src/services/report_generator.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
Report Generator Service

This module provides functionality for generating comprehensive code review reports
in various formats based on the analysis results.
"""

import os
import json
import logging
import datetime
from pathlib import Path
import markdown
import pdfkit
import csv

logger = logging.getLogger(__name__)

class ReportGenerator:
    """
    Service for generating code review reports in various formats.
    """
    
    def __init__(self, output_dir="reports"):
        """
        Initialize the ReportGenerator.
        
        Args:
            output_dir (str): Directory to save generated reports.
        """
        self.output_dir = output_dir
        os.makedirs(output_dir, exist_ok=True)
        logger.info(f"Initialized ReportGenerator with output directory: {output_dir}")
    
    def generate_report(self, repo_name, results, format_type="all"):
        """
        Generate a report based on the analysis results.
        
        Args:
            repo_name (str): Name of the repository.
            results (dict): Analysis results.
            format_type (str): Report format type (json, html, pdf, csv, or all).
        
        Returns:
            dict: Paths to the generated reports.
        """
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        report_name = f"{repo_name}_{timestamp}"
        report_paths = {}
        
        # Create report content
        report_content = self._create_report_content(repo_name, results)
        
        # Generate reports in requested formats
        if format_type in ["json", "all"]:
            json_path = self._generate_json_report(report_name, report_content)
            report_paths["json"] = json_path
        
        if format_type in ["html", "all"]:
            html_path = self._generate_html_report(report_name, report_content)
            report_paths["html"] = html_path
        
        if format_type in ["pdf", "all"]:
            pdf_path = self._generate_pdf_report(report_name, report_content)
            report_paths["pdf"] = pdf_path
        
        if format_type in ["csv", "all"]:
            csv_path = self._generate_csv_report(report_name, report_content)
            report_paths["csv"] = csv_path
        
        logger.info(f"Generated {len(report_paths)} report(s) for {repo_name}")
        return report_paths
    
    def _create_report_content(self, repo_name, results):
        """
        Create the content for the report.
        
        Args:
            repo_name (str): Name of the repository.
            results (dict): Analysis results.
        
        Returns:
            dict: Structured report content.
        """
        # Extract repository info
        repo_info = results.get("repository_info", {})
        
        # Extract language breakdown
        language_breakdown = results.get("language_breakdown", {})
        
        # Extract code analysis results
        code_analysis = results.get("code_analysis", {})
        
        # Extract security scan results
        security_scan = results.get("security_scan", {})
        
        # Extract performance analysis results
        performance_analysis = results.get("performance_analysis", {})
        
        # Extract AI review results
        ai_review = results.get("ai_review", {})
        
        # Calculate summary metrics
        summary_metrics = self._calculate_summary_metrics(results)
        
        # Create report structure
        report = {
            "metadata": {
                "repository_name": repo_name,
                "report_date": datetime.datetime.now().isoformat(),
                "repository_info": repo_info,
            },
            "summary": {
                "metrics": summary_metrics,
                "language_breakdown": language_breakdown,
                "executive_summary": ai_review.get("summary", "No AI review summary available."),
            },
            "code_quality": {
                "issues_by_language": code_analysis,
                "top_issues": self._extract_top_issues(code_analysis),
            },
            "security": {
                "vulnerabilities_by_language": security_scan,
                "critical_vulnerabilities": self._extract_critical_vulnerabilities(security_scan),
            },
            "performance": {
                "issues_by_language": performance_analysis.get("language_results", {}),
                "hotspots": performance_analysis.get("hotspots", []),
            },
            "ai_review": {
                "file_reviews": ai_review.get("reviews", {}),
                "summary": ai_review.get("summary", "No AI review summary available."),
            },
            "recommendations": self._generate_recommendations(results),
        }
        
        return report
    
    def _calculate_summary_metrics(self, results):
        """
        Calculate summary metrics from the analysis results.
        
        Args:
            results (dict): Analysis results.
        
        Returns:
            dict: Summary metrics.
        """
        metrics = {
            "total_files": results.get("repository_info", {}).get("file_count", 0),
            "repository_size": results.get("repository_info", {}).get("size", 0),
        }
        
        # Count code quality issues
        code_analysis = results.get("code_analysis", {})
        total_issues = 0
        critical_issues = 0
        for language, language_results in code_analysis.items():
            total_issues += language_results.get("issue_count", 0)
            for issue in language_results.get("issues", []):
                if issue.get("severity", "").lower() in ["critical", "high"]:
                    critical_issues += 1
        
        metrics["total_code_issues"] = total_issues
        metrics["critical_code_issues"] = critical_issues
        
        # Count security vulnerabilities
        security_scan = results.get("security_scan", {})
        total_vulnerabilities = 0
        critical_vulnerabilities = 0
        for language, language_results in security_scan.items():
            total_vulnerabilities += language_results.get("vulnerability_count", 0)
            for vuln in language_results.get("vulnerabilities", []):
                if vuln.get("severity", "").lower() in ["critical", "high"]:
                    critical_vulnerabilities += 1
        
        metrics["total_vulnerabilities"] = total_vulnerabilities
        metrics["critical_vulnerabilities"] = critical_vulnerabilities
        
        # Count performance issues
        performance_analysis = results.get("performance_analysis", {})
        total_performance_issues = 0
        for language, language_results in performance_analysis.get("language_results", {}).items():
            total_performance_issues += language_results.get("issue_count", 0)
        
        metrics["total_performance_issues"] = total_performance_issues
        metrics["performance_hotspots"] = len(performance_analysis.get("hotspots", []))
        
        # Calculate overall score (0-100)
        # This is a simple scoring algorithm that can be refined
        base_score = 100
        deductions = 0
        
        # Deduct for code issues (more weight for critical issues)
        if metrics["total_files"] > 0:
            code_issue_ratio = metrics["total_code_issues"] / metrics["total_files"]
            deductions += min(30, code_issue_ratio * 100)
            deductions += min(20, (metrics["critical_code_issues"] / metrics["total_files"]) * 200)
        
        # Deduct for security vulnerabilities (heavy weight for critical vulnerabilities)
        if metrics["total_files"] > 0:
            deductions += min(30, (metrics["total_vulnerabilities"] / metrics["total_files"]) * 150)
            deductions += min(40, (metrics["critical_vulnerabilities"] / metrics["total_files"]) * 300)
        
        # Deduct for performance issues
        if metrics["total_files"] > 0:
            deductions += min(20, (metrics["total_performance_issues"] / metrics["total_files"]) * 80)
            deductions += min(10, (metrics["performance_hotspots"] / metrics["total_files"]) * 100)
        
        metrics["overall_score"] = max(0, min(100, base_score - deductions))
        
        # Determine quality rating based on score
        if metrics["overall_score"] >= 90:
            metrics["quality_rating"] = "Excellent"
        elif metrics["overall_score"] >= 80:
            metrics["quality_rating"] = "Good"
        elif metrics["overall_score"] >= 70:
            metrics["quality_rating"] = "Satisfactory"
        elif metrics["overall_score"] >= 50:
            metrics["quality_rating"] = "Needs Improvement"
        else:
            metrics["quality_rating"] = "Poor"
        
        return metrics
    
    def _extract_top_issues(self, code_analysis, limit=10):
        """
        Extract the top code quality issues from the analysis results.
        
        Args:
            code_analysis (dict): Code analysis results.
            limit (int): Maximum number of issues to extract.
        
        Returns:
            list: Top code quality issues.
        """
        all_issues = []
        
        for language, language_results in code_analysis.items():
            for issue in language_results.get("issues", []):
                # Add language to the issue
                issue["language"] = language
                all_issues.append(issue)
        
        # Sort issues by severity and then by line count if available
        severity_order = {"critical": 0, "high": 1, "medium": 2, "low": 3, "info": 4}
        
        def issue_sort_key(issue):
            severity = issue.get("severity", "").lower()
            severity_value = severity_order.get(severity, 5)
            return (severity_value, -issue.get("line_count", 0))
        
        sorted_issues = sorted(all_issues, key=issue_sort_key)
        
        return sorted_issues[:limit]
    
    def _extract_critical_vulnerabilities(self, security_scan, limit=10):
        """
        Extract critical security vulnerabilities from the scan results.
        
        Args:
            security_scan (dict): Security scan results.
            limit (int): Maximum number of vulnerabilities to extract.
        
        Returns:
            list: Critical security vulnerabilities.
        """
        all_vulnerabilities = []
        
        for language, language_results in security_scan.items():
            for vuln in language_results.get("vulnerabilities", []):
                # Add language to the vulnerability
                vuln["language"] = language
                all_vulnerabilities.append(vuln)
        
        # Sort vulnerabilities by severity
        severity_order = {"critical": 0, "high": 1, "medium": 2, "low": 3, "info": 4}
        
        def vuln_sort_key(vuln):
            severity = vuln.get("severity", "").lower()
            severity_value = severity_order.get(severity, 5)
            return severity_value
        
        sorted_vulnerabilities = sorted(all_vulnerabilities, key=vuln_sort_key)
        
        return sorted_vulnerabilities[:limit]
    
    def _generate_recommendations(self, results):
        """
        Generate recommendations based on the analysis results.
        
        Args:
            results (dict): Analysis results.
        
        Returns:
            dict: Recommendations categorized by priority.
        """
        recommendations = {
            "high_priority": [],
            "medium_priority": [],
            "low_priority": [],
        }
        
        # Extract critical security vulnerabilities as high priority recommendations
        security_scan = results.get("security_scan", {})
        for language, language_results in security_scan.items():
            for vuln in language_results.get("vulnerabilities", []):
                if vuln.get("severity", "").lower() in ["critical", "high"]:
                    recommendations["high_priority"].append({
                        "type": "security",
                        "language": language,
                        "issue": vuln.get("issue", "Unknown vulnerability"),
                        "description": vuln.get("description", ""),
                        "file": vuln.get("file", ""),
                        "line": vuln.get("line", ""),
                        "recommendation": vuln.get("recommendation", "Fix this security vulnerability."),
                    })
        
        # Extract critical code quality issues as medium priority recommendations
        code_analysis = results.get("code_analysis", {})
        for language, language_results in code_analysis.items():
            for issue in language_results.get("issues", []):
                if issue.get("severity", "").lower() in ["critical", "high"]:
                    recommendations["medium_priority"].append({
                        "type": "code_quality",
                        "language": language,
                        "issue": issue.get("issue", "Unknown issue"),
                        "description": issue.get("description", ""),
                        "file": issue.get("file", ""),
                        "line": issue.get("line", ""),
                        "recommendation": issue.get("recommendation", "Address this code quality issue."),
                    })
        
        # Extract performance hotspots as medium priority recommendations
        performance_analysis = results.get("performance_analysis", {})
        for hotspot in performance_analysis.get("hotspots", []):
            recommendations["medium_priority"].append({
                "type": "performance",
                "language": hotspot.get("language", ""),
                "issue": "Performance Hotspot",
                "description": f"File contains {hotspot.get('issue_count', 0)} performance issues",
                "file": hotspot.get("file", ""),
                "recommendation": "Optimize this file to improve performance.",
            })
        
        # Extract other performance issues as low priority recommendations
        for language, language_results in performance_analysis.get("language_results", {}).items():
            for issue in language_results.get("issues", []):
                # Skip issues that are already part of hotspots
                if any(hotspot.get("file", "") == issue.get("file", "") for hotspot in performance_analysis.get("hotspots", [])):
                    continue
                
                recommendations["low_priority"].append({
                    "type": "performance",
                    "language": language,
                    "issue": issue.get("issue", "Unknown issue"),
                    "description": issue.get("description", ""),
                    "file": issue.get("file", ""),
                    "line": issue.get("line", ""),
                    "recommendation": issue.get("recommendation", "Consider optimizing this code."),
                })
        
        # Extract AI review suggestions as recommendations
        ai_review = results.get("ai_review", {})
        for file_path, review in ai_review.get("reviews", {}).items():
            for suggestion in review.get("suggestions", []):
                priority = "medium_priority"
                if "security" in suggestion.get("section", "").lower():
                    priority = "high_priority"
                elif "performance" in suggestion.get("section", "").lower():
                    priority = "low_priority"
                
                recommendations[priority].append({
                    "type": "ai_review",
                    "language": "",  # AI review doesn't specify language
                    "issue": suggestion.get("section", "AI Suggestion"),
                    "description": suggestion.get("description", ""),
                    "file": file_path,
                    "line": suggestion.get("line", ""),
                    "recommendation": suggestion.get("details", ""),
                })
        
        # Limit the number of recommendations in each category
        limit = 15
        recommendations["high_priority"] = recommendations["high_priority"][:limit]
        recommendations["medium_priority"] = recommendations["medium_priority"][:limit]
        recommendations["low_priority"] = recommendations["low_priority"][:limit]
        
        return recommendations
    
    def _generate_json_report(self, report_name, report_content):
        """
        Generate a JSON report.
        
        Args:
            report_name (str): Name of the report.
            report_content (dict): Report content.
        
        Returns:
            str: Path to the generated report.
        """
        report_path = os.path.join(self.output_dir, f"{report_name}.json")
        
        with open(report_path, "w", encoding="utf-8") as f:
            json.dump(report_content, f, indent=2, ensure_ascii=False)
        
        logger.info(f"Generated JSON report: {report_path}")
        return report_path
    
    def _generate_html_report(self, report_name, report_content):
        """
        Generate an HTML report.
        
        Args:
            report_name (str): Name of the report.
            report_content (dict): Report content.
        
        Returns:
            str: Path to the generated report.
        """
        report_path = os.path.join(self.output_dir, f"{report_name}.html")
        
        # Convert report content to markdown
        md_content = self._convert_to_markdown(report_content)
        
        # Convert markdown to HTML
        html_content = markdown.markdown(md_content, extensions=["tables", "fenced_code"])
        
        # Add CSS styling
        html_content = f"""
        <!DOCTYPE html>
        <html>
        <head>
            <meta charset="utf-8">
            <meta name="viewport" content="width=device-width, initial-scale=1">
            <title>Code Review Report: {report_content['metadata']['repository_name']}</title>
            <style>
                body {{font-family: Arial, sans-serif; line-height: 1.6; max-width: 1200px; margin: 0 auto; padding: 20px;}}
                h1, h2, h3, h4 {{color: #333; margin-top: 30px;}}
                h1 {{border-bottom: 2px solid #333; padding-bottom: 10px;}}
                h2 {{border-bottom: 1px solid #ccc; padding-bottom: 5px;}}
                table {{border-collapse: collapse; width: 100%; margin: 20px 0;}}
                th, td {{text-align: left; padding: 12px; border-bottom: 1px solid #ddd;}}
                th {{background-color: #f2f2f2;}}
                tr:hover {{background-color: #f5f5f5;}}
                .metric-card {{background-color: #f9f9f9; border-radius: 5px; padding: 15px; margin: 10px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1);}}
                .metric-value {{font-size: 24px; font-weight: bold; color: #333;}}
                .metric-label {{font-size: 14px; color: #666;}}
                .severity-critical {{color: #d9534f; font-weight: bold;}}
                .severity-high {{color: #f0ad4e; font-weight: bold;}}
                .severity-medium {{color: #5bc0de; font-weight: bold;}}
                .severity-low {{color: #5cb85c; font-weight: bold;}}
                .metrics-container {{display: flex; flex-wrap: wrap; gap: 20px; justify-content: space-between;}}
                .metric-card {{flex: 1; min-width: 200px;}}
                pre {{background-color: #f5f5f5; padding: 15px; border-radius: 5px; overflow-x: auto;}}
                code {{font-family: Consolas, Monaco, 'Andale Mono', monospace; font-size: 14px;}}
                .recommendation {{background-color: #f9f9f9; border-left: 4px solid #5bc0de; padding: 10px; margin: 10px 0;}}
                .high-priority {{border-left-color: #d9534f;}}
                .medium-priority {{border-left-color: #f0ad4e;}}
                .low-priority {{border-left-color: #5cb85c;}}
            </style>
        </head>
        <body>
            {html_content}
        </body>
        </html>
        """
        
        with open(report_path, "w", encoding="utf-8") as f:
            f.write(html_content)
        
        logger.info(f"Generated HTML report: {report_path}")
        return report_path
    
    def _generate_pdf_report(self, report_name, report_content):
        """
        Generate a PDF report.
        
        Args:
            report_name (str): Name of the report.
            report_content (dict): Report content.
        
        Returns:
            str: Path to the generated report.
        """
        report_path = os.path.join(self.output_dir, f"{report_name}.pdf")
        
        # First generate HTML report
        html_path = self._generate_html_report(f"{report_name}_temp", report_content)
        
        try:
            # Convert HTML to PDF using pdfkit
            pdfkit.from_file(html_path, report_path)
            
            # Remove temporary HTML file
            os.remove(html_path)
            
            logger.info(f"Generated PDF report: {report_path}")
            return report_path
        
        except Exception as e:
            logger.error(f"Error generating PDF report: {e}")
            return html_path
    
    def _generate_csv_report(self, report_name, report_content):
        """
        Generate a CSV report with issues and recommendations.
        
        Args:
            report_name (str): Name of the report.
            report_content (dict): Report content.
        
        Returns:
            str: Path to the generated report.
        """
        report_path = os.path.join(self.output_dir, f"{report_name}.csv")
        
        # Collect all issues and recommendations
        rows = []
        
        # Add code quality issues
        for language, language_results in report_content["code_quality"]["issues_by_language"].items():
            for issue in language_results.get("issues", []):
                rows.append({
                    "Type": "Code Quality",
                    "Language": language,
                    "Severity": issue.get("severity", ""),
                    "Issue": issue.get("issue", ""),
                    "Description": issue.get("description", ""),
                    "File": issue.get("file", ""),
                    "Line": issue.get("line", ""),
                    "Recommendation": issue.get("recommendation", ""),
                })
        
        # Add security vulnerabilities
        for language, language_results in report_content["security"]["vulnerabilities_by_language"].items():
            for vuln in language_results.get("vulnerabilities", []):
                rows.append({
                    "Type": "Security",
                    "Language": language,
                    "Severity": vuln.get("severity", ""),
                    "Issue": vuln.get("issue", ""),
                    "Description": vuln.get("description", ""),
                    "File": vuln.get("file", ""),
                    "Line": vuln.get("line", ""),
                    "Recommendation": vuln.get("recommendation", ""),
                })
        
        # Add performance issues
        for language, language_results in report_content["performance"]["issues_by_language"].items():
            for issue in language_results.get("issues", []):
                rows.append({
                    "Type": "Performance",
                    "Language": language,
                    "Severity": issue.get("severity", "Medium"),
                    "Issue": issue.get("issue", ""),
                    "Description": issue.get("description", ""),
                    "File": issue.get("file", ""),
                    "Line": issue.get("line", ""),
                    "Recommendation": issue.get("recommendation", ""),
                })
        
        # Add AI review suggestions
        for file_path, review in report_content["ai_review"]["file_reviews"].items():
            for suggestion in review.get("suggestions", []):
                rows.append({
                    "Type": "AI Review",
                    "Language": "",
                    "Severity": "",
                    "Issue": suggestion.get("section", ""),
                    "Description": suggestion.get("description", ""),
                    "File": file_path,
                    "Line": suggestion.get("line", ""),
                    "Recommendation": suggestion.get("details", ""),
                })
        
        # Write to CSV
        with open(report_path, "w", newline="", encoding="utf-8") as f:
            fieldnames = ["Type", "Language", "Severity", "Issue", "Description", "File", "Line", "Recommendation"]
            writer = csv.DictWriter(f, fieldnames=fieldnames)
            writer.writeheader()
            writer.writerows(rows)
        
        logger.info(f"Generated CSV report: {report_path}")
        return report_path
    
    def _convert_to_markdown(self, report_content):
        """
        Convert report content to markdown format.
        
        Args:
            report_content (dict): Report content.
        
        Returns:
            str: Markdown formatted report.
        """
        md = []
        
        # Title and metadata
        md.append(f"# Code Review Report: {report_content['metadata']['repository_name']}")
        md.append(f"**Report Date:** {report_content['metadata']['report_date']}")
        md.append("")
        
        # Repository info
        repo_info = report_content['metadata']['repository_info']
        md.append("## Repository Information")
        md.append(f"**Branch:** {repo_info.get('branch', 'N/A')}")
        md.append(f"**Commit:** {repo_info.get('commit', 'N/A')}")
        md.append(f"**Remote URL:** {repo_info.get('remote_url', 'N/A')}")
        md.append(f"**Size:** {repo_info.get('size', 0)} bytes")
        md.append(f"**File Count:** {repo_info.get('file_count', 0)}")
        md.append("")
        
        # Summary metrics
        md.append("## Executive Summary")
        metrics = report_content['summary']['metrics']
        md.append(f"**Overall Score:** {metrics.get('overall_score', 0)}/100")
        md.append(f"**Quality Rating:** {metrics.get('quality_rating', 'N/A')}")
        md.append("")
        md.append("### Key Metrics")
        md.append("| Metric | Value |")
        md.append("| ------ | ----- |")
        md.append(f"| Total Files | {metrics.get('total_files', 0)} |")
        md.append(f"| Code Quality Issues | {metrics.get('total_code_issues', 0)} |")
        md.append(f"| Critical Code Issues | {metrics.get('critical_code_issues', 0)} |")
        md.append(f"| Security Vulnerabilities | {metrics.get('total_vulnerabilities', 0)} |")
        md.append(f"| Critical Vulnerabilities | {metrics.get('critical_vulnerabilities', 0)} |")
        md.append(f"| Performance Issues | {metrics.get('total_performance_issues', 0)} |")
        md.append(f"| Performance Hotspots | {metrics.get('performance_hotspots', 0)} |")
        md.append("")
        
        # Language breakdown
        md.append("### Language Breakdown")
        language_breakdown = report_content['summary']['language_breakdown']
        md.append("| Language | Files | Lines | Percentage |")
        md.append("| -------- | ----- | ----- | ---------- |")
        for language, stats in language_breakdown.items():
            md.append(f"| {language} | {stats.get('files', 0)} | {stats.get('lines', 0)} | {stats.get('percentage', 0)}% |")
        md.append("")
        
        # Executive summary from AI review
        md.append("### Executive Summary")
        md.append(report_content['summary']['executive_summary'])
        md.append("")
        
        # Code quality issues
        md.append("## Code Quality Analysis")
        md.append("### Top Issues")
        top_issues = report_content['code_quality']['top_issues']
        if top_issues:
            md.append("| Severity | Language | Issue | File | Line |")
            md.append("| -------- | -------- | ----- | ---- | ---- |")
            for issue in top_issues:
                md.append(f"| {issue.get('severity', 'N/A')} | {issue.get('language', 'N/A')} | {issue.get('issue', 'N/A')} | {issue.get('file', 'N/A')} | {issue.get('line', 'N/A')} |")
        else:
            md.append("No code quality issues found.")
        md.append("")
        
        # Security vulnerabilities
        md.append("## Security Analysis")
        md.append("### Critical Vulnerabilities")
        critical_vulnerabilities = report_content['security']['critical_vulnerabilities']
        if critical_vulnerabilities:
            md.append("| Severity | Language | Vulnerability | File | Line |")
            md.append("| -------- | -------- | ------------- | ---- | ---- |")
            for vuln in critical_vulnerabilities:
                md.append(f"| {vuln.get('severity', 'N/A')} | {vuln.get('language', 'N/A')} | {vuln.get('issue', 'N/A')} | {vuln.get('file', 'N/A')} | {vuln.get('line', 'N/A')} |")
        else:
            md.append("No critical security vulnerabilities found.")
        md.append("")
        
        # Performance analysis
        md.append("## Performance Analysis")
        md.append("### Performance Hotspots")
        hotspots = report_content['performance']['hotspots']
        if hotspots:
            md.append("| Language | File | Issue Count |")
            md.append("| -------- | ---- | ----------- |")
            for hotspot in hotspots:
                md.append(f"| {hotspot.get('language', 'N/A')} | {hotspot.get('file', 'N/A')} | {hotspot.get('issue_count', 0)} |")
        else:
            md.append("No performance hotspots found.")
        md.append("")
        
        # Recommendations
        md.append("## Recommendations")
        
        # High priority recommendations
        md.append("### High Priority")
        high_priority = report_content['recommendations']['high_priority']
        if high_priority:
            for i, rec in enumerate(high_priority, 1):
                md.append(f"**{i}. {rec.get('issue', 'Recommendation')}**")
                md.append(f"- **Type:** {rec.get('type', 'N/A')}")
                md.append(f"- **File:** {rec.get('file', 'N/A')}")
                if rec.get('line'):
                    md.append(f"- **Line:** {rec.get('line')}")
                md.append(f"- **Description:** {rec.get('description', 'N/A')}")
                md.append(f"- **Recommendation:** {rec.get('recommendation', 'N/A')}")
                md.append("")
        else:
            md.append("No high priority recommendations.")
        md.append("")
        
        # Medium priority recommendations
        md.append("### Medium Priority")
        medium_priority = report_content['recommendations']['medium_priority']
        if medium_priority:
            for i, rec in enumerate(medium_priority, 1):
                md.append(f"**{i}. {rec.get('issue', 'Recommendation')}**")
                md.append(f"- **Type:** {rec.get('type', 'N/A')}")
                md.append(f"- **File:** {rec.get('file', 'N/A')}")
                if rec.get('line'):
                    md.append(f"- **Line:** {rec.get('line')}")
                md.append(f"- **Description:** {rec.get('description', 'N/A')}")
                md.append(f"- **Recommendation:** {rec.get('recommendation', 'N/A')}")
                md.append("")
        else:
            md.append("No medium priority recommendations.")
        md.append("")
        
        # Low priority recommendations
        md.append("### Low Priority")
        low_priority = report_content['recommendations']['low_priority']
        if low_priority:
            for i, rec in enumerate(low_priority, 1):
                md.append(f"**{i}. {rec.get('issue', 'Recommendation')}**")
                md.append(f"- **Type:** {rec.get('type', 'N/A')}")
                md.append(f"- **File:** {rec.get('file', 'N/A')}")
                if rec.get('line'):
                    md.append(f"- **Line:** {rec.get('line')}")
                md.append(f"- **Description:** {rec.get('description', 'N/A')}")
                md.append(f"- **Recommendation:** {rec.get('recommendation', 'N/A')}")
                md.append("")
        else:
            md.append("No low priority recommendations.")
        
        return "\n".join(md)