# src/services/orchestrator.py
"""
Simplified orchestrator for the self-improving code generator
This is a minimal implementation to get started
"""

import logging
from typing import Dict, Any, Optional
from ..core.technology_detector import TechnologyDetector
from ..utils.config import get_settings

logger = logging.getLogger(__name__)

class SelfImprovingCodeGenerator:
    """Main orchestrator for the self-improving code generation system"""
    
    def __init__(self, claude_api_key: str, database_url: str):
        self.claude_api_key = claude_api_key
        self.database_url = database_url
        self.settings = get_settings()
        
        # Initialize core components
        self.tech_detector = TechnologyDetector()
        
        logger.info("✅ Self-improving code generator initialized")
    
    async def analyze_and_improve_project(self, 
                                        project_id: str,
                                        project_path: str, 
                                        user_id: Optional[str] = None,
                                        target_quality: float = 0.85,
                                        max_iterations: int = 5) -> Dict[str, Any]:
        """
        Main method: Analyze project and iteratively improve until target quality
        
        This is a simplified version for initial implementation
        """
        
        try:
            logger.info(f"Starting analysis for project {project_id}")
            
            # Step 1: Detect technology stack
            tech_profile = await self.tech_detector.detect_technology_stack(project_path)
            logger.info(f"Detected technology: {tech_profile.name}")
            
            # Step 2: Simulate quality analysis (will be expanded)
            current_quality = 0.45  # Simulated initial quality
            
            # Step 3: Simulate improvements (will be expanded)
            improvements_applied = []
            iteration = 0
            
            while current_quality < target_quality and iteration < max_iterations:
                iteration += 1
                logger.info(f"Running improvement iteration {iteration}")
                
                # Simulate improvement application
                simulated_improvement = {
                    "iteration": iteration,
                    "type": "critical_fix",
                    "description": f"Applied critical fixes in iteration {iteration}",
                    "quality_boost": 0.15
                }
                
                improvements_applied.append(simulated_improvement)
                current_quality += simulated_improvement["quality_boost"]
                
                if current_quality >= target_quality:
                    logger.info(f"Target quality {target_quality} achieved!")
                    break
            
            return {
                "project_id": project_id,
                "iterations_completed": iteration,
                "initial_quality": 0.45,
                "final_quality": current_quality,
                "target_achieved": current_quality >= target_quality,
                "technology_stack": tech_profile.stack.value,
                "confidence": tech_profile.confidence_score,
                "improvements_applied": len(improvements_applied),
                "improvement_details": improvements_applied
            }
            
        except Exception as e:
            logger.error(f"Analysis failed for project {project_id}: {e}")
            raise
