#!/usr/bin/env python3
"""
IQKiller Enhanced Interview Orchestrator
Main analysis engine that coordinates all components for 30-60 second analysis
Enterprise-grade with 93%+ accuracy as mentioned in the documentation
"""

import asyncio
import logging
import time
from typing import Dict, Any, Optional, List, Tuple
from dataclasses import dataclass
from enum import Enum

# Local imports
from config import get_config
from llm_client import get_llm_client, generate_interview_content, generate_analysis_content
from micro.scrape import get_scraper, ScrapeResult
from salary_negotiation_simulator import get_simulator, get_random_scenario

# Simple text extraction function
def extract_text_from_content(content: str) -> str:
    """Extract text from content (placeholder for file processing)"""
    return content

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class AnalysisType(Enum):
    """Types of analysis available"""
    QUICK = "quick"
    FULL = "full"
    COMPREHENSIVE = "comprehensive"

@dataclass
class ResumeData:
    """Parsed resume information"""
    raw_text: str
    skills: List[str]
    experience_years: int
    education: List[str]
    previous_roles: List[str]
    key_achievements: List[str]
    contact_info: Dict[str, str]

@dataclass
class JobData:
    """Parsed job posting information"""
    raw_text: str
    company: str
    role: str
    location: str
    required_skills: List[str]
    preferred_skills: List[str]
    experience_level: str
    salary_range: Optional[str]
    benefits: List[str]
    responsibilities: List[str]

@dataclass
class AnalysisResult:
    """Complete analysis result"""
    match_score: float  # 0-100
    strengths: List[str]
    gaps: List[str]
    interview_questions: List[Dict[str, str]]
    preparation_advice: List[str]
    salary_insights: Dict[str, Any]
    negotiation_points: List[str]
    processing_time: float
    confidence_level: str
    action_items: List[str]

class EnhancedInterviewOrchestrator:
    """Main orchestrator for interview analysis and preparation"""
    
    def __init__(self, config=None):
        """Initialize orchestrator with all components"""
        self.config = config or get_config()
        self.llm_client = get_llm_client()
        self.scraper = get_scraper()
        self.negotiation_simulator = get_simulator()
        
    async def parse_resume(self, resume_content: str, is_file_content: bool = False) -> ResumeData:
        """Parse resume content and extract structured data"""
        
        start_time = time.time()
        logger.info("🔄 Parsing resume content...")
        
        # Extract text if it's a file
        if is_file_content:
            text_content = extract_text_from_content(resume_content)
        else:
            text_content = resume_content
        
        # Use LLM to extract structured data
        extraction_prompt = f"""
        Analyze this resume and extract structured information in JSON format:

        RESUME CONTENT:
        {text_content[:3000]}  # Limit for token efficiency

        Extract the following information:
        {{
            "skills": ["list of technical and soft skills"],
            "experience_years": number,
            "education": ["degree, school, year"],
            "previous_roles": ["job title at company"],
            "key_achievements": ["quantified achievements"],
            "contact_info": {{"email": "", "phone": "", "linkedin": "", "location": ""}}
        }}

        Be precise and only include information explicitly mentioned in the resume.
        """
        
        system_prompt = """You are an expert resume parser. Extract information accurately and return valid JSON only."""
        
        try:
            response = await generate_analysis_content(extraction_prompt, system_prompt)
            
            # Parse JSON response (with error handling)
            import json
            try:
                parsed_data = json.loads(response)
            except json.JSONDecodeError:
                # Fallback parsing if JSON is malformed
                logger.warning("LLM returned malformed JSON, using fallback parsing")
                parsed_data = self._fallback_resume_parse(text_content)
            
            processing_time = time.time() - start_time
            logger.info(f"✅ Resume parsed in {processing_time:.2f}s")
            
            return ResumeData(
                raw_text=text_content,
                skills=parsed_data.get("skills", []) if isinstance(parsed_data, dict) else [],
                experience_years=parsed_data.get("experience_years", 0) if isinstance(parsed_data, dict) else 0,
                education=parsed_data.get("education", []) if isinstance(parsed_data, dict) else [],
                previous_roles=parsed_data.get("previous_roles", []) if isinstance(parsed_data, dict) else [],
                key_achievements=parsed_data.get("key_achievements", []) if isinstance(parsed_data, dict) else [],
                contact_info=parsed_data.get("contact_info", {}) if isinstance(parsed_data, dict) else {}
            )
            
        except Exception as e:
            logger.error(f"❌ Resume parsing failed: {e}")
            # Return basic parsed data
            return self._fallback_resume_parse(text_content)
    
    async def parse_job_posting(self, job_input: str) -> JobData:
        """Parse job posting (URL or text) and extract structured data"""
        
        start_time = time.time()
        logger.info("🔄 Processing job posting...")
        
        # Determine if input is URL or text
        if job_input.startswith(('http://', 'https://', 'www.')):
            # It's a URL - scrape it
            logger.info(f"🌐 Scraping job URL: {job_input}")
            scrape_result = await self.scraper.scrape_job_posting(job_input)
            
            if scrape_result.success:
                job_text = scrape_result.content
                logger.info(f"✅ Scraped {len(job_text)} characters")
            else:
                logger.warning(f"⚠️  Scraping failed: {scrape_result.error}")
                return self._fallback_job_parse(job_input, f"Failed to scrape: {scrape_result.error}")
        else:
            # It's direct text
            job_text = job_input
            logger.info(f"📝 Using direct job text: {len(job_text)} characters")
        
        # Use LLM to extract structured job data
        extraction_prompt = f"""
        Analyze this job posting and extract structured information in JSON format:

        JOB POSTING:
        {job_text[:3000]}  # Limit for token efficiency

        Extract the following information:
        {{
            "company": "company name",
            "role": "job title/role",
            "location": "location (city, state/country or remote)",
            "required_skills": ["must-have skills"],
            "preferred_skills": ["nice-to-have skills"],
            "experience_level": "entry/mid/senior level",
            "salary_range": "salary range if mentioned",
            "benefits": ["benefits listed"],
            "responsibilities": ["key responsibilities"]
        }}

        Be precise and only include information explicitly mentioned.
        """
        
        system_prompt = """You are an expert job posting analyzer. Extract information accurately and return valid JSON only."""
        
        try:
            response = await generate_analysis_content(extraction_prompt, system_prompt)
            
            # Parse JSON response
            import json
            try:
                parsed_data = json.loads(response)
            except json.JSONDecodeError:
                logger.warning("LLM returned malformed JSON for job parsing")
                parsed_data = self._fallback_job_parse_data(job_text)
            
            processing_time = time.time() - start_time
            logger.info(f"✅ Job posting parsed in {processing_time:.2f}s")
            
            return JobData(
                raw_text=job_text,
                company=parsed_data.get("company", "Unknown Company"),
                role=parsed_data.get("role", "Unknown Role"),
                location=parsed_data.get("location", "Location not specified"),
                required_skills=parsed_data.get("required_skills", []),
                preferred_skills=parsed_data.get("preferred_skills", []),
                experience_level=parsed_data.get("experience_level", "Not specified"),
                salary_range=parsed_data.get("salary_range"),
                benefits=parsed_data.get("benefits", []),
                responsibilities=parsed_data.get("responsibilities", [])
            )
            
        except Exception as e:
            logger.error(f"❌ Job parsing failed: {e}")
            return self._fallback_job_parse(job_text, str(e))
    
    async def analyze_compatibility(self, resume_data: ResumeData, job_data: JobData) -> AnalysisResult:
        """Perform comprehensive compatibility analysis"""
        
        start_time = time.time()
        logger.info("🔄 Analyzing resume-job compatibility...")
        
        # Generate comprehensive analysis
        analysis_prompt = f"""
        Perform a detailed compatibility analysis between this resume and job posting:

        RESUME SUMMARY:
        - Experience: {resume_data.experience_years} years
        - Skills: {', '.join(resume_data.skills[:10])}
        - Previous Roles: {', '.join(resume_data.previous_roles[:3])}
        - Key Achievements: {', '.join(resume_data.key_achievements[:3])}

        JOB REQUIREMENTS:
        - Company: {job_data.company}
        - Role: {job_data.role}
        - Location: {job_data.location}
        - Required Skills: {', '.join(job_data.required_skills[:10])}
        - Preferred Skills: {', '.join(job_data.preferred_skills[:10])}
        - Experience Level: {job_data.experience_level}
        - Key Responsibilities: {', '.join(job_data.responsibilities[:5])}

        Provide analysis in this JSON format:
        {{
            "match_score": 85,
            "strengths": ["specific strengths with evidence"],
            "gaps": ["specific gaps and how to address them"],
            "interview_questions": [
                {{"question": "Tell me about...", "category": "technical", "difficulty": "medium"}},
                {{"question": "How would you...", "category": "behavioral", "difficulty": "easy"}}
            ],
            "preparation_advice": ["specific actionable advice"],
            "salary_insights": {{
                "market_range": "$X - $Y",
                "recommendation": "negotiate for $Z based on...",
                "factors": ["experience", "skills", "market demand"]
            }},
            "negotiation_points": ["specific points to emphasize"],
            "confidence_level": "high/medium/low",
            "action_items": ["immediate next steps"]
        }}

        Be specific, actionable, and realistic. Focus on match score accuracy.
        """
        
        system_prompt = """You are an expert career advisor and interview coach. Provide detailed, actionable analysis with accurate scoring based on resume-job fit."""
        
        try:
            response = await generate_analysis_content(analysis_prompt, system_prompt)
            
            # Parse analysis results
            import json
            try:
                analysis_data = json.loads(response)
            except json.JSONDecodeError:
                logger.warning("LLM returned malformed JSON for analysis")
                analysis_data = self._fallback_analysis_data(resume_data, job_data)
            
            processing_time = time.time() - start_time
            logger.info(f"✅ Analysis completed in {processing_time:.2f}s")
            
            return AnalysisResult(
                match_score=min(100, max(0, analysis_data.get("match_score", 75))),
                strengths=analysis_data.get("strengths", []),
                gaps=analysis_data.get("gaps", []),
                interview_questions=analysis_data.get("interview_questions", []),
                preparation_advice=analysis_data.get("preparation_advice", []),
                salary_insights=analysis_data.get("salary_insights", {}),
                negotiation_points=analysis_data.get("negotiation_points", []),
                processing_time=processing_time,
                confidence_level=analysis_data.get("confidence_level", "medium"),
                action_items=analysis_data.get("action_items", [])
            )
            
        except Exception as e:
            logger.error(f"❌ Analysis failed: {e}")
            return self._fallback_analysis(resume_data, job_data, str(e))
    
    async def full_analysis_pipeline(self, resume_content: str, job_input: str, 
                                   analysis_type: AnalysisType = AnalysisType.FULL) -> Dict[str, Any]:
        """Complete analysis pipeline from raw inputs to final results"""
        
        pipeline_start = time.time()
        logger.info(f"🚀 Starting {analysis_type.value} analysis pipeline...")
        
        try:
            # Step 1: Parse resume (parallel with job parsing)
            resume_task = asyncio.create_task(
                self.parse_resume(resume_content, is_file_content=False)
            )
            
            # Step 2: Parse job posting (parallel with resume parsing)
            job_task = asyncio.create_task(
                self.parse_job_posting(job_input)
            )
            
            # Wait for both parsing tasks to complete
            resume_data, job_data = await asyncio.gather(resume_task, job_task)
            
            # Step 3: Perform compatibility analysis
            analysis_result = await self.analyze_compatibility(resume_data, job_data)
            
            # Step 4: Get salary negotiation scenario (if full analysis)
            negotiation_scenario = None
            if analysis_type in [AnalysisType.FULL, AnalysisType.COMPREHENSIVE]:
                negotiation_scenario = get_random_scenario()
            
            total_time = time.time() - pipeline_start
            logger.info(f"✅ Pipeline completed in {total_time:.2f}s")
            
            # Compile final results
            results = {
                "success": True,
                "analysis_type": analysis_type.value,
                "processing_time": round(total_time, 2),
                "resume_data": {
                    "skills_count": len(resume_data.skills),
                    "experience_years": resume_data.experience_years,
                    "previous_roles_count": len(resume_data.previous_roles),
                    "achievements_count": len(resume_data.key_achievements)
                },
                "job_data": {
                    "company": job_data.company,
                    "role": job_data.role,
                    "location": job_data.location,
                    "required_skills_count": len(job_data.required_skills),
                    "experience_level": job_data.experience_level
                },
                "analysis": {
                    "match_score": analysis_result.match_score,
                    "strengths": analysis_result.strengths,
                    "gaps": analysis_result.gaps,
                    "interview_questions": analysis_result.interview_questions,
                    "preparation_advice": analysis_result.preparation_advice,
                    "salary_insights": analysis_result.salary_insights,
                    "negotiation_points": analysis_result.negotiation_points,
                    "confidence_level": analysis_result.confidence_level,
                    "action_items": analysis_result.action_items
                },
                "metadata": {
                    "timestamp": time.time(),
                    "version": "2.0",
                    "llm_provider": self.llm_client.get_status()["primary_provider"],
                    "scraping_method": "auto-detected"
                }
            }
            
            # Add negotiation scenario if included
            if negotiation_scenario:
                results["negotiation_scenario"] = {
                    "id": negotiation_scenario.id,
                    "title": negotiation_scenario.title,
                    "situation": negotiation_scenario.situation,
                    "question": negotiation_scenario.question,
                    "options": negotiation_scenario.options,
                    "difficulty": negotiation_scenario.difficulty,
                    "type": negotiation_scenario.type.value
                }
            
            return results
            
        except Exception as e:
            logger.error(f"❌ Pipeline failed: {e}")
            return {
                "success": False,
                "error": str(e),
                "processing_time": time.time() - pipeline_start,
                "fallback_message": "Analysis failed. Please try again or contact support."
            }
    
    def _fallback_resume_parse(self, text: str) -> ResumeData:
        """Fallback resume parsing when LLM fails"""
        
        # Basic keyword extraction
        import re
        
        skills = []
        for skill in ["Python", "JavaScript", "SQL", "Excel", "Communication", "Leadership"]:
            if skill.lower() in text.lower():
                skills.append(skill)
        
        # Basic experience extraction
        experience_match = re.search(r'(\d+)[\s\+]*years?\s+(?:of\s+)?experience', text, re.IGNORECASE)
        experience_years = int(experience_match.group(1)) if experience_match else 2
        
        return ResumeData(
            raw_text=text,
            skills=skills,
            experience_years=experience_years,
            education=["Education not parsed"],
            previous_roles=["Previous roles not parsed"],
            key_achievements=["Achievements not parsed"],
            contact_info={}
        )
    
    def _fallback_job_parse(self, text: str, error: str) -> JobData:
        """Fallback job parsing when scraping/LLM fails"""
        
        return JobData(
            raw_text=text,
            company="Company not detected",
            role="Role not detected",
            location="Location not specified",
            required_skills=["Skills not parsed"],
            preferred_skills=[],
            experience_level="Not specified",
            salary_range=None,
            benefits=[],
            responsibilities=["Responsibilities not parsed"]
        )
    
    def _fallback_job_parse_data(self, text: str) -> Dict[str, Any]:
        """Fallback structured job data parsing"""
        return {
            "company": "Unknown Company",
            "role": "Unknown Role",
            "location": "Location not specified",
            "required_skills": ["Skills not parsed"],
            "preferred_skills": [],
            "experience_level": "Not specified",
            "salary_range": None,
            "benefits": [],
            "responsibilities": ["Responsibilities not parsed"]
        }
    
    def _fallback_analysis_data(self, resume_data: ResumeData, job_data: JobData) -> Dict[str, Any]:
        """Fallback analysis data when LLM fails"""
        return {
            "match_score": 65,
            "strengths": ["Experience in relevant field", "Skills alignment"],
            "gaps": ["Analysis incomplete due to technical issues"],
            "interview_questions": [
                {"question": "Tell me about your experience", "category": "general", "difficulty": "easy"}
            ],
            "preparation_advice": ["Review your experience", "Practice common interview questions"],
            "salary_insights": {
                "market_range": "Market data unavailable",
                "recommendation": "Research salary ranges for this role",
                "factors": ["experience", "skills", "location"]
            },
            "negotiation_points": ["Highlight your experience"],
            "confidence_level": "medium",
            "action_items": ["Prepare for interview", "Research company"]
        }
    
    def _fallback_analysis(self, resume_data: ResumeData, job_data: JobData, error: str) -> AnalysisResult:
        """Fallback analysis result when LLM fails"""
        
        return AnalysisResult(
            match_score=65.0,
            strengths=["Experience in relevant field"],
            gaps=["Analysis incomplete due to technical issues"],
            interview_questions=[{"question": "Tell me about yourself", "category": "general", "difficulty": "easy"}],
            preparation_advice=["Review the job description", "Prepare STAR method examples"],
            salary_insights={"recommendation": "Research market rates"},
            negotiation_points=["Highlight your experience"],
            processing_time=1.0,
            confidence_level="low",
            action_items=["Retry analysis", "Check your inputs"]
        )
    
    def get_status(self) -> Dict[str, Any]:
        """Get orchestrator status and component health"""
        
        return {
            "llm_status": self.llm_client.get_status(),
            "scraper_status": self.scraper.get_status(),
            "config": {
                "analysis_timeout": self.config.max_analysis_time,
                "retry_attempts": self.config.retry_attempts
            },
            "version": "2.0",
            "features": {
                "firecrawl_scraping": self.scraper.get_status()["firecrawl_available"],
                "multi_llm_fallback": self.llm_client.get_status()["fallback_available"],
                "salary_negotiation": True
            }
        }

# Global orchestrator instance
_orchestrator: Optional[EnhancedInterviewOrchestrator] = None

def get_orchestrator() -> EnhancedInterviewOrchestrator:
    """Get global orchestrator instance"""
    global _orchestrator
    if _orchestrator is None:
        _orchestrator = EnhancedInterviewOrchestrator()
    return _orchestrator

async def quick_analysis(resume_content: str, job_input: str) -> Dict[str, Any]:
    """Convenience function for quick analysis"""
    orchestrator = get_orchestrator()
    return await orchestrator.full_analysis_pipeline(resume_content, job_input, AnalysisType.QUICK)

async def full_analysis(resume_content: str, job_input: str) -> Dict[str, Any]:
    """Convenience function for full analysis"""
    orchestrator = get_orchestrator()
    return await orchestrator.full_analysis_pipeline(resume_content, job_input, AnalysisType.FULL)

if __name__ == "__main__":
    async def test_orchestrator():
        """Test the orchestrator functionality"""
        orchestrator = EnhancedInterviewOrchestrator()
        
        print("🧪 Testing Enhanced Interview Orchestrator")
        print("=" * 60)
        
        # Print status
        status = orchestrator.get_status()
        print("📊 Orchestrator Status:")
        for key, value in status.items():
            print(f"  {key}: {value}")
        
        # Test with sample data
        sample_resume = """
        John Doe
        Software Engineer
        5 years experience in Python, JavaScript, SQL
        Led team of 3 developers at TechCorp
        Built scalable web applications serving 10k+ users
        """
        
        sample_job = """
        Senior Software Engineer at InnovateTech
        Requirements: 3+ years Python, JavaScript, team leadership
        Responsibilities: Lead development team, architect solutions
        Location: San Francisco, CA
        Salary: $120,000 - $150,000
        """
        
        print(f"\n🔄 Testing analysis pipeline...")
        
        try:
            result = await orchestrator.full_analysis_pipeline(sample_resume, sample_job)
            
            if result["success"]:
                print(f"✅ Analysis completed in {result['processing_time']}s")
                print(f"🎯 Match Score: {result['analysis']['match_score']}%")
                print(f"💪 Strengths: {len(result['analysis']['strengths'])}")
                print(f"📋 Interview Questions: {len(result['analysis']['interview_questions'])}")
                print(f"🔍 Confidence: {result['analysis']['confidence_level']}")
            else:
                print(f"❌ Analysis failed: {result.get('error', 'Unknown error')}")
                
        except Exception as e:
            print(f"❌ Test failed: {e}")
        
        print("=" * 60)
    
    # Run test
    asyncio.run(test_orchestrator())