Upload 2 files

pre.py

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+import pandas as pd
+import streamlit as st
+import csv
+import io
+
+def preprocess_csv(input_bytes):
+    text = input_bytes.decode()  # Decode bytes to text
+    output = io.StringIO()
+    writer = csv.writer(output)
+
+    for row in csv.reader(io.StringIO(text)):  # Read text as csv
+        if len(row) > 5:
+            row = row[0:5] + [','.join(row[5:])]  # Combine extra fields into one
+        writer.writerow(row)
+
+    output.seek(0)  # go to the start of the StringIO object
+    return output
+
+def load_data(file):
+    column_names = [
+        'Functional area',
+        'Scenario name',
+        'Start datetime',
+        'End datetime',
+        'Status',
+        'Error message'
+    ]
+    data = pd.read_csv(file, header=None, names=column_names)
+    return data
+
+def fill_missing_data(data, column_index, value):
+    data.iloc[:, column_index] = data.iloc[:, column_index].fillna(value)
+    return data

second.py

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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 Failures(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