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import pandas as pd
import plotly.express as px
import streamlit as st
from process_kpi.process_gsm_capacity import analyze_gsm_data
from utils.convert_to_excel import ( # Import convert_dfs from the appropriate module
convert_gsm_dfs,
)
from utils.kpi_analysis_utils import GsmCapacity
st.title(" 📊 GSM Capacity Analysis")
doc_col, image_col = st.columns(2)
with doc_col:
st.write(
"""
The report should be run with a minimum of 3 days of data.
- Dump file required
- Daily Cell level KPI report in CSV format
- BH Cell level KPI report in CSV format
"""
)
with image_col:
st.image("./assets/gsm_capacity.png", width=250)
file1, file2, file3 = st.columns(3)
with file1:
uploaded_dump = st.file_uploader("Upload Dump file in xlsb format", type="xlsb")
with file2:
uploaded_daily_report = st.file_uploader(
"Upload Daily Report in CSV format", type="csv"
)
with file3:
uploaded_bh_report = st.file_uploader(
"Upload Busy Hour Report in CSV format", type="csv"
)
col1, col2 = st.columns(2)
threshold_col1, threshold_col2 = st.columns(2)
threshold_col3, threshold_col4 = st.columns(2)
max_traffic_threshold_col1, operational_neighbours_distance_col1 = st.columns(2)
if (
uploaded_dump is not None
and uploaded_daily_report is not None
and uploaded_bh_report is not None
):
# WbtsCapacity.final_results = None
with col1:
number_of_kpi_days = st.number_input(
"Number of days for analysis",
min_value=3,
max_value=30,
value=7,
)
with col2:
number_of_threshold_days = st.number_input(
"Number of days for threshold",
min_value=1,
max_value=30,
value=3,
)
with threshold_col1:
availability_threshold = st.number_input(
"Availability Threshold", min_value=1, max_value=100, value=95
)
with threshold_col2:
tch_abis_fails_threshold = st.number_input(
"TCH ABIS Fails Threshold", min_value=0, value=10
)
with threshold_col3:
sdcch_blocking_threshold = st.number_input(
"SDDCH Blocking Threshold", min_value=0.1, value=0.5
)
with threshold_col4:
tch_blocking_threshold = st.number_input(
"TCH Blocking Threshold", min_value=0.1, value=0.5
)
with max_traffic_threshold_col1:
max_traffic_threshold = st.number_input(
"TCH Utilization Max Traffic Threshold", min_value=0, value=90
)
with operational_neighbours_distance_col1:
operational_neighbours_distance = st.number_input(
"Operational Neighbours Distance", min_value=0, value=1
)
if st.button("Analyze Data", type="primary"):
dfs = analyze_gsm_data(
dump_path=uploaded_dump,
daily_report_path=uploaded_daily_report,
bh_report_path=uploaded_bh_report,
number_of_kpi_days=number_of_kpi_days,
number_of_threshold_days=number_of_threshold_days,
availability_threshold=availability_threshold,
tch_abis_fails_threshold=tch_abis_fails_threshold,
sdcch_blocking_threshold=sdcch_blocking_threshold,
tch_blocking_threshold=tch_blocking_threshold,
max_traffic_threshold=max_traffic_threshold,
operational_neighbours_distance=operational_neighbours_distance,
)
if dfs is not None:
gsm_analysis_df: pd.DataFrame = dfs[0]
bh_kpi_df: pd.DataFrame = dfs[1]
daily_kpi_df: pd.DataFrame = dfs[2]
distance_df: pd.DataFrame = dfs[3]
GsmCapacity.final_results = convert_gsm_dfs(
[gsm_analysis_df, distance_df, bh_kpi_df, daily_kpi_df],
["GSM_Analysis", "Distance", "BH_KPI_Analysis", "Daily_KPI_Analysis"],
)
# GsmCapacity.final_results = convert_gsm_dfs(
# [gsm_analysis_df, bh_kpi_df, daily_kpi_df],
# ["GSM_Analysis", "BH_KPI_Analysis", "Daily_KPI_Analysis"],
# )
if GsmCapacity.final_results is not None:
st.download_button(
on_click="ignore",
type="primary",
label="Download the Analysis Report",
data=GsmCapacity.final_results,
file_name="GSM_Analysis_Report.xlsx",
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
)
st.write(daily_kpi_df)
# Add dataframe and ploty bar chart with "Final comment" distribution in gsm_analysis_df in 2 columns
final_comments_df = (
gsm_analysis_df.groupby("Final comment").size().reset_index(name="count")
)
fig = px.bar(
final_comments_df,
x="Final comment",
y="count",
title="Final comment distribution",
)
fig.update_layout(height=1000)
fig.update_traces(texttemplate="%{value}", textposition="outside")
st.plotly_chart(fig, use_container_width=True)
st.write(final_comments_df)
# Add dataframe and ploty bar chart with "Final comment summary" distribution in gsm_analysis_df in 2 columns
final_comments_summary_df = (
gsm_analysis_df.groupby("Final comment summary")
.size()
.reset_index(name="count")
)
# Add Pie chart with "Final comment summary" distribution in gsm_analysis_df in 2 columns
st.markdown("***")
st.markdown(":blue[**Final comment summary distribution**]")
final_comments_summary_col1, final_comments_summary_col2 = st.columns((1, 3))
with final_comments_summary_col1:
st.write(final_comments_summary_df)
with final_comments_summary_col2:
fig = px.pie(
final_comments_summary_df,
names="Final comment summary",
values="count",
hover_name="Final comment summary",
hover_data=["count"],
title="GSM Analysis comment distribution",
)
fig.update_layout(height=800)
fig.update_traces(
texttemplate="%{label}: %{value}",
textfont_size=15,
textposition="outside",
)
st.plotly_chart(fig, use_container_width=True)
# Add dataframe and ploty bar chart with "BH Congestion status" distribution in gsm_analysis_df in 2 columns
st.markdown("***")
st.markdown(":blue[**BH Congestion status distribution**]")
bh_congestion_status_df = (
gsm_analysis_df.groupby("BH Congestion status")
.size()
.reset_index(name="count")
)
# Replace "" cell in "BH Congestion status" with "No Congestion"
bh_congestion_status_df["BH Congestion status"] = bh_congestion_status_df[
"BH Congestion status"
].replace("", "No Congestion")
# Replace "nan, nan" cell in "BH Congestion status" with "No KPI"
bh_congestion_status_df["BH Congestion status"] = bh_congestion_status_df[
"BH Congestion status"
].replace("nan, nan", "No KPI")
bh_congestion_status_col1, bh_congestion_status_col2 = st.columns((2, 1))
with bh_congestion_status_col2:
st.write(bh_congestion_status_df)
with bh_congestion_status_col1:
fig = px.pie(
bh_congestion_status_df,
names="BH Congestion status",
values="count",
hover_name="BH Congestion status",
hover_data=["count"],
title="BH Congestion status distribution",
)
fig.update_layout(height=800)
fig.update_traces(
texttemplate="%{label}: %{value}",
textfont_size=15,
textposition="outside",
)
st.plotly_chart(fig, use_container_width=True)
# Add dataframe and ploty pie chart with "operational_comment" distribution in gsm_analysis_df in 2 columns
st.markdown("***")
st.markdown(":blue[**Operational comments distribution**]")
operational_comments_df = (
gsm_analysis_df.groupby("operational_comment")
.size()
.reset_index(name="count")
)
operational_comments_col1, operational_comments_col2 = st.columns((1, 2))
with operational_comments_col1:
st.write(operational_comments_df)
with operational_comments_col2:
fig = px.pie(
operational_comments_df,
names="operational_comment",
values="count",
hover_name="operational_comment",
hover_data=["count"],
title="Operational comments distribution",
)
fig.update_layout(height=600)
fig.update_traces(
texttemplate="%{label}: %{value}",
textfont_size=15,
textposition="outside",
)
st.plotly_chart(fig, use_container_width=True)
# create a map plotly with gsm_analysis_df and max_tch_call_blocking_bh
st.markdown("***")
st.markdown(":blue[**Max TCH Call Blocking BH distribution**]")
fig = px.scatter_mapbox(
gsm_analysis_df.dropna(
subset=["max_tch_call_blocking_bh", "Latitude", "Longitude"]
),
lat="Latitude",
lon="Longitude",
color=[
"red" if val > tch_blocking_threshold else "green"
for val in gsm_analysis_df[
"max_tch_call_blocking_bh"
].dropna() # .values
],
size="max_tch_call_blocking_bh",
zoom=10,
height=600,
title="Max TCH Call Blocking BH distribution",
)
fig.update_layout(mapbox_style="open-street-map")
st.plotly_chart(fig, use_container_width=True)
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