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| import pandas as pd | |
| import streamlit as st | |
| import csv | |
| import io | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from pre import preprocess_csv, load_data, fill_missing_data | |
| def double_main(uploaded_file1,uploaded_file2): | |
| # st.title('Single CSV Analyzer') | |
| if uploaded_file1 is not None and uploaded_file2 is not None: | |
| # Process the csv files with header | |
| filet_1 = uploaded_file1.read() | |
| processed_output_1 = preprocess_csv(filet_1) | |
| processed_file_1 = io.StringIO(processed_output_1.getvalue()) | |
| data_1 = load_data(processed_file_1) | |
| filet_2 = uploaded_file2.read() | |
| processed_output_2 = preprocess_csv(filet_2) | |
| processed_file_2 = io.StringIO(processed_output_2.getvalue()) | |
| data_2 = load_data(processed_file_2) | |
| data_1 = fill_missing_data(data_1, 4, 0) | |
| data_1['Start datetime'] = pd.to_datetime(data_1['Start datetime'], errors='coerce') | |
| data_1['End datetime'] = pd.to_datetime(data_1['End datetime'], errors='coerce') | |
| data_1['Time spent'] = (data_1['End datetime'] - data_1['Start datetime']).dt.total_seconds() | |
| data_2 = fill_missing_data(data_2, 4, 0) | |
| data_2['Start datetime'] = pd.to_datetime(data_2['Start datetime'], errors='coerce') | |
| data_2['End datetime'] = pd.to_datetime(data_2['End datetime'], errors='coerce') | |
| data_2['Time spent'] = (data_2['End datetime'] - data_2['Start datetime']).dt.total_seconds() | |
| # Determine which DataFrame is older | |
| if data_1['Start datetime'].min() < data_2['Start datetime'].min(): | |
| older_df = data_1 | |
| newer_df = data_2 | |
| else: | |
| older_df = data_2 | |
| newer_df = data_1 | |
| older_df['Time spent'] = pd.to_datetime(older_df['Time spent'], unit='s').dt.strftime('%M:%S') | |
| newer_df['Time spent'] = pd.to_datetime(newer_df['Time spent'], unit='s').dt.strftime('%M:%S') | |
| older_datetime = older_df['Start datetime'].min() | |
| newer_datetime = newer_df['Start datetime'].min() | |
| st.write(f"The older csv started on {older_datetime}") | |
| st.write(f"The newer csv started on {newer_datetime}") | |
| # Merge dataframes on 'scenario name' | |
| merged_df = pd.merge(older_df, newer_df, on=['Functional area', 'Scenario name'], suffixes=('_old', '_new')) | |
| # Filter scenarios that were failing and are still failing | |
| fail_to_fail_scenarios = merged_df[(merged_df['Status_old'] == 'FAILED') & (merged_df['Status_new'] == 'FAILED')] | |
| st.markdown("### Consistent Failures(previously failing, now failing)") | |
| fail_count = len(fail_to_fail_scenarios) | |
| st.write(f"Failing scenarios Count: {fail_count}") | |
| # Select columns for display | |
| columns_to_display = ['Functional area', 'Scenario name', 'Error message_old', 'Error message_new'] | |
| # Display the selected columns using st.write | |
| st.write(fail_to_fail_scenarios[columns_to_display]) | |
| # Filter scenarios that were passing and now failing | |
| pass_to_fail_scenarios = merged_df[(merged_df['Status_old'] == 'PASSED') & (merged_df['Status_new'] == 'FAILED')] | |
| st.markdown("### New Failures(previously passing, now failing)") | |
| pass_fail_count = len(pass_to_fail_scenarios) | |
| st.write(f"Failing scenarios Count: {pass_fail_count}") | |
| # Select columns for display | |
| columns_to_display = ['Functional area', 'Scenario name', 'Error message_new', 'Time spent_old','Time spent_new',] | |
| # Display the selected columns using st.write | |
| st.write(pass_to_fail_scenarios[columns_to_display]) | |
| # Filter scenarios that were failing and now passing | |
| fail_to_pass_scenarios = merged_df[(merged_df['Status_old'] == 'FAILED') & (merged_df['Status_new'] == 'PASSED')] | |
| # Display filtered dataframe in Streamlit app | |
| st.markdown("### New Passes(previously failing, now passing)") | |
| pass_count = len(fail_to_pass_scenarios) | |
| st.write(f"Passing scenarios Count: {pass_count}") | |
| # Select columns for display | |
| columns_to_display = ['Functional area', 'Scenario name', 'Error message_old', 'Time spent_old','Time spent_new',] | |
| # Display the selected columns using st.write | |
| st.write(fail_to_pass_scenarios[columns_to_display]) | |
| pass |