Create multiple.py

multiple.py

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+import pandas as pd
+import streamlit as st
+import matplotlib.pyplot as plt
+import numpy as np
+from pre import preprocess_uploaded_file
+
+# Define the function to perform analysis
+def perform_analysis(uploaded_dataframes):
+    # Concatenate all dataframes into a single dataframe
+    combined_data = pd.concat(uploaded_dataframes, ignore_index=True)
+
+    # Display scenarios with status "failed" grouped by functional area
+    failed_scenarios = combined_data[combined_data['Status'] == 'FAILED']
+    passed_scenarios = combined_data[combined_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('Environment')[['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':
+                # 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=(12, 10))
+                bars = 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', fontsize=10)
+                # 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), fontsize=10)
+                
+                # Display individual numbers on y-axis
+                for bar in bars:
+                    height = bar.get_height()
+                    plt.text(bar.get_x() + bar.get_width() / 2, height, str(int(height)),
+                             ha='center', va='bottom')  # Reduce font size of individual numbers
+
+                plt.tight_layout()  # Add this line to adjust layout
+                st.pyplot(plt)      
+    pass
+
+def multiple_main():
+
+    # Get the number of environments from the user
+    num_environments = st.number_input("Enter the number of environments", min_value=1, value=1, step=1)
+
+    # Initialize list to store uploaded dataframes
+    uploaded_dataframes = []
+
+    # Loop through the number of environments and create file uploaders
+    for i in range(num_environments):
+        uploaded_files = st.file_uploader(f"Upload CSV files for Environment {i + 1}", type="csv", accept_multiple_files=True)
+        
+        for uploaded_file in uploaded_files:
+            # Preprocess the uploaded CSV file
+            data = preprocess_uploaded_file(uploaded_file)
+
+            
+            # Extract environment name from filename
+            filename = uploaded_file.name
+            environment = filename.split('_Puppeteer')[0]
+            
+            # Add environment column to the dataframe
+            data['Environment'] = environment
+            
+            # Append the dataframe to the list
+            uploaded_dataframes.append(data)
+    
+    # Check if any files were uploaded
+    if uploaded_dataframes:
+        # Perform analysis for uploaded data
+        perform_analysis(uploaded_dataframes)
+    else:
+        st.write("Please upload at least one CSV file.")
+
+pass