Create app.py

app.py

@@ -0,0 +1,146 @@
+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()