codenuk_frontend_mine/FRONTEND_AI_INTEGRATION.md

Frontend AI Analysis Integration

Overview

This document describes the complete integration of AI analysis functionality into the frontend, including real-time monitoring, progress tracking, and API gateway communication.

Architecture

Frontend → API Gateway → AI Analysis Service → Git Integration Service
    ↓           ↓              ↓                      ↓
Real-time UI → HTTP API → Repository Info → Local File Access
    ↓           ↓              ↓                      ↓
Progress     → WebSocket → Analysis → Direct File System
Monitoring   → Stream    → Processing → Access

Components Implemented

1. API Routes (/app/api/ai/repository/)

/analyze/route.ts

• Purpose: Start AI analysis for a repository
• Method: POST
• Input: { repository_id, user_id, output_format, max_files }
• Output: Analysis result with stats and report path

/stream/route.ts

• Purpose: Real-time streaming of analysis progress
• Method: GET
• Input: repository_id, user_id as query parameters
• Output: Server-Sent Events stream with progress updates

/report/[filename]/route.ts

• Purpose: Download analysis reports
• Method: GET
• Input: Filename as URL parameter
• Output: File download with appropriate headers

2. React Hooks (/hooks/useAIAnalysis.ts)

useAIAnalysis Hook

• State Management: Analysis progress, results, errors
• Functions:
  • startAnalysis(): Initiates analysis with options
  • stopAnalysis(): Stops ongoing analysis
  • resetAnalysis(): Resets all state
• Real-time Updates: Uses EventSource for streaming updates

3. UI Components (/components/ai/)

AnalysisMonitor.tsx

• Real-time Progress: Shows analysis progress with percentage
• Status Indicators: Loading, error, and completion states
• Statistics Display: File counts, quality scores, languages
• Download Functionality: Direct report download

AnalysisModal.tsx

• Modal Interface: Full-screen analysis interface
• Progress Tracking: Real-time progress updates
• Error Handling: User-friendly error messages
• Result Display: Comprehensive analysis results

4. Integration Points

GitHub Repos Page (/app/github/repos/page.tsx)

• AI Analysis Button: Triggers analysis modal
• Repository Context: Passes repository ID and name
• User Context: Handles user authentication
• Modal Integration: Seamless modal experience

Real-time Features

1. Progress Monitoring

interface AnalysisProgress {
  current_chunk: number
  total_chunks: number
  processed_files: number
  total_files: number
  percentage: number
}

2. Status Updates

• Ready: Analysis not started
• Analyzing: In progress with real-time updates
• Complete: Analysis finished with results
• Error: Analysis failed with error message

3. Statistics Display

• File Metrics: Total files, lines of code
• Quality Scores: Overall code quality rating
• Language Breakdown: Programming languages detected
• Quality Distribution: High/medium/low quality files

API Gateway Integration

1. Service Communication

All communication flows through the API gateway:

Frontend → Next.js API Routes → External Services

2. Environment Configuration

# Service URLs
GIT_INTEGRATION_SERVICE_URL=http://git-integration:8012
AI_ANALYSIS_SERVICE_URL=http://ai-analysis-service:8022

# Public URLs (for client-side)
NEXT_PUBLIC_GIT_INTEGRATION_SERVICE_URL=http://localhost:8012
NEXT_PUBLIC_AI_ANALYSIS_SERVICE_URL=http://localhost:8022

3. Error Handling

• Service Unavailable: Graceful fallback messages
• Network Errors: Retry mechanisms
• Analysis Failures: User-friendly error display

User Experience Flow

1. Analysis Initiation

1. User clicks "AI Analysis" button on repository card
2. Modal opens with analysis interface
3. User can start analysis or close modal

2. Real-time Progress

1. Analysis starts with progress indicator
2. Real-time updates show:
   • Files processed
   • Chunks processed
   • Percentage complete
3. User can stop analysis if needed

3. Results Display

1. Analysis completes with success indicator
2. Statistics displayed:
   • Total files and lines
   • Quality score
   • Language breakdown
   • Quality distribution
3. Download button for report

4. Error Handling

1. Error messages displayed clearly
2. Retry options provided
3. User can close and try again

Technical Implementation

1. State Management

const {
  isAnalyzing,      // Analysis in progress
  progress,         // Real-time progress
  result,          // Analysis results
  error,           // Error messages
  startAnalysis,   // Start function
  stopAnalysis,    // Stop function
  resetAnalysis    // Reset function
} = useAIAnalysis()

2. Real-time Updates

// EventSource for streaming updates
const eventSource = new EventSource(streamUrl)

eventSource.onmessage = (event) => {
  const data = JSON.parse(event.data)
  // Update progress, handle completion, etc.
}

3. File Download

const handleDownloadReport = async () => {
  const response = await fetch(`/api/ai/repository/report/${filename}`)
  const blob = await response.blob()
  // Create download link and trigger download
}

Security Considerations

1. Authentication

• User ID passed through all requests
• JWT tokens for service authentication
• Repository access validation

2. Input Validation

• Repository ID validation
• User ID validation
• File size limits
• Content type validation

3. Error Handling

• No sensitive information in error messages
• Graceful degradation on service failures
• User-friendly error display

Performance Optimizations

1. Caching

• Analysis results cached for 24 hours
• File content hashing for change detection
• Redis-based caching system

2. Rate Limiting

• 90 requests per minute to Claude API
• Request queuing and throttling
• Progress updates to prevent timeouts

3. Content Optimization

• Large files truncated intelligently
• Important code sections preserved
• Token limit compliance

Deployment Considerations

1. Environment Variables

# Required for production
GIT_INTEGRATION_SERVICE_URL=https://git-integration.yourdomain.com
AI_ANALYSIS_SERVICE_URL=https://ai-analysis.yourdomain.com

2. CORS Configuration

// API routes handle CORS
app.add_middleware(
  CORSMiddleware,
  allow_origins=["*"],
  allow_credentials=True,
  allow_methods=["*"],
  allow_headers=["*"],
)

3. Error Monitoring

• Console logging for debugging
• Error tracking for production
• Performance monitoring

Testing

1. Unit Tests

• Hook functionality testing
• Component rendering tests
• API route testing

2. Integration Tests

• End-to-end analysis flow
• Real-time update testing
• Error scenario testing

3. Performance Tests

• Large repository handling
• Concurrent analysis testing
• Memory usage monitoring

Future Enhancements

1. Advanced Features

• Batch analysis for multiple repositories
• Analysis scheduling
• Custom analysis parameters

2. UI Improvements

• Analysis history
• Comparison between repositories
• Advanced filtering and sorting

3. Performance

• Background processing
• Queue management
• Resource optimization

This integration provides a complete, production-ready AI analysis system with real-time monitoring, progress tracking, and seamless user experience through the API gateway architecture.