Update app.py

app.py

@@ -176,7 +176,7 @@ def parse_excel(data_bytes: bytes):
     xl = pd.ExcelFile(bio)
     return {sh: xl.parse(sh) for sh in xl.sheet_names}
 
-def read_book_bytes(b: bytes): 
+def read_book_bytes(b: bytes):
     return parse_excel(b) if b else {}
 
 def normalize_df(df: pd.DataFrame) -> pd.DataFrame:
@@ -774,8 +774,8 @@ 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 = normalize_df(book[name].copy())
-        if not ensure_cols(df, FEATURES): 
-            st.markdown('<div class="st-message-box st-error">Missing required feature columns.</div>', unsafe_allow_html=True); 
+        if not ensure_cols(df, FEATURES):
+            st.markdown('<div class="st-message-box st-error">Missing required feature columns.</div>', unsafe_allow_html=True)
             st.stop()
 
         pred_raw = model.predict(df[FEATURES])
@@ -783,7 +783,7 @@ if st.session_state.app_step == "validate":
         try:
             df["GR_Actual"] = to_actual_series(df, TARGET, ACTUAL_COL, TARGET_TRANSFORM)
         except Exception:
-            st.markdown('<div class="st-message-box st-error">Validation sheet must include actual GR (or a target column that can be inverse-transformed).</div>', unsafe_allow_html=True); 
+            st.markdown('<div class="st-message-box st-error">Validation sheet must include actual GR (or a target column that can be inverse-transformed).</div>', unsafe_allow_html=True)
             st.stop()
 
         st.session_state.results["Validate"]=df
@@ -858,15 +858,15 @@ if st.session_state.app_step == "predict":
         if up is not None:
             book = read_book_bytes(up.getvalue()); name = list(book.keys())[0]
             df = normalize_df(book[name].copy())
-            if not ensure_cols(df, FEATURES):  
-                st.markdown('<div class="st-message-box st-error">Missing required feature columns.</div>', unsafe_allow_html=True)  
+            if not ensure_cols(df, FEATURES):
+                st.markdown('<div class="st-message-box st-error">Missing required feature columns.</div>', unsafe_allow_html=True)
                 st.stop()
             pred_raw = model.predict(df[FEATURES])
             df["GR_Pred"] = inverse_target(np.asarray(pred_raw, dtype=float), TARGET_TRANSFORM)
             st.session_state.results["PredictOnly"]=df
             ranges = st.session_state.train_ranges
             oor_pct = 0.0
-            oor_tbl = None 
+            oor_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)
@@ -885,7 +885,7 @@ if st.session_state.app_step == "predict":
             }
             st.session_state.results["oor_tbl_pred"] = oor_tbl
 
-    if st.sidebar.button("⬅ Back to Case Building", use_container_width=True): 
+    if st.sidebar.button("⬅ Back to Case Building", use_container_width=True):
         st.session_state.app_step="dev"
         st.rerun()
 
@@ -899,16 +899,16 @@ if st.session_state.app_step == "predict":
             table = pd.DataFrame({
                 "Metric": ["# points","Pred min","Pred max","Pred mean","Pred std","OOR %"],
                 "Value":  [sv["n"],
-                            round(sv["pred_min"],2),
-                            round(sv["pred_max"],2),
-                            round(sv["pred_mean"],2),
-                            round(sv["pred_std"],2),
-                            f'{sv["oor"]:.1f}%']
+                           round(sv["pred_min"],2),
+                           round(sv["pred_max"],2),
+                           round(sv["pred_mean"],2),
+                           round(sv["pred_std"],2),
+                           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.")
-            
+
             if st.session_state.results.get("oor_tbl_pred") is not None:
                 st.markdown('<div class="st-message-box st-warning">Some inputs fall outside **training min–max** ranges.</div>', unsafe_allow_html=True)
                 st.write("*Out-of-range rows (vs. Training min–max):*")
@@ -975,4 +975,6 @@ st.markdown("""
 <hr>
 <div style='text-align:center;color:#6b7280;font-size:1.0em;'>
     © 2025 Smart Thinking AI-Solutions Team. All rights reserved.<br>
-    Contact: <a href="mailto:smartthinking@smartthinking.com.sa">smartthinking@smartthinking
+    Contact: <a href="mailto:smartthinking@smartthinking.com.sa">smartthinking@smartthinking.com.sa</a>
+</div>
+""", unsafe_allow_html=True)