workflow-demo.py

#!/usr/bin/env python3
"""
Warp + GitHub MCP Workflow Demo

This script simulates what would happen when using Warp AI with GitHub MCP integration.
It demonstrates automated code analysis, issue creation, and project management workflows.
"""

import json
import subprocess
import re
from typing import List, Dict, Tuple

class CodeAnalyzer:
    """Analyzes Python code for common issues and patterns."""
    
    def __init__(self):
        self.issues = []
    
    def analyze_file(self, filepath: str) -> List[Dict]:
        """Analyze a Python file for code quality issues."""
        issues = []
        
        try:
            with open(filepath, 'r') as f:
                lines = f.readlines()
            
            for line_num, line in enumerate(lines, 1):
                # Check for TODO/FIXME comments
                if 'TODO' in line or 'FIXME' in line:
                    issues.append({
                        'type': 'todo',
                        'line': line_num,
                        'message': f"TODO/FIXME found: {line.strip()}",
                        'severity': 'low'
                    })
                
                # Check for hardcoded strings (potential config issues)
                if re.search(r'["\'][^"\']*\.(com|org|net)["\']', line):
                    issues.append({
                        'type': 'hardcoded',
                        'line': line_num,
                        'message': "Hardcoded URL/domain found",
                        'severity': 'medium'
                    })
                
                # Check for missing error handling
                if 'open(' in line and 'try:' not in ''.join(lines[max(0, line_num-3):line_num]):
                    issues.append({
                        'type': 'error_handling',
                        'line': line_num,
                        'message': "File operation without error handling",
                        'severity': 'high'
                    })
                
                # Check for inefficient patterns
                if 'for ' in line and 'if ' in line and 'return' in line:
                    issues.append({
                        'type': 'performance',
                        'line': line_num,
                        'message': "Potential linear search pattern",
                        'severity': 'medium'
                    })
        
        except Exception as e:
            print(f"Error analyzing {filepath}: {e}")
        
        return issues

class GitHubWorkflow:
    """Simulates GitHub operations that would be available through MCP."""
    
    def __init__(self):
        pass
    
    def create_improvement_issue(self, analysis_result: Dict) -> Dict:
        """Create a GitHub issue based on code analysis results."""
        
        # Group issues by type
        issue_groups = {}
        for issue in analysis_result['issues']:
            issue_type = issue['type']
            if issue_type not in issue_groups:
                issue_groups[issue_type] = []
            issue_groups[issue_type].append(issue)
        
        created_issues = []
        
        for issue_type, issues in issue_groups.items():
            title = self._generate_issue_title(issue_type, len(issues))
            body = self._generate_issue_body(issue_type, issues, analysis_result['file'])
            
            # In real MCP integration, this would create actual GitHub issues
            issue_data = {
                'title': title,
                'body': body,
                'labels': [issue_type, f"severity-{issues[0]['severity']}"],
                'file': analysis_result['file'],
                'count': len(issues)
            }
            created_issues.append(issue_data)
            
            print(f"📝 Would create issue: '{title}' with {len(issues)} occurrences")
        
        return created_issues
    
    def _generate_issue_title(self, issue_type: str, count: int) -> str:
        """Generate appropriate issue titles based on analysis results."""
        titles = {
            'todo': f"Code cleanup: {count} TODO/FIXME comment{'s' if count > 1 else ''} need attention",
            'hardcoded': f"Configuration: {count} hardcoded value{'s' if count > 1 else ''} found",
            'error_handling': f"Bug: Missing error handling in {count} location{'s' if count > 1 else ''}",
            'performance': f"Performance: {count} inefficient pattern{'s' if count > 1 else ''} detected"
        }
        return titles.get(issue_type, f"Code issue: {issue_type} ({count} occurrences)")
    
    def _generate_issue_body(self, issue_type: str, issues: List[Dict], filepath: str) -> str:
        """Generate detailed issue descriptions."""
        body = f"## Automated Code Analysis Report\n\n"
        body += f"**File:** `{filepath}`\n"
        body += f"**Issue Type:** {issue_type.title()}\n"
        body += f"**Occurrences:** {len(issues)}\n\n"
        
        body += "### Details\n\n"
        for issue in issues[:5]:  # Limit to first 5 for readability
            body += f"- **Line {issue['line']}:** {issue['message']}\n"
        
        if len(issues) > 5:
            body += f"- ... and {len(issues) - 5} more occurrences\n"
        
        body += f"\n### Recommended Actions\n\n"
        recommendations = {
            'todo': "- Review and complete TODO items\n- Convert FIXMEs to proper issues\n- Remove completed TODOs",
            'hardcoded': "- Move hardcoded values to configuration files\n- Use environment variables\n- Implement configuration management",
            'error_handling': "- Add try-catch blocks for file operations\n- Implement proper error logging\n- Add validation for external dependencies",
            'performance': "- Consider using dictionaries for O(1) lookups\n- Implement caching where appropriate\n- Profile code for bottlenecks"
        }
        body += recommendations.get(issue_type, "- Review and address the identified issues")
        
        return body
    
    def get_repository_stats(self) -> Dict:
        """Get repository statistics (simulated)."""
        try:
            # This simulates what MCP would provide
            result = subprocess.run(['gh', 'repo', 'view', '--json', 'name,description,stargazerCount,forkCount,primaryLanguage'], 
                                  capture_output=True, text=True, check=True)
            return json.loads(result.stdout)
        except Exception as e:
            print(f"Error getting repo stats: {e}")
            return {}

def main():
    """Main workflow demonstration."""
    print("🚀 Warp + GitHub MCP Workflow Demo")
    print("=" * 50)
    
    # Initialize components
    analyzer = CodeAnalyzer()
    github = GitHubWorkflow()
    
    # Step 1: Analyze code
    print("\n📊 Step 1: Analyzing code quality...")
    code_files = ['app.py']  # In real scenario, this would scan the entire project
    
    all_analysis_results = []
    for file_path in code_files:
        print(f"   Analyzing {file_path}...")
        issues = analyzer.analyze_file(file_path)
        analysis_result = {
            'file': file_path,
            'issues': issues,
            'total_issues': len(issues)
        }
        all_analysis_results.append(analysis_result)
        print(f"   Found {len(issues)} potential issues")
    
    # Step 2: Get repository context
    print("\n🔍 Step 2: Gathering repository context...")
    repo_stats = github.get_repository_stats()
    if repo_stats:
        print(f"   Repository: {repo_stats.get('name', 'Unknown')}")
        print(f"   Description: {repo_stats.get('description', 'N/A')}")
        print(f"   Primary Language: {repo_stats.get('primaryLanguage', {}).get('name', 'Unknown')}")
    
    # Step 3: Create improvement suggestions
    print("\n📋 Step 3: Creating improvement suggestions...")
    all_created_issues = []
    for analysis_result in all_analysis_results:
        if analysis_result['issues']:
            created_issues = github.create_improvement_issue(analysis_result)
            all_created_issues.extend(created_issues)
    
    # Step 4: Summary report
    print("\n📈 Step 4: Workflow Summary")
    print("=" * 30)
    print(f"Files analyzed: {len(all_analysis_results)}")
    print(f"Total issues found: {sum(r['total_issues'] for r in all_analysis_results)}")
    print(f"Issues that would be created: {len(all_created_issues)}")
    
    if all_created_issues:
        print("\n📝 Issues to be created:")
        for issue in all_created_issues:
            print(f"   • {issue['title']}")
            print(f"     Labels: {', '.join(issue['labels'])}")
    
    print("\n✅ Workflow complete! This demonstrates how Warp AI with GitHub MCP can:")
    print("   • Automatically analyze code quality")
    print("   • Create structured GitHub issues")
    print("   • Provide actionable improvement suggestions")
    print("   • Integrate seamlessly with your development workflow")

if __name__ == "__main__":
    main()