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| import pandas as pd | |
| import streamlit as st | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from pre import preprocess_uploaded_file | |
| # Define the function to perform analysis | |
| def perform_analysis(uploaded_dataframes): | |
| # Concatenate all dataframes into a single dataframe | |
| combined_data = pd.concat(uploaded_dataframes, ignore_index=True) | |
| # Display scenarios with status "failed" grouped by functional area | |
| failed_scenarios = combined_data[combined_data['Status'] == 'FAILED'] | |
| passed_scenarios = combined_data[combined_data['Status'] == 'PASSED'] | |
| # Display total count of failures | |
| fail_count = len(failed_scenarios) | |
| st.markdown(f"Failing scenarios Count: {fail_count}") | |
| # Display total count of Passing | |
| pass_count = len(passed_scenarios) | |
| st.markdown(f"Passing scenarios Count: {pass_count}") | |
| # Use radio buttons for selecting status | |
| selected_status = st.radio("Select a status", ['Failed', 'Passed']) | |
| # Determine which scenarios to display based on selected status | |
| if selected_status == 'Failed': | |
| unique_areas = np.append(failed_scenarios['Functional area'].unique(), "All") | |
| selected_scenarios = failed_scenarios | |
| elif selected_status == 'Passed': | |
| unique_areas = np.append(passed_scenarios['Functional area'].unique(), "All") | |
| selected_scenarios = passed_scenarios | |
| else: | |
| selected_scenarios = None | |
| if selected_scenarios is not None: | |
| # st.write(f"Scenarios with status '{selected_status}' grouped by functional area:") | |
| st.markdown(f"### Scenarios with status '{selected_status}' grouped by functional area:") | |
| # Select a range of functional areas to filter scenarios | |
| selected_functional_areas = st.multiselect("Select functional areas", unique_areas, ["All"]) | |
| if "All" in selected_functional_areas: | |
| filtered_scenarios = selected_scenarios | |
| else: | |
| filtered_scenarios = selected_scenarios[selected_scenarios['Functional area'].isin(selected_functional_areas)] | |
| if not selected_functional_areas: # Check if the list is empty | |
| st.error("Please select at least one functional area.") | |
| else: | |
| # Calculate the average time spent for each functional area | |
| average_time_spent_seconds = filtered_scenarios.groupby('Functional area')['Time spent'].mean().reset_index() | |
| # Convert average time spent from seconds to minutes and seconds format | |
| average_time_spent_seconds['Time spent'] = pd.to_datetime(average_time_spent_seconds['Time spent'], unit='s').dt.strftime('%M:%S') | |
| # Group by functional area and get the start datetime for sorting | |
| start_datetime_group = filtered_scenarios.groupby('Functional area')['Start datetime'].min().reset_index() | |
| end_datetime_group = filtered_scenarios.groupby('Functional area')['End datetime'].max().reset_index() | |
| # Calculate the total time spent for each functional area (difference between end and start datetime) | |
| total_time_spent_seconds = (end_datetime_group['End datetime'] - start_datetime_group['Start datetime']).dt.total_seconds() | |
| # Convert total time spent from seconds to minutes and seconds format | |
| total_time_spent_seconds = pd.to_datetime(total_time_spent_seconds, unit='s').dt.strftime('%M:%S') | |
| # Merge the average_time_spent_seconds with start_datetime_group and end_datetime_group | |
| average_time_spent_seconds = average_time_spent_seconds.merge(start_datetime_group, on='Functional area') | |
| average_time_spent_seconds = average_time_spent_seconds.merge(end_datetime_group, on='Functional area') | |
| average_time_spent_seconds['Total Time Spent'] = total_time_spent_seconds | |
| # Filter scenarios based on selected functional area | |
| if selected_status == 'Failed': | |
| grouped_filtered_scenarios = filtered_scenarios.groupby('Environment')[['Functional area', 'Scenario Name', 'Error Message','Time spent(m:s)','Start datetime']].apply(lambda x: x.reset_index(drop=True)) | |
| elif selected_status == 'Passed': | |
| grouped_filtered_scenarios = filtered_scenarios.groupby('Functional area')[['Scenario Name', 'Time spent(m:s)']].apply(lambda x: x.reset_index(drop=True)) | |
| else: | |
| grouped_filtered_scenarios = None | |
| grouped_filtered_scenarios.reset_index(inplace=True) | |
| grouped_filtered_scenarios.drop(columns=['level_1'], inplace=True) | |
| grouped_filtered_scenarios.index = grouped_filtered_scenarios.index + 1 | |
| st.dataframe(grouped_filtered_scenarios) | |
| # Sort the average time spent table by start datetime | |
| average_time_spent_seconds = average_time_spent_seconds.sort_values(by='Start datetime') | |
| # Display average time spent on each functional area in a table | |
| st.markdown("### Total and Average Time Spent on Each Functional Area") | |
| average_time_spent_seconds.index = average_time_spent_seconds.index + 1 | |
| # Rename the columns for clarity | |
| average_time_spent_seconds.rename(columns={'Start datetime': 'Start Datetime', 'End datetime': 'End Datetime', 'Time spent':'Average Time Spent'}, inplace=True) | |
| # Rearrange the columns | |
| average_time_spent_seconds = average_time_spent_seconds[['Functional area', 'Total Time Spent', 'Start Datetime', 'End Datetime', 'Average Time Spent']] | |
| st.dataframe(average_time_spent_seconds) | |
| # Check if selected_status is 'Failed' and grouped_filtered_scenarifos length is less than or equal to 400 | |
| if selected_status != 'Passed': | |
| # Create and display bar graph of errors by functional area | |
| st.write(f"### Bar graph showing number of '{selected_status}' scenarios in each functional area:") | |
| error_counts = grouped_filtered_scenarios['Functional area'].value_counts() | |
| plt.figure(figsize=(12, 10)) | |
| bars = plt.bar(error_counts.index, error_counts.values) | |
| plt.xlabel('Functional Area') | |
| plt.ylabel('Number of Failures') | |
| plt.title(f"Number of '{selected_status}' scenarios by Functional Area") | |
| plt.xticks(rotation=45, ha='right', fontsize=10) | |
| # Set y-axis limits and ticks for consistent interval of 1 | |
| y_max = max(error_counts.values) + 1 | |
| plt.ylim(0, y_max) | |
| plt.yticks(range(0, y_max, 1), fontsize=10) | |
| # Display individual numbers on y-axis | |
| for bar in bars: | |
| height = bar.get_height() | |
| plt.text(bar.get_x() + bar.get_width() / 2, height, str(int(height)), | |
| ha='center', va='bottom') # Reduce font size of individual numbers | |
| plt.tight_layout() # Add this line to adjust layout | |
| st.pyplot(plt) | |
| pass | |
| def multiple_main(): | |
| # Get the number of environments from the user | |
| num_environments = st.number_input("Enter the number of environments", min_value=1, value=1, step=1) | |
| # Initialize list to store uploaded dataframes | |
| uploaded_dataframes = [] | |
| # Loop through the number of environments and create file uploaders | |
| for i in range(num_environments): | |
| uploaded_files = st.file_uploader(f"Upload CSV or XLSX files for Environment {i + 1}", type=["csv", "xlsx"], accept_multiple_files=True) | |
| for uploaded_file in uploaded_files: | |
| # Preprocess the uploaded file | |
| data = preprocess_uploaded_file(uploaded_file) | |
| # Append the dataframe to the list | |
| uploaded_dataframes.append(data) | |
| # Check if any files were uploaded | |
| if uploaded_dataframes: | |
| # Perform analysis for uploaded data | |
| perform_analysis(uploaded_dataframes) | |
| else: | |
| st.write("Please upload at least one file.") | |
| pass | |