Update app.py

app.py

@@ -5,12 +5,12 @@ import pandas as pd
 import numpy as np
 import joblib
 import matplotlib
-matplotlib.use("Agg")  # safe non-GUI backend for cloud
+matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
 
 # =========================
-# Defaults (overridden by models/meta.json or model.feature_names_in_)
+# Defaults
 # =========================
 FEATURES = ["Q, gpm", "SPP(psi)", "T (kft.lbf)", "WOB (klbf)", "ROP (ft/h)"]
 TARGET = "UCS"
@@ -18,12 +18,7 @@ MODELS_DIR = Path("models")
 DEFAULT_MODEL = MODELS_DIR / "ucs_rf.joblib"
 MODEL_FALLBACKS = [MODELS_DIR / "model.joblib", MODELS_DIR / "model.pkl"]
 
-# Colors for plots
-COLORS = {
-    "pred": "#1f77b4",   # blue (predicted)
-    "actual": "#f2b702", # yellow (actual)
-    "ref": "#5a5a5a"     # grey 1:1 line
-}
+COLORS = {"pred": "#1f77b4", "actual": "#f2b702", "ref": "#5a5a5a"}
 
 # =========================
 # Page / Theme
@@ -39,7 +34,7 @@ st.markdown(
     .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; }  /* bigger logo */
+    .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; }
@@ -54,7 +49,7 @@ st.markdown(
 try:
     dialog = st.dialog
 except AttributeError:
-    # Back-compat shim if st.dialog isn't available
+    # Fallback (expander) if st.dialog is unavailable
     def dialog(title):
         def deco(fn):
             def wrapper(*args, **kwargs):
@@ -64,7 +59,6 @@ except AttributeError:
         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()
 
 def rmse(y_true, y_pred): return float(np.sqrt(mean_squared_error(y_true, y_pred)))
@@ -176,8 +170,7 @@ def make_index_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 in sheet", ha="center", va="center")
-        ax.axis("off")
-        return fig
+        ax.axis("off"); return fig
     width_per = 2.2
     fig_h = 7.0
     fig, axes = plt.subplots(1, n, figsize=(width_per * n, fig_h), sharey=True, dpi=100)
@@ -230,7 +223,7 @@ def preview_modal_val(book: dict[str, pd.DataFrame], feature_cols: list[str]):
     with t2: st.dataframe(stats_table(df, feature_cols), use_container_width=True)
 
 # =========================
-# Model presence (local or optional download)
+# Model presence
 # =========================
 MODEL_URL = _get_model_url()
 
@@ -265,7 +258,7 @@ except Exception as e:
     st.error(f"Failed to load model: {model_path}\n{e}")
     st.stop()
 
-# Meta overrides or inference
+# Meta overrides / inference
 meta_path = MODELS_DIR / "meta.json"
 if meta_path.exists():
     try:
@@ -285,12 +278,14 @@ if "train_ranges" not in st.session_state: st.session_state.train_ranges = None
 if "dev_ready" not in st.session_state: st.session_state.dev_ready = False
 if "dev_file_loaded" not in st.session_state: st.session_state.dev_file_loaded = False
 if "dev_previewed" not in st.session_state: st.session_state.dev_previewed = False
+if "dev_file_signature" not in st.session_state: st.session_state.dev_file_signature = None
+if "dev_preview_request" not in st.session_state: st.session_state.dev_preview_request = False
 
 if ("Train" in st.session_state.results) or ("Test" in st.session_state.results):
     st.session_state.dev_ready = True
 
 # =========================
-# Hero header (logo + title)
+# Hero header
 # =========================
 st.markdown(
     f"""
@@ -332,70 +327,83 @@ if st.session_state.app_step == "intro":
         st.session_state.app_step = "dev"; st.rerun()
 
 # =========================
-# MODEL DEVELOPMENT (Train/Test)
+# MODEL DEVELOPMENT
 # =========================
 if st.session_state.app_step == "dev":
     st.sidebar.header("Model Development Data")
-
     dev_label = "Upload Data (Excel)" if not st.session_state.get("dev_file_name") else "Replace data (Excel)"
     train_test_file = st.sidebar.file_uploader(dev_label, type=["xlsx","xls"], key="dev_upload")
 
-    # If a file is uploaded, parse once and set flags
+    # Detect new/changed file by signature (name + size)
     if train_test_file is not None:
-        st.session_state.dev_file_name = train_test_file.name
-        _book_tmp = read_book(train_test_file)
-        if _book_tmp:
-            first_df = next(iter(_book_tmp.values()))
-            st.sidebar.caption(f"**Data loaded:** {train_test_file.name} • {first_df.shape[0]} rows × {first_df.shape[1]} cols")
+        try:
+            size = train_test_file.size
+        except Exception:
+            size = len(train_test_file.getvalue())
+        sig = (train_test_file.name, size)
+        if sig != st.session_state.dev_file_signature:
+            # NEW upload => reset state
+            st.session_state.dev_file_signature = sig
+            st.session_state.dev_file_name = train_test_file.name
+            _book_tmp = read_book(train_test_file)
+            if _book_tmp:
+                first_df = next(iter(_book_tmp.values()))
+                st.sidebar.caption(f"**Data loaded:** {train_test_file.name} • {first_df.shape[0]} rows × {first_df.shape[1]} cols")
+                st.session_state.dev_file_loaded = True
+                st.session_state.dev_previewed = False
+                st.session_state.dev_ready = False
+        else:
+            # Same file, keep existing state
             st.session_state.dev_file_loaded = True
-            # Reset preview + readiness on new upload
-            st.session_state.dev_previewed = False
-            st.session_state.dev_ready = False
 
-    # Preview button (modal)
+    # Sidebar actions
     preview_btn = st.sidebar.button("Preview data", use_container_width=True, disabled=not st.session_state.dev_file_loaded)
     if preview_btn and st.session_state.dev_file_loaded and train_test_file is not None:
-        _book = read_book(train_test_file)
-        st.session_state.dev_previewed = True   # mark previewed
-        preview_modal_dev(_book, FEATURES)
+        st.session_state.dev_preview_request = True  # defer opening modal until after helper renders
 
     run_btn = st.sidebar.button("Run Model", type="primary", use_container_width=True)
 
-    # Proceed button BELOW run, visible always; enables after first successful run
-    st.sidebar.button(
+    proceed_clicked = st.sidebar.button(
         "Proceed to Prediction ▶",
         use_container_width=True,
-        disabled=not st.session_state.dev_ready,
-        on_click=(lambda: st.session_state.update(app_step="predict")) if st.session_state.dev_ready else None,
+        disabled=not st.session_state.dev_ready
     )
+    if proceed_clicked and st.session_state.dev_ready:
+        st.session_state.app_step = "predict"; st.rerun()
+
+    # ----- ALWAYS-ON TOP: Title + helper -----
+    helper_top = st.container()
+    with helper_top:
+        st.subheader("Model Development")
+        if st.session_state.dev_ready:
+            st.success("Case has been built and results are displayed below.")
+        elif st.session_state.dev_file_loaded and st.session_state.dev_previewed:
+            st.info("Previewed ✓ — now click **Run Model** to build the case.")
+        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 user clicked preview, open modal *after* helper so helper stays on top
+    if st.session_state.dev_preview_request and train_test_file is not None:
+        _book = read_book(train_test_file)
+        st.session_state.dev_previewed = True
+        st.session_state.dev_preview_request = False
+        preview_modal_dev(_book, FEATURES)
 
-    # ----- Title & helper (ALWAYS visible; state-aware) -----
-    st.subheader("Model Development")
-    if st.session_state.dev_ready:
-        st.success("Case has been built and results are displayed below.")
-    elif st.session_state.dev_file_loaded and st.session_state.dev_previewed:
-        st.info("Previewed ✓ — now click **Run Model** to build the case.")
-    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.**")
-
+    # Run model
     if run_btn and train_test_file is not None:
         with st.status("Processing…", expanded=False) as status:
             book = read_book(train_test_file)
             if not book: status.update(label="Failed to read workbook.", state="error"); st.stop()
             status.update(label="Workbook read ✓")
-
-            # Internally still expect Train/Test sheets
             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:
                 status.update(label="Workbook must include Train/Training/training2 and Test/Testing/testing2.", state="error"); st.stop()
-
             df_tr = book[sh_train].copy(); df_te = book[sh_test].copy()
             if not (ensure_cols(df_tr, FEATURES + [TARGET]) and ensure_cols(df_te, FEATURES + [TARGET])):
                 status.update(label="Missing required columns.", state="error"); st.stop()
-
             status.update(label="Columns validated ✓"); status.update(label="Predicting…")
 
             df_tr["UCS_Pred"] = model.predict(df_tr[FEATURES])
@@ -416,11 +424,11 @@ if st.session_state.app_step == "dev":
             tr_min = df_tr[FEATURES].min().to_dict(); tr_max = df_tr[FEATURES].max().to_dict()
             st.session_state.train_ranges = {f:(float(tr_min[f]), float(tr_max[f])) for f in FEATURES}
 
-            st.session_state.dev_ready = True  # enable Proceed button immediately
+            st.session_state.dev_ready = True
             status.update(label="Done ✓", state="complete"); toast("Model run complete 🚀")
-            st.rerun()  # refresh UI to reflect new helper & enabled button
+            st.rerun()
 
-    # Results
+    # Results (if available)
     if ("Train" in st.session_state.results) or ("Test" in st.session_state.results):
         tab1, tab2 = st.tabs(["Training", "Testing"])
         if "Train" in st.session_state.results:
@@ -469,7 +477,6 @@ if st.session_state.app_step == "predict":
     val_label = "Upload Validation Excel" if not st.session_state.get("val_file_name") else "Replace data (Excel)"
     validation_file = st.sidebar.file_uploader(val_label, type=["xlsx","xls"], key="val_upload")
     if validation_file is not None:
-        st.session_state.val_file_name = validation_file.name
         _book_tmp = read_book(validation_file)
         if _book_tmp:
             first_df = next(iter(_book_tmp.values()))
@@ -545,14 +552,10 @@ if st.session_state.app_step == "predict":
             else:
                 st.info("Actual UCS values are not available in the validation data. Cross-plot cannot be generated.")
         with right:
-            st.pyplot(
-                depth_or_index_track(
-                    st.session_state.results["Validate"],
-                    title=None,
-                    include_actual=(TARGET in st.session_state.results["Validate"].columns)
-                ),
-                use_container_width=True
-            )
+            st.pyplot(depth_or_index_track(
+                st.session_state.results["Validate"], title=None,
+                include_actual=(TARGET in st.session_state.results["Validate"].columns)
+            ), use_container_width=True)
 
         if oor_table is not None:
             st.write("*Out-of-range rows (vs. Training min–max):*")
@@ -574,13 +577,14 @@ if st.session_state.app_step == "predict":
             st.warning(str(e))
 
 # =========================
-# Footer (with website link)
+# Footer
 # =========================
 st.markdown("---")
 st.markdown(
     "<div style='text-align:center; color:#6b7280;'>"
     "ST_GeoMech_UCS • © Smart Thinking • "
-    "<a href='https://www.smartthinking.com.sa' target='_blank'>smartthinking.com.sa</a>"
+    "Visit our Website: "
+         "<a href='https://www.smartthinking.com.sa' target='_blank'>Visit our Website: smartthinking.com.sa</a>"
     "</div>",
     unsafe_allow_html=True
 )