Update app.py

app.py

@@ -19,7 +19,7 @@ from sklearn.metrics import mean_squared_error, mean_absolute_error
 # Constants (GR)
 # =========================
 APP_NAME = "ST_Log_GR"
-TAGLINE  = "Gamma Ray Prediction"
+TAGLINE  = "Real-Time Gamma Ray Prediction Using Drilling Data"
 
 FEATURES = ["GPM", "SPP", "RPM", "WOB", "T", "ROP"]
 
@@ -690,68 +690,54 @@ if st.session_state.app_step == "validate":
             st.write("*Out-of-range rows (vs. Training min–max):*")
             df_centered_rounded(st.session_state.results["oor_tbl"])
 
+
 # =========================
 # PREDICTION (no actual GR)
 # =========================
 if st.session_state.app_step == "predict":
-    # ... (existing code for sidebar and sticky header)
-
-    if go_btn and up is not None:
-        # ... (existing code to load data and predict)
+    st.sidebar.header("Prediction (No Actual GR)")
+    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)):
+        st.session_state.show_preview_modal = True
+    
+    # Corrected logic: Move the code that depends on the button's click
+    # to be immediately after the button is defined.
+    if st.sidebar.button("Predict", type="primary", use_container_width=True):
+        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);  
+                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
+            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["GR_Pred"].min()),
+                "pred_max":float(df["GR_Pred"].max()),
+                "pred_mean":float(df["GR_Pred"].mean()),
+                "pred_std":float(df["GR_Pred"].std(ddof=0)),
+                "oor":oor_pct
+            }
+    
+    if st.sidebar.button("⬅ Back to Case Building", use_container_width=True): st.session_state.app_step="dev"; st.rerun()
 
-        ranges = st.session_state.train_ranges
-        oor_pct = 0.0
-        oor_tbl = None # Initialize OOR table as 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():
-                oor_tbl = df.loc[any_viol, FEATURES].copy()
-                for c in FEATURES:
-                    if pd.api.types.is_numeric_dtype(oor_tbl[c]): oor_tbl[c] = oor_tbl[c].round(2)
-                oor_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["PredictOnly"]=df
-        st.session_state.results["sv_pred"]={
-            "n":len(df),
-            "pred_min":float(df["GR_Pred"].min()),
-            "pred_max":float(df["GR_Pred"].max()),
-            "pred_mean":float(df["GR_Pred"].mean()),
-            "pred_std":float(df["GR_Pred"].std(ddof=0)),
-            "oor":oor_pct
-        }
-        st.session_state.results["oor_tbl_pred"] = oor_tbl # Save the OOR table for prediction
+    sticky_header("Prediction", "Upload a dataset with the feature columns (no **GR**).")
 
     if "PredictOnly" in st.session_state.results:
-        df = st.session_state.results["PredictOnly"]
-        sv = st.session_state.results["sv_pred"]
-
-        col_left, col_right = st.columns([2,3], gap="large")
-        with col_left:
-            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}%']
-            })
-            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.")
-            
-            # Add this check and display logic to the prediction section
-            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):*")
-                df_centered_rounded(st.session_state.results["oor_tbl_pred"])
-
-        with col_right:
-            st.plotly_chart(
-                track_plot(df, include_actual=False, pred_col="GR_Pred", actual_col="GR"),
-                use_container_width=False, config={"displayModeBar": False, "scrollZoom": True}
-            )
+        # ... (Rest of the code to display the results)
 
 # =========================
 # Preview modal (re-usable)