min().to_dict(); tr_max = tr[FEATURES].max().to_dict()
         st.session_state.train_ranges = {f:(float(tr_min[f]), float(tr_max[f])) for f in FEATURES}
@@ -435,7 +435,7 @@ if st.session_state.app_step == "dev":
             c1.metric("R²", f"{m['R2']:.4f}"); c2.metric("RMSE", f"{m['RMSE']:.4f}"); c3.metric("MAE", f"{m['MAE']:.4f}")
             left, mid, right = st.columns(PLOT_COLS, gap="small")
             with left:
-                st.plotly_chart(cross_plot(df[TARGET], df["UCS_Pred, psi"]),
+                st.plotly_chart(cross_plot(df[TARGET], df["UCS_Pred"]),
                                 use_container_width=False, config=PLOT_CFG)
             with mid:
                 st.write("")  # spacer
@@ -467,14 +467,14 @@ if st.session_state.app_step == "validate":
 
     info, success = make_notice()
     st.subheader("Validate the Model")
-    info("Upload a dataset with the same <b>features</b> and <b>UCS, psi</b> to evaluate performance.")
+    info("Upload a dataset with the same <b>features</b> and <b>UCS</b> to evaluate performance.")
 
     if go_btn and up is not None:
         book = read_book_bytes(up.getvalue())
         name = find_sheet(book, ["Validation","Validate","validation2","Val","val"]) or list(book.keys())[0]
         df = book[name].copy()
         if not ensure_cols(df, FEATURES+[TARGET]): st.stop()
-        df["UCS_Pred, psi"] = model.predict(df[FEATURES])
+        df["UCS_Pred"] = model.predict(df[FEATURES])
         st.session_state.results["Validate"]=df
 
         ranges = st.session_state.train_ranges; oor_pct = 0.0; tbl=None
@@ -484,8 +484,8 @@ if st.session_state.app_step == "validate":
             if any_viol.any():
                 tbl = df.loc[any_viol, FEATURES].copy()
                 tbl["Violations"] = pd.DataFrame({f:(df[f]<ranges[f][0])|(df[f]>ranges[f][1]) for f in FEATURES}).loc[any_viol].apply(lambda r:", ".join([c for c,v in r.items() if v]), axis=1)
-        st.session_state.results["m_val"]={"R2":r2_score(df[TARGET],df["UCS_Pred, psi"]), "RMSE":rmse(df[TARGET],df["UCS_Pred, psi"]), "MAE":mean_absolute_error(df[TARGET],df["UCS_Pred"])}
-        st.session_state.results["sv_val"]={"n":len(df),"pred_min":float(df["UCS_Pred, psi"].min()),"pred_max":float(df["UCS_Pred, psi"].max()),"oor":oor_pct}
+        st.session_state.results["m_val"]={"R2":r2_score(df[TARGET],df["UCS_Pred"]), "RMSE":rmse(df[TARGET],df["UCS_Pred"]), "MAE":mean_absolute_error(df[TARGET],df["UCS_Pred"])}
+        st.session_state.results["sv_val"]={"n":len(df),"pred_min":float(df["UCS_Pred"].min()),"pred_max":float(df["UCS_Pred"].max()),"oor":oor_pct}
         st.session_state.results["oor_tbl"]=tbl
         st.rerun()
 
@@ -497,7 +497,7 @@ if st.session_state.app_step == "validate":
         left, mid, right = st.columns(PLOT_COLS, gap="small")
         with left:
             st.plotly_chart(cross_plot(st.session_state.results["Validate"][TARGET],
-                                       st.session_state.results["Validate"]["UCS_Pred, psi"]),
+                                       st.session_state.results["Validate"]["UCS_Pred"]),
                             use_container_width=False, config=PLOT_CFG)
         with mid:
             st.write("")
@@ -528,7 +528,7 @@ if st.session_state.app_step == "predict":
 
     info, _success = make_notice()
     st.subheader("Prediction")
-    info("Upload a dataset with the feature columns (no <b>UCS, psi</b>).")
+    info("Upload a dataset with the feature columns (no <b>UCS</b>).")
 
     if go_btn and up is not None:
         book = read_book_bytes(up.getvalue()); name = list(book.keys())[0]
@@ -543,10 +543,10 @@ if st.session_state.app_step == "predict":
             oor_pct = float(any_viol.mean()*100.0)
         st.session_state.results["sv_pred"]={
             "n":len(df),
-            "pred_min":float(df["UCS_Pred, psi"].min()),
-            "pred_max":float(df["UCS_Pred, psi"].max()),
-            "pred_mean":float(df["UCS_Pred, psi"].mean()),
-            "pred_std":float(df["UCS_Pred, psi"].std(ddof=0)),
+            "pred_min":float(df["UCS_Pred"].min()),
+            "pred_max":float(df["UCS_Pred"].max()),
+            "pred_mean":float(df["UCS_Pred"].mean()),
+            "pred_std":float(df["UCS_Pred"].std(ddof=0)),
             "oor":oor_pct
         }
         st.rerun()