app.py

import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
import numpy as np
from second import double_main
from multiple import multiple_main
from pre import preprocess_uploaded_file, add_app_description



def single_main(uploaded_file):  
    # st.title('multi CSV Analyzer')

    # uploaded_file = st.file_uploader("Upload CSV file", type="csv")

    if uploaded_file is not None:
       # Process the csv files with header
        data = preprocess_uploaded_file(uploaded_file)
        # st.write(data)

       # Display scenarios with status "failed" grouped by functional area
        failed_scenarios = data[data['Status'] == 'FAILED']
        passed_scenarios = data[data['Status'] == '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'])

        # Determine which scenarios to display based on selected status
        if selected_status == 'Failed':
            unique_areas = np.append(failed_scenarios['Functional area'].unique(), "All")
            selected_scenarios = failed_scenarios
        elif selected_status == 'Passed':
            unique_areas = np.append(passed_scenarios['Functional area'].unique(), "All")
            selected_scenarios = passed_scenarios
        else:  
            selected_scenarios = None
        
        if selected_scenarios is not None:
            # st.write(f"Scenarios with status '{selected_status}' grouped by functional area:")
            st.markdown(f"### Scenarios with status '{selected_status}' grouped by functional area:")
            
            # Select a range of functional areas to filter scenarios
            selected_functional_areas = st.multiselect("Select functional areas", unique_areas, ["All"])
           
            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

def main():
    add_app_description()

    # Initially we are in multi file processing mode
    if "mode" not in st.session_state:
        st.session_state["mode"] = "multi"

    mode_display = f'## Current mode: {st.session_state["mode"].title()} mode'
    st.sidebar.markdown(mode_display)

    # Add a button to switch between modes
    btn_label = "Switch to Compare mode" if st.session_state["mode"] == "multi" else "Switch to Multi mode"
    if st.sidebar.button(btn_label):
        if st.session_state["mode"] == "multi":
            st.session_state["mode"] = "compare"
        else:
            st.session_state["mode"] = "multi"

    # Only show the second file uploader in compare mode
    if st.session_state["mode"] == "multi":
        multiple_main()
    else:
        uploaded_file_1 = st.sidebar.file_uploader("Upload CSV file 1", type="csv")
        uploaded_file_2 = st.sidebar.file_uploader("Upload CSV file 2", type="csv")
        if uploaded_file_1 is not None and uploaded_file_2 is not None:
            double_main(uploaded_file_1, uploaded_file_2)

if __name__ == "__main__":
    main()