Update app.py

app.py

@@ -0,0 +1,303 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import glob
+import re
+import os
+
+# ======================
+# PAGE CONFIG
+# ======================
+st.set_page_config(
+    page_title="HF KPI Dashboard",
+    layout="wide"
+)
+
+# ======================
+# TAB NAVIGATION
+# ======================
+tab1, tab2 = st.tabs([
+    "📊 Dashboard KPI Monitoring Daily",
+    "📈 Dashboard Capacity & Utilization"
+])
+
+# ======================
+# LOAD DATA (SHARED)
+# ======================
+@st.cache_data
+def load_data():
+    files = (
+        glob.glob("src/LTE_CELL_DAILY *.csv") +
+        glob.glob("src/LTE_CELL_DAILY *.csv.gz")
+    )
+    if not files:
+        return None
+
+    dfs = []
+    for f in files:
+        tmp = pd.read_csv(f, compression="infer")
+
+        if "DATE_ID" in tmp.columns:
+            tmp["DATE_ID"] = pd.to_datetime(
+                tmp["DATE_ID"].astype(str).str.strip(),
+                format="%m/%d/%Y",
+                errors="coerce"
+            )
+
+        if tmp["DATE_ID"].isna().all():
+            m = re.search(r"(\d{8})", os.path.basename(f))
+            if m:
+                tmp["DATE_ID"] = pd.to_datetime(m.group(1), format="%Y%m%d")
+
+        dfs.append(tmp)
+
+    df = pd.concat(dfs, ignore_index=True)
+    df = df.dropna(subset=["DATE_ID"])
+    df["DATE_ID"] = df["DATE_ID"].dt.normalize()
+    return df
+
+
+df_raw = load_data()
+if df_raw is None or df_raw.empty:
+    st.error("Data CSV / CSV.GZ tidak ditemukan")
+    st.stop()
+
+# ======================
+# KPI ALIAS
+# ======================
+COLUMN_ALIAS = {
+    "Intra-Frequency Handover Out Success Rate":
+        "Intra_Frequency_HO_Out_Success_Rate"
+}
+for src, dst in COLUMN_ALIAS.items():
+    if src in df_raw.columns:
+        df_raw[dst] = df_raw[src]
+
+# ======================
+# FILTER (GLOBAL)
+# ======================
+with tab1:
+    st.title("DASHBOARDS KPI MONITORING DAILY")
+    st.caption("📊 Dashboard By Muhammad Defri")
+
+    st.subheader("Filter")
+
+    selected_sites = st.multiselect(
+        "Site ID",
+        sorted(df_raw["ERBS"].dropna().unique())
+    )
+
+    if not selected_sites:
+        st.warning("⚠️ Input Site ID")
+        st.stop()
+
+    df = df_raw[df_raw["ERBS"].isin(selected_sites)]
+
+    col_start, col_end = st.columns(2)
+
+    with col_start:
+        start = st.date_input(
+            "Start Date",
+            df["DATE_ID"].min().date(),
+            min_value=df_raw["DATE_ID"].min().date(),
+            max_value=df_raw["DATE_ID"].max().date()
+        )
+
+    with col_end:
+        end = st.date_input(
+            "End Date",
+            df_raw["DATE_ID"].max().date(),
+            min_value=start,
+            max_value=df_raw["DATE_ID"].max().date()
+        )
+
+    df = df[
+        (df["DATE_ID"] >= pd.to_datetime(start)) &
+        (df["DATE_ID"] < pd.to_datetime(end) + pd.Timedelta(days=1))
+    ]
+
+    # ======================
+    # MAP BAND & SECTOR (UNCHANGED)
+    # ======================
+    def map_band(cell):
+        if pd.isna(cell): 
+            return "OTHER"
+        cell = str(cell).upper()
+        if re.search(r"(MT|IT)", cell): return "L900"
+        if re.search(r"(ML|IL|RL)", cell): return "L1800"
+        if re.search(r"(MR|RR|IR)", cell): return "L2100"
+        if re.search(r"(ME|MF|MV|IE|IF|IV|VE|VF|VV)", cell): return "L2300"
+        return "OTHER"
+
+    def map_sector(cell):
+        if pd.isna(cell):
+            return "SEC_OTHER"
+        cell = str(cell).upper().strip()
+        m = re.search(r"(RR|RL|IR|IL)(\d{2})$", cell)
+        if m:
+            sec = int(m.group(2)) // 10
+            return f"SEC{sec}" if sec in (1, 2, 3) else "SEC_OTHER"
+        m = re.search(r"(\d{1,2})$", cell)
+        if m:
+            sec = ((int(m.group(1)) - 1) % 3) + 1
+            return f"SEC{sec}"
+        return "SEC_OTHER"
+
+    df["BAND"] = df["EUTRANCELLFDD"].apply(map_band)
+    df["SECTOR"] = df["EUTRANCELLFDD"].apply(map_sector)
+
+    available_bands = ["L900", "L1800", "L2100", "L2300"]
+    selected_bands = st.multiselect("Band", available_bands, default=available_bands)
+    df = df[df["BAND"].isin(selected_bands)]
+
+    # ======================
+    # GLOBAL DATE AXIS
+    # ======================
+    X_MIN = pd.to_datetime(start)
+    X_MAX = pd.to_datetime(end) + pd.Timedelta(days=1) - pd.Timedelta(seconds=1)
+    FULL_DATES = pd.DataFrame({
+        "DATE_ID": pd.date_range(start=X_MIN, end=pd.to_datetime(end), freq="D")
+    })
+    DTICK = "D1"
+
+    # ======================
+    # AUTO UNIT
+    # ======================
+    def auto_unit(d, kpi):
+        d = d.copy()
+        max_val = d[kpi].max()
+        if pd.isna(max_val):
+            return d.round(2), "GB"
+        if max_val >= 1024:
+            d[kpi] = (d[kpi] / 1024).round(2)
+            return d, "TB"
+        d[kpi] = d[kpi].round(2)
+        return d, "GB"
+
+    # ======================
+    # SECTOR CHART (UNCHANGED)
+    # ======================
+    def sector_chart(df, sector, kpi, title, is_pct=False):
+        base = df[df["SECTOR"] == sector]
+        if base.empty or kpi not in base.columns:
+            return None
+
+        frames = []
+        for cell in sorted(base["EUTRANCELLFDD"].unique()):
+            g = base[base["EUTRANCELLFDD"] == cell][["DATE_ID", kpi]]
+            g = FULL_DATES.merge(g, on="DATE_ID", how="left")
+            g["EUTRANCELLFDD"] = cell
+            frames.append(g)
+
+        d = pd.concat(frames, ignore_index=True)
+
+        if ("TRAFFIC" in kpi.upper()) or ("PAYLOAD" in kpi.upper()):
+            d, unit = auto_unit(d, kpi)
+            fig = px.area(d, x="DATE_ID", y=kpi, color="EUTRANCELLFDD",
+                          title=f"{title} ({unit})")
+        else:
+            fig = px.line(d, x="DATE_ID", y=kpi,
+                          color="EUTRANCELLFDD",
+                          title=title, markers=True)
+
+        fig.update_layout(
+            height=420,
+            dragmode=False,
+            xaxis=dict(tickformat="%d-%b", tickangle=-45),
+            legend=dict(orientation="h", y=-0.35, x=0.5, xanchor="center")
+        )
+
+        fig.update_xaxes(
+            range=[X_MIN, X_MAX],
+            tickmode="linear",
+            dtick=DTICK,
+            fixedrange=True
+        )
+
+        if is_pct:
+            fig.update_yaxes(range=[0, 100])
+
+        return fig
+
+    KPI_LIST = [
+        ("Radio_Network_Availability_Rate","Availability",True),
+        ("Session_Abnormal_Release","Session Abnormal Release",False),
+        ("RRC_Setup_Success_Rate_Service","RRC Setup Success Rate",True),
+        ("ERAB_Setup_Success_Rate_All","ERAB Setup Success Rate",True),
+        ("Session_Setup_Success_Rate","Session Setup Success Rate",True),
+        ("Intra_Frequency_HO_Out_Success_Rate","Intra-Frequency HO Out",True),
+        ("inter_freq_HO","Inter Frequency HO",False),
+        ("UL_RSSI_dbm","UL RSSI (dBm)",False),
+        ("Average_CQI_nonHOME","Average CQI",False),
+        ("SE_2","Spectral Efficiency",False),
+        ("Total_Traffic_Volume_new","Total Traffic Volume New",False),
+        ("CA_PAYLOAD_GB","CA Payload (GB)",False),
+    ]
+
+    for kpi, title, is_pct in KPI_LIST:
+        st.markdown("---")
+        c1, c2, c3 = st.columns(3)
+        for col, sec in zip([c1, c2, c3], ["SEC1","SEC2","SEC3"]):
+            with col:
+                fig = sector_chart(df, sec, kpi, f"{title} - {sec}", is_pct)
+                if fig:
+                    st.plotly_chart(
+                        fig,
+                        use_container_width=True,
+                        config={"displayModeBar": False, "scrollZoom": False}
+                    )
+
+# ======================
+# DASHBOARD 2
+# ======================
+with tab2:
+    st.title("DASHBOARD CAPACITY & UTILIZATION")
+    st.caption("📈 Clean • Enterprise • Management Ready")
+
+    KPI_LIST_2 = [
+        "DL_Resource_Block_Utilizing_Rate",
+        "UL_Resource_Block_Utilizing_Rate",
+        "User_Uplink_Average_Throughput",
+        "DL_PDCP_User_Throughput",
+        "Cell_Downlink_Average_Throughput",
+        "Cell_Uplink_Average_Throughput",
+        "LTE_CSFB_SR",
+        "Maximum_User_Number_RrcConn",
+        "PRB_MAX",
+        "AU_MAX",
+        "Max DL Cell Downlink Throughput",
+        "Max UL Cell Downlink Throughput"
+    ]
+
+    def simple_kpi_chart(df, kpi):
+        if kpi not in df.columns:
+            return None
+
+        d = df.groupby("DATE_ID")[kpi].mean().reset_index()
+        d = FULL_DATES.merge(d, on="DATE_ID", how="left")
+
+        fig = px.area(d, x="DATE_ID", y=kpi, title=kpi)
+        fig.update_layout(
+            height=420,
+            dragmode=False,
+            xaxis=dict(tickformat="%d-%b"),
+        )
+        fig.update_xaxes(
+            range=[X_MIN, X_MAX],
+            tickmode="linear",
+            dtick=DTICK,
+            fixedrange=True
+        )
+        return fig
+
+    for i in range(0, len(KPI_LIST_2), 3):
+        cols = st.columns(3)
+        for col, kpi in zip(cols, KPI_LIST_2[i:i+3]):
+            with col:
+                fig = simple_kpi_chart(df, kpi)
+                if fig:
+                    st.plotly_chart(
+                        fig,
+                        use_container_width=True,
+                        config={"displayModeBar": False, "scrollZoom": False}
+                    )