Update second.py

second.py

@@ -1,4 +1,5 @@
-import pandas as pd
+# Import necessary libraries
+import pandas as pd 
 import streamlit as st
 import csv
 import io
@@ -6,63 +7,107 @@ import matplotlib.pyplot as plt
 import numpy as np
 from pre import preprocess_uploaded_file
 
+# Main function to process 2 uploaded CSV files 
+def double_main(uploaded_file1,uploaded_file2):
 
-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
-        data_1 = preprocess_uploaded_file(uploaded_file1)
-        data_2 = preprocess_uploaded_file(uploaded_file2)
-        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
+  # Check if both files are uploaded
+  if uploaded_file1 is not None and uploaded_file2 is not None:
+
+    # Preprocess the uploaded CSV files
+    data_1 = preprocess_uploaded_file(uploaded_file1) 
+    data_2 = preprocess_uploaded_file(uploaded_file2)
+
+    # Determine which file is older and newer
+    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
+
+    # Convert time columns to MM:SS format  
+    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')
+  
+    # Get start datetime of each file  
+    older_datetime = older_df['Start datetime'].min()
+    newer_datetime = newer_df['Start datetime'].min()
+    
+    # Display start datetime of each file
+    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')]
+    
+    # Display Consistent Failures section
+    st.markdown("### Consistent Failures(previously failing, now failing)")
+
+    # Get failing scenarios count
+    fail_count = len(fail_to_fail_scenarios)
+    st.write(f"Failing scenarios Count: {fail_count}")
+
+    # Display filtered dataframe 
+    columns_to_display1 = ['Functional area', 'Scenario name', 'Error message_old', 'Error message_new']
+    st.write(fail_to_fail_scenarios[columns_to_display1])
+
+    # Filter scenarios that were passing and now failing
+    pass_to_fail_scenarios = merged_df[(merged_df['Status_old'] == 'PASSED') & (merged_df['Status_new'] == 'FAILED')]
+
+    # Display New Failures section 
+    st.markdown("### New Failures(previously passing, now failing)")
+
+    # Get failing scenarios count
+    pass_fail_count = len(pass_to_fail_scenarios)
+    st.write(f"Failing scenarios Count: {pass_fail_count}")
+
+    # Display filtered dataframe
+    columns_to_display2 = ['Functional area', 'Scenario name', 'Error message_new', 'Time spent_old','Time spent_new',] 
+    st.write(pass_to_fail_scenarios[columns_to_display2])
+
+    # 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 New Passes section
+    st.markdown("### New Passes(previously failing, now passing)")
+    
+    # Get passing scenarios count
+    pass_count = len(fail_to_pass_scenarios)
+    st.write(f"Passing scenarios Count: {pass_count}")
+
+    # Display filtered dataframe
+    columns_to_display3 = ['Functional area', 'Scenario name', 'Error message_old', 'Time spent_old','Time spent_new',]
+    st.write(fail_to_pass_scenarios[columns_to_display3])
+
+
+    # Create a Pandas Excel writer using XlsxWriter as the engine
+    excel_writer = pd.ExcelWriter('comparison_results.xlsx', engine='xlsxwriter')
+    
+    # Write each section to a separate sheet
+    fail_to_fail_scenarios.loc[:, columns_to_display1].to_excel(excel_writer, sheet_name='Consistent Failures', index=False)
+    pass_to_fail_scenarios.loc[:, columns_to_display2].to_excel(excel_writer, sheet_name='New Failures', index=False)
+    fail_to_pass_scenarios.loc[:, columns_to_display3].to_excel(excel_writer, sheet_name='New Passes', index=False)
+
+
+
+    # Add a sheet to store information about CSV versions
+    csv_version_sheet = excel_writer.book.add_worksheet('CSV Details')
+    
+    # Write the CSV version information
+    csv_version_sheet.write('A1', 'Older CSV:')
+    csv_version_sheet.write('B1', 'Newer CSV:')
+    csv_version_sheet.write('A2', older_df['Start datetime'].min().strftime('%Y-%m-%d %H:%M:%S'))
+    csv_version_sheet.write('B2', newer_df['Start datetime'].min().strftime('%Y-%m-%d %H:%M:%S'))
+    
+    # Save the Excel file
+    excel_writer.save()
+
+# Create a Download Excel button
+    st.markdown("### Download Excel Report")
+    st.markdown("Click below to download the comparison results in Excel format:")
+    with open('comparison_results.xlsx', 'rb') as excel_file:
+        excel_bytes = excel_file.read()
+    st.download_button(label='Download Excel Report', data=excel_bytes, file_name='comparison_results.xlsx', key='excel-download')