Update app.py

app.py

@@ -15,21 +15,20 @@ import plotly.graph_objects as go
 from sklearn.metrics import mean_squared_error, mean_absolute_error
 
 # =========================
-# Constants (simple & robust)
+# Constants
 # =========================
 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 = 500; CROSS_H = 500      # square cross-plot (Build + Validate)
+CROSS_W = 420; CROSS_H = 420      # square cross-plot (original look)
 TRACK_W = 400; TRACK_H = 950      # log-strip style (all pages)
 FONT_SZ  = 13
-PLOT_COLS = [14, 0.5, 10]         # 3-column band: left • spacer • right (Build + Validate)
+PLOT_COLS = [14, 0.5, 10]         # 3-column band: left • spacer • right
 CROSS_NUDGE = 0.5                 # inner columns [CROSS_NUDGE : 1] → bigger = more right
 
 # =========================
@@ -38,27 +37,38 @@ CROSS_NUDGE = 0.5                 # inner columns [CROSS_NUDGE : 1] → bigger =
 st.set_page_config(page_title="ST_GeoMech_UCS", page_icon="logo.png", layout="wide")
 st.markdown("<style>header, footer{visibility:hidden !important;}</style>", unsafe_allow_html=True)
 
-# Hide drag-n-drop helper texts inside uploaders; keep the Browse button
 st.markdown(
     """
     <style>
       .stApp { background:#fff; }
       section[data-testid="stSidebar"] { background:#F6F9FC; }
       .block-container { padding-top:.5rem; padding-bottom:.5rem; }
+
+      /* Buttons look */
       .stButton>button { background:#007bff; color:#fff; font-weight:600; border-radius:8px; border:none; }
       .stButton>button:hover { background:#0056b3; }
+
+      /* Brand header */
       .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; }
 
-      /* Remove drag & drop copy and limit line in uploader */
+      /* Remove drag & drop + limit lines — keep Browse button */
+      [data-testid="stFileUploadDropzone"] p,
       [data-testid="stFileUploadDropzone"] [data-testid="stFileUploaderInstructions"],
-      [data-testid="stFileUploadDropzone"] [data-testid="stCaptionContainer"],
-      .stFileUploader .dz-message {display:none !important;}
+      [data-testid="stFileUploadDropzone"] [data-testid="stCaptionContainer"]{
+        display:none !important;
+      }
 
-      /* Center our HTML tables */
+      /* Pinned title/helper area */
+      .pinned-top{
+        position:sticky; top:0; z-index:999; background:#fff; padding-top:4px;
+      }
+
+      /* Center every table cell we render via HTML */
       .table-center table { margin-left:auto; margin-right:auto; border-collapse:collapse; }
       .table-center table th, .table-center table td {
         text-align:center !important; padding:6px 10px; border:1px solid #e5e7eb;
@@ -70,7 +80,7 @@ st.markdown(
 )
 
 # =========================
-# Password gate (define first, then call)
+# Password gate
 # =========================
 def inline_logo(path="logo.png") -> str:
     try:
@@ -137,29 +147,15 @@ 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) -> float:
     return float(np.sqrt(mean_squared_error(y_true, y_pred)))
 
 def corrcoef_safe(y_true, y_pred) -> float:
     a = pd.Series(y_true, dtype=float)
     b = pd.Series(y_pred, dtype=float)
-    a = a.replace([np.inf, -np.inf], np.nan).dropna()
-    b = b.replace([np.inf, -np.inf], np.nan).dropna()
-    n = min(len(a), len(b))
-    if n == 0:
-        return float("nan")
-    return float(np.corrcoef(a.iloc[:n], b.iloc[:n])[0, 1])
+    m = np.isfinite(a) & np.isfinite(b)
+    if not m.any(): return float("nan")
+    return float(np.corrcoef(a[m], b[m])[0, 1])
 
 @st.cache_resource(show_spinner=False)
 def load_model(model_path: str):
@@ -236,7 +232,6 @@ def cross_plot(actual, pred):
         margin=dict(l=64, r=18, t=10, b=48), hovermode="closest",
         font=dict(size=FONT_SZ)
     )
-    # identical x & y ranges/ticks; stays locked on zoom
     axis_common = dict(
         range=[lo, hi], ticks="outside", tickformat=",.0f",
         tick0=tick0, dtick=step,
@@ -254,14 +249,13 @@ def track_plot(df, include_actual=True):
         y = pd.Series(df[depth_col]).astype(float)
         ylab = depth_col
         y_min, y_max = float(y.min()), float(y.max())
-        y_range = [y_max, y_min]   # reversed for log profile style
+        y_range = [y_max, y_min]
     else:
         y = pd.Series(np.arange(1, len(df) + 1))
         ylab = "Point Index"
         y_min, y_max = float(y.min()), float(y.max())
         y_range = [y_max, y_min]
 
-    # X (UCS) range & ticks
     x_series = pd.Series(df.get("UCS_Pred", pd.Series(dtype=float))).astype(float)
     if include_actual and TARGET in df.columns:
         x_series = pd.concat([x_series, pd.Series(df[TARGET]).astype(float)], ignore_index=True)
@@ -318,8 +312,7 @@ def preview_tracks(df: pd.DataFrame, cols: list[str]):
     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
+        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)
@@ -360,7 +353,7 @@ def preview_modal(book: dict[str, pd.DataFrame]):
                 html_table_center(tbl, index=False)
 
 # =========================
-# Load model (simple)
+# Load model
 # =========================
 def ensure_model() -> Path|None:
     for p in [DEFAULT_MODEL, *MODEL_FALLBACKS]:
@@ -456,6 +449,17 @@ if st.session_state.app_step == "dev":
             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")
 
+    # ---- Pinned title/helper FIRST (so it never appears below preview) ----
+    st.markdown('<div class="pinned-top">', unsafe_allow_html=True)
+    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.**")
+    st.markdown('</div>', unsafe_allow_html=True)
+
     # Preview button ALWAYS enabled
     if st.sidebar.button("Preview data", use_container_width=True):
         if not st.session_state.dev_file_loaded:
@@ -468,17 +472,6 @@ if st.session_state.app_step == "dev":
     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"])
@@ -513,7 +506,7 @@ if st.session_state.app_step == "dev":
         c1.metric("R", f"{m['R']:.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
+            pad, plotcol = left.columns([CROSS_NUDGE, 1])
             with plotcol:
                 st.plotly_chart(cross_plot(df[TARGET], df["UCS_Pred"]),
                                 use_container_width=False, config={"displayModeBar": False, "scrollZoom": True})
@@ -540,20 +533,21 @@ if st.session_state.app_step == "validate":
             df0 = next(iter(book.values()))
             st.sidebar.caption(f"**Data loaded:** {up.name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
 
+    # pinned title/helper first
+    st.markdown('<div class="pinned-top">', unsafe_allow_html=True)
+    st.subheader("Validate the Model")
+    st.write("Upload a dataset with the same **features** and **UCS** to evaluate performance.")
+    st.markdown('</div>', unsafe_allow_html=True)
+
     if st.sidebar.button("Preview data", use_container_width=True):
         if up is None:
             st.warning("Upload an Excel file first, then preview.")
         else:
             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()
 
-    # pinned
-    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]
@@ -616,19 +610,20 @@ if st.session_state.app_step == "predict":
             df0 = next(iter(book.values()))
             st.sidebar.caption(f"**Data loaded:** {up.name} • {df0.shape[0]} rows × {df0.shape[1]} cols")
 
+    # pinned title/helper first
+    st.markdown('<div class="pinned-top">', unsafe_allow_html=True)
+    st.subheader("Prediction")
+    st.write("Upload a dataset with the feature columns (no **UCS**).")
+    st.markdown('</div>', unsafe_allow_html=True)
+
     if st.sidebar.button("Preview data", use_container_width=True):
         if up is None:
             st.warning("Upload an Excel file first, then preview.")
         else:
             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()
 
-    # pinned
-    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()