Update app.py

app.py

@@ -4,6 +4,8 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import joblib
+import matplotlib
+matplotlib.use("Agg")  # safe non-GUI backend for cloud
 import matplotlib.pyplot as plt
 from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
 
@@ -35,11 +37,11 @@ st.markdown(
     <style>
     .stApp { background: #FFFFFF; }
     section[data-testid="stSidebar"] { background: #F6F9FC; }
-    .block-container { padding-top: .5rem; padding-bottom: .5rem; } /* less vertical padding */
+    .block-container { padding-top: .5rem; padding-bottom: .5rem; }
     .stButton>button{ background:#007bff; color:#fff; font-weight:bold; border-radius:8px; border:none; padding:10px 24px; }
     .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; }  /* enlarged logo */
+    .st-hero .brand { width:110px; height:110px; object-fit:contain; }  /* bigger logo */
     .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; }
@@ -51,6 +53,19 @@ st.markdown(
 # =========================
 # Helpers
 # =========================
+
+# Modal dialog fallback for older Streamlit
+try:
+    dialog = st.dialog  # Streamlit ≥1.31
+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 _get_model_url():
     """Read optional MODEL_URL from environment only (avoid st.secrets banner)."""
     return (os.environ.get("MODEL_URL", "") or "").strip()
@@ -107,34 +122,27 @@ def depth_or_index_track(df, title=None, include_actual=True):
     Narrow, tall track: predicted solid blue; actual dotted yellow.
     Works for either Depth on Y or Index on Y.
     """
-    # Find depth-like column if available
     depth_col = next((c for c in df.columns if 'depth' in str(c).lower()), None)
-
-    # Narrow width, tall height for logging look
     fig_w = 3.1
     fig_h = 7.6 if depth_col is not None else 7.2
     fig, ax = plt.subplots(figsize=(fig_w, fig_h), dpi=100)
 
     if depth_col is not None:
-        ax.plot(df["UCS_Pred"], df[depth_col],
-                '-', lw=1.8, color=COLORS["pred"], label="UCS_Pred")
+        ax.plot(df["UCS_Pred"], df[depth_col], '-', lw=1.8, color=COLORS["pred"], label="UCS_Pred")
         if include_actual and TARGET in df.columns:
-            ax.plot(df[TARGET], df[depth_col],
-                    ':', lw=2.0, color=COLORS["actual"], alpha=0.95, label="UCS (actual)")
+            ax.plot(df[TARGET], df[depth_col], ':', lw=2.0, color=COLORS["actual"], alpha=0.95, label="UCS (actual)")
         ax.set_ylabel(depth_col); ax.set_xlabel("UCS")
         ax.xaxis.set_label_position('top'); ax.xaxis.tick_top(); ax.invert_yaxis()
     else:
         idx = np.arange(1, len(df) + 1)
-        ax.plot(df["UCS_Pred"], idx,
-                '-', lw=1.8, color=COLORS["pred"], label="UCS_Pred")
+        ax.plot(df["UCS_Pred"], idx, '-', lw=1.8, color=COLORS["pred"], label="UCS_Pred")
         if include_actual and TARGET in df.columns:
-            ax.plot(df[TARGET], idx,
-                    ':', lw=2.0, color=COLORS["actual"], alpha=0.95, label="UCS (actual)")
+            ax.plot(df[TARGET], idx, ':', lw=2.0, color=COLORS["actual"], alpha=0.95, label="UCS (actual)")
         ax.set_ylabel("Point Index"); ax.set_xlabel("UCS")
         ax.xaxis.set_label_position('top'); ax.xaxis.tick_top(); ax.invert_yaxis()
 
     ax.grid(True, linestyle=":", alpha=0.4)
-    if title: ax.set_title(title, pad=8)  # keep no title by passing None
+    if title: ax.set_title(title, pad=8)
     ax.legend(loc="best")
     return fig
 
@@ -173,6 +181,54 @@ def inline_logo(path="logo.png") -> str:
     except Exception:
         return ""
 
+# ---------- Preview modal helpers ----------
+
+def make_index_tracks(df: pd.DataFrame, cols: list[str]):
+    """Build a side-by-side index track figure (log-style) for given columns."""
+    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 in sheet", ha="center", va="center")
+        ax.axis("off")
+        return fig
+    width_per = 2.2  # width per track
+    fig_h = 7.0
+    fig, axes = plt.subplots(1, n, figsize=(width_per * n, fig_h), 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="#333333")
+        ax.set_xlabel(col)
+        ax.xaxis.set_label_position('top'); ax.xaxis.tick_top(); ax.invert_yaxis()
+        ax.grid(True, linestyle=":", alpha=0.3)
+    axes[0].set_ylabel("Point Index")
+    return fig
+
+def stats_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({"Feature": [], "Min": [], "Max": [], "Mean": [], "Std": []})
+    out = df[cols].agg(['min', 'max', 'mean', 'std']).T
+    out = out.rename(columns={"min": "Min", "max": "Max", "mean": "Mean", "std": "Std"})
+    out = out.reset_index().rename(columns={"index": "Feature"})
+    return out
+
+@dialog("Preview data")
+def preview_modal(book: dict[str, pd.DataFrame], default_cols: list[str]):
+    if not book:
+        st.info("No data loaded yet.")
+        return
+    sheet = st.selectbox("Sheet", list(book.keys()), index=0)
+    df = book[sheet]
+    present = [c for c in default_cols if c in df.columns]
+    tab_tracks, tab_summary = st.tabs(["Tracks", "Summary"])
+    with tab_tracks:
+        st.pyplot(make_index_tracks(df, present), use_container_width=True)
+    with tab_summary:
+        st.dataframe(stats_table(df, present), use_container_width=True)
+
 # =========================
 # Model presence (local or optional download)
 # =========================
@@ -278,6 +334,12 @@ if st.session_state.app_step == "dev":
     st.sidebar.header("Model Development Data")
     train_test_file = st.sidebar.file_uploader("Upload Data (Excel)", type=["xlsx","xls"], key="dev_upload")
 
+    # Preview button (uses modal)
+    preview_btn = st.sidebar.button("Preview data", use_container_width=True, disabled=(train_test_file is None))
+    if preview_btn and train_test_file is not None:
+        _book = read_book(train_test_file)
+        preview_modal(_book, FEATURES)
+
     run_btn = st.sidebar.button("Run Model", type="primary", use_container_width=True)
 
     # Proceed button BELOW run, always visible; enables immediately after first successful run
@@ -339,8 +401,7 @@ if st.session_state.app_step == "dev":
                 df = st.session_state.results["Train"]; m = st.session_state.results["metrics_train"]
                 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}")
-                # Narrow track column for log-look
-                left, right = st.columns([0.9, 0.55])
+                left, right = st.columns([0.9, 0.55])  # slim track column
                 with left:
                     st.pyplot(cross_plot(df[TARGET], df["UCS_Pred"], "Training: Actual vs Predicted"), use_container_width=True)
                 with right:
@@ -380,6 +441,13 @@ if st.session_state.app_step == "dev":
 if st.session_state.app_step == "predict":
     st.sidebar.header("Prediction (Validation)")
     validation_file = st.sidebar.file_uploader("Upload Validation Excel", type=["xlsx","xls"], key="val_upload")
+
+    # Preview button for validation data
+    preview_val_btn = st.sidebar.button("Preview data", use_container_width=True, disabled=(validation_file is None))
+    if preview_val_btn and validation_file is not None:
+        _book = read_book(validation_file)
+        preview_modal(_book, FEATURES)
+
     predict_btn = st.sidebar.button("Predict", type="primary", use_container_width=True)
     st.sidebar.button("⬅ Back", on_click=lambda: st.session_state.update(app_step="dev"), use_container_width=True)
 
@@ -428,7 +496,6 @@ if st.session_state.app_step == "predict":
         st.subheader("Validation Results")
         sv = st.session_state.results["summary_val"]; oor_table = st.session_state.results.get("oor_table")
 
-        # Show OOR warning above the plots when applicable
         if sv["oor_pct"] > 0:
             st.warning("Some validation inputs fall outside the **training min–max** ranges. Interpret predictions with caution.")