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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
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()
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