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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.
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