Update app.py

app.py

@@ -1,3 +1,357 @@
+# app.py — ST_GeoMech_YM (mirrors your UCS GUI, adapted for Young's Modulus)
+import io, json, os, base64, math
+from pathlib import Path
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from datetime import datetime
+
+# Matplotlib for PREVIEW modal and the CROSS-PLOT (static)
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from matplotlib.ticker import FuncFormatter
+
+import plotly.graph_objects as go
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+
+# =========================
+# Constants (Ym variant)
+# =========================
+APP_NAME = "ST_GeoMech_YM"
+TAGLINE  = "Real-Time Young's Modulus Tracking"
+
+FEATURES  = ["WOB(klbf)", "TORQUE(kft.lbf)", "SPP(psi)", "RPM(1/min)", "ROP(ft/h)", "Flow Rate, gpm"]
+TARGET    = "Actual Ym"
+PRED_COL  = "Ym_Pred"
+
+MODELS_DIR = Path("models")
+DEFAULT_MODEL = MODELS_DIR / "ym_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
+CROSS_H = 350
+TRACK_H = 1000
+TRACK_W = 500
+FONT_SZ = 13
+BOLD_FONT = "Arial Black, Arial, sans-serif"
+
+# =========================
+# Page / CSS
+# =========================
+st.set_page_config(page_title=APP_NAME, page_icon="logo.png", layout="wide")
+
+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; }
+</style>
+""", unsafe_allow_html=True)
+
+# Sticky helpers
+st.markdown("""
+<style>
+.main .block-container { overflow: unset !important; }
+div[data-testid="stVerticalBlock"] { overflow: unset !important; }
+</style>
+""", unsafe_allow_html=True)
+
+# Hide uploader helper text
+st.markdown("""
+<style>
+section[data-testid="stFileUploader"] div[data-testid="stMarkdownContainer"]{display:none !important;}
+section[data-testid="stFileUploader"] [data-testid="stFileUploaderDropzone"] > div:first-child{display:none !important;}
+section[data-testid="stFileUploader"] [data-testid="stFileUploaderInstructions"]{display:none !important;}
+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)
+st.markdown("""
+<style>
+div[data-testid="stExpander"] > details > summary {
+ 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; z-index: 9; background: #fff; padding-top: 6px;
+}
+</style>
+""", unsafe_allow_html=True)
+
+# Center text in all pandas Styler tables
+TABLE_CENTER_CSS = [
+    dict(selector="th", props=[("text-align", "center")]),
+    dict(selector="td", props=[("text-align", "center")]),
+]
+
+# Message box CSS
+st.markdown("""
+<style>
+.st-message-box { background-color: #f0f2f6; color: #333; 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; }
+</style>
+""", unsafe_allow_html=True)
+
+# =========================
+# 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 ""
+
+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;'>{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()
+
+add_password_gate()
+
+# =========================
+# Utilities
+# =========================
+def rmse(y_true, y_pred) -> float:
+    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")
+    # Guard constant series
+    if np.all(a == a[0]) or np.all(p == p[0]): 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)
+
+@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}
+
+def read_book_bytes(b: bytes): return parse_excel(b) if b else {}
+
+def _normalize_columns(df: pd.DataFrame) -> pd.DataFrame:
+    out = df.copy()
+    out.columns = [c.strip() for c in out.columns]
+    # Fix flow-rate typo variants
+    out = out.rename(columns={
+        "Fow Rate, gpm": "Flow Rate, gpm",
+        "Fow Rate, gpm ": "Flow Rate, gpm"
+    })
+    return out
+
+def ensure_cols(df: pd.DataFrame, cols: list[str]) -> bool:
+    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
+
+def _nice_tick0(xmin: float, step: float = 0.1) -> float:
+    # Rounded start tick for continuous Ym scales (unit-agnostic)
+    return step * math.floor(xmin / step) if np.isfinite(xmin) else xmin
+
+def df_centered_rounded(df: pd.DataFrame, hide_index=True):
+    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)
+
+# === Excel export helpers =================================================
+def _excel_engine() -> str:
+    try:
+        import xlsxwriter  # noqa: F401
+        return "xlsxwriter"
+    except Exception:
+        return "openpyxl"
+
+def _excel_safe_name(name: str) -> str:
+    bad = '[]:*?/\\'
+    safe = ''.join('_' if ch in bad else ch for ch in str(name))
+    return safe[:31]
+
+def _round_numeric(df: pd.DataFrame, ndigits: int = 3) -> pd.DataFrame:
+    out = df.copy()
+    for c in out.columns:
+        if pd.api.types.is_float_dtype(out[c]) or pd.api.types.is_integer_dtype(out[c]):
+            out[c] = pd.to_numeric(out[c], errors="coerce").round(ndigits)
+    return out
+
+def _summary_table(df: pd.DataFrame, cols: list[str]) -> pd.DataFrame:
+    cols = [c for c in cols if c in df.columns]
+    if not cols:
+        return pd.DataFrame()
+    tbl = (df[cols]
+            .agg(['min','max','mean','std'])
+            .T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"})
+            .reset_index(names="Field"))
+    return _round_numeric(tbl, 3)
+
+def _train_ranges_df(ranges: dict[str, tuple[float, float]]) -> pd.DataFrame:
+    if not ranges:
+        return pd.DataFrame()
+    df = pd.DataFrame(ranges).T.reset_index()
+    df.columns = ["Feature", "Min", "Max"]
+    return _round_numeric(df, 3)
+
+def _excel_autofit(writer, sheet_name: str, df: pd.DataFrame, min_w: int = 8, max_w: int = 40):
+    """Auto-fit columns when using xlsxwriter."""
+    try:
+        import xlsxwriter  # noqa: F401
+    except Exception:
+        return
+    ws = writer.sheets[sheet_name]
+    for i, col in enumerate(df.columns):
+        series = df[col].astype(str)
+        max_len = max([len(str(col))] + series.map(len).tolist())
+        ws.set_column(i, i, max(min_w, min(max_len + 2, max_w)))
+    ws.freeze_panes(1, 0)
+
+def _add_sheet(sheets: dict, order: list, name: str, df: pd.DataFrame, ndigits: int):
+    if df is None or df.empty:
+        return
+    sheets[name] = _round_numeric(df, ndigits)
+    order.append(name)
+
+def _available_sections() -> list[str]:
+    """Compute which sections exist, to pre-check them in the export UI."""
+    res = st.session_state.get("results", {})
+    sections = []
+    if "Train" in res:        sections += ["Training","Training_Metrics","Training_Summary"]
+    if "Test" in res:         sections += ["Testing","Testing_Metrics","Testing_Summary"]
+    if "Validate" in res:     sections += ["Validation","Validation_Metrics","Validation_Summary","Validation_OOR"]
+    if "PredictOnly" in res:  sections += ["Prediction","Prediction_Summary"]
+    if st.session_state.get("train_ranges"): sections += ["Training_Ranges"]
+    sections += ["Info"]
+    return sections
+
+def build_export_workbook(selected: list[str], ndigits: int = 3, do_autofit: bool = True) -> tuple[bytes|None, str|None, list[str]]:
+    """Builds an in-memory Excel workbook for selected sheets; fixed rounding to 3 decimals."""
+    res = st.session_state.get("results", {})
+    if not res:
+        return None, None, []
+
+    sheets: dict[str, pd.DataFrame] = {}
+    order: list[str] = []
+
+    # Training
+    if "Training" in selected and "Train" in res:
+        _add_sheet(sheets, order, "Training", res["Train"], ndigits)
+    if "Training_Metrics" in selected and res.get("m_train"):
+        _add_sheet(sheets, order, "Training_Metrics", pd.DataFrame([res["m_train"]]), ndigits)
+    if "Training_Summary" in selected and "Train" in res:
+        tr_cols = FEATURES + [c for c in [TARGET, PRED_COL] if c in res["Train"].columns]
+        _add_sheet(sheets, order, "Training_Summary", _summary_table(res["Train"], tr_cols), ndigits)
+
+    # Testing
+    if "Testing" in selected and "Test" in res:
+        _add_sheet(sheets, order, "Testing", res["Test"], ndigits)
+    if "Testing_Metrics" in selected and res.get("m_test"):
+        _add_sheet(sheets, order, "Testing_Metrics", pd.DataFrame([res["m_test"]]), ndigits)
+    if "Testing_Summary" in selected and "Test" in res:
+        te_cols = FEATURES + [c for c in [TARGET, PRED_COL] if c in res["Test"].columns]
+        _add_sheet(sheets, order, "Testing_Summary", _summary_table(res["Test"], te_cols), ndigits)
+
+    # Validation
+    if "Validation" in selected and "Validate" in res:
+        _add_sheet(sheets, order, "Validation", res["Validate"], ndigits)
+    if "Validation_Metrics" in selected and res.get("m_val"):
+        _add_sheet(sheets, order, "Validation_Metrics", pd.DataFrame([res["m_val"]]), ndigits)
+    if "Validation_Summary" in selected and res.get("sv_val"):
+        _add_sheet(sheets, order, "Validation_Summary", pd.DataFrame([res["sv_val"]]), ndigits)
+    if "Validation_OOR" in selected and isinstance(res.get("oor_tbl"), pd.DataFrame) and not res["oor_tbl"].empty:
+        _add_sheet(sheets, order, "Validation_OOR", res["oor_tbl"].reset_index(drop=True), ndigits)
+
+    # Prediction
+    if "Prediction" in selected and "PredictOnly" in res:
+        _add_sheet(sheets, order, "Prediction", res["PredictOnly"], ndigits)
+    if "Prediction_Summary" in selected and res.get("sv_pred"):
+        _add_sheet(sheets, order, "Prediction_Summary", pd.DataFrame([res["sv_pred"]]), ndigits)
+
+    # Training ranges
+    if "Training_Ranges" in selected and st.session_state.get("train_ranges"):
+        rr = _train_ranges_df(st.session_state["train_ranges"])
+        _add_sheet(sheets, order, "Training_Ranges", rr, ndigits)
+
+    # Info
+    if "Info" in selected:
+        info = pd.DataFrame([
+            {"Key": "AppName",    "Value": APP_NAME},
+            {"Key": "Tagline",    "Value": TAGLINE},
+            {"Key": "Target",     "Value": TARGET},
+            {"Key": "PredColumn", "Value": PRED_COL},
+            {"Key": "Features",   "Value": ", ".join(FEATURES)},
+            {"Key": "ExportedAt", "Value": datetime.now().strftime("%Y-%m-%d %H:%M:%S")},
+        ])
+        _add_sheet(sheets, order, "Info", info, ndigits)
+
+    if not order:
+        return None, None, []
+
+    bio = io.BytesIO()
+    engine = _excel_engine()
+    with pd.ExcelWriter(bio, engine=engine) as writer:
+        for name in order:
+            df = sheets[name]
+            sheet = _excel_safe_name(name)
+            df.to_excel(writer, sheet_name=sheet, index=False)
+            if do_autofit:
+                _excel_autofit(writer, sheet, df)
+    bio.seek(0)
+
+    fname = f"YM_Export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx"
+    return bio.getvalue(), fname, order
+
 def render_export_button(phase_key: str, default_sections: list[str]) -> None:
     """Export UI — dropdown checklists for sheets & options; fixed rounding=3."""
     st.divider()
@@ -30,19 +384,18 @@ def render_export_button(phase_key: str, default_sections: list[str]) -> None:
         help="Choose extra export behaviors.",
         key=f"opts_{phase_key}",
     )
-    do_autofit = "Auto-fit columns & freeze header" in export_options
-    add_timestamp = "Append timestamp to filename" in export_options
+    do_autofit  = "Auto-fit columns & freeze header" in export_options
+    add_stamp   = "Append timestamp to filename" in export_options
 
     base_name = st.text_input("Base filename", value="YM_Export", key=f"basename_{phase_key}")
 
-    # Build workbook (fixed rounding=3) and optionally auto-fit columns
+    # Build workbook (fixed rounding=3)
     data, _, names = build_export_workbook(selected=selected_sheets, ndigits=3, do_autofit=do_autofit)
 
     if names:
         st.caption("Will include: " + ", ".join(names))
 
-    # Compose filename
-    suffix = "_" + datetime.now().strftime("%Y%m%d_%H%M%S") if add_timestamp else ""
+    suffix = "_" + datetime.now().strftime("%Y%m%d_%H%M%S") if add_stamp else ""
     file_name = (base_name or "YM_Export") + suffix + ".xlsx"
 
     st.download_button(
@@ -53,3 +406,595 @@ def render_export_button(phase_key: str, default_sections: list[str]) -> None:
         disabled=(data is None),
         key=f"download_{phase_key}",
     )
+
+# =========================
+# Cross plot (Matplotlib) — auto-scaled for Ym
+# =========================
+def cross_plot_static(actual, pred, xlabel="Actual Ym", ylabel="Predicted Ym"):
+    a = pd.Series(actual, dtype=float)
+    p = pd.Series(pred, dtype=float)
+
+    lo = float(min(a.min(), p.min()))
+    hi = float(max(a.max(), p.max()))
+    pad = 0.03 * (hi - lo if hi > lo else 1.0)
+    lo2, hi2 = lo - pad, hi + pad
+
+    ticks = np.linspace(lo2, hi2, 5)
+
+    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([lo2, hi2], [lo2, hi2], linestyle="--", linewidth=1.2, color=COLORS["ref"])
+
+    ax.set_xlim(lo2, hi2)
+    ax.set_ylim(lo2, hi2)
+    ax.set_xticks(ticks)
+    ax.set_yticks(ticks)
+    ax.set_aspect("equal", adjustable="box")
+
+    # Generic numeric formatting (2 decimals)
+    fmt = FuncFormatter(lambda x, _: f"{x:.2f}")
+    ax.xaxis.set_major_formatter(fmt)
+    ax.yaxis.set_major_formatter(fmt)
+
+    ax.set_xlabel(xlabel, fontweight="bold", fontsize=10, color="black")
+    ax.set_ylabel(ylabel, 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
+
+# =========================
+# Track plot (Plotly)
+# =========================
+def track_plot(df, include_actual=True):
+    # Depth (or index) on Y
+    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 range from prediction/actual
+    x_series = pd.Series(df.get(PRED_COL, 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=max((xmax - xmin) / 10.0, 0.1))
+
+    fig = go.Figure()
+    if PRED_COL in df.columns:
+        fig.add_trace(go.Scatter(
+            x=df[PRED_COL], y=y, mode="lines",
+            line=dict(color=COLORS["pred"], width=1.8),
+            name=PRED_COL,
+            hovertemplate=f"{PRED_COL}: "+"%{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=f"{TARGET} (actual)",
+            hovertemplate=f"{TARGET}: "+"%{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=""
+    )
+
+    # X axis with NO decimals
+    fig.update_xaxes(
+        title_text="Ym",
+        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",   # integers, thousands separated
+        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]
+    depth_col = next((c for c in df.columns if 'depth' in str(c).lower()), None)
+    if depth_col is not None:
+        idx = pd.to_numeric(df[depth_col], errors="coerce")
+    else:
+        idx = pd.Series(np.arange(1, len(df) + 1))
+    for ax, col in zip(axes, cols):
+        ax.plot(pd.to_numeric(df[col], errors="coerce"), 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(depth_col if depth_col else "Point Index")
+    return fig
+
+# Modal wrapper
+try:
+    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 = _normalize_columns(book[name])
+            t1, t2 = st.tabs(["Tracks", "Summary"])
+            with t1:
+                st.pyplot(preview_tracks(df, FEATURES), use_container_width=True)
+            with t2:
+                present = [c for c in FEATURES if c in df.columns]
+                if present:
+                    tbl = (df[present]
+                            .agg(['min','max','mean','std'])
+                            .T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"}))
+                    df_centered_rounded(tbl.reset_index(names="Feature"))
+                else:
+                    st.info("No expected feature columns found to summarize.")
+
+# =========================
+# 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
+
+mpath = ensure_model()
+if not mpath:
+    st.error("Model not found. Upload models/ym_rf.joblib (or set MODEL_URL).")
+    st.stop()
+try:
+    model = load_model(str(mpath))
+except Exception as e:
+    st.error(f"Failed to load model: {e}")
+    st.stop()
+
+# ---------- Load meta (optional) ----------
+meta = {}
+meta_candidates = [MODELS_DIR / "meta.json", MODELS_DIR / "ym_meta.json"]
+meta_path = next((p for p in meta_candidates if p.exists()), None)
+if meta_path:
+    try:
+        meta = json.loads(meta_path.read_text(encoding="utf-8"))
+        FEATURES = meta.get("features", FEATURES)
+        TARGET   = meta.get("target", TARGET)
+    except Exception as e:
+        st.warning(f"Could not parse meta file ({meta_path.name}): {e}")
+
+# Optional: version mismatch warning
+import numpy as _np, sklearn as _skl
+mv = meta.get("versions", {})
+if mv:
+    msg = []
+    if mv.get("numpy") and mv["numpy"] != _np.__version__:
+        msg.append(f"NumPy {mv['numpy']} expected, running {_np.__version__}")
+    if mv.get("scikit_learn") and mv["scikit_learn"] != _skl.__version__:
+        msg.append(f"scikit-learn {mv['scikit_learn']} expected, running {_skl.__version__}")
+    if msg:
+        st.warning("Environment mismatch: " + " | ".join(msg))
+
+# =========================
+# Session state
+# =========================
+st.session_state.setdefault("app_step", "intro")
+st.session_state.setdefault("results", {})
+st.session_state.setdefault("train_ranges", None)
+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)
+
+# =========================
+# 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;'>{APP_NAME}</div>
+        <div style='color:#667085;'>{TAGLINE}</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
+    )
+
+# =========================
+# 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 Young's Modulus (Ym) from drilling data.")
+    st.subheader("How It Works")
+    st.markdown(
+        "1) **Upload your data to build the case and preview the model performance.** \n"
+        "2) Click **Run Model** to compute metrics and plots. \n"
+        "3) **Proceed to Validation** (with actual Ym) or **Proceed to Prediction** (no Ym)."
+    )
+    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
+        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()
+
+    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 = _normalize_columns(book[sh_train].copy())
+        te = _normalize_columns(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[PRED_COL] = model.predict(tr[FEATURES])
+        te[PRED_COL] = 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[PRED_COL]),
+            "RMSE": rmse(tr[TARGET], tr[PRED_COL]),
+            "MAE": mean_absolute_error(tr[TARGET], tr[PRED_COL])
+        }
+        st.session_state.results["m_test"]={
+            "R": pearson_r(te[TARGET], te[PRED_COL]),
+            "RMSE": rmse(te[TARGET], te[PRED_COL]),
+            "MAE": mean_absolute_error(te[TARGET], te[PRED_COL])
+        }
+
+        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, include_actual=True),
+                use_container_width=False,
+                config={"displayModeBar": False, "scrollZoom": True}
+            )
+        with col_cross:
+            st.pyplot(cross_plot_static(df[TARGET], df[PRED_COL]), 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"])
+
+        # Export UI for this phase
+        default_sections = _available_sections()
+        render_export_button(phase_key="dev", default_sections=default_sections)
+
+# =========================
+# VALIDATION (with actual Ym)
+# =========================
+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
+    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 **Actual Ym** 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 = _normalize_columns(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[PRED_COL] = 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[PRED_COL]),
+            "RMSE": rmse(df[TARGET], df[PRED_COL]),
+            "MAE": mean_absolute_error(df[TARGET], df[PRED_COL])
+        }
+        st.session_state.results["sv_val"]={"n":len(df), "pred_min":float(df[PRED_COL].min()), "pred_max":float(df[PRED_COL].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"], include_actual=True),
+                use_container_width=False,
+                config={"displayModeBar": False, "scrollZoom": True}
+            )
+        with col_cross:
+            st.pyplot(
+                cross_plot_static(st.session_state.results["Validate"][TARGET],
+                                  st.session_state.results["Validate"][PRED_COL]),
+                use_container_width=False
+            )
+
+        # Export UI for this phase
+        default_sections = _available_sections()
+        render_export_button(phase_key="validate", default_sections=default_sections)
+
+        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 Ym)
+# =========================
+if st.session_state.app_step == "predict":
+    st.sidebar.header("Prediction (No Actual Ym)")
+    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 **Actual Ym**).")
+
+    if go_btn and up is not None:
+        book = read_book_bytes(up.getvalue()); name = list(book.keys())[0]
+        df = _normalize_columns(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[PRED_COL] = 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[PRED_COL].min()),
+            "pred_max":float(df[PRED_COL].max()),
+            "pred_mean":float(df[PRED_COL].mean()),
+            "pred_std":float(df[PRED_COL].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"],3),
+                          round(sv["pred_max"],3),
+                          round(sv["pred_mean"],3),
+                          round(sv["pred_std"],3),
+                          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,
+                config={"displayModeBar": False, "scrollZoom": True}
+            )
+
+        # Export UI for this phase
+        default_sections = _available_sections()
+        render_export_button(phase_key="predict", default_sections=default_sections)
+
+# =========================
+# Run preview modal after all other elements
+# =========================
+if st.session_state.show_preview_modal:
+    # Select the correct workbook bytes for this 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 = _normalize_columns(book_to_preview[name])
+                    t1, t2 = st.tabs(["Tracks", "Summary"])
+                    with t1:
+                        st.pyplot(preview_tracks(df, FEATURES), use_container_width=True)
+                    with t2:
+                        feat_present = [c for c in FEATURES if c in df.columns]
+                        if not feat_present:
+                            st.info("No feature columns found to summarize.")
+                        else:
+                            tbl = (
+                                df[feat_present]
+                                  .agg(['min','max','mean','std'])
+                                  .T.rename(columns={"min":"Min","max":"Max","mean":"Mean","std":"Std"})
+                                  .reset_index(names="Feature")
+                            )
+                            df_centered_rounded(tbl)
+
+    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;'>
+    © 2025 Smart Thinking AI-Solutions Team. All rights reserved.<br>
+    Website: <a href="https://smartthinking.com.sa" target="_blank" rel="noopener noreferrer">smartthinking.com.sa</a>
+</div>
+""", unsafe_allow_html=True)