Update app.py

app.py

@@ -25,9 +25,11 @@ MODEL_FALLBACKS = [MODELS_DIR / "model.joblib", MODELS_DIR / "model.pkl"]
 COLORS = {"pred": "#1f77b4", "actual": "#f2b702", "ref": "#5a5a5a"}
 
 # ---- Plot sizing controls ----
-CROSS_W = 450      # px (matplotlib figure size; Streamlit will still scale)
-CROSS_H = 450
+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 = 800       # px (plotly width)
 FONT_SZ  = 13
 BOLD_FONT = "Arial Black, Arial, sans-serif"  # used for bold axis titles & ticks
 
@@ -107,6 +109,34 @@ TABLE_CENTER_CSS = [
     dict(selector="td", props=[("text-align", "center")]),
 ]
 
+# NEW: CSS for the message box
+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;
+}
+</style>
+""", unsafe_allow_html=True)
+
 # =========================
 # Password gate
 # =========================
@@ -158,7 +188,7 @@ def rmse(y_true, y_pred) -> float:
 
 def pearson_r(y_true, y_pred) -> float:
     a = np.asarray(y_true, dtype=float)
-    p = np.asarray(y_pred, dtype=float)
+    p = np.asarray(y_pred,   dtype=float)
     if a.size < 2: return float("nan")
     return float(np.corrcoef(a, p)[0, 1])
 
@@ -207,7 +237,7 @@ def df_centered_rounded(df: pd.DataFrame, hide_index=True):
 # =========================
 def cross_plot_static(actual, pred):
     a = pd.Series(actual, dtype=float)
-    p = pd.Series(pred,    dtype=float)
+    p = pd.Series(pred,   dtype=float)
 
     fixed_min, fixed_max = 6000, 10000
     ticks = np.arange(fixed_min, fixed_max + 1, 1000)
@@ -284,7 +314,9 @@ def track_plot(df, include_actual=True):
         ))
 
     fig.update_layout(
-        height=TRACK_H, width=None,  # width auto-fits the column
+        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"),
@@ -509,10 +541,12 @@ if st.session_state.app_step == "dev":
         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()
+            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.error("Missing required columns."); st.stop()
+            st.markdown('<div class="st-message-box st-error">Missing required columns.</div>', unsafe_allow_html=True)
+            st.stop()
         tr["UCS_Pred"] = model.predict(tr[FEATURES])
         te["UCS_Pred"] = model.predict(te[FEATURES])
 
@@ -530,7 +564,7 @@ if st.session_state.app_step == "dev":
 
         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.")
+        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)
@@ -543,7 +577,7 @@ if st.session_state.app_step == "dev":
         with col_track:
             st.plotly_chart(
                 track_plot(df, include_actual=True),
-                use_container_width=True,
+                use_container_width=False, # Set to False to honor the width in track_plot()
                 config={"displayModeBar": False, "scrollZoom": True}
             )
 
@@ -577,7 +611,7 @@ if st.session_state.app_step == "validate":
         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()
+        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["UCS_Pred"] = model.predict(df[FEATURES])
         st.session_state.results["Validate"]=df
 
@@ -613,11 +647,12 @@ if st.session_state.app_step == "validate":
         with col_track:
             st.plotly_chart(
                 track_plot(st.session_state.results["Validate"], include_actual=True),
-                use_container_width=True, config={"displayModeBar": False, "scrollZoom": True}
+                use_container_width=False, # Set to False to honor the width in track_plot()
+                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 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"])
@@ -643,7 +678,7 @@ if st.session_state.app_step == "predict":
     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()
+        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["UCS_Pred"] = model.predict(df[FEATURES])
         st.session_state.results["PredictOnly"]=df
 
@@ -674,13 +709,14 @@ if st.session_state.app_step == "predict":
                           round(sv["pred_std"],2),
                           f'{sv["oor"]:.1f}%']
             })
-            st.success("Predictions ready ✓")
+            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=True, config={"displayModeBar": False, "scrollZoom": True}
+                use_container_width=False, # Set to False to honor the width in track_plot()
+                config={"displayModeBar": False, "scrollZoom": True}
             )
 
 # =========================
@@ -693,20 +729,20 @@ if st.session_state.show_preview_modal:
         book_to_preview = read_book_bytes(st.session_state.dev_file_bytes)
     elif st.session_state.app_step in ["validate", "predict"] and up is not None:
         book_to_preview = read_book_bytes(up.getvalue())
-    
+
     # Use a try-except block to handle cases where 'up' might be None
     # and the logic tries to access its attributes.
     try:
         if st.session_state.app_step == "validate" and up is not None:
-             book_to_preview = read_book_bytes(up.getvalue())
+              book_to_preview = read_book_bytes(up.getvalue())
         elif st.session_state.app_step == "predict" and up is not None:
-             book_to_preview = read_book_bytes(up.getvalue())
+              book_to_preview = read_book_bytes(up.getvalue())
     except NameError:
         book_to_preview = {}
 
     with st.expander("Preview data", expanded=True):
         if not book_to_preview:
-            st.info("No data loaded yet.")
+            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)
@@ -718,12 +754,12 @@ if st.session_state.show_preview_modal:
                         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"}))
+                                 .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
-    
+
 # =========================
 # Footer
 # =========================