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import pandas as pd
import streamlit as st
import plotly.graph_objects as go
from pre import preprocess_uploaded_file
def generate_weekly_report(uploaded_files):
if not uploaded_files:
st.error("No files uploaded. Please upload CSV files for analysis.")
return
combined_data = pd.DataFrame()
for uploaded_file in uploaded_files:
data = preprocess_uploaded_file(uploaded_file)
combined_data = pd.concat([combined_data, data], ignore_index=True)
if combined_data.empty:
st.error("No data found in the uploaded files. Please check the file contents.")
return
failed_data = combined_data[combined_data['Status'] == 'FAILED']
if failed_data.empty:
st.warning("No failed scenarios found in the uploaded data.")
return
failed_data['Date'] = pd.to_datetime(failed_data['Start datetime']).dt.date
# UI for selecting environments and functional areas
environments = combined_data['Environment'].unique()
selected_environments = st.multiselect("Select Environments", options=environments, default=environments)
all_functional_areas = failed_data['Functional area'].unique()
area_choice = st.radio("Choose Functional Areas to Display", ['All', 'Select Functional Areas'])
if area_choice == 'Select Functional Areas':
selected_functional_areas = st.multiselect("Select Functional Areas", options=all_functional_areas)
if not selected_functional_areas:
st.error("Please select at least one functional area.")
return
else:
selected_functional_areas = all_functional_areas
# Date range selection
min_date = failed_data['Date'].min()
max_date = failed_data['Date'].max()
col1, col2 = st.columns(2)
with col1:
start_date = st.date_input("Start Date", min_value=min_date, max_value=max_date, value=min_date)
with col2:
end_date = st.date_input("End Date", min_value=min_date, max_value=max_date, value=max_date)
# Filter data based on selections and date range
filtered_data = failed_data[
(failed_data['Environment'].isin(selected_environments)) &
(failed_data['Date'] >= start_date) &
(failed_data['Date'] <= end_date)
]
if area_choice == 'Select Functional Areas':
filtered_data = filtered_data[filtered_data['Functional area'].isin(selected_functional_areas)]
# Group by Date, Environment, and Functional area
daily_failures = filtered_data.groupby(['Date', 'Environment', 'Functional area']).size().unstack(level=[1, 2], fill_value=0)
# Y-axis scaling option
y_axis_scale = st.radio("Y-axis Scaling", ["Fixed", "Dynamic"])
# Create an interactive plot using Plotly
fig = go.Figure()
for env in selected_environments:
if env in daily_failures.columns.levels[0]:
env_data = daily_failures[env]
if area_choice == 'All':
total_failures = env_data.sum(axis=1)
fig.add_trace(go.Scatter(x=daily_failures.index, y=total_failures,
mode='lines+markers', name=f'{env} - All Areas'))
else:
for area in selected_functional_areas:
if area in env_data.columns:
fig.add_trace(go.Scatter(x=daily_failures.index, y=env_data[area],
mode='lines+markers', name=f'{env} - {area}'))
fig.update_layout(
title='Failure Rates Comparison Across Environments Over Time',
xaxis_title='Date',
yaxis_title='Number of Failures',
legend_title='Environment - Functional Area',
hovermode='closest'
)
if y_axis_scale == "Fixed":
fig.update_yaxes(rangemode="tozero")
else:
pass
# Use st.plotly_chart to display the interactive chart
st.plotly_chart(fig, use_container_width=True)
# Add interactivity for scenario details
st.write("Select a date and environment to see detailed scenario information:")
selected_date = st.date_input("Select a date", min_value=start_date, max_value=end_date, value=start_date)
selected_env = st.selectbox("Select an environment", options=selected_environments)
if selected_date and selected_env:
st.write(f"### Detailed Scenarios for {selected_date} - {selected_env}")
day_scenarios = filtered_data[(filtered_data['Date'] == selected_date) &
(filtered_data['Environment'] == selected_env)]
if not day_scenarios.empty:
st.dataframe(day_scenarios[['Functional area', 'Scenario name', 'Error message', 'Time spent(m:s)']])
else:
st.write("No failing scenarios found for the selected date and environment.")
# Summary Statistics
st.write("### Summary Statistics")
for env in selected_environments:
env_data = filtered_data[filtered_data['Environment'] == env]
total_failures = len(env_data)
if len(daily_failures) > 0:
avg_daily_failures = total_failures / len(daily_failures)
if env in daily_failures.columns.levels[0]:
max_daily_failures = daily_failures[env].sum(axis=1).max()
min_daily_failures = daily_failures[env].sum(axis=1).min()
else:
max_daily_failures = min_daily_failures = 0
else:
avg_daily_failures = max_daily_failures = min_daily_failures = 0
st.write(f"**{env}**:")
st.write(f" - Total Failures: {total_failures}")
st.write(f" - Average Daily Failures: {avg_daily_failures:.2f}")
st.write(f" - Max Daily Failures: {max_daily_failures}")
st.write(f" - Min Daily Failures: {min_daily_failures}")
if area_choice == 'Select Functional Areas':
st.write("\n **Failures by Functional Area:**")
for area in selected_functional_areas:
area_total = len(env_data[env_data['Functional area'] == area])
st.write(f" - {area}: {area_total}")
st.write("---")
# Display raw data for verification
if st.checkbox("Show Raw Data"):
st.write(daily_failures) |