multiselect areas and chart update

multiselect functional areas and chart updated based on failing scenarios

app.py

@@ -1,7 +1,7 @@
 import pandas as pd
 import streamlit as st
 import csv
-import io
+import io, os
 import matplotlib.pyplot as plt
 import numpy as np
 from second import double_main
@@ -9,7 +9,6 @@ from pre import preprocess_csv, load_data, fill_missing_data
 
 
 
-
 def single_main(uploaded_file):  
     # st.title('Single CSV Analyzer')
 
@@ -33,7 +32,14 @@ def single_main(uploaded_file):
        # Display scenarios with status "failed" grouped by functional area
         failed_scenarios = data[data['Status'] == 'FAILED']
         passed_scenarios = data[data['Status'] == 'PASSED']
-        # selected_status = st.selectbox("Select a status", ['Failed', 'Passed'])
+
+        # Display total count of failures
+        fail_count = len(failed_scenarios)
+        st.markdown(f"Failing scenarios Count: {fail_count}")
+        # Display total count of Passing
+        pass_count = len(passed_scenarios)
+        st.markdown(f"Passing scenarios Count: {pass_count}")
+
          # Use radio buttons for selecting status
         selected_status = st.radio("Select a status", ['Failed', 'Passed'])
 
@@ -41,11 +47,9 @@ def single_main(uploaded_file):
         if selected_status == 'Failed':
             unique_areas = np.append(failed_scenarios['Functional area'].unique(), "All")
             selected_scenarios = failed_scenarios
-            selected_functional_area = st.selectbox("Select a functional area", unique_areas, index=len(unique_areas)-1)        
         elif selected_status == 'Passed':
             unique_areas = np.append(passed_scenarios['Functional area'].unique(), "All")
             selected_scenarios = passed_scenarios
-            selected_functional_area = st.selectbox("Select a functional area", unique_areas, index=len(unique_areas)-1)        
         else:  
             selected_scenarios = None
         
@@ -53,68 +57,76 @@ def single_main(uploaded_file):
             # st.write(f"Scenarios with status '{selected_status}' grouped by functional area:")
             st.markdown(f"### Scenarios with status '{selected_status}' grouped by functional area:")
             
-            # Handle the "All" option
-           # Filter scenarios based on selected functional area
-            if selected_functional_area != "All":
-                filtered_scenarios = selected_scenarios[selected_scenarios['Functional area'] == selected_functional_area]
-            else:
-                filtered_scenarios = selected_scenarios
-
-             # Calculate the average time spent for each functional area
-            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
-            average_time_spent_seconds = average_time_spent_seconds.merge(start_datetime_group, on='Functional area')
-
-            # Filter scenarios based on selected functional area
-            if selected_status == 'Failed':
-                grouped_filtered_failed_scenarios = filtered_scenarios.groupby('Functional area')[['Scenario name', 'Error message','Time spent(m:s)']].apply(lambda x: x.reset_index(drop=True))
-            elif selected_status == 'Passed':
-                grouped_filtered_failed_scenarios = filtered_scenarios.groupby('Functional area')[['Scenario name', 'Time spent(m:s)']].apply(lambda x: x.reset_index(drop=True))
-            else:  
-                grouped_filtered_failed_scenarios = None
-           
-            
-            grouped_filtered_failed_scenarios.reset_index(inplace=True)
-            grouped_filtered_failed_scenarios.drop(columns=['level_1'], inplace=True)
-            # grouped_filtered_failed_scenarios['level_1'] = index
-            grouped_filtered_failed_scenarios.index = grouped_filtered_failed_scenarios.index + 1
+            # Select a range of functional areas to filter scenarios
+            selected_functional_areas = st.multiselect("Select functional areas", unique_areas, ["All"])
            
-            st.dataframe(grouped_filtered_failed_scenarios)     
-        
-         # Display total count of failures
-            fail_count = len(failed_scenarios)
-            st.write(f"Failing scenarios Count: {fail_count}")
-        # Display total count of Passing
-            pass_count = len(passed_scenarios)
-            st.write(f"Passing scenarios Count: {pass_count}")
-
-            # 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")
-            average_time_spent_seconds.index = average_time_spent_seconds.index + 1
-            st.dataframe(average_time_spent_seconds)
-
-        # Create and display bar graph of errors by functional area
-            st.write("### Bar graph showing number of failures in each functional area:")
-            error_counts = failed_scenarios['Functional area'].value_counts()
-            plt.figure(figsize=(10, 6))
-            plt.bar(error_counts.index, error_counts.values)
-            plt.xlabel('Functional Area')
-            plt.ylabel('Number of Errors')
-            plt.title('Number of Errors by Functional Area')
-            plt.xticks(rotation=45, ha='right')
-            plt.tight_layout()  # Add this line to adjust layout
-            st.pyplot(plt)
+            if "All" in selected_functional_areas:
+                filtered_scenarios = selected_scenarios
+            else:
+                filtered_scenarios = selected_scenarios[selected_scenarios['Functional area'].isin(selected_functional_areas)]
             
+            if not selected_functional_areas:  # Check if the list is empty
+                st.error("Please select at least one functional area.")
+            else:
+             # Calculate the average time spent for each functional area
+                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
+                average_time_spent_seconds = average_time_spent_seconds.merge(start_datetime_group, on='Functional area')
+
+                # Filter scenarios based on selected functional area
+                if selected_status == 'Failed':
+                    grouped_filtered_scenarios = filtered_scenarios.groupby('Functional area')[['Scenario name', 'Error message','Time spent(m:s)']].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)     
+
+                # 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")
+                average_time_spent_seconds.index = average_time_spent_seconds.index + 1
+                st.dataframe(average_time_spent_seconds)
+
+                # Check if selected_status is 'Failed' and grouped_filtered_scenarios length is less than or equal to 400
+                if selected_status != 'Passed' and len(grouped_filtered_scenarios) <= 400:
+                    # 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:")
+                    error_counts = grouped_filtered_scenarios['Functional area'].value_counts()
+                    plt.figure(figsize=(10, 6))
+                    plt.bar(error_counts.index, error_counts.values)
+                    plt.xlabel('Functional Area')
+                    plt.ylabel('Number of Failures')
+                    plt.title(f"Number of '{selected_status}' scenarios by Functional Area")
+                    plt.xticks(rotation=45, ha='right')
+
+                    # Set y-axis limits and ticks for consistent interval of 1
+                    y_max = max(error_counts.values) + 1
+                    plt.ylim(0, y_max)
+                    plt.yticks(range(0, y_max, 1))
+
+                    # Display individual numbers on y-axis
+                    for i, count in enumerate(error_counts.values):
+                        plt.text(i, count, str(count), ha='center', va='bottom')
+
+                    plt.tight_layout()  # Add this line to adjust layout
+                    st.pyplot(plt)      
         else:
             st.write("### No scenarios with status 'failed' found.")  
     pass