import pandas as pd
import streamlit as st
import csv
import io
import matplotlib.pyplot as plt
import numpy as np

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


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

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

    if uploaded_file is not None:
        # Process the csv file
        column_names = ["Functional area", "Scenario name", "Start datetime", "End datetime", "Status", "Error message"]

        filet = uploaded_file.read()
        processed_output = preprocess_csv(filet)
        processed_file = io.StringIO(processed_output.getvalue())
        data = load_data(processed_file)

        data = fill_missing_data(data, 4, 0)
        data['Start datetime'] = pd.to_datetime(data['Start datetime'], errors='coerce')
        data['End datetime'] = pd.to_datetime(data['End datetime'], errors='coerce')
        data['Time spent'] = (data['End datetime'] - data['Start datetime']).dt.total_seconds()

        # st.write(data)

       # 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'])
         # 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
            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
        
        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:")
            
            # 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
            
            grouped_filtered_failed_scenarios = filtered_scenarios.groupby('Functional area')[['Scenario name', 'Error message']].apply(lambda x: x.reset_index(drop=True))
            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")
            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)

        else:
            st.write("### No scenarios with status 'failed' found.")  
    pass

def main():
    st.title('CSV Analyser')
    
    uploaded_file = st.file_uploader("Upload CSV file", type="csv")

    if uploaded_file is not None:
        single_main(uploaded_file)  # Load the main app when the file is uploaded
        
if __name__ == "__main__":
    main()