Update app.py

app.py

@@ -1,3 +1,4 @@
+# app.py
 import io, json, os, base64
 from pathlib import Path
 import streamlit as st
@@ -5,14 +6,12 @@ import pandas as pd
 import numpy as np
 import joblib
 
-# Matplotlib remains for the preview modal (tracks + stats)
+# keep matplotlib ONLY for the preview modal (static thumbnails)
 import matplotlib
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 
-# Plotly for the main interactive charts
 import plotly.graph_objects as go
-
 from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
 
 # =========================
@@ -98,9 +97,9 @@ def find_sheet(book, names):
         if nm.lower() in low2orig: return low2orig[nm.lower()]
     return None
 
-# ---------- Plotly (interactive) charts ----------
+# ---------- Interactive plotting (full outline, bold axis titles) ----------
 def cross_plot_interactive(actual, pred, size=(3.9, 3.9)):
-    """Interactive cross-plot: blue points, dashed 1:1, equal axes, no title, numeric ticks."""
+    """Interactive cross-plot: blue points, dashed 1:1, equal axes, no title, numeric ticks, full box outline."""
     a = pd.Series(actual).astype(float)
     p = pd.Series(pred).astype(float)
     lo = float(np.nanmin([a.min(), p.min()]))
@@ -115,14 +114,14 @@ def cross_plot_interactive(actual, pred, size=(3.9, 3.9)):
         x=a, y=p, mode="markers",
         marker=dict(size=6, color=COLORS["pred"]),
         hovertemplate="Actual: %{x:.2f}<br>Pred: %{y:.2f}<extra></extra>",
-        showlegend=False, name="Points"
+        showlegend=False
     ))
 
     # 1:1 line (dashed grey)
     fig.add_trace(go.Scatter(
         x=[x0, x1], y=[x0, x1], mode="lines",
         line=dict(color=COLORS["ref"], width=1.2, dash="dash"),
-        hoverinfo="skip", showlegend=False, name="1:1"
+        hoverinfo="skip", showlegend=False
     ))
 
     fig.update_layout(
@@ -130,17 +129,18 @@ def cross_plot_interactive(actual, pred, size=(3.9, 3.9)):
         margin=dict(l=50, r=10, t=10, b=36),
         hovermode="closest", font=dict(size=13)
     )
+    # FULL OUTLINE via mirror=True (top/right) + showline
     fig.update_xaxes(
         title_text="<b>Actual UCS</b>",
         range=[x0, x1], ticks="outside",
-        showline=True, linewidth=1.2, linecolor="#444",
+        showline=True, linewidth=1.2, linecolor="#444", mirror=True,
         showgrid=True, gridcolor="rgba(0,0,0,0.12)",
         tickformat=",.0f", automargin=True
     )
     fig.update_yaxes(
         title_text="<b>Predicted UCS</b>",
         range=[x0, x1], ticks="outside",
-        showline=True, linewidth=1.2, linecolor="#444",
+        showline=True, linewidth=1.2, linecolor="#444", mirror=True,
         showgrid=True, gridcolor="rgba(0,0,0,0.12)",
         tickformat=",.0f", scaleanchor="x", scaleratio=1,
         automargin=True
@@ -150,7 +150,7 @@ def cross_plot_interactive(actual, pred, size=(3.9, 3.9)):
     return fig
 
 def depth_or_index_track_interactive(df, title=None, include_actual=True):
-    """Interactive UCS track: blue solid pred, yellow dotted actual, legend inside; x on top; y inverted."""
+    """Interactive UCS track: blue solid pred, yellow dotted actual, legend inside; x on top; full box outline."""
     depth_col = next((c for c in df.columns if 'depth' in str(c).lower()), None)
     if depth_col is not None:
         y = df[depth_col]; y_label = depth_col
@@ -187,21 +187,22 @@ def depth_or_index_track_interactive(df, title=None, include_actual=True):
         width=int(3.1 * 100),
         height=int((7.6 if depth_col is not None else 7.2) * 100),
     )
+    # FULL OUTLINE via mirror=True (adds bottom/right sides)
     fig.update_xaxes(
         title_text="<b>UCS</b>", side="top",
-        ticks="outside", showline=True, linewidth=1.2, linecolor="#444",
+        ticks="outside", showline=True, linewidth=1.2, linecolor="#444", mirror=True,
         showgrid=True, gridcolor="rgba(0,0,0,0.12)",
         tickformat=",.0f", automargin=True
     )
     fig.update_yaxes(
         title_text=f"<b>{y_label}</b>", autorange="reversed",
-        ticks="outside", showline=True, linewidth=1.2, linecolor="#444",
+        ticks="outside", showline=True, linewidth=1.2, linecolor="#444", mirror=True,
         showgrid=True, gridcolor="rgba(0,0,0,0.12)",
         automargin=True
     )
     return fig
 
-# ---------- Preview modal helpers (matplotlib) ----------
+# ---------- Preview modal helpers (matplotlib static) ----------
 def make_index_tracks(df: pd.DataFrame, cols: list[str]):
     cols = [c for c in cols if c in df.columns]
     n = len(cols)
@@ -304,12 +305,19 @@ if meta_path.exists():
         FEATURES = meta.get("features", FEATURES); TARGET = meta.get("target", TARGET)
     except Exception: pass
 else:
-    infer = None
-    try:
-        if hasattr(model, "feature_names_in_") and len(getattr(model, "feature_names_in_")):
-            infer = [str(x) for x in model.feature_names_in_]
-    except Exception:
-        pass
+    def infer_features_from_model(m):
+        try:
+            if hasattr(m, "feature_names_in_") and len(getattr(m, "feature_names_in_")):
+                return [str(x) for x in m.feature_names_in_]
+        except Exception: pass
+        try:
+            if hasattr(m, "steps") and len(m.steps):
+                last = m.steps[-1][1]
+                if hasattr(last, "feature_names_in_") and len(last.feature_names_in_):
+                    return [str(x) for x in last.feature_names_in_]
+        except Exception: pass
+        return None
+    infer = infer_features_from_model(model)
     if infer: FEATURES = infer
 
 # =========================
@@ -397,13 +405,13 @@ if st.session_state.app_step == "dev":
             file_bytes = train_test_file.getvalue()
             size = len(file_bytes)
         except Exception:
-            file_bytes = b""
-            size = 0
+            file_bytes = b""; size = 0
         sig = (train_test_file.name, size)
         if sig != st.session_state.dev_file_signature and size > 0:
             st.session_state.dev_file_signature = sig
             st.session_state.dev_file_name = train_test_file.name
             st.session_state.dev_file_bytes = file_bytes
+            # Inspect first sheet for rows/cols
             _book_tmp = read_book_bytes(file_bytes)
             if _book_tmp:
                 first_df = next(iter(_book_tmp.values()))
@@ -448,7 +456,7 @@ if st.session_state.app_step == "dev":
         else:
             st.write("**Upload your data to build a case, then run the model to review development performance.**")
 
-    # If user clicked preview, open modal after helper (so helper stays on top)
+    # If user clicked preview, open modal *after* helper so helper stays on top
     if st.session_state.dev_preview_request and st.session_state.dev_file_bytes:
         _book = read_book_bytes(st.session_state.dev_file_bytes)
         st.session_state.dev_previewed = True
@@ -538,15 +546,17 @@ if st.session_state.app_step == "dev":
         summary_df = pd.DataFrame(rows) if rows else None
         try:
             buf = io.BytesIO()
-            import openpyxl  # noqa
             with pd.ExcelWriter(buf, engine="openpyxl") as xw:
                 for name, frame in sheets.items():
                     frame.to_excel(xw, sheet_name=name[:31], index=False)
                 if summary_df is not None:
                     summary_df.to_excel(xw, sheet_name="Summary", index=False)
-            st.download_button("Export Development Results to Excel",
-                data=buf.getvalue(), file_name="UCS_Dev_Results.xlsx",
-                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
+            st.download_button(
+                "Export Development Results to Excel",
+                data=buf.getvalue(),
+                file_name="UCS_Dev_Results.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+            )
         except Exception as e:
             st.warning(str(e))
 
@@ -618,9 +628,19 @@ if st.session_state.app_step == "predict":
         if sv["oor_pct"] > 0:
             st.warning("Some validation inputs fall outside the **training min–max** ranges. Interpret predictions with caution.")
 
-        c1,c2,c3,c4 = st.columns(4)
-        c1.metric("points", f"{sv['n_points']}"); c2.metric("Pred min", f"{sv['pred_min']:.2f}")
-        c3.metric("Pred max", f"{sv['pred_max']:.2f}"); c4.metric("OOR %", f"{sv['oor_pct']:.1f}%")
+        # Metrics like Development (R² / RMSE / MAE)
+        metrics_val = st.session_state.results.get("metrics_val")
+        if metrics_val is not None:
+            c1, c2, c3 = st.columns(3)
+            c1.metric("R²",   f"{metrics_val['R2']:.4f}")
+            c2.metric("RMSE", f"{metrics_val['RMSE']:.4f}")
+            c3.metric("MAE",  f"{metrics_val['MAE']:.4f}")
+        else:
+            # Fallback only if actual UCS isn't provided
+            c1, c2, c3 = st.columns(3)
+            c1.metric("# points", f"{sv['n_points']}")
+            c2.metric("Pred min", f"{sv['pred_min']:.2f}")
+            c3.metric("Pred max", f"{sv['pred_max']:.2f}")
 
         left, right = st.columns([0.9, 0.55])
         with left:
@@ -658,15 +678,17 @@ if st.session_state.app_step == "predict":
         summary_df = pd.DataFrame(rows) if rows else None
         try:
             buf = io.BytesIO()
-            import openpyxl  # noqa
             with pd.ExcelWriter(buf, engine="openpyxl") as xw:
-                for nm, fr in sheets.items():
-                    fr.to_excel(xw, sheet_name=nm[:31], index=False)
+                for name, frame in sheets.items():
+                    frame.to_excel(xw, sheet_name=name[:31], index=False)
                 if summary_df is not None:
                     summary_df.to_excel(xw, sheet_name="Summary", index=False)
-            st.download_button("Export Validation Results to Excel",
-                data=buf.getvalue(), file_name="UCS_Validation_Results.xlsx",
-                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")
+            st.download_button(
+                "Export Validation Results to Excel",
+                data=buf.getvalue(),
+                file_name="UCS_Validation_Results.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+            )
         except Exception as e:
             st.warning(str(e))