Änderung: Filterfunktionen costobjects.py implementiert

app/data/sql/ora_kostenobjekte.sql

@@ -6,7 +6,7 @@ select
     KOSTENOBJEKT as obj,
     BEZEICHNUNG,
     VERANTWORTLICHER,
-    FELD_2_X30 as VORGESETZER,
+    FELD_2_X30 as vorgesetzter,
     ZUORDNUNGSGRUPPE_1 as zgrp1,
     ZUORDNUNGSGRUPPE_2 as zgrp2,
     ZUORDNUNGSGRUPPE_3 as zgrp3,
@@ -14,6 +14,9 @@ select
     ZUORDNUNGSGRUPPE_5 as zgrp5,
     ZUORDNUNGSGRUPPE_6 as zgrp6,
     FELD_1_X30 as fertigung,
-    OBJEKTGRUPPE as objgrp
+    OBJEKTGRUPPE as objgrp,
+    sysdate
 from
     pkos
+where
+    objekttyp in ('01', '02')

app/pages/costobjects.py

@@ -5,41 +5,149 @@ from data.db import load_data
 from auth_runtime import require_login
 from ui.sidebar import build_sidebar, hide_sidebar_if_logged_out
 from auth import get_fullname_for_user
+import numpy as np
 
-# hide_sidebar_if_logged_out()
 
 st.set_page_config(page_title="Co-App Home", page_icon="🏠", layout="wide")
 
 authenticator = require_login()
 st.session_state["authenticator"] = authenticator
 
-@st.cache_data
-def cache_data():
-    return load_data("ora_kostenobjekte","oracle")
 
-def render_report():
+
+@st.cache_data
+def cache_data() -> pd.DataFrame:
+    """
+    Load and cache the base dataset for this page.
+
+    Streamlit reruns the script on every interaction; caching avoids
+    repeated I/O and makes filtering feel instant.
+    """
+    try:
+        df = load_data("ora_kostenobjekte", "oracle")
+        return df
+    except:
+        st.warning("Fehler beim Laden der Daten", icon="⚠️")
+
+
+
+def sidebar_filters(df) -> dict:
+    """
+    Render the sidebar UI and return the current filter selections.
+
+    This function should contain UI concerns only (widgets, layout),
+    and not data filtering logic, to keep the code maintainable.
+    """
+    st.sidebar.header("Filter")
+    
+    if st.sidebar.button("Refresh (Global)"):
+        cache_data.clear()
+        st.rerun()
+    
+    year = st.sidebar.selectbox(label="Jahr", options=(2025, 2026), index=1)
+    filter_text = st.sidebar.text_input(label="Textsuche", placeholder="Suche Objekt, Text, Verantwortlicher")
+    col_s1, col_s2 = st.sidebar.columns(2)
+    with col_s1:
+        typ = st.sidebar.selectbox(label="Typ", options=sorted(df["typ"].dropna().unique()), index=1)
+    with col_s2:
+        obj = st.sidebar.multiselect("obj", sorted(df["obj"].dropna().unique()))
+    zgrp1 = st.sidebar.multiselect("ZGrp1", sorted(df["zgrp1"].dropna().unique()))
+    zgrp2 = st.sidebar.multiselect("ZGrp2", sorted(df["zgrp2"].dropna().unique()))
+    zgrp3 = st.sidebar.multiselect("ZGrp3", sorted(df["zgrp3"].dropna().unique()))
+    
+    return {"year": year, "filter_text": filter_text, "typ": typ, "obj": obj, "zgrp1": zgrp1, "zgrp2": zgrp2, "zgrp3": zgrp3}
+
+
+
+
+def build_mask(df: pd.DataFrame, sidebar_filter: dict) -> np.ndarray:
+    """
+    Build a boolean mask based on filter selections.
+
+    The mask approach keeps the logic readable and makes it easy
+    to add more conditions later.
+    """
+
+    mask = np.ones(len(df), dtype=bool)
+
+    filter_text = (sidebar_filter.get("filter_text") or "").strip()
+    if filter_text:
+        m1 = df["bezeichnung"].astype("string").str.contains(filter_text, case=False, na=False, regex=False)
+        m2 = df["verantwortlicher"].astype("string").str.contains(filter_text, case=False, na=False, regex=False)
+        m3 = df["vorgesetzter"].astype("string").str.contains(filter_text, case=False, na=False, regex=False)
+        mask &= (m1 | m2 | m3)
+
+    if sidebar_filter["year"]:
+        mask &= df["jahr"].eq(sidebar_filter["year"])
+    if sidebar_filter["typ"]:
+        mask &= df["typ"].eq(sidebar_filter["typ"])
+    if sidebar_filter["obj"]:
+        mask &= df["obj"].isin(sidebar_filter["obj"])
+    if sidebar_filter["zgrp1"]:
+        mask &= df["zgrp1"].isin(sidebar_filter["zgrp1"])
+    if sidebar_filter["zgrp2"]:
+        mask &= df["zgrp2"].isin(sidebar_filter["zgrp2"])
+    if sidebar_filter["zgrp3"]:
+        mask &= df["zgrp3"].isin(sidebar_filter["zgrp3"])
+    return mask
+
+
+
+
+def render_table(df):
+    """
+    Render the result table.
+
+    Keep this function presentation-only: it should not modify data.
+    """
+    st.dataframe(df, hide_index=True, width="stretch", height="stretch")
+
+
+def page():
+    """
+    Page entry point: orchestrates data loading, UI, filtering, and rendering.
+    """
+
+    # -----------------------------
+    # Data loading (cached)
+    # ----------------------------- 
     df = cache_data()
 
-    zgrp1_options = st.multiselect(
-        "Zuordnungsgruppe1",
-        ["SPI", "KULA", "AT"]
-    )
 
-    year = 2026
+    # -----------------------------
+    # UI (Sidebar)
+    # -----------------------------
+    sidebar_filter = sidebar_filters(df)
+
+
+    # -----------------------------
+    # Business logic (filtering)
+    # -----------------------------
+    mask = build_mask(df, sidebar_filter)
+
+
+    # -----------------------------
+    # Presentation
+    # -----------------------------
+
+    df = df.sort_values(by="obj", key=lambda s: pd.to_numeric(s, errors="coerce"))
+    df_clean = df[df.columns[:-1]] # letzte Spalten entfernen (sysdate)
+    df_display = df_clean.loc[mask]
+    render_table(df_display)
+    
+    col1, col2 = st.columns(2)
+    data_as_of = df["sysdate"].max()
+    row_count = len(df_display)
+    
+    with col1:
+        st.text(f"Datenstand: {data_as_of}")
+        st.text("Datenquelle: Oracle (PENTA)")
+    with col2:
+        st.markdown(f"<div style='text-align: right;'>Anzahl Zeilen: {row_count}</div>", unsafe_allow_html=True)
     
-    df_actual_year = df[df["jahr"] == year]
-    df_actual_year = df_actual_year.sort_values(
-        by="obj",
-        key=lambda s: pd.to_numeric(s, errors="coerce")
-    )
 
-    if zgrp1_options:
-        df_view = df_actual_year[df_actual_year["zgrp1"].isin(zgrp1_options)]
-    else:
-        df_view = df_actual_year
 
-    st.dataframe(df_view, height=600, hide_index=True, width="stretch")
 
 
 if __name__ == "__main__":
-    df = render_report()
+    df = page()