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