multiple.py

import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
import numpy as np
from pre import preprocess_uploaded_file
from jira_integration import (
    render_jira_login, 
    get_current_sprint, 
    get_regression_board,
    get_sprint_issues,
    calculate_points,
    create_regression_task,
    generate_task_content,
    calculate_story_points,
    get_project_metadata,
    get_field_dependencies,
    get_dependent_field_value,
    get_boards,
    get_functional_area_values,
    map_functional_area,
    get_customer_field_values,
    map_customer_value
)
from datetime import datetime, timedelta
import plotly.express as px
import plotly.graph_objects as go
import os
from dotenv import load_dotenv
import json
import logging
load_dotenv()
JIRA_SERVER = os.getenv("JIRA_SERVER")
# Initialize session state variables
if 'filtered_scenarios_df' not in st.session_state:
    st.session_state.filtered_scenarios_df = None
if 'task_content' not in st.session_state:
    st.session_state.task_content = None
if 'total_story_points' not in st.session_state:
    st.session_state.total_story_points = 0
if 'completed_points' not in st.session_state:
    st.session_state.completed_points = 0
if 'current_page' not in st.session_state:
    st.session_state.current_page = "analysis"
if 'task_df' not in st.session_state:
    st.session_state.task_df = None
if 'task_environment' not in st.session_state:
    st.session_state.task_environment = None
if 'last_task_key' not in st.session_state:
    st.session_state.last_task_key = None
if 'last_task_url' not in st.session_state:
    st.session_state.last_task_url = None
if 'show_success' not in st.session_state:
    st.session_state.show_success = False

# Get logger from jira_integration
logger = logging.getLogger("multiple")

# Function to capture button clicks with manual callback
def handle_task_button_click(summary, description, formatted_env, filtered_df):
    logger.info("=== Task button clicked - Starting debug logging ===")
    try:
        logger.info(f"Summary: {summary}")
        logger.info(f"Description length: {len(description)}")
        logger.info(f"Environment: {formatted_env}")
        logger.info(f"DataFrame shape: {filtered_df.shape}")
        
        # Get metadata for field values
        metadata = get_project_metadata("RS")
        if not metadata:
            logger.error("Could not get project metadata")
            return False
            
        # Check if this is an ILR environment
        is_ilr_environment = any(env in formatted_env.upper() if formatted_env else False 
                               for env in ["LEGAL_WISE_NR", "LIFE_WISE_NR", "SCORPION_NR", "TALKSURE"])
        
        # Extract functional area from filtered scenarios
        functional_areas = []
        if "Functional area" in filtered_df.columns:
            functional_areas = filtered_df["Functional area"].unique().tolist()
            logger.info(f"Extracted functional areas: {functional_areas}")
        
        # Map functional area using metadata
        functional_area_parent = "ILR" if is_ilr_environment else "R&I"
        functional_area_child = None
        
        # Set child value based on environment for ILR
        if is_ilr_environment:
            if "LEGAL_WISE_NR" in formatted_env.upper():
                functional_area_child = "LEZA - LegalWise"
            elif "LIFE_WISE_NR" in formatted_env.upper():
                functional_area_child = "LEZA - LifeWise"
            elif "SCORPION_NR" in formatted_env.upper():
                functional_area_child = "LEZA - Scorpion"
            elif "TALKSURE" in formatted_env.upper():
                functional_area_child = "Talksure"
        else:
            # Use standard R&I mapping
            _, functional_area_child = map_functional_area(
                functional_areas[0] if functional_areas else "Data Exchange",
                metadata
            )
        
        logger.info(f"Mapped functional area to parent: {functional_area_parent}, child: {functional_area_child}")
        
        # Get customer field values and map environment
        customer_values = get_customer_field_values(metadata)
        parent_value, child_value = map_customer_value(formatted_env, customer_values)
        logger.info(f"Mapped customer values - Parent: {parent_value}, Child: {child_value}")
        
        # Calculate story points based on number of scenarios
        story_points = calculate_story_points(len(filtered_df))
        logger.info(f"Calculated story points: {story_points}")
        
        # Prepare issue dictionary with all required fields
        issue_dict = {
            "project": {"key": "RS"},
            "summary": summary,
            "description": description,
            "issuetype": {"name": "Story"},
            "components": [{"name": "Maintenance (Regression)"}],
            "customfield_10427": {
                "value": parent_value,
                "child": {
                    "value": child_value
                }
            },
            "customfield_12730": {"value": "Non-Business Critical"},  # Regression Type field
            "customfield_13430": {"value": str(len(filtered_df))},  # Number of Scenarios
            "customfield_13100": {
                "value": functional_area_parent,
                "child": {
                    "value": functional_area_child
                }
            },
            "assignee": {"name": st.session_state.jira_username},
            "customfield_10002": story_points  # Story Points field
        }
        
        # Log the complete issue dictionary
        logger.info("=== Task Creation Values ===")
        logger.info(f"Complete issue dictionary: {json.dumps(issue_dict, indent=2)}")
        
        # Create the actual Jira task
        task_key = create_regression_task(
            summary=summary, 
            description=description, 
            environment=formatted_env, 
            filtered_scenarios_df=filtered_df,
            project_key="RS"
        )
        
        if task_key:
            # Set session state variables for success message
            st.session_state.last_task_key = task_key
            st.session_state.last_task_url = f"{JIRA_SERVER}/browse/{task_key}"
            st.session_state.show_success = True
            logger.info(f"Successfully created task: {task_key}")
            return True
        else:
            st.error("❌ Failed to create Jira task. Check logs for details.")
            return False
                
    except Exception as e:
        logger.exception(f"Error in handle_task_button_click: {str(e)}")
        st.error(f"❌ Error preparing task: {str(e)}")
        import traceback
        error_trace = traceback.format_exc()
        logger.error(f"Full traceback: {error_trace}")
        st.error(error_trace)
        return False
    finally:
        logger.info("=== Ending handle_task_button_click function ===")

# 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 debugging information
    # st.write("Combined data shape:", combined_data.shape)
    # st.write("Unique functional areas in combined data:", combined_data['Functional area'].nunique())
    # st.write("Sample of combined data:", combined_data.head())

    # 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.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:
            # Display count of filtered scenarios
            st.write(f"Number of filtered scenarios: {len(filtered_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()
            end_datetime_group = filtered_scenarios.groupby('Functional area')['End datetime'].max().reset_index()
           
            # Calculate the total time spent for each functional area (difference between end and start datetime)
            total_time_spent_seconds = (end_datetime_group['End datetime'] - start_datetime_group['Start datetime']).dt.total_seconds()

            # Convert total time spent from seconds to minutes and seconds format
            total_time_spent_seconds = pd.to_datetime(total_time_spent_seconds, unit='s').dt.strftime('%M:%S')

           # Merge the average_time_spent_seconds with start_datetime_group and end_datetime_group
            average_time_spent_seconds = average_time_spent_seconds.merge(start_datetime_group, on='Functional area')
            average_time_spent_seconds = average_time_spent_seconds.merge(end_datetime_group, on='Functional area')   
            average_time_spent_seconds['Total Time Spent'] = total_time_spent_seconds

            
             # Filter scenarios based on selected functional area
            if selected_status == 'Failed':
                # Define columns in the exact order they appear in the table
                columns_to_keep = [
                    'Environment',
                    'Functional area',
                    'Scenario Name',
                    'Error Message',
                    'Failed Step',
                    'Time spent(m:s)',
                    'Start datetime'
                ]
                # Check if Failed Step column exists
                if 'Failed Step' in filtered_scenarios.columns:
                    grouped_filtered_scenarios = filtered_scenarios[columns_to_keep].copy()
                else:
                    columns_to_keep.remove('Failed Step')
                    grouped_filtered_scenarios = filtered_scenarios[columns_to_keep].copy()
            elif selected_status == 'Passed':
                grouped_filtered_scenarios = filtered_scenarios[[
                    'Environment',
                    'Functional area',
                    'Scenario Name',
                    'Time spent(m:s)'
                ]].copy()
            else:  
                grouped_filtered_scenarios = None
            
            # Only proceed if we have data
            if grouped_filtered_scenarios is not None:
                # Reset the index to start from 1
                grouped_filtered_scenarios.index = range(1, len(grouped_filtered_scenarios) + 1)
                st.dataframe(grouped_filtered_scenarios)

            # Show task creation button if:
            # 1. User is authenticated
            # 2. Status is Failed
            # 3. Exactly one functional area is selected (not "All")
            if ('jira_client' in st.session_state and 
                st.session_state.jira_client and 
                selected_status == 'Failed' and 
                len(selected_functional_areas) == 1 and 
                "All" not in selected_functional_areas):
                
                # If we have a recently created task, show the success message first
                if st.session_state.show_success and st.session_state.last_task_key:
                    st.success("✅ Task created successfully!")
                    
                    # Display task link in a more prominent way
                    st.markdown(
                        f"""
                        <div style='padding: 10px; border-radius: 5px; border: 1px solid #90EE90; margin: 10px 0;'>
                            <h3 style='margin: 0; color: #90EE90;'>Task Details</h3>
                            <p style='margin: 10px 0;'>Task Key: {st.session_state.last_task_key}</p>
                            <a href='{st.session_state.last_task_url}' target='_blank' 
                               style='background-color: #90EE90; color: black; padding: 5px 10px; 
                                      border-radius: 3px; text-decoration: none; display: inline-block;'>
                                View Task in Jira
                            </a>
                        </div>
                        """,
                        unsafe_allow_html=True
                    )
                    
                    # Add a button to create another task
                    col1, col2, col3 = st.columns([1, 2, 1])
                    with col2:
                        if st.button("Create Another Task", key="create_another", use_container_width=True):
                            # Clear all task-related state
                            st.session_state.task_content = None
                            st.session_state.last_task_key = None
                            st.session_state.last_task_url = None
                            st.session_state.show_success = False
                            st.rerun()
                else:
                    environment = filtered_scenarios['Environment'].iloc[0]
                    # Create columns for compact layout
                    col1, col2, col3 = st.columns([1, 2, 1])
                    with col2:
                        if st.button("📝 Log Jira Task", use_container_width=True):
                            # Use the properly structured DataFrame for task creation
                            task_df = grouped_filtered_scenarios.copy()
                            expected_columns = [
                                'Environment',
                                'Functional area',
                                'Scenario Name',
                                'Error Message',
                                'Failed Step',
                                'Time spent(m:s)',
                                'Start datetime'
                            ]
                            missing_columns = [col for col in expected_columns if col not in task_df.columns]
                            if missing_columns:
                                st.error(f"Missing required columns: {', '.join(missing_columns)}")
                                st.error("Please ensure your data includes all required columns")
                                return
                            
                            # Generate task content
                            summary, description = generate_task_content(task_df)
                            if summary and description:
                                # Call the task creation function
                                success = handle_task_button_click(summary, description, environment, task_df)
                                if success:
                                    st.rerun()  # Refresh the page to show success message

            # Check if selected_status is 'Failed' and show bar graph
            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()
                
                # Only create the graph if there are errors to display
                if not error_counts.empty:
                    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)
                else:
                    st.info(f"No '{selected_status}' scenarios found to display in the graph.")
    pass

def display_story_points_stats(force_refresh=False):
    """Display story points statistics from current sprint"""
    if not st.session_state.jira_client:
        return
        
    try:
        with st.spinner("Fetching sprint data..."):
            # Get regression board
            board = get_regression_board("RS")
            if not board:
                return
            
            # Get current sprint
            sprint = get_current_sprint(board['id'])
            if not sprint:
                return
            
            # Get sprint issues
            issues = get_sprint_issues(board['id'], sprint.id, board['estimation_field'])
            if not issues:
                return
            
            # Calculate points
            issues_data, total_points, completed_points, in_progress_points = calculate_points(issues, board['estimation_field'])
            
            # Update session state
            st.session_state.total_story_points = total_points
            st.session_state.completed_points = completed_points
            
            # Create compact metrics display
            metrics_container = st.container()
            with metrics_container:
                # Show sprint info
                st.info(f"Current Sprint: {sprint.name}")
                
                # Show metrics in a compact format
                cols = st.columns(4)
                with cols[0]:
                    st.metric("Total", f"{total_points:.1f}")
                with cols[1]:
                    st.metric("Done", f"{completed_points:.1f}")
                with cols[2]:
                    st.metric("In Progress", f"{in_progress_points:.1f}")
                with cols[3]:
                    completion_rate = (completed_points / total_points * 100) if total_points > 0 else 0
                    st.metric("Complete", f"{completion_rate:.1f}%")
                
                # Show progress bar
                progress = completed_points / total_points if total_points > 0 else 0
                st.progress(progress)
                
                # Add refresh button
                if st.button("🔄 Refresh", key="refresh_stats", use_container_width=True):
                    st.session_state.last_refresh = datetime.now()
                    return
    except Exception as e:
        st.error(f"Error updating story points: {str(e)}")

def show_task_creation_section(filtered_df, environment):
    """Display the task creation section with detailed functional area mapping information."""
    
    if "Functional area" in filtered_df.columns and len(filtered_df) > 0:
        functional_areas = filtered_df["Functional area"].unique().tolist()
        functional_area = functional_areas[0] if functional_areas else None
        logger.debug(f"Found functional areas: {functional_areas}")
        
        # Get project metadata to access allowed values
        metadata = get_project_metadata("RS")
        if metadata:
            # Create expandable section for field structure
            with st.expander("Functional Area Field Structure", expanded=False):
                func_field = metadata['all_fields'].get('customfield_13100', {})
                if func_field and 'allowedValues' in func_field:
                    st.write("Available parent-child mappings:")
                    for parent in func_field['allowedValues']:
                        if isinstance(parent, dict):
                            parent_value = parent.get('value', 'Unknown')
                            st.markdown(f"**Parent: {parent_value}**")
                            if 'cascadingOptions' in parent:
                                child_values = [child.get('value') for child in parent['cascadingOptions'] if child.get('value')]
                                st.write("Child options:")
                                for child in sorted(child_values):
                                    st.write(f"  • {child}")
                                st.write("")
            
            # Display current functional area and mapping attempt
            st.subheader("Functional Area Mapping")
            col1, col2 = st.columns(2)
            
            with col1:
                st.markdown("**Input Functional Area:**")
                st.info(functional_area)
                
                st.markdown("**Split Parts:**")
                parts = functional_area.split(' - ')
                for i, part in enumerate(parts, 1):
                    st.write(f"{i}. {part}")
            
            with col2:
                # Try to map the functional area
                parent, child = map_functional_area(functional_area, metadata)
                st.markdown("**Mapped Values:**")
                st.success(f"Parent: {parent}")
                st.success(f"Child: {child}")
                
                # Show normalized form
                st.markdown("**Normalized Form:**")
                norm_area = functional_area.lower().replace(' ', '-')
                st.info(norm_area)
            
            # Add warning if using default mapping
            if parent == "R&I" and child == "Data Exchange" and functional_area.lower() != "data exchange":
                st.warning("""
                ⚠️ Using default mapping (R&I/Data Exchange). This might not be the best match.
                Please check the 'Functional Area Field Structure' above for available values.
                """)
    else:
        logger.warning("No functional area found in data")
        st.warning("No functional area information found in the data")
    
    # Create task button
    if st.button("Create Task", key="create_task_button"):
        handle_task_button_click(filtered_df, environment)

def multiple_main():
    # Initialize session state variables
    if 'current_page' not in st.session_state:
        st.session_state.current_page = "upload"
    if 'task_df' not in st.session_state:
        st.session_state.task_df = None
    if 'selected_files' not in st.session_state:
        st.session_state.selected_files = []
    if 'uploaded_files' not in st.session_state:
        st.session_state.uploaded_files = []
    if 'filtered_scenarios_df' not in st.session_state:
        st.session_state.filtered_scenarios_df = None
    if 'show_success' not in st.session_state:
        st.session_state.show_success = False

    if 'jira_server' not in st.session_state:
        st.session_state.jira_server = JIRA_SERVER
    # Initialize session state for sprint data if not exists
    if 'sprint_data_initialized' not in st.session_state:
        st.session_state.sprint_data_initialized = False
    
    # Add Jira login to sidebar (only once)
    with st.sidebar:
        st.subheader("Jira Integration (Optional)")
        
        # Only render login if not already authenticated
        if 'is_authenticated' not in st.session_state:
            st.session_state.is_authenticated = render_jira_login()
        else:
            # Just display the status without re-rendering the login
            if st.session_state.is_authenticated:
                st.success("Connected to Jira")
            else:
                # Allow re-login if not authenticated
                st.session_state.is_authenticated = render_jira_login()
        
        # Only show story points in sidebar if authenticated
        if st.session_state.is_authenticated and st.session_state.jira_client:
            st.markdown("---")
            st.subheader("Sprint Progress")
            
            # Only fetch sprint data once or when refresh is clicked
            if not st.session_state.sprint_data_initialized:
                display_story_points_stats(force_refresh=True)
                st.session_state.sprint_data_initialized = True
            else:
                display_story_points_stats(force_refresh=False)
    
    
    
    # Initialize session state for uploaded data
    if 'uploaded_data' not in st.session_state:
        st.session_state.uploaded_data = None
    if 'last_refresh' not in st.session_state:
        st.session_state.last_refresh = None
    
    # Check if we're in task creation mode
    if st.session_state.current_page == "create_task" and st.session_state.task_df is not None:
        # Add a back button
        if st.button("⬅️ Back to Analysis"):
            st.session_state.current_page = "analysis"
            st.rerun()
            return
        
        # Show task creation section
        show_task_creation_section(st.session_state.task_df, st.session_state.task_environment)
        return
    
    # Main analysis page
    uploaded_files = st.file_uploader("Upload CSV or Excel files", 
                                    type=['csv', 'xlsx'], 
                                    accept_multiple_files=True)
    
    # Process uploaded files and store in session state
    if uploaded_files:
        all_data = []
        for file in uploaded_files:
            try:
                df = preprocess_uploaded_file(file)
                all_data.append(df)
            except Exception as e:
                st.error(f"Error processing {file.name}: {str(e)}")
        
        if all_data:
            # Store the processed data in session state
            st.session_state.uploaded_data = all_data
    
    # Use data from session state for analysis
    if st.session_state.uploaded_data:
        # Perform analysis for uploaded data
        perform_analysis(st.session_state.uploaded_data)
        
        # Get combined data for Jira integration
        combined_df = pd.concat(st.session_state.uploaded_data, ignore_index=True)
        
          
    else:
        st.write("Please upload at least one file.")

if __name__ == "__main__":
    st.set_page_config(layout="wide")
    multiple_main()