import streamlit as st
import pandas as pd
import plotly.express as px

st.title("📊 DASHBOARD KPI MONITORING DAILY")

# Upload CSV
uploaded = st.file_uploader("Upload KPI CSV", type=["csv"])

if uploaded:

    df = pd.read_csv(uploaded)

    df["DATE_ID"] = pd.to_datetime(df["DATE_ID"])

    # ========================
    # FILTER SECTION
    # ========================

    st.subheader("Filter")

    site_filter = st.multiselect(
        "Site ID",
        df["SITE_ID"].unique(),
        default=df["SITE_ID"].unique()
    )

    band_filter = st.multiselect(
        "Band",
        df["BAND"].unique(),
        default=df["BAND"].unique()
    )

    start_date = st.date_input("Start Date", df["DATE_ID"].min())
    end_date = st.date_input("End Date", df["DATE_ID"].max())

    # Apply filter
    df_filtered = df[
        (df["SITE_ID"].isin(site_filter)) &
        (df["BAND"].isin(band_filter)) &
        (df["DATE_ID"].between(pd.to_datetime(start_date), pd.to_datetime(end_date)))
    ]

    # ========================
    # KPI CHART PER SECTOR
    # ========================

    sectors = df_filtered["SECTOR"].unique()

    st.subheader("Availability")

    cols = st.columns(len(sectors))

    for i, sector in enumerate(sectors):

        sector_data = df_filtered[df_filtered["SECTOR"] == sector]

        fig = px.line(
            sector_data,
            x="DATE_ID",
            y="Availability",
            title=f"Availability - {sector}"
        )

        cols[i].plotly_chart(fig, use_container_width=True)

    # ========================
    # SESSION ABNORMAL
    # ========================

    st.subheader("Session Abnormal Release")

    cols2 = st.columns(len(sectors))

    for i, sector in enumerate(sectors):

        sector_data = df_filtered[df_filtered["SECTOR"] == sector]

        fig = px.line(
            sector_data,
            x="DATE_ID",
            y="Session_Abnormal",
            title=f"Session Abnormal - {sector}"
        )

        cols2[i].plotly_chart(fig, use_container_width=True)