KPI_HOURLY / src /streamlit_app.py
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Update src/streamlit_app.py
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import streamlit as st
import pandas as pd
import plotly.express as px
st.title("๐Ÿ“Š 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)