Delete app.py

app.py

@@ -1,658 +0,0 @@
-import io, json, os, base64, math
-from pathlib import Path
-import streamlit as st
-import pandas as pd
-import numpy as np
-import joblib
-
-# matplotlib only for PREVIEW modal
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
-
-import plotly.graph_objects as go
-from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
-
-# =========================
-# Constants (simple & robust)
-# =========================
-FEATURES = ["Q, gpm", "SPP(psi)", "T (kft.lbf)", "WOB (klbf)", "ROP (ft/h)"]
-TARGET = "UCS"
-MODELS_DIR = Path("models")
-DEFAULT_MODEL = MODELS_DIR / "ucs_rf.joblib"
-MODEL_FALLBACKS = [MODELS_DIR / "model.joblib", MODELS_DIR / "model.pkl"]
-COLORS = {"pred": "#1f77b4", "actual": "#f2b702", "ref": "#5a5a5a"}
-
-# ---- Plot sizing controls (edit here) ----
-CROSS_W = 450; CROSS_H = 450      # square cross-plot (Build + Validate)
-TRACK_W = 400; TRACK_H = 950      # log-strip style (all pages)
-FONT_SZ  = 15
-PLOT_COLS = [30, 1, 20]         # 3-column band: left • spacer • right (Build + Validate)
-CROSS_NUDGE = 0.02                # push cross-plot to the RIGHT inside its band:
-                                  # inner columns [CROSS_NUDGE : 1] → bigger = more right
-
-# =========================
-# Page / CSS
-# =========================
-st.set_page_config(page_title="ST_GeoMech_UCS", page_icon="logo.png", layout="wide")
-st.markdown("""
-<style>
-/* ✅ Hides 'Drag and drop file here' and file size note */
-section[data-testid="stFileUploader"] div[data-testid="stMarkdownContainer"] {
-    display: none !important;
-}
-</style>
-""", unsafe_allow_html=True)
-st.markdown("<style>header, footer{visibility:hidden !important;}</style>", unsafe_allow_html=True)
-st.markdown(
-    """
-    <style>
-      .stApp { background:#fff; }
-      section[data-testid="stSidebar"] { background:#F6F9FC; }
-      .block-container { padding-top:.5rem; padding-bottom:.5rem; }
-      .stButton>button { background:#007bff; color:#fff; font-weight:600; border-radius:8px; border:none; }
-      .stButton>button:hover { background:#0056b3; }
-      .st-hero { display:flex; align-items:center; gap:16px; padding-top: 4px; }
-      .st-hero .brand { width:110px; height:110px; object-fit:contain; }
-      .st-hero h1 { margin:0; line-height:1.05; }
-      .st-hero .tagline { margin:2px 0 0 2px; color:#6b7280; font-size:1.05rem; font-style:italic; }
-      [data-testid="stBlock"]{ margin-top:0 !important; }
-    </style>
-    """,
-    unsafe_allow_html=True
-)
-
-# =========================
-# Password gate (define first, then call)
-# =========================
-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.markdown(
-            f"""
-            <div style="display:flex;align-items:center;gap:14px;margin:8px 0 6px 0;">
-              <img src="{inline_logo()}" style="width:56px;height:56px;object-fit:contain"/>
-              <div>
-                <div style="font-size:1.9rem;font-weight:800;">ST_GeoMech_UCS</div>
-                <div style="color:#667085;">Smart Thinking • Secure Access</div>
-              </div>
-            </div>
-            <div style="font-size:1.25rem;font-weight:700;margin:8px 0 4px 0;">Protected Area</div>
-            <div style="color:#6b7280;margin-bottom:14px;">
-              Set <code>APP_PASSWORD</code> in <b>Settings → Secrets</b> (or environment) and restart.
-            </div>
-            """,
-            unsafe_allow_html=True,
-        )
-        st.stop()
-
-    if st.session_state.get("auth_ok", False):
-        return
-
-    st.markdown(
-        f"""
-        <div style="display:flex;align-items:center;gap:14px;margin:8px 0 6px 0;">
-          <img src="{inline_logo()}" style="width:56px;height:56px;object-fit:contain"/>
-          <div>
-            <div style="font-size:1.9rem;font-weight:800;">ST_GeoMech_UCS</div>
-            <div style="color:#667085;">Smart Thinking • Secure Access</div>
-          </div>
-        </div>
-        <div style="font-size:1.25rem;font-weight:700;margin:8px 0 4px 0;">Protected</div>
-        <div style="color:#6b7280;margin-bottom:14px;">Please enter your access key to continue.</div>
-        """,
-        unsafe_allow_html=True
-    )
-
-    pwd = st.text_input("Access key", type="password", placeholder="••••••••")
-    if st.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
-# =========================
-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 rmse(y_true, y_pred):
-    return float(np.sqrt(mean_squared_error(y_true, y_pred)))
-
-@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 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
-
-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: int = 100) -> float:
-    return step * math.floor(xmin / step) if np.isfinite(xmin) else xmin
-
-# ---------- cross_plot ----------
-def cross_plot(actual, pred):
-    a = pd.Series(actual).astype(float)
-    p = pd.Series(pred).astype(float)
-
-    fixed_min = 6000
-    fixed_max = 10000
-    tick_spacing = 500
-    tick_start = 6000
-
-    fig = go.Figure()
-
-    # Scatter points
-    fig.add_trace(go.Scatter(
-        x=a, y=p, mode="markers",
-        marker=dict(size=6, color=COLORS["pred"]),
-        hovertemplate="Actual: %{x:.0f}<br>Pred: %{y:.0f}<extra></extra>",
-        showlegend=False
-    ))
-
-    # 1:1 diagonal line
-    fig.add_trace(go.Scatter(
-        x=[fixed_min, fixed_max], y=[fixed_min, fixed_max], mode="lines",
-        line=dict(color=COLORS["ref"], width=1.2, dash="dash"),
-        hoverinfo="skip", showlegend=False
-    ))
-
-    fig.update_layout(
-        width=CROSS_W, height=CROSS_H,
-        paper_bgcolor="#fff", plot_bgcolor="#fff",
-        margin=dict(l=64, r=18, t=10, b=48),
-        hovermode="closest",
-        font=dict(size=FONT_SZ),
-        dragmode=False
-    )
-
-fig.update_xaxes(
-    title_text="<b>Actual UCS (psi)</b>",
-    title_font=dict(size=18, family="Arial", color="#000"),
-    range=[6000, 10000],
-    tick0=6000,
-    dtick=500,
-    ticks="outside",
-    tickformat=",.0f",
-    showline=True, linewidth=1.2, linecolor="#444", mirror=True,
-    showgrid=True, gridcolor="rgba(0,0,0,0.12)",
-    constrain="domain",                # ensures no stretching
-    scaleanchor="y", scaleratio=1,     # enforce 1:1 with y-axis
-    automargin=False
-)
-
-fig.update_yaxes(
-    title_text="<b>Predicted UCS (psi)</b>",
-    title_font=dict(size=18, family="Arial", color="#000"),
-    range=[6000, 10000],
-    tick0=6000,
-    dtick=500,
-    ticks="outside",
-    tickformat=",.0f",
-    showline=True, linewidth=1.2, linecolor="#444", mirror=True,
-    showgrid=True, gridcolor="rgba(0,0,0,0.12)",
-    constrain="domain",                # ensures no stretching
-    automargin=False
-)
-
-return fig
-
-# ---------- track_plot ----------
-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:
-        y = pd.Series(df[depth_col]).astype(float)
-        ylab = depth_col
-    else:
-        y = pd.Series(np.arange(1, len(df) + 1))
-        ylab = "Point Index"
-
-    y_range = [float(y.max()), float(y.min())]
-
-    x_series = pd.Series(df.get("UCS_Pred", pd.Series(dtype=float))).astype(float)
-    if include_actual and TARGET in df.columns:
-        x_series = pd.concat([x_series, pd.Series(df[TARGET]).astype(float)], ignore_index=True)
-
-    x_lo, x_hi = float(x_series.min()), float(x_series.max())
-    x_pad = 0.03 * (x_hi - x_lo if x_hi > x_lo else 1.0)
-    xmin, xmax = x_lo - x_pad, x_hi + x_pad
-    tick0 = _nice_tick0(xmin, step=100)
-
-    fig = go.Figure()
-
-    fig.add_trace(go.Scatter(
-        x=df["UCS_Pred"], y=y, mode="lines",
-        line=dict(color=COLORS["pred"], width=1.8),
-        name="UCS_Pred",
-        hovertemplate="UCS_Pred: %{x:.0f}<br>" + ylab + ": %{y}<extra></extra>"
-    ))
-
-    if include_actual and TARGET in df.columns:
-        fig.add_trace(go.Scatter(
-            x=df[TARGET], y=y, mode="lines",
-            line=dict(color=COLORS["actual"], width=2.0, dash="dot"),
-            name="UCS (actual)",
-            hovertemplate="UCS (actual): %{x:.0f}<br>" + ylab + ": %{y}<extra></extra>"
-        ))
-
-    fig.update_layout(
-        width=TRACK_W, height=TRACK_H,
-        paper_bgcolor="#fff", plot_bgcolor="#fff",
-        margin=dict(l=72, r=18, t=36, b=48),
-        hovermode="closest",
-        font=dict(size=FONT_SZ),
-        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="<b>UCS (psi)</b>",
-        title_font=dict(size=18, family="Arial", color="#000"),
-        side="top", range=[xmin, xmax],
-        tick0=tick0, tickmode="auto", tickformat=",.0f",
-        ticks="outside",
-        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"<b>{ylab}</b>",
-        title_font=dict(size=18, family="Arial", color="#000"),
-        range=y_range,
-        ticks="outside",
-        showline=True, linewidth=1.2, linecolor="#444", mirror=True,
-        showgrid=True, gridcolor="rgba(0,0,0,0.12)", automargin=True
-    )
-
-    return fig
-# ---------- Preview modal (matplotlib) ----------
-def preview_tracks(df: pd.DataFrame, cols: list[str]):
-    cols = [c for c in cols if c in df.columns]
-    n = len(cols)
-    if n == 0:
-        fig, ax = plt.subplots(figsize=(4, 2))
-        ax.text(0.5,0.5,"No selected columns",ha="center",va="center")
-        ax.axis("off")
-        return fig
-    fig, axes = plt.subplots(1, n, figsize=(2.2*n, 7.0), sharey=True, dpi=100)
-    if n == 1: axes = [axes]
-    idx = np.arange(1, len(df) + 1)
-    for ax, col in zip(axes, cols):
-        ax.plot(df[col], idx, '-', lw=1.4, color="#333")
-        ax.set_xlabel(col); ax.xaxis.set_label_position('top'); ax.xaxis.tick_top(); ax.invert_yaxis()
-        ax.grid(True, linestyle=":", alpha=0.3)
-        for s in ax.spines.values(): s.set_visible(True)
-    axes[0].set_ylabel("Point Index")
-    return fig
-
-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
-
-@dialog("Preview data")
-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"})
-                st.dataframe(tbl.reset_index(names="Feature"), use_container_width=True)
-
-# =========================
-# Load model (simple)
-# =========================
-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/ucs_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()
-
-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
-
-# =========================
-# 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)
-
-# =========================
-# Hero
-# =========================
-st.markdown(
-    f"""
-    <div class="st-hero">
-      <img src="{inline_logo()}" class="brand" />
-      <div>
-        <h1>ST_GeoMech_UCS</h1>
-        <div class="tagline">Real-Time UCS Tracking While Drilling</div>
-      </div>
-    </div>
-    """,
-    unsafe_allow_html=True,
-)
-
-# =========================
-# INTRO
-# =========================
-if st.session_state.app_step == "intro":
-    st.header("Welcome!")
-    st.markdown("This software is developed by *Smart Thinking AI-Solutions Team* to estimate UCS from drilling data.")
-    st.subheader("How It Works")
-    st.markdown(
-        "1) **Upload your data to build the case and preview the performance of our model.**  \n"
-        "2) Click **Run Model** to compute metrics and plots.  \n"
-        "3) **Proceed to Validation** (with actual UCS) or **Proceed to Prediction** (no UCS)."
-    )
-    if st.button("Start Showcase", type="primary"):
-        st.session_state.app_step = "dev"; st.rerun()
-
-# =========================
-# CASE BUILDING
-# =========================
-if st.session_state.app_step == "dev":
-    st.sidebar.header("Case Building")
-    up = st.sidebar.file_uploader("Upload Train/Test Excel", 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):
-        preview_modal(read_book_bytes(st.session_state.dev_file_bytes))
-        st.session_state.dev_preview = True
-
-    run = st.sidebar.button("Run Model", type="primary", use_container_width=True)
-    # always available nav
-    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()
-
-    # ---- Pinned helper at the very top of the page ----
-    helper_top = st.container()
-    with helper_top:
-        st.subheader("Case Building")
-        if st.session_state.dev_file_loaded and st.session_state.dev_preview:
-            st.info("Previewed ✓ — now click **Run Model**.")
-        elif st.session_state.dev_file_loaded:
-            st.info("📄 **Preview uploaded data** using the sidebar button, then click **Run Model**.")
-        else:
-            st.write("**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.error("Workbook must include Train/Training/training2 and Test/Testing/testing2 sheets."); st.stop()
-        tr = book[sh_train].copy(); te = book[sh_test].copy()
-        if not (ensure_cols(tr, FEATURES+[TARGET]) and ensure_cols(te, FEATURES+[TARGET])):
-            st.error("Missing required columns."); st.stop()
-        tr["UCS_Pred"] = model.predict(tr[FEATURES])
-        te["UCS_Pred"] = model.predict(te[FEATURES])
-
-        st.session_state.results["Train"]=tr; st.session_state.results["Test"]=te
-        st.session_state.results["m_train"]={"R2":r2_score(tr[TARGET],tr["UCS_Pred"]), "RMSE":rmse(tr[TARGET],tr["UCS_Pred"]), "MAE":mean_absolute_error(tr[TARGET],tr["UCS_Pred"])}
-        st.session_state.results["m_test"] ={"R2":r2_score(te[TARGET],te["UCS_Pred"]), "RMSE":rmse(te[TARGET],te["UCS_Pred"]), "MAE":mean_absolute_error(te[TARGET],te["UCS_Pred"])}
-
-        tr_min = tr[FEATURES].min().to_dict(); tr_max = tr[FEATURES].max().to_dict()
-        st.session_state.train_ranges = {f:(float(tr_min[f]), float(tr_max[f])) for f in FEATURES}
-        st.success("Case has been built and results are displayed below.")
-
-    def _dev_block(df, m):
-        c1,c2,c3 = st.columns(3)
-        c1.metric("R²", f"{m['R2']:.4f}"); c2.metric("RMSE", f"{m['RMSE']:.4f}"); c3.metric("MAE", f"{m['MAE']:.4f}")
-        left, spacer, right = st.columns(PLOT_COLS)
-        with left:
-            pad, plotcol = left.columns([CROSS_NUDGE, 1])  # shift cross-plot right inside its band
-            with plotcol:
-                st.plotly_chart(
-                    cross_plot(df[TARGET], df["UCS_Pred"]),
-                    use_container_width=False,
-                    config={"displayModeBar": False, "scrollZoom": True}
-                )
-        with right:
-            st.plotly_chart(
-                track_plot(df, include_actual=True),
-                use_container_width=False,
-                config={"displayModeBar": False, "scrollZoom": True}
-            )
-
-    if "Train" in st.session_state.results or "Test" in st.session_state.results:
-        tab1, tab2 = st.tabs(["Training", "Testing"])
-        if "Train" in st.session_state.results:
-            with tab1: _dev_block(st.session_state.results["Train"], st.session_state.results["m_train"])
-        if "Test" in st.session_state.results:
-            with tab2: _dev_block(st.session_state.results["Test"],  st.session_state.results["m_test"])
-
-# =========================
-# VALIDATION (with actual UCS)
-# =========================
-if st.session_state.app_step == "validate":
-    st.sidebar.header("Validate the Model")
-    up = st.sidebar.file_uploader("Upload Validation Excel", type=["xlsx","xls"])
-    if up is not None:
-        book = read_book_bytes(up.getvalue())
-        if book:
-            df0 = next(iter(book.values()))
-            st.sidebar.caption(f"**Data loaded:** {up.name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
-    if st.sidebar.button("Preview data", use_container_width=True, disabled=(up is None)):
-        preview_modal(read_book_bytes(up.getvalue()))
-    go_btn = st.sidebar.button("Predict", type="primary", use_container_width=True)
-    if st.sidebar.button("⬅ Back to Case Building", use_container_width=True): st.session_state.app_step="dev"; st.rerun()
-    if st.sidebar.button("Proceed to Prediction ▶", use_container_width=True): st.session_state.app_step="predict"; st.rerun()
-
-    st.subheader("Validate the Model")
-    st.write("Upload a dataset with the same **features** and **UCS** to evaluate performance.")
-
-    if go_btn and up is not None:
-        book = read_book_bytes(up.getvalue())
-        name = find_sheet(book, ["Validation","Validate","validation2","Val","val"]) or list(book.keys())[0]
-        df = book[name].copy()
-        if not ensure_cols(df, FEATURES+[TARGET]): st.error("Missing required columns."); st.stop()
-        df["UCS_Pred"] = model.predict(df[FEATURES])
-        st.session_state.results["Validate"]=df
-
-        ranges = st.session_state.train_ranges; oor_pct = 0.0; tbl=None
-        if ranges:
-            any_viol = pd.DataFrame({f:(df[f]<ranges[f][0])|(df[f]>ranges[f][1]) for f in FEATURES}).any(axis=1)
-            oor_pct = float(any_viol.mean()*100.0)
-            if any_viol.any():
-                tbl = df.loc[any_viol, FEATURES].copy()
-                tbl["Violations"] = pd.DataFrame({f:(df[f]<ranges[f][0])|(df[f]>ranges[f][1]) for f in FEATURES}).loc[any_viol].apply(lambda r:", ".join([c for c,v in r.items() if v]), axis=1)
-        st.session_state.results["m_val"]={"R2":r2_score(df[TARGET],df["UCS_Pred"]), "RMSE":rmse(df[TARGET],df["UCS_Pred"]), "MAE":mean_absolute_error(df[TARGET],df["UCS_Pred"])}
-        st.session_state.results["sv_val"]={"n":len(df),"pred_min":float(df["UCS_Pred"].min()),"pred_max":float(df["UCS_Pred"].max()),"oor":oor_pct}
-        st.session_state.results["oor_tbl"]=tbl
-
-    if "Validate" in st.session_state.results:
-        m = st.session_state.results["m_val"]
-        c1,c2,c3 = st.columns(3)
-        c1.metric("R²", f"{m['R2']:.4f}"); c2.metric("RMSE", f"{m['RMSE']:.4f}"); c3.metric("MAE", f"{m['MAE']:.4f}")
-
-        left, spacer, right = st.columns(PLOT_COLS)
-        with left:
-            pad, plotcol = left.columns([CROSS_NUDGE, 1])  # same nudge
-            with plotcol:
-                st.plotly_chart(
-                    cross_plot(st.session_state.results["Validate"][TARGET],
-                               st.session_state.results["Validate"]["UCS_Pred"]),
-                    use_container_width=False, config={"displayModeBar": False, "scrollZoom": True}
-                )
-        with right:
-            st.plotly_chart(
-                track_plot(st.session_state.results["Validate"], include_actual=True),
-                use_container_width=False, config={"displayModeBar": False, "scrollZoom": True}
-            )
-
-        sv = st.session_state.results["sv_val"]
-        if sv["oor"] > 0: st.warning("Some inputs fall outside **training min–max** ranges.")
-        if st.session_state.results["oor_tbl"] is not None:
-            st.write("*Out-of-range rows (vs. Training min–max):*")
-            st.dataframe(st.session_state.results["oor_tbl"], use_container_width=True)
-
-# =========================
-# PREDICTION (no actual UCS)
-# =========================
-if st.session_state.app_step == "predict":
-    st.sidebar.header("Prediction (No Actual UCS)")
-    up = st.sidebar.file_uploader("Upload Prediction Excel", type=["xlsx","xls"])
-    if up is not None:
-        book = read_book_bytes(up.getvalue())
-        if book:
-            df0 = next(iter(book.values()))
-            st.sidebar.caption(f"**Data loaded:** {up.name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
-    if st.sidebar.button("Preview data", use_container_width=True, disabled=(up is None)):
-        preview_modal(read_book_bytes(up.getvalue()))
-    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()
-
-    st.subheader("Prediction")
-    st.write("Upload a dataset with the feature columns (no **UCS**).")
-
-    if go_btn and up is not None:
-        book = read_book_bytes(up.getvalue()); name = list(book.keys())[0]
-        df = book[name].copy()
-        if not ensure_cols(df, FEATURES): st.error("Missing required columns."); st.stop()
-        df["UCS_Pred"] = model.predict(df[FEATURES])
-        st.session_state.results["PredictOnly"]=df
-
-        ranges = st.session_state.train_ranges; oor_pct = 0.0
-        if ranges:
-            any_viol = pd.DataFrame({f:(df[f]<ranges[f][0])|(df[f]>ranges[f][1]) for f in FEATURES}).any(axis=1)
-            oor_pct = float(any_viol.mean()*100.0)
-        st.session_state.results["sv_pred"]={
-            "n":len(df),
-            "pred_min":float(df["UCS_Pred"].min()),
-            "pred_max":float(df["UCS_Pred"].max()),
-            "pred_mean":float(df["UCS_Pred"].mean()),
-            "pred_std":float(df["UCS_Pred"].std(ddof=0)),
-            "oor":oor_pct
-        }
-
-    if "PredictOnly" in st.session_state.results:
-        df = st.session_state.results["PredictOnly"]; sv = st.session_state.results["sv_pred"]
-
-        left, spacer, right = st.columns(PLOT_COLS)
-        with left:
-            table = pd.DataFrame({
-                "Metric": ["# points","Pred min","Pred max","Pred mean","Pred std","OOR %"],
-                "Value":  [sv["n"], sv["pred_min"], sv["pred_max"], sv["pred_mean"], sv["pred_std"], f'{sv["oor"]:.1f}%']
-            })
-            st.success("Predictions ready ✓")
-            st.dataframe(table, use_container_width=True, hide_index=True)
-            st.caption("**★ OOR** = % of rows whose input features fall outside the training min–max range.")
-        with right:
-            st.plotly_chart(
-                track_plot(df, include_actual=False),
-                use_container_width=False, config={"displayModeBar": False, "scrollZoom": True}
-            )
-
-# =========================
-# Footer
-# =========================
-st.markdown("---")
-st.markdown(
-    """
-    <div style='text-align:center; color:#6b7280; line-height:1.6'>
-      ST_GeoMech_UCS • © Smart Thinking<br/>
-      <strong>Visit our website:</strong> <a href='https://www.smartthinking.com.sa' target='_blank'>smartthinking.com.sa</a>
-    </div>
-    """,
-    unsafe_allow_html=True
-)