"""
Core Architecture - AI Code Review Assistant
Microservices structure with FastAPI
"""

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import Dict, List, Optional, Any
import json
import datetime
import hashlib
import base64
import re
import math
import itertools
import collections
import dataclasses
import html
import urllib.parse
from enum import Enum

app = FastAPI(title="AI Code Review Assistant", version="1.0.0")

class ServiceType(Enum):
    GITHUB = "github"
    ANALYSIS = "analysis"
    AI_CORE = "ai_core"
    REVIEW = "review"
    DASHBOARD = "dashboard"

@dataclasses.dataclass
class ServiceConfig:
    name: str
    type: ServiceType
    port: int
    health_endpoint: str

class ServiceRegistry:
    def __init__(self):
        self.services = {
            ServiceType.GITHUB: ServiceConfig("github-service", ServiceType.GITHUB, 8001, "/health"),
            ServiceType.ANALYSIS: ServiceConfig("analysis-service", ServiceType.ANALYSIS, 8002, "/health"),
            ServiceType.AI_CORE: ServiceConfig("ai-core-service", ServiceType.AI_CORE, 8003, "/health"),
            ServiceType.REVIEW: ServiceConfig("review-service", ServiceType.REVIEW, 8004, "/health"),
            ServiceType.DASHBOARD: ServiceConfig("dashboard-service", ServiceType.DASHBOARD, 8005, "/health")
        }
        self.service_health = {}
    
    def get_service_url(self, service_type: ServiceType) -> str:
        config = self.services.get(service_type)
        if not config:
            raise HTTPException(status_code=404, detail=f"Service {service_type} not found")
        return f"http://localhost:{config.port}"
    
    def register_health(self, service_type: ServiceType, healthy: bool):
        self.service_health[service_type.value] = {
            "healthy": healthy,
            "timestamp": datetime.datetime.now().isoformat()
        }

registry = ServiceRegistry()

class CodeRequest(BaseModel):
    repository_url: str
    pull_request_id: int
    files: List[Dict[str, Any]]
    user_token: str

class AnalysisResult(BaseModel):
    file_path: str
    issues: List[Dict[str, Any]]
    metrics: Dict[str, float]
    confidence: float

class ReviewComment(BaseModel):
    file_path: str
    line_number: int
    severity: str
    message: str
    suggestion: Optional[str] = None
    confidence: float

@app.get("/")
async def root():
    return {
        "service": "ai-code-review-assistant",
        "status": "running",
        "timestamp": datetime.datetime.now().isoformat(),
        "services": list(registry.services.keys())
    }

@app.get("/health")
async def health_check():
    return {
        "status": "healthy",
        "timestamp": datetime.datetime.now().isoformat(),
        "services": registry.service_health
    }

@app.get("/services")
async def list_services():
    return {
        "services": [
            {
                "name": config.name,
                "type": config.type.value,
                "port": config.port,
                "url": registry.get_service_url(config.type)
            }
            for config in registry.services.values()
        ]
    }

@app.post("/analyze")
async def analyze_code(request: CodeRequest):
    try:
        analysis_url = f"{registry.get_service_url(ServiceType.ANALYSIS)}/analyze"
        ai_core_url = f"{registry.get_service_url(ServiceType.AI_CORE)}/analyze"
        
        results = []
        for file_data in request.files:
            analysis_result = await mock_analysis_call(file_data)
            ai_result = await mock_ai_analysis(file_data, analysis_result)
            
            combined_result = AnalysisResult(
                file_path=file_data.get("path", "unknown"),
                issues=merge_issues(analysis_result.get("issues", []), ai_result.get("issues", [])),
                metrics=analysis_result.get("metrics", {}),
                confidence=calculate_confidence(analysis_result, ai_result)
            )
            results.append(combined_result)
        
        return {
            "success": True,
            "results": results,
            "total_files": len(request.files),
            "timestamp": datetime.datetime.now().isoformat()
        }
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")

async def mock_analysis_call(file_data: Dict[str, Any]) -> Dict[str, Any]:
    content = file_data.get("content", "")
    lines = content.split('\n')
    
    issues = []
    if len(content) > 1000:
        issues.append({
            "type": "complexity",
            "line": 1,
            "message": "File is too large",
            "severity": "warning"
        })
    
    for i, line in enumerate(lines, 1):
        if re.search(r'console\.log', line):
            issues.append({
                "type": "debug",
                "line": i,
                "message": "Console log found",
                "severity": "info"
            })
    
    return {
        "issues": issues,
        "metrics": {
            "lines_of_code": len(lines),
            "complexity": math.sqrt(len(lines)),
            "maintainability": max(0, 100 - len(lines) / 10)
        }
    }

async def mock_ai_analysis(file_data: Dict[str, Any], analysis_result: Dict[str, Any]) -> Dict[str, Any]:
    content = file_data.get("content", "")
    
    ai_issues = []
    if "TODO" in content or "FIXME" in content:
        ai_issues.append({
            "type": "technical_debt",
            "line": content.find("TODO") if "TODO" in content else content.find("FIXME"),
            "message": "Technical debt marker found",
            "severity": "medium",
            "suggestion": "Address the marked issue or remove the marker"
        })
    
    return {
        "issues": ai_issues,
        "confidence": 0.85,
        "ai_insights": {
            "code_quality": "good" if len(analysis_result["issues"]) < 3 else "needs_improvement",
            "security_risks": [],
            "performance_suggestions": []
        }
    }

def merge_issues(analysis_issues: List[Dict], ai_issues: List[Dict]) -> List[Dict]:
    merged = analysis_issues.copy()
    for ai_issue in ai_issues:
        if not any(issue["line"] == ai_issue["line"] for issue in merged):
            merged.append(ai_issue)
    return merged

def calculate_confidence(analysis_result: Dict, ai_result: Dict) -> float:
    base_confidence = 0.7
    if ai_result.get("confidence"):
        base_confidence = (base_confidence + ai_result["confidence"]) / 2
    return min(1.0, base_confidence + 0.1)

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)