Update second.py

second.py

@@ -4,35 +4,16 @@ import csv
 import io
 import matplotlib.pyplot as plt
 import numpy as np
-from pre import preprocess_csv, load_data, fill_missing_data
+from pre import preprocess_uploaded_file
 
 
 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
+        # 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
@@ -77,7 +58,7 @@ def double_main(uploaded_file1,uploaded_file2):
         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)")
+        st.markdown("### New Failures(previously failing, now passing)")
         pass_count = len(fail_to_pass_scenarios)
         st.write(f"Passing scenarios Count: {pass_count}")
         # Select columns for display