Update multiple.py

multiple.py

@@ -49,31 +49,49 @@ def perform_analysis(uploaded_dataframes):
             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()
-            # Merge average_time_spent_seconds and start_datetime_group
+            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')
-            # Filter scenarios based on selected 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)']].apply(lambda x: x.reset_index(drop=True))
+                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['level_1'] = index
             grouped_filtered_scenarios.index = grouped_filtered_scenarios.index + 1
-            st.dataframe(grouped_filtered_scenarios)     
+            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("### Average Time Spent on Each Functional Area")
+            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_scenarios length is less than or equal to 400
+            # 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:")
@@ -114,14 +132,6 @@ def multiple_main():
         for uploaded_file in uploaded_files:
             # Preprocess the uploaded CSV file
             data = preprocess_uploaded_file(uploaded_file)
-
-            
-            # Extract environment name from filename
-            filename = uploaded_file.name
-            environment = filename.split('_Puppeteer')[0]
-            
-            # Add environment column to the dataframe
-            data['Environment'] = environment
             
             # Append the dataframe to the list
             uploaded_dataframes.append(data)