Update app.py

app.py

@@ -30,8 +30,8 @@ CROSS_H = 250
 TRACK_H = 1000 # px (plotly height; width auto-fits column)
 # NEW: Add a TRACK_W variable to control the width
 TRACK_W = 500 # px (plotly width)
-FONT_SZ  = 13
-BOLD_FONT = "Arial Black, Arial, sans-serif"  # used for bold axis titles & ticks
+FONT_SZ = 13
+BOLD_FONT = "Arial Black, Arial, sans-serif" # used for bold axis titles & ticks
 
 # =========================
 # Page / CSS
@@ -41,16 +41,16 @@ st.set_page_config(page_title="ST_GeoMech_UCS", page_icon="logo.png", layout="wi
 # General CSS (logo helpers etc.)
 st.markdown("""
 <style>
-  .brand-logo { width: 200px; height: auto; object-fit: contain; }
-  .sidebar-header { display:flex; align-items:center; gap:12px; }
-  .sidebar-header .text h1 { font-size: 1.05rem; margin:0; line-height:1.1; }
-  .sidebar-header .text .tag { font-size: .85rem; color:#6b7280; margin:2px 0 0; }
-  .centered-container {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    text-align: center;
-  }
+ .brand-logo { width: 200px; height: auto; object-fit: contain; }
+ .sidebar-header { display:flex; align-items:center; gap:12px; }
+ .sidebar-header .text h1 { font-size: 1.05rem; margin:0; line-height:1.1; }
+ .sidebar-header .text .tag { font-size: .85rem; color:#6b7280; margin:2px 0 0; }
+ .centered-container {
+   display: flex;
+   flex-direction: column;
+   align-items: center;
+   text-align: center;
+ }
 </style>
 """, unsafe_allow_html=True)
 
@@ -59,12 +59,12 @@ st.markdown("""
 <style>
 /* This targets the main content area */
 .main .block-container {
-    overflow: unset !important;
+    overflow: unset !important;
 }
 
 /* This targets the vertical block that holds all your elements */
 div[data-testid="stVerticalBlock"] {
-    overflow: unset !important;
+    overflow: unset !important;
 }
 </style>
 """, unsafe_allow_html=True)
@@ -87,52 +87,52 @@ section[data-testid="stFileUploader"] p, section[data-testid="stFileUploader"] s
 st.markdown("""
 <style>
 div[data-testid="stExpander"] > details > summary {
-  position: sticky;
-  top: 0;
-  z-index: 10;
-  background: #fff;
-  border-bottom: 1px solid #eee;
+ position: sticky;
+ top: 0;
+ z-index: 10;
+ background: #fff;
+ border-bottom: 1px solid #eee;
 }
 div[data-testid="stExpander"] div[data-baseweb="tab-list"] {
-  position: sticky;
-  top: 42px;    /* adjust if your expander header height differs */
-  z-index: 9;
-  background: #fff;
-  padding-top: 6px;
+ position: sticky;
+ top: 42px;    /* adjust if your expander header height differs */
+ z-index: 9;
+ background: #fff;
+ padding-top: 6px;
 }
 </style>
 """, unsafe_allow_html=True)
 
 # Center text in all pandas Styler tables (headers + cells)
 TABLE_CENTER_CSS = [
-    dict(selector="th", props=[("text-align", "center")]),
-    dict(selector="td", props=[("text-align", "center")]),
+    dict(selector="th", props=[("text-align", "center")]),
+    dict(selector="td", props=[("text-align", "center")]),
 ]
 
 # NEW: CSS for the message box
 st.markdown("""
 <style>
 .st-message-box {
-    background-color: #f0f2f6;
-    color: #333333;
-    padding: 10px;
-    border-radius: 10px;
-    border: 1px solid #e6e9ef;
+    background-color: #f0f2f6;
+    color: #333333;
+    padding: 10px;
+    border-radius: 10px;
+    border: 1px solid #e6e9ef;
 }
 .st-message-box.st-success {
-    background-color: #d4edda;
-    color: #155724;
-    border-color: #c3e6cb;
+    background-color: #d4edda;
+    color: #155724;
+    border-color: #c3e6cb;
 }
 .st-message-box.st-warning {
-    background-color: #fff3cd;
-    color: #856404;
-    border-color: #ffeeba;
+    background-color: #fff3cd;
+    color: #856404;
+    border-color: #ffeeba;
 }
 .st-message-box.st-error {
-    background-color: #f8d7da;
-    color: #721c24;
-    border-color: #f5c6cb;
+    background-color: #f8d7da;
+    color: #721c24;
+    border-color: #f5c6cb;
 }
 </style>
 """, unsafe_allow_html=True)
@@ -141,42 +141,42 @@ st.markdown("""
 # Password gate
 # =========================
 def inline_logo(path="logo.png") -> str:
-    try:
-        p = Path(path)
-        if not p.exists(): return ""
-        return f"data:image/png;base64,{base64.b64encode(p.read_bytes()).decode('ascii')}"
-    except Exception:
-        return ""
+    try:
+        p = Path(path)
+        if not p.exists(): return ""
+        return f"data:image/png;base64,{base64.b64encode(p.read_bytes()).decode('ascii')}"
+    except Exception:
+        return ""
 
 def add_password_gate() -> None:
-    try:
-        required = st.secrets.get("APP_PASSWORD", "")
-    except Exception:
-        required = os.environ.get("APP_PASSWORD", "")
-
-    if not required:
-        st.warning("Set APP_PASSWORD in Secrets (or environment) and restart.")
-        st.stop()
-
-    if st.session_state.get("auth_ok", False):
-        return
-
-    st.sidebar.markdown(f"""
-        <div class="centered-container">
-            <img src="{inline_logo('logo.png')}" style="width: 200px; height: auto; object-fit: contain;">
-            <div style='font-weight:800;font-size:1.2rem; margin-top: 10px;'>ST_GeoMech_UCS</div>
-            <div style='color:#667085;'>Smart Thinking • Secure Access</div>
-        </div>
-        """, unsafe_allow_html=True
-    )
-    pwd = st.sidebar.text_input("Access key", type="password", placeholder="••••••••")
-    if st.sidebar.button("Unlock", type="primary"):
-        if pwd == required:
-            st.session_state.auth_ok = True
-            st.rerun()
-        else:
-            st.error("Incorrect key.")
-    st.stop()
+    try:
+        required = st.secrets.get("APP_PASSWORD", "")
+    except Exception:
+        required = os.environ.get("APP_PASSWORD", "")
+
+    if not required:
+        st.warning("Set APP_PASSWORD in Secrets (or environment) and restart.")
+        st.stop()
+
+    if st.session_state.get("auth_ok", False):
+        return
+
+    st.sidebar.markdown(f"""
+        <div class="centered-container">
+            <img src="{inline_logo('logo.png')}" style="width: 200px; height: auto; object-fit: contain;">
+            <div style='font-weight:800;font-size:1.2rem; margin-top: 10px;'>ST_GeoMech_UCS</div>
+            <div style='color:#667085;'>Smart Thinking • Secure Access</div>
+        </div>
+        """, unsafe_allow_html=True
+    )
+    pwd = st.sidebar.text_input("Access key", type="password", placeholder="••••••••")
+    if st.sidebar.button("Unlock", type="primary"):
+        if pwd == required:
+            st.session_state.auth_ok = True
+            st.rerun()
+        else:
+            st.error("Incorrect key.")
+    st.stop()
 
 add_password_gate()
 
@@ -184,260 +184,260 @@ add_password_gate()
 # Utilities
 # =========================
 def rmse(y_true, y_pred) -> float:
-    return float(np.sqrt(mean_squared_error(y_true, y_pred)))
+    return float(np.sqrt(mean_squared_error(y_true, y_pred)))
 
 def pearson_r(y_true, y_pred) -> float:
-    a = np.asarray(y_true, dtype=float)
-    p = np.asarray(y_pred,   dtype=float)
-    if a.size < 2: return float("nan")
-    return float(np.corrcoef(a, p)[0, 1])
+    a = np.asarray(y_true, dtype=float)
+    p = np.asarray(y_pred, dtype=float)
+    if a.size < 2: return float("nan")
+    return float(np.corrcoef(a, p)[0, 1])
 
 @st.cache_resource(show_spinner=False)
 def load_model(model_path: str):
-    return joblib.load(model_path)
+    return joblib.load(model_path)
 
 @st.cache_data(show_spinner=False)
 def parse_excel(data_bytes: bytes):
-    bio = io.BytesIO(data_bytes)
-    xl = pd.ExcelFile(bio)
-    return {sh: xl.parse(sh) for sh in xl.sheet_names}
+    bio = io.BytesIO(data_bytes)
+    xl = pd.ExcelFile(bio)
+    return {sh: xl.parse(sh) for sh in xl.sheet_names}
 
 def read_book_bytes(b: bytes): return parse_excel(b) if b else {}
 
 def ensure_cols(df, cols):
-    miss = [c for c in cols if c not in df.columns]
-    if miss:
-        st.error(f"Missing columns: {miss}\nFound: {list(df.columns)}")
-        return False
-    return True
+    miss = [c for c in cols if c not in df.columns]
+    if miss:
+        st.error(f"Missing columns: {miss}\nFound: {list(df.columns)}")
+        return False
+    return True
 
 def find_sheet(book, names):
-    low2orig = {k.lower(): k for k in book.keys()}
-    for nm in names:
-        if nm.lower() in low2orig: return low2orig[nm.lower()]
-    return None
+    low2orig = {k.lower(): k for k in book.keys()}
+    for nm in names:
+        if nm.lower() in low2orig: return low2orig[nm.lower()]
+    return None
 
 def _nice_tick0(xmin: float, step: int = 100) -> float:
-    return step * math.floor(xmin / step) if np.isfinite(xmin) else xmin
+    return step * math.floor(xmin / step) if np.isfinite(xmin) else xmin
 
 def df_centered_rounded(df: pd.DataFrame, hide_index=True):
-    """Center headers & cells; format numeric columns to 2 decimals."""
-    out = df.copy()
-    numcols = out.select_dtypes(include=[np.number]).columns
-    styler = (
-        out.style
-            .format({c: "{:.2f}" for c in numcols})
-            .set_properties(**{"text-align": "center"})
-            .set_table_styles(TABLE_CENTER_CSS)
-    )
-    st.dataframe(styler, use_container_width=True, hide_index=hide_index)
+    """Center headers & cells; format numeric columns to 2 decimals."""
+    out = df.copy()
+    numcols = out.select_dtypes(include=[np.number]).columns
+    styler = (
+        out.style
+            .format({c: "{:.2f}" for c in numcols})
+            .set_properties(**{"text-align": "center"})
+            .set_table_styles(TABLE_CENTER_CSS)
+    )
+    st.dataframe(styler, use_container_width=True, hide_index=hide_index)
 
 # =========================
 # Cross plot (Matplotlib, fixed limits & ticks)
 # =========================
 def cross_plot_static(actual, pred):
-    a = pd.Series(actual, dtype=float)
-    p = pd.Series(pred,   dtype=float)
+    a = pd.Series(actual, dtype=float)
+    p = pd.Series(pred, dtype=float)
 
-    fixed_min, fixed_max = 6000, 10000
-    ticks = np.arange(fixed_min, fixed_max + 1, 1000)
+    fixed_min, fixed_max = 6000, 10000
+    ticks = np.arange(fixed_min, fixed_max + 1, 1000)
 
-    dpi = 110
-    fig, ax = plt.subplots(
-        figsize=(CROSS_W / dpi, CROSS_H / dpi),
-        dpi=dpi,
-        constrained_layout=False
-    )
+    dpi = 110
+    fig, ax = plt.subplots(
+        figsize=(CROSS_W / dpi, CROSS_H / dpi),
+        dpi=dpi,
+        constrained_layout=False
+    )
 
-    ax.scatter(a, p, s=14, c=COLORS["pred"], alpha=0.9, linewidths=0)
-    ax.plot([fixed_min, fixed_max], [fixed_min, fixed_max],
-            linestyle="--", linewidth=1.2, color=COLORS["ref"])
+    ax.scatter(a, p, s=14, c=COLORS["pred"], alpha=0.9, linewidths=0)
+    ax.plot([fixed_min, fixed_max], [fixed_min, fixed_max],
+            linestyle="--", linewidth=1.2, color=COLORS["ref"])
 
-    ax.set_xlim(fixed_min, fixed_max)
-    ax.set_ylim(fixed_min, fixed_max)
-    ax.set_xticks(ticks)
-    ax.set_yticks(ticks)
-    ax.set_aspect("equal", adjustable="box")  # true 45°
+    ax.set_xlim(fixed_min, fixed_max)
+    ax.set_ylim(fixed_min, fixed_max)
+    ax.set_xticks(ticks)
+    ax.set_yticks(ticks)
+    ax.set_aspect("equal", adjustable="box") # true 45°
 
-    fmt = FuncFormatter(lambda x, _: f"{int(x):,}")
-    ax.xaxis.set_major_formatter(fmt)
-    ax.yaxis.set_major_formatter(fmt)
+    fmt = FuncFormatter(lambda x, _: f"{int(x):,}")
+    ax.xaxis.set_major_formatter(fmt)
+    ax.yaxis.set_major_formatter(fmt)
 
-    ax.set_xlabel("Actual UCS (psi)", fontweight="bold", fontsize=4, color="black")
-    ax.set_ylabel("Predicted UCS (psi)", fontweight="bold", fontsize=4, color="black")
-    ax.tick_params(labelsize=2, colors="black")
+    ax.set_xlabel("Actual UCS (psi)", fontweight="bold", fontsize=4, color="black")
+    ax.set_ylabel("Predicted UCS (psi)", fontweight="bold", fontsize=4, color="black")
+    ax.tick_params(labelsize=2, colors="black")
 
-    ax.grid(True, linestyle=":", alpha=0.3)
-    for spine in ax.spines.values():
-        spine.set_linewidth(1.1)
-        spine.set_color("#444")
+    ax.grid(True, linestyle=":", alpha=0.3)
+    for spine in ax.spines.values():
+        spine.set_linewidth(1.1)
+        spine.set_color("#444")
 
-    fig.subplots_adjust(left=0.16, bottom=0.16, right=0.98, top=0.98)
-    return fig
+    fig.subplots_adjust(left=0.16, bottom=0.16, right=0.98, top=0.98)
+    return fig
 
 # =========================
 # Track plot (Plotly)
 # =========================
 def track_plot(df, include_actual=True):
-    depth_col = next((c for c in df.columns if 'depth' in str(c).lower()), None)
-    if depth_col is not None:
-        y = pd.Series(df[depth_col]).astype(float)
-        ylab = depth_col
-        y_range = [float(y.max()), float(y.min())]  # reverse
-    else:
-        y = pd.Series(np.arange(1, len(df) + 1))
-        ylab = "Point Index"
-        y_range = [float(y.max()), float(y.min())]
-
-    # X (UCS) range & ticks
-    x_series = pd.Series(df.get("UCS_Pred", pd.Series(dtype=float))).astype(float)
-    if include_actual and TARGET in df.columns:
-        x_series = pd.concat([x_series, pd.Series(df[TARGET]).astype(float)], ignore_index=True)
-    x_lo, x_hi = float(x_series.min()), float(x_series.max())
-    x_pad = 0.03 * (x_hi - x_lo if x_hi > x_lo else 1.0)
-    xmin, xmax = x_lo - x_pad, x_hi + x_pad
-    tick0 = _nice_tick0(xmin, step=100)
-
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(
-        x=df["UCS_Pred"], y=y, mode="lines",
-        line=dict(color=COLORS["pred"], width=1.8),
-        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)",
-            hovertemplate="UCS (actual): %{x:.0f}<br>"+ylab+": %{y}<extra></extra>"
-        ))
-
-    fig.update_layout(
-        height=TRACK_H,
-        width=TRACK_W, # Set the width here
-        autosize=False, # Disable autosizing to respect the width
-        paper_bgcolor="#fff", plot_bgcolor="#fff",
-        margin=dict(l=64, r=16, t=36, b=48), hovermode="closest",
-        font=dict(size=FONT_SZ, color="#000"),
-        legend=dict(
-            x=0.98, y=0.05, xanchor="right", yanchor="bottom",
-            bgcolor="rgba(255,255,255,0.75)", bordercolor="#ccc", borderwidth=1
-        ),
-        legend_title_text=""
-    )
-
-    # Bold, black axis titles & ticks
-    fig.update_xaxes(
-        title_text="UCS (psi)",
-        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",
-        tickmode="auto",
-        tick0=tick0,
-        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=ylab,
-        title_font=dict(size=20, family=BOLD_FONT, color="#000"),
-        tickfont=dict(size=15, family=BOLD_FONT, color="#000"),
-        range=y_range,
-        ticks="outside",
-        showline=True, linewidth=1.2, linecolor="#444", mirror=True,
-        showgrid=True, gridcolor="rgba(0,0,0,0.12)", automargin=True
-    )
-
-    return fig
+    depth_col = next((c for c in df.columns if 'depth' in str(c).lower()), None)
+    if depth_col is not None:
+        y = pd.Series(df[depth_col]).astype(float)
+        ylab = depth_col
+        y_range = [float(y.max()), float(y.min())] # reverse
+    else:
+        y = pd.Series(np.arange(1, len(df) + 1))
+        ylab = "Point Index"
+        y_range = [float(y.max()), float(y.min())]
+
+    # X (UCS) range & ticks
+    x_series = pd.Series(df.get("UCS_Pred", pd.Series(dtype=float))).astype(float)
+    if include_actual and TARGET in df.columns:
+        x_series = pd.concat([x_series, pd.Series(df[TARGET]).astype(float)], ignore_index=True)
+    x_lo, x_hi = float(x_series.min()), float(x_series.max())
+    x_pad = 0.03 * (x_hi - x_lo if x_hi > x_lo else 1.0)
+    xmin, xmax = x_lo - x_pad, x_hi + x_pad
+    tick0 = _nice_tick0(xmin, step=100)
+
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=df["UCS_Pred"], y=y, mode="lines",
+        line=dict(color=COLORS["pred"], width=1.8),
+        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)",
+            hovertemplate="UCS (actual): %{x:.0f}<br>"+ylab+": %{y}<extra></extra>"
+        ))
+
+    fig.update_layout(
+        height=TRACK_H,
+        width=TRACK_W, # Set the width here
+        autosize=False, # Disable autosizing to respect the width
+        paper_bgcolor="#fff", plot_bgcolor="#fff",
+        margin=dict(l=64, r=16, t=36, b=48), hovermode="closest",
+        font=dict(size=FONT_SZ, color="#000"),
+        legend=dict(
+            x=0.98, y=0.05, xanchor="right", yanchor="bottom",
+            bgcolor="rgba(255,255,255,0.75)", bordercolor="#ccc", borderwidth=1
+        ),
+        legend_title_text=""
+    )
+
+    # Bold, black axis titles & ticks
+    fig.update_xaxes(
+        title_text="UCS (psi)",
+        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",
+        tickmode="auto",
+        tick0=tick0,
+        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=ylab,
+        title_font=dict(size=20, family=BOLD_FONT, color="#000"),
+        tickfont=dict(size=15, family=BOLD_FONT, color="#000"),
+        range=y_range,
+        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 (matplotlib) ----------
 def preview_tracks(df: pd.DataFrame, cols: list[str]):
-    cols = [c for c in cols if c in df.columns]
-    n = len(cols)
-    if n == 0:
-        fig, ax = plt.subplots(figsize=(4, 2))
-        ax.text(0.5,0.5,"No selected columns",ha="center",va="center"); ax.axis("off")
-        return fig
-    fig, axes = plt.subplots(1, n, figsize=(2.2*n, 7.0), sharey=True, dpi=100)
-    if n == 1: axes = [axes]
-    idx = np.arange(1, len(df) + 1)
-    for ax, col in zip(axes, cols):
-        ax.plot(df[col], idx, '-', lw=1.4, color="#333")
-        ax.set_xlabel(col); ax.xaxis.set_label_position('top'); ax.xaxis.tick_top(); ax.invert_yaxis()
-        ax.grid(True, linestyle=":", alpha=0.3)
-        for s in ax.spines.values(): s.set_visible(True)
-    axes[0].set_ylabel("Point Index")
-    return fig
+    cols = [c for c in cols if c in df.columns]
+    n = len(cols)
+    if n == 0:
+        fig, ax = plt.subplots(figsize=(4, 2))
+        ax.text(0.5,0.5,"No selected columns",ha="center",va="center"); ax.axis("off")
+        return fig
+    fig, axes = plt.subplots(1, n, figsize=(2.2*n, 7.0), sharey=True, dpi=100)
+    if n == 1: axes = [axes]
+    idx = np.arange(1, len(df) + 1)
+    for ax, col in zip(axes, cols):
+        ax.plot(df[col], idx, '-', lw=1.4, color="#333")
+        ax.set_xlabel(col); ax.xaxis.set_label_position('top'); ax.xaxis.tick_top(); ax.invert_yaxis()
+        ax.grid(True, linestyle=":", alpha=0.3)
+        for s in ax.spines.values(): s.set_visible(True)
+    axes[0].set_ylabel("Point Index")
+    return fig
 
 # Modal wrapper (Streamlit compatibility)
 try:
-    dialog = st.dialog
+    dialog = st.dialog
 except AttributeError:
-    def dialog(title):
-        def deco(fn):
-            def wrapper(*args, **kwargs):
-                with st.expander(title, expanded=True):
-                    return fn(*args, **kwargs)
-            return wrapper
-        return deco
+    def dialog(title):
+        def deco(fn):
+            def wrapper(*args, **kwargs):
+                with st.expander(title, expanded=True):
+                    return fn(*args, **kwargs)
+            return wrapper
+        return deco
 
 def preview_modal(book: dict[str, pd.DataFrame]):
-    if not book:
-        st.info("No data loaded yet."); return
-    names = list(book.keys())
-    tabs = st.tabs(names)
-    for t, name in zip(tabs, names):
-        with t:
-            df = book[name]
-            t1, t2 = st.tabs(["Tracks", "Summary"])
-            with t1:
-                st.pyplot(preview_tracks(df, FEATURES), use_container_width=True)
-            with t2:
-                tbl = (df[FEATURES]
-                        .agg(['min','max','mean','std'])
-                        .T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"}))
-                df_centered_rounded(tbl.reset_index(names="Feature"))
+    if not book:
+        st.info("No data loaded yet."); return
+    names = list(book.keys())
+    tabs = st.tabs(names)
+    for t, name in zip(tabs, names):
+        with t:
+            df = book[name]
+            t1, t2 = st.tabs(["Tracks", "Summary"])
+            with t1:
+                st.pyplot(preview_tracks(df, FEATURES), use_container_width=True)
+            with t2:
+                tbl = (df[FEATURES]
+                        .agg(['min','max','mean','std'])
+                        .T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"}))
+                df_centered_rounded(tbl.reset_index(names="Feature"))
 
 # =========================
 # Load model
 # =========================
 def ensure_model() -> Path|None:
-    for p in [DEFAULT_MODEL, *MODEL_FALLBACKS]:
-        if p.exists() and p.stat().st_size > 0: return p
-    url = os.environ.get("MODEL_URL", "")
-    if not url: return None
-    try:
-        import requests
-        DEFAULT_MODEL.parent.mkdir(parents=True, exist_ok=True)
-        with requests.get(url, stream=True, timeout=30) as r:
-            r.raise_for_status()
-            with open(DEFAULT_MODEL, "wb") as f:
-                for chunk in r.iter_content(1<<20):
-                    if chunk: f.write(chunk)
-        return DEFAULT_MODEL
-    except Exception:
-        return None
+    for p in [DEFAULT_MODEL, *MODEL_FALLBACKS]:
+        if p.exists() and p.stat().st_size > 0: return p
+    url = os.environ.get("MODEL_URL", "")
+    if not url: return None
+    try:
+        import requests
+        DEFAULT_MODEL.parent.mkdir(parents=True, exist_ok=True)
+        with requests.get(url, stream=True, timeout=30) as r:
+            r.raise_for_status()
+            with open(DEFAULT_MODEL, "wb") as f:
+                for chunk in r.iter_content(1<<20):
+                    if chunk: f.write(chunk)
+        return DEFAULT_MODEL
+    except Exception:
+        return None
 
 mpath = ensure_model()
 if not mpath:
-    st.error("Model not found. Upload models/ucs_rf.joblib (or set MODEL_URL).")
-    st.stop()
+    st.error("Model not found. Upload models/ucs_rf.joblib (or set MODEL_URL).")
+    st.stop()
 try:
-    model = load_model(str(mpath))
+    model = load_model(str(mpath))
 except Exception as e:
-    st.error(f"Failed to load model: {e}")
-    st.stop()
+    st.error(f"Failed to load model: {e}")
+    st.stop()
 
 meta_path = MODELS_DIR / "meta.json"
 if meta_path.exists():
-    try:
-        meta = json.loads(meta_path.read_text(encoding="utf-8"))
-        FEATURES = meta.get("features", FEATURES); TARGET = meta.get("target", TARGET)
-    except Exception:
-        pass
+    try:
+        meta = json.loads(meta_path.read_text(encoding="utf-8"))
+        FEATURES = meta.get("features", FEATURES); TARGET = meta.get("target", TARGET)
+    except Exception:
+        pass
 
 # =========================
 # Session state
@@ -455,343 +455,330 @@ st.session_state.setdefault("show_preview_modal", False) # New state variable
 # Branding in Sidebar
 # =========================
 st.sidebar.markdown(f"""
-    <div class="centered-container">
-        <img src="{inline_logo('logo.png')}" style="width: 200px; height: auto; object-fit: contain;">
-        <div style='font-weight:800;font-size:1.2rem;'>ST_GeoMech_UCS</div>
-        <div style='color:#667085;'>Real-Time UCS Tracking While Drilling</div>
-    </div>
-    """, unsafe_allow_html=True
+    <div class="centered-container">
+        <img src="{inline_logo('logo.png')}" style="width: 200px; height: auto; object-fit: contain;">
+        <div style='font-weight:800;font-size:1.2rem;'>ST_GeoMech_UCS</div>
+        <div style='color:#667085;'>Real-Time UCS Tracking While Drilling</div>
+    </div>
+    """, unsafe_allow_html=True
 )
 
 # =========================
 # Reusable Sticky Header Function
 # =========================
 def sticky_header(title, message):
-    st.markdown(
-        f"""
-        <style>
-        .sticky-container {{
-            position: sticky;
-            top: 0;
-            background-color: white;
-            z-index: 100;
-            padding-top: 10px;
-            padding-bottom: 10px;
-            border-bottom: 1px solid #eee;
-        }}
-        </style>
-        <div class="sticky-container">
-            <h3>{title}</h3>
-            <p>{message}</p>
-        </div>
-        """,
-        unsafe_allow_html=True
-    )
+    st.markdown(
+        f"""
+        <style>
+        .sticky-container {{
+            position: sticky;
+            top: 0;
+            background-color: white;
+            z-index: 100;
+            padding-top: 10px;
+            padding-bottom: 10px;
+            border-bottom: 1px solid #eee;
+        }}
+        </style>
+        <div class="sticky-container">
+            <h3>{title}</h3>
+            <p>{message}</p>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
 
 # =========================
 # INTRO
 # =========================
 if st.session_state.app_step == "intro":
-    st.header("Welcome!")
-    st.markdown("This software is developed by *Smart Thinking AI-Solutions Team* to estimate UCS from drilling data.")
-    st.subheader("How It Works")
-    st.markdown(
-        "1) **Upload your data to build the case and preview the performance of our model.** \n"
-        "2) Click **Run Model** to compute metrics and plots.  \n"
-        "3) **Proceed to Validation** (with actual UCS) or **Proceed to Prediction** (no UCS)."
-    )
-    if st.button("Start Showcase", type="primary"):
-        st.session_state.app_step = "dev"; st.rerun()
+    st.header("Welcome!")
+    st.markdown("This software is developed by *Smart Thinking AI-Solutions Team* to estimate UCS from drilling data.")
+    st.subheader("How It Works")
+    st.markdown(
+        "1) **Upload your data to build the case and preview the performance of our model.** \n"
+        "2) Click **Run Model** to compute metrics and plots. \n"
+        "3) **Proceed to Validation** (with actual UCS) or **Proceed to Prediction** (no UCS)."
+    )
+    if st.button("Start Showcase", type="primary"):
+        st.session_state.app_step = "dev"; st.rerun()
 
 # =========================
 # CASE BUILDING
 # =========================
 if st.session_state.app_step == "dev":
-    st.sidebar.header("Case Building")
-    up = st.sidebar.file_uploader("Upload Your Data File", type=["xlsx","xls"])
-    if up is not None:
-        st.session_state.dev_file_bytes = up.getvalue()
-        st.session_state.dev_file_name = up.name
-        st.session_state.dev_file_loaded = True
-        st.session_state.dev_preview = False
-    if st.session_state.dev_file_loaded:
-        tmp = read_book_bytes(st.session_state.dev_file_bytes)
-        if tmp:
-            df0 = next(iter(tmp.values()))
-            st.sidebar.caption(f"**Data loaded:** {st.session_state.dev_file_name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
-
-    if st.sidebar.button("Preview data", use_container_width=True, disabled=not st.session_state.dev_file_loaded):
-        st.session_state.show_preview_modal = True  # Set state to show modal
-        st.session_state.dev_preview = True
-
-    run = st.sidebar.button("Run Model", type="primary", use_container_width=True)
-    if st.sidebar.button("Proceed to Validation ▶", use_container_width=True): st.session_state.app_step="validate"; st.rerun()
-    if st.sidebar.button("Proceed to Prediction ▶", use_container_width=True): st.session_state.app_step="predict"; st.rerun()
-
-    # Apply sticky header
-    if st.session_state.dev_file_loaded and st.session_state.dev_preview:
-        sticky_header("Case Building", "Previewed ✓ — now click **Run Model**.")
-    elif st.session_state.dev_file_loaded:
-        sticky_header("Case Building", "📄 **Preview uploaded data** using the sidebar button, then click **Run Model**.")
-    else:
-        sticky_header("Case Building", "**Upload your data to build a case, then run the model to review development performance.**")
-
-    if run and st.session_state.dev_file_bytes:
-        book = read_book_bytes(st.session_state.dev_file_bytes)
-        sh_train = find_sheet(book, ["Train","Training","training2","train","training"])
-        sh_test  = find_sheet(book, ["Test","Testing","testing2","test","testing"])
-        if sh_train is None or sh_test is None:
-            st.markdown('<div class="st-message-box st-error">Workbook must include Train/Training/training2 and Test/Testing/testing2 sheets.</div>', unsafe_allow_html=True)
-            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.markdown('<div class="st-message-box st-error">Missing required columns.</div>', unsafe_allow_html=True)
-            st.stop()
-        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"]={
-            "R": pearson_r(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"]={
-            "R": pearson_r(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}
-        st.markdown('<div class="st-message-box st-success">Case has been built and results are displayed below.</div>', unsafe_allow_html=True)
-
-    def _dev_block(df, m):
-        c1,c2,c3 = st.columns(3)
-        c1.metric("R", f"{m['R']:.2f}")
-        c2.metric("RMSE", f"{m['RMSE']:.2f}")
-        c3.metric("MAE", f"{m['MAE']:.2f}")
-
-        # NEW: Footer for metric abbreviations
-        st.markdown("""
-            <div style='text-align: left; font-size: 0.8em; color: #6b7280; margin-top: -16px; margin-bottom: 8px;'>
-                <strong>R:</strong> Pearson Correlation Coefficient<br>
-                <strong>RMSE:</strong> Root Mean Square Error<br>
-                <strong>MAE:</strong> Mean Absolute Error
-            </div>
-        """, unsafe_allow_html=True)
-
-        # 2-column layout, big gap (prevents overlap)
-        col_cross, col_track = st.columns([3, 2], gap="large")
-        with col_cross:
-            st.pyplot(cross_plot_static(df[TARGET], df["UCS_Pred"]), use_container_width=False)
-        with col_track:
-            st.plotly_chart(
-                track_plot(df, include_actual=True),
-                use_container_width=False, # Set to False to honor the width in track_plot()
-                config={"displayModeBar": False, "scrollZoom": True}
-            )
-
-    if "Train" in st.session_state.results or "Test" in st.session_state.results:
-        tab1, tab2 = st.tabs(["Training", "Testing"])
-        if "Train" in st.session_state.results:
-            with tab1: _dev_block(st.session_state.results["Train"], st.session_state.results["m_train"])
-        if "Test" in st.session_state.results:
-            with tab2: _dev_block(st.session_state.results["Test"],  st.session_state.results["m_test"])
+    st.sidebar.header("Case Building")
+    up = st.sidebar.file_uploader("Upload Your Data File", type=["xlsx","xls"])
+    if up is not None:
+        st.session_state.dev_file_bytes = up.getvalue()
+        st.session_state.dev_file_name = up.name
+        st.session_state.dev_file_loaded = True
+        st.session_state.dev_preview = False
+    if st.session_state.dev_file_loaded:
+        tmp = read_book_bytes(st.session_state.dev_file_bytes)
+        if tmp:
+            df0 = next(iter(tmp.values()))
+            st.sidebar.caption(f"**Data loaded:** {st.session_state.dev_file_name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
+
+    if st.sidebar.button("Preview data", use_container_width=True, disabled=not st.session_state.dev_file_loaded):
+        st.session_state.show_preview_modal = True # Set state to show modal
+        st.session_state.dev_preview = True
+
+    run = st.sidebar.button("Run Model", type="primary", use_container_width=True)
+    if st.sidebar.button("Proceed to Validation ▶", use_container_width=True): st.session_state.app_step="validate"; st.rerun()
+    if st.sidebar.button("Proceed to Prediction ▶", use_container_width=True): st.session_state.app_step="predict"; st.rerun()
+
+    # Apply sticky header
+    if st.session_state.dev_file_loaded and st.session_state.dev_preview:
+        sticky_header("Case Building", "Previewed ✓ — now click **Run Model**.")
+    elif st.session_state.dev_file_loaded:
+        sticky_header("Case Building", "📄 **Preview uploaded data** using the sidebar button, then click **Run Model**.")
+    else:
+        sticky_header("Case Building", "**Upload your data to build a case, then run the model to review development performance.**")
+
+    if run and st.session_state.dev_file_bytes:
+        book = read_book_bytes(st.session_state.dev_file_bytes)
+        sh_train = find_sheet(book, ["Train","Training","training2","train","training"])
+        sh_test = find_sheet(book, ["Test","Testing","testing2","test","testing"])
+        if sh_train is None or sh_test is None:
+            st.markdown('<div class="st-message-box st-error">Workbook must include Train/Training/training2 and Test/Testing/testing2 sheets.</div>', unsafe_allow_html=True)
+            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.markdown('<div class="st-message-box st-error">Missing required columns.</div>', unsafe_allow_html=True)
+            st.stop()
+        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"]={
+            "R": pearson_r(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"]={
+            "R": pearson_r(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}
+        st.markdown('<div class="st-message-box st-success">Case has been built and results are displayed below.</div>', unsafe_allow_html=True)
+
+    def _dev_block(df, m):
+        c1,c2,c3 = st.columns(3)
+        c1.metric("R", f"{m['R']:.2f}")
+        c2.metric("RMSE", f"{m['RMSE']:.2f}")
+        c3.metric("MAE", f"{m['MAE']:.2f}")
+
+        # NEW: Footer for metric abbreviations
+        st.markdown("""
+            <div style='text-align: left; font-size: 0.8em; color: #6b7280; margin-top: -16px; margin-bottom: 8px;'>
+                <strong>R:</strong> Pearson Correlation Coefficient<br>
+                <strong>RMSE:</strong> Root Mean Square Error<br>
+                <strong>MAE:</strong> Mean Absolute Error
+            </div>
+        """, unsafe_allow_html=True)
+
+        # 2-column layout, big gap (prevents overlap)
+        col_cross, col_track = st.columns([3, 2], gap="large")
+        with col_cross:
+            st.pyplot(cross_plot_static(df[TARGET], df["UCS_Pred"]), use_container_width=False)
+        with col_track:
+            st.plotly_chart(
+                track_plot(df, include_actual=True),
+                use_container_width=False, # Set to False to honor the width in track_plot()
+                config={"displayModeBar": False, "scrollZoom": True}
+            )
+
+    if "Train" in st.session_state.results or "Test" in st.session_state.results:
+        tab1, tab2 = st.tabs(["Training", "Testing"])
+        if "Train" in st.session_state.results:
+            with tab1: _dev_block(st.session_state.results["Train"], st.session_state.results["m_train"])
+        if "Test" in st.session_state.results:
+            with tab2: _dev_block(st.session_state.results["Test"], st.session_state.results["m_test"])
 
 # =========================
 # VALIDATION (with actual UCS)
 # =========================
 if st.session_state.app_step == "validate":
-    st.sidebar.header("Validate the Model")
-    up = st.sidebar.file_uploader("Upload Validation Excel", type=["xlsx","xls"])
-    if up is not None:
-        book = read_book_bytes(up.getvalue())
-        if book:
-            df0 = next(iter(book.values()))
-            st.sidebar.caption(f"**Data loaded:** {up.name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
-    if st.sidebar.button("Preview data", use_container_width=True, disabled=(up is None)):
-        st.session_state.show_preview_modal = True  # Set state to show modal
-    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()
-    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 **UCS** 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.markdown('<div class="st-message-box st-error">Missing required columns.</div>', unsafe_allow_html=True); st.stop()
-        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
-        if ranges:
-            any_viol = pd.DataFrame({f:(df[f]<ranges[f][0])|(df[f]>ranges[f][1]) for f in FEATURES}).any(axis=1)
-            oor_pct = float(any_viol.mean()*100.0)
-            if any_viol.any():
-                tbl = df.loc[any_viol, FEATURES].copy()
-                for c in FEATURES:
-                    if pd.api.types.is_numeric_dtype(tbl[c]): tbl[c] = tbl[c].round(2)
-                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"]={
-            "R": pearson_r(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
-
-    if "Validate" in st.session_state.results:
-        m = st.session_state.results["m_val"]
-        c1,c2,c3 = st.columns(3)
-        c1.metric("R", f"{m['R']:.2f}")
-        c2.metric("RMSE", f"{m['RMSE']:.2f}")
-        c3.metric("MAE", f"{m['MAE']:.2f}")
-
-        # NEW: Footer for metric abbreviations
-        st.markdown("""
-            <div style='text-align: left; font-size: 0.8em; color: #6b7280; margin-top: -16px; margin-bottom: 8px;'>
-                <strong>R:</strong> Pearson Correlation Coefficient<br>
-                <strong>RMSE:</strong> Root Mean Square Error<br>
-                <strong>MAE:</strong> Mean Absolute Error
-            </div>
-        """, unsafe_allow_html=True)
-    
-        col_cross, col_track = st.columns([3, 2], gap="large")
-        with col_cross:
-            st.pyplot(
-                cross_plot_static(st.session_state.results["Validate"][TARGET],
-                                     st.session_state.results["Validate"]["UCS_Pred"]),
-                use_container_width=False
-            )
-        with col_track:
-            st.plotly_chart(
-                track_plot(st.session_state.results["Validate"], include_actual=True),
-                use_container_width=False, # Set to False to honor the width in track_plot()
-                config={"displayModeBar": False, "scrollZoom": True}
-            )
-
-        sv = st.session_state.results["sv_val"]
-        if sv["oor"] > 0: st.markdown('<div class="st-message-box st-warning">Some inputs fall outside **training min–max** ranges.</div>', unsafe_allow_html=True)
-        if st.session_state.results["oor_tbl"] is not None:
-            st.write("*Out-of-range rows (vs. Training min–max):*")
-            df_centered_rounded(st.session_state.results["oor_tbl"])
+    st.sidebar.header("Validate the Model")
+    up = st.sidebar.file_uploader("Upload Validation Excel", type=["xlsx","xls"])
+    if up is not None:
+        book = read_book_bytes(up.getvalue())
+        if book:
+            df0 = next(iter(book.values()))
+            st.sidebar.caption(f"**Data loaded:** {up.name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
+    if st.sidebar.button("Preview data", use_container_width=True, disabled=(up is None)):
+        st.session_state.show_preview_modal = True # Set state to show modal
+    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()
+    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 **UCS** 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.markdown('<div class="st-message-box st-error">Missing required columns.</div>', unsafe_allow_html=True); st.stop()
+        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
+        if ranges:
+            any_viol = pd.DataFrame({f:(df[f]<ranges[f][0])|(df[f]>ranges[f][1]) for f in FEATURES}).any(axis=1)
+            oor_pct = float(any_viol.mean()*100.0)
+            if any_viol.any():
+                tbl = df.loc[any_viol, FEATURES].copy()
+                for c in FEATURES:
+                    if pd.api.types.is_numeric_dtype(tbl[c]): tbl[c] = tbl[c].round(2)
+                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"]={
+            "R": pearson_r(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
+
+    if "Validate" in st.session_state.results:
+        m = st.session_state.results["m_val"]
+        c1,c2,c3 = st.columns(3)
+        c1.metric("R", f"{m['R']:.2f}")
+        c2.metric("RMSE", f"{m['RMSE']:.2f}")
+        c3.metric("MAE", f"{m['MAE']:.2f}")
+
+        # NEW: Footer for metric abbreviations
+        st.markdown("""
+            <div style='text-align: left; font-size: 0.8em; color: #6b7280; margin-top: -16px; margin-bottom: 8px;'>
+                <strong>R:</strong> Pearson Correlation Coefficient<br>
+                <strong>RMSE:</strong> Root Mean Square Error<br>
+                <strong>MAE:</strong> Mean Absolute Error
+            </div>
+        """, unsafe_allow_html=True)
+       
+        col_cross, col_track = st.columns([3, 2], gap="large")
+        with col_cross:
+            st.pyplot(
+                cross_plot_static(st.session_state.results["Validate"][TARGET],
+                                 st.session_state.results["Validate"]["UCS_Pred"]),
+                use_container_width=False
+            )
+        with col_track:
+            st.plotly_chart(
+                track_plot(st.session_state.results["Validate"], include_actual=True),
+                use_container_width=False, # Set to False to honor the width in track_plot()
+                config={"displayModeBar": False, "scrollZoom": True}
+            )
+
+        sv = st.session_state.results["sv_val"]
+        if sv["oor"] > 0: st.markdown('<div class="st-message-box st-warning">Some inputs fall outside **training min–max** ranges.</div>', unsafe_allow_html=True)
+        if st.session_state.results["oor_tbl"] is not None:
+            st.write("*Out-of-range rows (vs. Training min–max):*")
+            df_centered_rounded(st.session_state.results["oor_tbl"])
 
 # =========================
 # PREDICTION (no actual UCS)
 # =========================
 if st.session_state.app_step == "predict":
-    st.sidebar.header("Prediction (No Actual UCS)")
-    up = st.sidebar.file_uploader("Upload Prediction Excel", type=["xlsx","xls"])
-    if up is not None:
-        book = read_book_bytes(up.getvalue())
-        if book:
-            df0 = next(iter(book.values()))
-            st.sidebar.caption(f"**Data loaded:** {up.name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
-    if st.sidebar.button("Preview data", use_container_width=True, disabled=(up is None)):
-        st.session_state.show_preview_modal = True  # Set state to show modal
-    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 **UCS**).")
-
-    if go_btn and up is not None:
-        book = read_book_bytes(up.getvalue()); name = list(book.keys())[0]
-        df = book[name].copy()
-        if not ensure_cols(df, FEATURES): st.markdown('<div class="st-message-box st-error">Missing required columns.</div>', unsafe_allow_html=True); st.stop()
-        df["UCS_Pred"] = model.predict(df[FEATURES])
-        st.session_state.results["PredictOnly"]=df
-
-        ranges = st.session_state.train_ranges; oor_pct = 0.0
-        if ranges:
-            any_viol = pd.DataFrame({f:(df[f]<ranges[f][0])|(df[f]>ranges[f][1]) for f in FEATURES}).any(axis=1)
-            oor_pct = float(any_viol.mean()*100.0)
-        st.session_state.results["sv_pred"]={
-            "n":len(df),
-            "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
-        }
-
-    if "PredictOnly" in st.session_state.results:
-        df = st.session_state.results["PredictOnly"]; sv = st.session_state.results["sv_pred"]
-
-        col_left, col_right = st.columns([2,3], gap="large")
-        with col_left:
-            table = pd.DataFrame({
-                "Metric": ["# points","Pred min","Pred max","Pred mean","Pred std","OOR %"],
-                "Value":  [sv["n"],
-                          round(sv["pred_min"],2),
-                          round(sv["pred_max"],2),
-                          round(sv["pred_mean"],2),
-                          round(sv["pred_std"],2),
-                          f'{sv["oor"]:.1f}%']
-            })
-            st.markdown('<div class="st-message-box st-success">Predictions ready ✓</div>', unsafe_allow_html=True)
-            df_centered_rounded(table, hide_index=True)
-            st.caption("**★ OOR** = % of rows whose input features fall outside the training min–max range.")
-        with col_right:
-            st.plotly_chart(
-                track_plot(df, include_actual=False),
-                use_container_width=False, # Set to False to honor the width in track_plot()
-                config={"displayModeBar": False, "scrollZoom": True}
-            )
+    st.sidebar.header("Prediction (No Actual UCS)")
+    up = st.sidebar.file_uploader("Upload Prediction Excel", type=["xlsx","xls"])
+    if up is not None:
+        book = read_book_bytes(up.getvalue())
+        if book:
+            df0 = next(iter(book.values()))
+            st.sidebar.caption(f"**Data loaded:** {up.name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
+    if st.sidebar.button("Preview data", use_container_width=True, disabled=(up is None)):
+        st.session_state.show_preview_modal = True # Set state to show modal
+    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 **UCS**).")
+
+    if go_btn and up is not None:
+        book = read_book_bytes(up.getvalue()); name = list(book.keys())[0]
+        df = book[name].copy()
+        if not ensure_cols(df, FEATURES): st.markdown('<div class="st-message-box st-error">Missing required columns.</div>', unsafe_allow_html=True); st.stop()
+        df["UCS_Pred"] = model.predict(df[FEATURES])
+        st.session_state.results["PredictOnly"]=df
+
+        ranges = st.session_state.train_ranges; oor_pct = 0.0
+        if ranges:
+            any_viol = pd.DataFrame({f:(df[f]<ranges[f][0])|(df[f]>ranges[f][1]) for f in FEATURES}).any(axis=1)
+            oor_pct = float(any_viol.mean()*100.0)
+        st.session_state.results["sv_pred"]={
+            "n":len(df),
+            "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
+        }
+
+    if "PredictOnly" in st.session_state.results:
+        df = st.session_state.results["PredictOnly"]; sv = st.session_state.results["sv_pred"]
+
+        col_left, col_right = st.columns([2,3], gap="large")
+        with col_left:
+            table = pd.DataFrame({
+                "Metric": ["# points","Pred min","Pred max","Pred mean","Pred std","OOR %"],
+                "Value": [sv["n"],
+                          round(sv["pred_min"],2),
+                          round(sv["pred_max"],2),
+                          round(sv["pred_mean"],2),
+                          round(sv["pred_std"],2),
+                          f'{sv["oor"]:.1f}%']
+            })
+            st.markdown('<div class="st-message-box st-success">Predictions ready ✓</div>', unsafe_allow_html=True)
+            df_centered_rounded(table, hide_index=True)
+            st.caption("**★ OOR** = % of rows whose input features fall outside the training min–max range.")
+        with col_right:
+            st.plotly_chart(
+                track_plot(df, include_actual=False),
+                use_container_width=False, # Set to False to honor the width in track_plot()
+                config={"displayModeBar": False, "scrollZoom": True}
+            )
 
 # =========================
 # Run preview modal after all other elements
 # =========================
 if st.session_state.show_preview_modal:
-    # Get the correct book based on the current app step
-    book_to_preview = {}
-    if st.session_state.app_step == "dev":
-        book_to_preview = read_book_bytes(st.session_state.dev_file_bytes)
-    elif st.session_state.app_step in ["validate", "predict"] and up is not None:
-        book_to_preview = read_book_bytes(up.getvalue())
-
-    # Use a try-except block to handle cases where 'up' might be None
-    # and the logic tries to access its attributes.
-    try:
-        if st.session_state.app_step == "validate" and up is not None:
-              book_to_preview = read_book_bytes(up.getvalue())
-        elif st.session_state.app_step == "predict" and up is not None:
-    ��         book_to_preview = read_book_bytes(up.getvalue())
-    except NameError:
-        book_to_preview = {}
-
-    with st.expander("Preview data", expanded=True):
-        if not book_to_preview:
-            st.markdown('<div class="st-message-box">No data loaded yet.</div>', unsafe_allow_html=True)
-        else:
-            names = list(book_to_preview.keys())
-            tabs = st.tabs(names)
-            for t, name in zip(tabs, names):
-                with t:
-                    df = book_to_preview[name]
-                    t1, t2 = st.tabs(["Tracks", "Summary"])
-                    with t1:
-                        st.pyplot(preview_tracks(df, FEATURES), use_container_width=True)
-                    with t2:
-                        tbl = (df[FEATURES]
-                                 .agg(['min','max','mean','std'])
-                                 .T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"}))
-                        df_centered_rounded(tbl.reset_index(names="Feature"))
-    # Reset the state variable after the modal is displayed
-    st.session_state.show_preview_modal = False
+    # Get the correct book based on the current app step
+    book_to_preview = {}
+    if st.session_state.app_step == "dev":
+        book_to_preview = read_book_bytes(st.session_state.dev_file_bytes)
+    elif st.session_state.app_step in ["validate", "predict"] and 'up' in locals() and up is not None:
+        book_to_preview = read_book_bytes(up.getvalue())
+
+    with st.expander("Preview data", expanded=True):
+        if not book_to_preview:
+            st.markdown('<div class="st-message-box">No data loaded yet.</div>', unsafe_allow_html=True)
+        else:
+            names = list(book_to_preview.keys())
+            tabs = st.tabs(names)
+            for t, name in zip(tabs, names):
+                with t:
+                    df = book_to_preview[name]
+                    t1, t2 = st.tabs(["Tracks", "Summary"])
+                    with t1:
+                        st.pyplot(preview_tracks(df, FEATURES), use_container_width=True)
+                    with t2:
+                        tbl = (df[FEATURES]
+                                 .agg(['min','max','mean','std'])
+                                 .T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"}))
+                        df_centered_rounded(tbl.reset_index(names="Feature"))
+    # Reset the state variable after the modal is displayed
+    st.session_state.show_preview_modal = False
 
 # =========================
 # Footer
 # =========================
-st.markdown("---")
-st.markdown(
-    """
-    <div style='text-align:center; color:#6b7280; line-height:1.6'>
-      ST_GeoMech_UCS • © Smart Thinking<br/>
-      <strong>Visit our website:</strong> <a href='https://www.smartthinking.com.sa' target='_blank'>smartthinking.com.sa</a>
-    </div>
-    """,
-    unsafe_allow_html=True
-)
+st.markdown("""
+<br><br><br>
+<hr>
+<div style='text-align:center;color:#6b7280;font-size:0.8em;'>
+    © 2024 Smart Thinking AI-Solutions Team. All rights reserved.
+</div>
+""", unsafe_allow_html=True)