REVENUE EXCEL REPORT GENERATION TASK === YOUR MISSION === Create a professional Excel report from arranged_financial_data.json focusing ONLY on revenue data. Generate a business-ready revenue analysis report with 100% success rate. You are using gemini-2.5-flash with thinking budget optimization and RestrictedPythonTools for automatic path correction and package management. === WHAT TO CREATE === • Professional Excel file with revenue-focused worksheets • Clean, business-ready formatting for executives • Focus exclusively on revenue analysis and visualization • File ready for immediate business use === MANDATORY EXECUTION SEQUENCE === **STEP 1: Environment Setup (30 seconds)** ```python # RestrictedPythonTools automatically installs packages when needed # Just use run_python_code() - packages will be auto-installed import pandas as pd import openpyxl print("Packages will be auto-installed by RestrictedPythonTools") ``` **STEP 2: Revenue Data Loading (30 seconds)** - read_file('arranged_financial_data.json') - Parse and validate revenue data structure - Count revenue categories and data points - Log: "Revenue data loaded: X categories, Y revenue points" **STEP 3: Revenue Excel Script Creation (3 minutes)** Create 'generate_revenue_report.py' with this EXACT structure: ```python #!/usr/bin/env python3 import os import sys import json import pandas as pd from openpyxl import Workbook from openpyxl.styles import Font, PatternFill, Border, Side, Alignment from datetime import datetime import logging # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) def main(): try: # Load revenue data logger.info('Loading revenue data from arranged_financial_data.json') with open('arranged_financial_data.json', 'r', encoding='utf-8') as f: revenue_data = json.load(f) # Create professional workbook logger.info('Creating revenue analysis workbook') wb = Workbook() wb.remove(wb.active) # Remove default sheet # Define professional styling header_font = Font(bold=True, color='FFFFFF', size=12) header_fill = PatternFill(start_color='1F4E79', end_color='1F4E79', fill_type='solid') data_font = Font(size=11) # Process each revenue category revenue_categories = ['Company_Overview', 'Total_Revenue', 'Segment_Revenue', 'Regional_Revenue', 'Data_Quality'] for category_name in revenue_categories: if category_name in revenue_data: logger.info(f'Creating worksheet: {category_name}') category_data = revenue_data[category_name] ws = wb.create_sheet(title=category_name) # Add professional headers headers = ['Revenue Item', 'Amount', 'Currency/Unit', 'Period', 'Confidence Score'] for col, header in enumerate(headers, 1): cell = ws.cell(row=1, column=col, value=header) cell.font = header_font cell.fill = header_fill cell.alignment = Alignment(horizontal='center', vertical='center') # Add revenue data data_rows = category_data.get('data', []) for row_idx, data_row in enumerate(data_rows, 2): ws.cell(row=row_idx, column=1, value=data_row.get('item', '')).font = data_font ws.cell(row=row_idx, column=2, value=data_row.get('value', '')).font = data_font ws.cell(row=row_idx, column=3, value=data_row.get('unit', '')).font = data_font ws.cell(row=row_idx, column=4, value=data_row.get('period', '')).font = data_font ws.cell(row=row_idx, column=5, value=data_row.get('confidence', '')).font = data_font # Auto-size columns for professional appearance for column in ws.columns: max_length = 0 column_letter = column[0].column_letter for cell in column: try: if len(str(cell.value or '')) > max_length: max_length = len(str(cell.value or '')) except: pass adjusted_width = min(max(max_length + 2, 15), 50) ws.column_dimensions[column_letter].width = adjusted_width # Add borders for professional look thin_border = Border( left=Side(style='thin'), right=Side(style='thin'), top=Side(style='thin'), bottom=Side(style='thin') ) for row in ws.iter_rows(min_row=1, max_row=len(data_rows)+1, min_col=1, max_col=5): for cell in row: cell.border = thin_border # Save with professional filename timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') filename = f'Revenue_Analysis_Report_{timestamp}.xlsx' wb.save(filename) logger.info(f'Revenue report saved as: {filename}') # Verify file creation and quality if os.path.exists(filename): file_size = os.path.getsize(filename) if file_size > 5000: # Minimum 5KB logger.info(f'SUCCESS: Revenue report created successfully') logger.info(f'File: {filename} ({file_size:,} bytes)') logger.info(f'Worksheets: {len(wb.sheetnames)}') print(f'REVENUE_REPORT_SUCCESS: {filename}') return filename else: raise Exception(f'File too small ({file_size} bytes), likely corrupted') else: raise Exception('Excel file was not created') except FileNotFoundError as e: logger.error(f'Revenue data file not found: {str(e)}') sys.exit(1) except json.JSONDecodeError as e: logger.error(f'Invalid JSON in revenue data: {str(e)}') sys.exit(1) except Exception as e: logger.error(f'Error creating revenue report: {str(e)}') import traceback logger.error(f'Traceback: {traceback.format_exc()}') sys.exit(1) if __name__ == '__main__': result = main() print(f'COMPLETED: {result}') ``` **STEP 4: Script Execution with RestrictedPythonTools (2 minutes)** - Use run_python_code([complete_script]) for direct execution with auto-healing - OR save_python_file('generate_revenue_report.py', [complete_script]) + run_shell_command('python generate_revenue_report.py') - RestrictedPythonTools automatically handles path correction and directory constraints - Automatic package installation and error recovery built-in - If execution fails, RestrictedPythonTools will attempt automatic recovery **STEP 5: Excel File Verification (CRITICAL - 30 seconds)** - list_files() to check if Excel file exists in directory - If Excel file NOT found in list_files(), retry script execution immediately - run_shell_command('ls -la *Revenue*.xlsx') for detailed file info - run_shell_command('du -h *Revenue*.xlsx') to verify file size > 5KB - NEVER report success without Excel file confirmed in list_files() === REVENUE REPORT SPECIFICATIONS === **File Structure:** - Filename: Revenue_Analysis_Report_YYYYMMDD_HHMMSS.xlsx - 5 worksheets focusing exclusively on revenue data - Professional corporate formatting throughout **Worksheet Details:** 1. **Company_Overview** - Company info, document metadata 2. **Total_Revenue** - Consolidated revenue figures and totals 3. **Segment_Revenue** - Revenue by business segment/division 4. **Regional_Revenue** - Revenue by geographic region 5. **Data_Quality** - Confidence scores and data validation **Professional Formatting:** - Headers: Bold white text on navy blue background (#1F4E79) - Data: Clean 11pt font with professional alignment - Borders: Thin borders around all data cells - Columns: Auto-sized for optimal readability (15-50 characters) - Layout: Business-ready presentation format === ERROR HANDLING PROCEDURES === **Package Installation Issues:** - Try: pip install --user openpyxl pandas - Try: python3 -m pip install openpyxl pandas - Try: pip install --no-cache-dir openpyxl **Revenue Data Loading Issues:** - Verify arranged_financial_data.json exists - Check JSON syntax and structure - Ensure revenue categories are present **Excel Generation Issues:** - Log exact openpyxl error messages - Try simplified formatting if complex formatting fails - Check file write permissions in directory - Verify Python version compatibility **File Verification Issues:** - Check file exists and has reasonable size (>5KB) - Verify Excel file can be opened without corruption - Confirm all expected worksheets are present === SUCCESS CRITERIA === Revenue Excel generation is successful ONLY if: ✓ openpyxl package installed without errors ✓ Revenue data loaded and parsed successfully ✓ Python script executed without errors ✓ Excel file created with proper filename format ✓ File size > 5KB indicating data was written ✓ All 5 revenue worksheets present and populated ✓ Professional formatting applied consistently ✓ File opens without corruption in Excel === PROFESSIONAL FEATURES === Your Excel report MUST include: - **Corporate Design**: Professional navy blue headers with white text - **Business Layout**: Clean, executive-ready formatting - **Data Integrity**: All original revenue values preserved exactly - **User Experience**: Auto-sized columns, proper alignment, clear borders - **File Management**: Timestamped filename for version control - **Quality Assurance**: Comprehensive error handling and validation === FINAL VALIDATION CHECKLIST === Before reporting success, verify: □ All required packages installed successfully □ Revenue data JSON loaded and parsed correctly □ Python script saved and executed without errors □ Excel file created with timestamped filename □ File size indicates successful data population (>5KB) □ All 5 revenue worksheets present and properly named □ Revenue data populated correctly in each worksheet □ Professional formatting applied consistently □ No execution errors or warnings in output □ File can be opened by Excel applications Execute now. Focus EXCLUSIVELY on revenue data visualization. Create a professional, publication-ready revenue analysis report for business executives.