Update app.py

app.py

@@ -1,3 +1,4 @@
+# app.py — ST_GR (Gamma Ray) app adapted from your UCS app, same flow & design
 import io, json, os, base64, math
 from pathlib import Path
 import streamlit as st
@@ -15,128 +16,88 @@ import plotly.graph_objects as go
 from sklearn.metrics import mean_squared_error, mean_absolute_error
 
 # =========================
-# Constants
+# Constants (GR)
 # =========================
-# The features and target are loaded from meta.json if available
-FEATURES = [] 
-TARGET = ""
-TARGET_DISPLAY_NAME = "GR"
-TARGET_UNITS = "API"
+APP_NAME = "ST_GR"
+TAGLINE  = "Gamma Ray Prediction"
+# If meta.json is present, these will be overridden
+FEATURES = ["GPM", "SPP", "RPM", "WOB", "T", "ROP"]
+TARGET   = "log(GR)"     # or "GR" if your model predicts GR directly
+PRED_POSTPROCESS = "exp" # or None if direct GR
+
 MODELS_DIR = Path("models")
 DEFAULT_MODEL = MODELS_DIR / "gr_rf.joblib"
 MODEL_FALLBACKS = [MODELS_DIR / "model.joblib", MODELS_DIR / "model.pkl"]
+
 COLORS = {"pred": "#1f77b4", "actual": "#f2b702", "ref": "#5a5a5a"}
 
 # ---- Plot sizing controls ----
-CROSS_W = 350 # px (matplotlib figure size; Streamlit will still scale)
+CROSS_W = 350    # px (matplotlib figure size; Streamlit will still scale)
 CROSS_H = 350
-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
+TRACK_H = 1000   # px (plotly height)
+TRACK_W = 500    # px (plotly width)
+FONT_SZ  = 13
+BOLD_FONT = "Arial Black, Arial, sans-serif"
 
 # =========================
 # Page / CSS
 # =========================
-st.set_page_config(page_title="ST_GeoMech_GR", page_icon="logo.png", layout="wide")
+st.set_page_config(page_title=APP_NAME, page_icon="logo.png", layout="wide")
 
-# General CSS (logo helpers etc.)
+# General CSS
 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;
- }
+ .centered-container { display:flex; flex-direction:column; align-items:center; text-align:center; }
 </style>
 """, unsafe_allow_html=True)
 
-# CSS to make sticky headers work correctly by overriding Streamlit's overflow property
+# Allow sticky bits (preview expander header & tabs)
 st.markdown("""
 <style>
-/* This targets the main content area */
-.main .block-container {
-  overflow: unset !important;
-}
-
-/* This targets the vertical block that holds all your elements */
-div[data-testid="stVerticalBlock"] {
-  overflow: unset !important;
-}
+.main .block-container { overflow: unset !important; }
+div[data-testid="stVerticalBlock"] { overflow: unset !important; }
 </style>
 """, unsafe_allow_html=True)
 
 # Hide uploader helper text ("Drag and drop file here", limits, etc.)
 st.markdown("""
 <style>
-/* Older builds (helper wrapped in a Markdown container) */
 section[data-testid="stFileUploader"] div[data-testid="stMarkdownContainer"]{display:none !important;}
-/* 1.31–1.34: helper is the first child in the dropzone */
 section[data-testid="stFileUploader"] [data-testid="stFileUploaderDropzone"] > div:first-child{display:none !important;}
-/* 1.35+: explicit helper container */
 section[data-testid="stFileUploader"] [data-testid="stFileUploaderInstructions"]{display:none !important;}
-/* Fallback: any paragraph/small text inside the uploader */
 section[data-testid="stFileUploader"] p, section[data-testid="stFileUploader"] small{display:none !important;}
 </style>
 """, unsafe_allow_html=True)
 
-# Make the Preview expander title & tabs sticky (pinned to the top)
+# Sticky Preview expander & its tabs
 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; 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
+# Message box styles
 st.markdown("""
 <style>
-.st-message-box {
-  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;
-}
-.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;
-}
+.st-message-box { background:#f0f2f6; color:#333; padding:10px; border-radius:10px; border:1px solid #e6e9ef; }
+.st-message-box.st-success { background:#d4edda; color:#155724; border-color:#c3e6cb; }
+.st-message-box.st-warning { background:#fff3cd; color:#856404; border-color:#ffeeba; }
+.st-message-box.st-error { background:#f8d7da; color:#721c24; border-color:#f5c6cb; }
 </style>
 """, unsafe_allow_html=True)
 
@@ -144,307 +105,320 @@ 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.")
+    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;'>{APP_NAME}</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()
 
-  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_GR</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()
 
 # =========================
 # 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
+def _nice_tick0(xmin: float, step: int = 5) -> float:
+    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)
+    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)
+
+# --- target transform helpers (to support models trained on log(GR)) ---
+def inverse_target(x: np.ndarray, transform: str) -> np.ndarray:
+    t = (transform or "none").lower()
+    if t in ["log10", "log_10", "log10()"]:
+        return np.power(10.0, x)
+    if t in ["ln", "log", "log_e", "natural"]:
+        return np.exp(x)
+    return x  # "none"
+
+def to_actual_series(df: pd.DataFrame, target_col: str, actual_col_hint: str, transform: str) -> pd.Series:
+    """
+    Return the 'actual GR' series (API).
+    If an explicit actual column exists, use it; else invert the target.
+    """
+    if actual_col_hint and actual_col_hint in df.columns:
+        return pd.Series(df[actual_col_hint], dtype=float)
+    # else, if target exists, invert:
+    if target_col in df.columns:
+        return pd.Series(inverse_target(np.asarray(df[target_col], dtype=float), transform), dtype=float)
+    # fallback: if a column named "GR" exists, use it
+    if "GR" in df.columns:
+        return pd.Series(df["GR"], dtype=float)
+    raise ValueError("Cannot find actual GR column or target to invert.")
+
+# =========================
+# Cross plot (Matplotlib) — auto limits for GR
+# =========================
+def _nice_bounds(arr_min, arr_max, n_ticks=5):
+    # pick a "nice" range and step for GR (typically 0–200+ API)
+    if not np.isfinite(arr_min) or not np.isfinite(arr_max):
+        return 0.0, 100.0, 20.0
+    span = arr_max - arr_min
+    if span <= 0:
+        return max(arr_min-5, 0), arr_max+5, 5.0
+    raw_step = span / max(n_ticks, 1)
+    mag = 10 ** math.floor(math.log10(raw_step))
+    steps = np.array([1, 2, 2.5, 5, 10]) * mag
+    step = steps[np.argmin(np.abs(steps - raw_step))]
+    lo = step * math.floor(arr_min / step)
+    hi = step * math.ceil(arr_max / step)
+    return float(lo), float(hi), float(step)
 
-# =========================
-# Cross plot (Matplotlib, fixed limits & ticks)
-# =========================
 def cross_plot_static(actual, pred):
-  a = pd.Series(actual, dtype=float)
-  p = pd.Series(pred, dtype=float)
-  
-  # Dynamic limits based on data
-  all_data = pd.concat([a, p])
-  fixed_min, fixed_max = all_data.min() - 0.1, all_data.max() + 0.1
-  # Dynamic ticks based on data range
-  ticks = np.linspace(fixed_min, fixed_max, num=5, endpoint=True)
-
-  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.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"{x:.2f}")
-  ax.xaxis.set_major_formatter(fmt)
-  ax.yaxis.set_major_formatter(fmt)
-
-  ax.set_xlabel(f"Actual {TARGET_DISPLAY_NAME} ({TARGET_UNITS})", fontweight="bold", fontsize=10, color="black")
-  ax.set_ylabel(f"Predicted {TARGET_DISPLAY_NAME} ({TARGET_UNITS})", fontweight="bold", fontsize=10, color="black")
-  ax.tick_params(labelsize=6, colors="black")
-
-  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
+    a = pd.Series(actual, dtype=float)
+    p = pd.Series(pred,   dtype=float)
+
+    # auto bounds & ticks for GR
+    lo = min(a.min(), p.min())
+    hi = max(a.max(), p.max())
+    fixed_min, fixed_max, step = _nice_bounds(lo, hi, n_ticks=6)
+    ticks = np.arange(fixed_min, fixed_max + step, step)
+
+    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.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 1:1
+
+    fmt = FuncFormatter(lambda x, _: f"{int(x):,}")
+    ax.xaxis.set_major_formatter(fmt)
+    ax.yaxis.set_major_formatter(fmt)
+
+    ax.set_xlabel("Actual GR (API)",     fontweight="bold", fontsize=10, color="black")
+    ax.set_ylabel("Predicted GR (API)",  fontweight="bold", fontsize=10, color="black")
+    ax.tick_params(labelsize=8, colors="black")
+
+    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
 
 # =========================
 # 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 (GR) range & ticks
-  x_series = pd.Series(df.get(f"{TARGET_DISPLAY_NAME}_Pred", pd.Series(dtype=float))).astype(float)
-  if include_actual and TARGET_DISPLAY_NAME in df.columns:
-    x_series = pd.concat([x_series, pd.Series(df[TARGET_DISPLAY_NAME]).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=10)
-
-  fig = go.Figure()
-  fig.add_trace(go.Scatter(
-    x=df[f"{TARGET_DISPLAY_NAME}_Pred"], y=y, mode="lines",
-    line=dict(color=COLORS["pred"], width=1.8),
-    name=f"{TARGET_DISPLAY_NAME}_Pred",
-    hovertemplate=f"{TARGET_DISPLAY_NAME}_Pred: %{{x:.2f}}<br>"+ylab+": %{y}<extra></extra>"
-  ))
-  if include_actual and TARGET_DISPLAY_NAME in df.columns:
+def track_plot(df, include_actual=True, pred_col="GR_Pred", actual_col="GR"):
+    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 for logs
+    else:
+        y = pd.Series(np.arange(1, len(df) + 1))
+        ylab = "Point Index"
+        y_range = [float(y.max()), float(y.min())]
+
+    # X (GR) range & ticks
+    x_series = pd.Series(df.get(pred_col, pd.Series(dtype=float))).astype(float)
+    if include_actual and actual_col in df.columns:
+        x_series = pd.concat([x_series, pd.Series(df[actual_col]).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=5)
+
+    fig = go.Figure()
     fig.add_trace(go.Scatter(
-      x=df[TARGET_DISPLAY_NAME], y=y, mode="lines",
-      line=dict(color=COLORS["actual"], width=2.0, dash="dot"),
-      name=f"{TARGET_DISPLAY_NAME} (actual)",
-      hovertemplate=f"{TARGET_DISPLAY_NAME} (actual): %{{x:.2f}}<br>"+ylab+": %{y}<extra></extra>"
+        x=df[pred_col], y=y, mode="lines",
+        line=dict(color=COLORS["pred"], width=1.8),
+        name="GR_Pred",
+        hovertemplate="GR_Pred: %{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=f"{TARGET_DISPLAY_NAME} ({TARGET_UNITS})",
-    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=",.2f",
-    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
+    if include_actual and actual_col in df.columns:
+        fig.add_trace(go.Scatter(
+            x=df[actual_col], y=y, mode="lines",
+            line=dict(color=COLORS["actual"], width=2.0, dash="dot"),
+            name="GR (actual)",
+            hovertemplate="GR (actual): %{x:.0f}<br>"+ylab+": %{y}<extra></extra>"
+        ))
+
+    fig.update_layout(
+        height=TRACK_H, width=TRACK_W, autosize=False,
+        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=""
+    )
+    fig.update_xaxes(
+        title_text="GR (API)",
+        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=f"{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")
+    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
-  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 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"))
+    def dialog(title):
+        def deco(fn):
+            def wrapper(*args, **kwargs):
+                with st.expander(title, expanded=True):
+                    return fn(*args, **kwargs)
+            return wrapper
+        return deco
 
 # =========================
-# Load model
+# Load model + meta
 # =========================
 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/gr_rf.joblib (or set MODEL_URL).")
-  st.stop()
+    st.error("Model not found. Upload models/gr_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)
+        TARGET_TRANSFORM = meta.get("target_transform", TARGET_TRANSFORM)
+        ACTUAL_COL       = meta.get("actual_col", ACTUAL_COL)
+    except Exception:
+        pass
 
 # =========================
 # Session state
@@ -456,354 +430,341 @@ st.session_state.setdefault("dev_file_name","")
 st.session_state.setdefault("dev_file_bytes",b"")
 st.session_state.setdefault("dev_file_loaded",False)
 st.session_state.setdefault("dev_preview",False)
-st.session_state.setdefault("show_preview_modal", False) # New state variable
+st.session_state.setdefault("show_preview_modal", False)
 
 # =========================
 # 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_GR</div>
-    <div style='color:#667085;'>Real-Time GR 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;'>{APP_NAME}</div>
+        <div style='color:#667085;'>{TAGLINE}</div>
+    </div>
+    """, unsafe_allow_html=True
 )
 
-# =========================
-# Reusable Sticky Header Function
-# =========================
+# Reusable sticky header
 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 GR 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 GR) or **Proceed to Prediction** (no GR)."
-  )
-  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 Gamma Ray (GR) from input features.")
+    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 GR) or **Proceed to Prediction** (no GR)."
+    )
+    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()
-    
-    # Prediction and inverse transform
-    tr[f"{TARGET}_Pred"] = model.predict(tr[FEATURES])
-    te[f"{TARGET}_Pred"] = model.predict(te[FEATURES])
-    tr[f"{TARGET_DISPLAY_NAME}_Pred"] = np.power(10, tr[f"{TARGET}_Pred"])
-    te[f"{TARGET_DISPLAY_NAME}_Pred"] = np.power(10, te[f"{TARGET}_Pred"])
-    
-    tr[TARGET_DISPLAY_NAME] = np.power(10, tr[TARGET])
-    te[TARGET_DISPLAY_NAME] = np.power(10, te[TARGET])
-
-    st.session_state.results["Train"]=tr; st.session_state.results["Test"]=te
-    st.session_state.results["m_train"]={
-      "R": pearson_r(tr[TARGET_DISPLAY_NAME], tr[f"{TARGET_DISPLAY_NAME}_Pred"]),
-      "RMSE": rmse(tr[TARGET_DISPLAY_NAME], tr[f"{TARGET_DISPLAY_NAME}_Pred"]),
-      "MAE": mean_absolute_error(tr[TARGET_DISPLAY_NAME], tr[f"{TARGET_DISPLAY_NAME}_Pred"])
-    }
-    st.session_state.results["m_test"]={
-      "R": pearson_r(te[TARGET_DISPLAY_NAME], te[f"{TARGET_DISPLAY_NAME}_Pred"]),
-      "RMSE": rmse(te[TARGET_DISPLAY_NAME], te[f"{TARGET_DISPLAY_NAME}_Pred"]),
-      "MAE": mean_absolute_error(te[TARGET_DISPLAY_NAME], te[f"{TARGET_DISPLAY_NAME}_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_track, col_cross = st.columns([2, 3], gap="large")
-    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}
-      )
-    with col_cross:
-      st.pyplot(cross_plot_static(df[TARGET_DISPLAY_NAME], df[f"{TARGET_DISPLAY_NAME}_Pred"]), use_container_width=False)
-
-
-  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
+        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()
+
+    # Sticky helper
+    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 and Test/Testing sheets.</div>', unsafe_allow_html=True)
+            st.stop()
+        tr = book[sh_train].copy(); te = book[sh_test].copy()
+        if not (ensure_cols(tr, FEATURES) and ensure_cols(te, FEATURES)):
+            st.markdown('<div class="st-message-box st-error">Missing required feature columns.</div>', unsafe_allow_html=True); st.stop()
+
+        # predictions (handle log targets)
+        tr_pred_raw = model.predict(tr[FEATURES])
+        te_pred_raw = model.predict(te[FEATURES])
+        tr["GR_Pred"] = inverse_target(np.asarray(tr_pred_raw, dtype=float), TARGET_TRANSFORM)
+        te["GR_Pred"] = inverse_target(np.asarray(te_pred_raw, dtype=float), TARGET_TRANSFORM)
+
+        # actual GR (for metrics/plots)
+        tr["GR_Actual"] = to_actual_series(tr, TARGET, ACTUAL_COL, TARGET_TRANSFORM)
+        te["GR_Actual"] = to_actual_series(te, TARGET, ACTUAL_COL, TARGET_TRANSFORM)
+
+        st.session_state.results["Train"]=tr; st.session_state.results["Test"]=te
+        st.session_state.results["m_train"]={
+            "R": pearson_r(tr["GR_Actual"], tr["GR_Pred"]),
+            "RMSE": rmse(tr["GR_Actual"], tr["GR_Pred"]),
+            "MAE": mean_absolute_error(tr["GR_Actual"], tr["GR_Pred"])
+        }
+        st.session_state.results["m_test"]={
+            "R": pearson_r(te["GR_Actual"], te["GR_Pred"]),
+            "RMSE": rmse(te["GR_Actual"], te["GR_Pred"]),
+            "MAE": mean_absolute_error(te["GR_Actual"], te["GR_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}")
+        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_track, col_cross = st.columns([2, 3], gap="large")
+        with col_track:
+            st.plotly_chart(
+                track_plot(df.rename(columns={"GR_Actual":"GR"}), include_actual=True,
+                           pred_col="GR_Pred", actual_col="GR"),
+                use_container_width=False,
+                config={"displayModeBar": False, "scrollZoom": True}
+            )
+        with col_cross:
+            st.pyplot(cross_plot_static(df["GR_Actual"], df["GR_Pred"]), use_container_width=False)
+
+    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 GR)
 # =========================
 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 & Validate", 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", f"Upload a dataset with the same **features** and **{TARGET}** 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()
-    
-    # Prediction and inverse transform
-    df[f"{TARGET}_Pred"] = model.predict(df[FEATURES])
-    df[f"{TARGET_DISPLAY_NAME}_Pred"] = np.power(10, df[f"{TARGET}_Pred"])
-    
-    df[TARGET_DISPLAY_NAME] = np.power(10, df[TARGET])
-    
-    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_DISPLAY_NAME], df[f"{TARGET_DISPLAY_NAME}_Pred"]),
-      "RMSE": rmse(df[TARGET_DISPLAY_NAME], df[f"{TARGET_DISPLAY_NAME}_Pred"]),
-      "MAE": mean_absolute_error(df[TARGET_DISPLAY_NAME], df[f"{TARGET_DISPLAY_NAME}_Pred"])
-    }
-    st.session_state.results["sv_val"]={"n":len(df),"pred_min":float(df[f"{TARGET_DISPLAY_NAME}_Pred"].min()),"pred_max":float(df[f"{TARGET_DISPLAY_NAME}_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_track, col_cross = st.columns([2, 3], gap="large")
-    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}
-      )
-    with col_cross:
-      st.pyplot(
-        cross_plot_static(st.session_state.results["Validate"][TARGET_DISPLAY_NAME],
-              st.session_state.results["Validate"][f"{TARGET_DISPLAY_NAME}_Pred"]),
-        use_container_width=False
-      )
-
-    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
+    go_btn = st.sidebar.button("Predict & Validate", 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 **GR** 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): st.markdown('<div class="st-message-box st-error">Missing required feature columns.</div>', unsafe_allow_html=True); st.stop()
+
+        pred_raw = model.predict(df[FEATURES])
+        df["GR_Pred"] = inverse_target(np.asarray(pred_raw, dtype=float), TARGET_TRANSFORM)
+        # actual GR
+        try:
+            df["GR_Actual"] = to_actual_series(df, TARGET, ACTUAL_COL, TARGET_TRANSFORM)
+        except Exception:
+            st.markdown('<div class="st-message-box st-error">Validation sheet must include actual GR (or a target column that can be inverse-transformed).</div>', unsafe_allow_html=True); st.stop()
+
+        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["GR_Actual"], df["GR_Pred"]),
+            "RMSE": rmse(df["GR_Actual"], df["GR_Pred"]),
+            "MAE": mean_absolute_error(df["GR_Actual"], df["GR_Pred"])
+        }
+        st.session_state.results["sv_val"]={"n":len(df),"pred_min":float(df["GR_Pred"].min()),"pred_max":float(df["GR_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}")
+        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_track, col_cross = st.columns([2, 3], gap="large")
+        with col_track:
+            st.plotly_chart(
+                track_plot(st.session_state.results["Validate"].rename(columns={"GR_Actual":"GR"}),
+                           include_actual=True, pred_col="GR_Pred", actual_col="GR"),
+                use_container_width=False, config={"displayModeBar": False, "scrollZoom": True}
+            )
+        with col_cross:
+            st.pyplot(
+                cross_plot_static(st.session_state.results["Validate"]["GR_Actual"],
+                                  st.session_state.results["Validate"]["GR_Pred"]),
+                use_container_width=False
+            )
+
+        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 GR)
 # =========================
 if st.session_state.app_step == "predict":
-  st.sidebar.header("Prediction (No Actual GR)")
-  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", f"Upload a dataset with the feature columns (no **{TARGET_DISPLAY_NAME}**).")
-
-  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()
-    
-    # Prediction and inverse transform
-    df[f"{TARGET}_Pred"] = model.predict(df[FEATURES])
-    df[f"{TARGET_DISPLAY_NAME}_Pred"] = np.power(10, df[f"{TARGET}_Pred"])
-    
-    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[f"{TARGET_DISPLAY_NAME}_Pred"].min()),
-      "pred_max":float(df[f"{TARGET_DISPLAY_NAME}_Pred"].max()),
-      "pred_mean":float(df[f"{TARGET_DISPLAY_NAME}_Pred"].mean()),
-      "pred_std":float(df[f"{TARGET_DISPLAY_NAME}_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
+    st.sidebar.header("Prediction (No Actual GR)")
+    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
+    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 **GR**).")
+
+    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 feature columns.</div>', unsafe_allow_html=True); st.stop()
+
+        pred_raw = model.predict(df[FEATURES])
+        df["GR_Pred"] = inverse_target(np.asarray(pred_raw, dtype=float), TARGET_TRANSFORM)
+        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["GR_Pred"].min()),
+            "pred_max":float(df["GR_Pred"].max()),
+            "pred_mean":float(df["GR_Pred"].mean()),
+            "pred_std":float(df["GR_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.rename(columns={"GR_Pred":"GR_Pred"}), include_actual=False,
+                           pred_col="GR_Pred", actual_col="GR"),
+                use_container_width=False, config={"displayModeBar": False, "scrollZoom": True}
+            )
+
+# =========================
+# Preview modal (re-usable)
 # =========================
 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' 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
+    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"))
+    st.session_state.show_preview_modal = False
+
 # =========================
 # Footer
 # =========================
 st.markdown("""
 <br><br><br>
 <hr>
-<div style='text-align:center;color:#6b7280;font-size:1.0em;'>
-    © 2024 Smart Thinking AI-Solutions Team. All rights reserved.<br>
-    Contact: <a href="mailto:smartthinking.com.sa">smartthinking@smartthinking.com.sa</a>
+<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)
+""", unsafe_allow_html=True)