#!/usr/bin/env python3
"""
Generate a formatted data analysis report from database statistics.

Usage:
    python generate_report.py <table_name> [--output report.md]
"""

import argparse
import json
import sys
from datetime import datetime
from typing import Any


def format_number(n: int | float) -> str:
    """Format large numbers with commas."""
    if isinstance(n, float):
        return f"{n:,.2f}"
    return f"{n:,}"


def generate_report(table_name: str, stats: dict[str, Any]) -> str:
    """Generate a markdown report from table statistics."""
    
    report = f"""# Data Analysis Report: {table_name}

Generated: {datetime.now().strftime('%Y-%m-%d %H:%M')}

## Overview

- **Total Rows**: {format_number(stats.get('row_count', 0))}
- **Columns**: {len(stats.get('columns', []))}
- **Analysis Type**: Automated Data Profiling

## Column Summary

| Column | Type | Nulls | Unique | Notes |
|--------|------|-------|--------|-------|
"""
    
    for col in stats.get('columns', []):
        null_pct = col.get('null_pct', 0)
        null_badge = "⚠️ " if null_pct > 50 else ""
        unique_badge = "🎲 " if col.get('unique_ratio', 0) > 0.9 else ""
        
        notes = []
        if null_pct > 50:
            notes.append("High nulls")
        if col.get('unique_ratio', 0) > 0.9:
            notes.append("Near-unique")
        if col.get('is_date'):
            notes.append("Date range: {} to {}".format(
                col.get('min_date', '?'), col.get('max_date', '?')))
        
        note_str = ", ".join(notes) if notes else "-"
        
        report += f"| {col['name']} | {col['type']} | {null_pct:.1f}% {null_badge}| {format_number(col.get('unique', 0))} {unique_badge}| {note_str} |\n"
    
    # Data quality section
    report += "\n## Data Quality Assessment\n\n"
    
    issues = []
    warnings = []
    
    for col in stats.get('columns', []):
        if col.get('null_pct', 0) > 50:
            issues.append(f"- **{col['name']}**: {col['null_pct']:.1f}% null values")
        elif col.get('null_pct', 0) > 20:
            warnings.append(f"- **{col['name']}**: {col['null_pct']:.1f}% null values")
    
    if issues:
        report += "### ⚠️ Issues Found\n\n"
        report += "\n".join(issues) + "\n\n"
    
    if warnings:
        report += "### 📋 Warnings\n\n"
        report += "\n".join(warnings) + "\n\n"
    
    if not issues and not warnings:
        report += "✅ No major data quality issues detected.\n\n"
    
    # Recommendations
    report += """## Recommendations

1. **Review high-null columns** for data collection issues
2. **Check date ranges** are within expected bounds
3. **Validate unique constraints** on ID columns
4. **Consider indexing** frequently queried columns

---

*Report generated by PostgreSQL Analyzer Skill*
"""
    
    return report


def main():
    parser = argparse.ArgumentParser(description='Generate data analysis report')
    parser.add_argument('table_name', help='Table name analyzed')
    parser.add_argument('--stats', help='JSON file with statistics', default='-')
    parser.add_argument('--output', '-o', help='Output file', default='-')
    
    args = parser.parse_args()
    
    # Read stats
    if args.stats == '-':
        stats = json.load(sys.stdin)
    else:
        with open(args.stats) as f:
            stats = json.load(f)
    
    # Generate report
    report = generate_report(args.table_name, stats)
    
    # Output
    if args.output == '-':
        print(report)
    else:
        with open(args.output, 'w') as f:
            f.write(report)
        print(f"Report written to {args.output}")


if __name__ == '__main__':
    main()