Human Resources & Recruitment AI

from typing import Dict, Any, List, Optional, Tuple
import asyncio
from datetime import datetime, timedelta
import numpy as np
from dataclasses import dataclass

class HRRecruitmentLLM:
    """Advanced HR & Recruitment AI Platform
    Integrates resume screening, candidate matching, and bias reduction
    Based on HRGraph research for 30-50% efficiency gains"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.knowledge_graph = HRKnowledgeGraph()
        self.bias_detector = BiasReductionEngine()
        self.skills_analyzer = SkillsMatchingAI()
        self.engagement_predictor = EmployeeEngagementAI()
        
    async def screen_candidates(
        self,
        job_posting: Dict[str, Any],
        resumes: List[Dict[str, Any]],
        diversity_targets: Optional[Dict[str, float]] = None
    ) -> List[Dict[str, Any]]:
        """Screen resumes with bias reduction and skill matching"""
        
        # 1. Extract job requirements and build skill profile
        job_requirements = await self.extract_job_requirements(job_posting)
        required_skills = await self.knowledge_graph.expand_skills(
            job_requirements['skills'],
            include_related=True,
            include_synonyms=True
        )
        
        # 2. Process each resume
        screened_candidates = []
        
        for resume in resumes:
            # Anonymize to reduce bias
            anonymized = await self.bias_detector.anonymize_resume(resume)
            
            # Extract candidate skills and experience
            candidate_profile = await self.extract_candidate_profile(
                anonymized,
                include_implicit_skills=True
            )
            
            # Calculate match score using LLM
            match_analysis = await self.llm.chat.completions.create(
                model="claude-3-opus-20240229",
                messages=[{
                    "role": "system",
                    "content": """You are an expert HR recruiter analyzing candidate fit.
                    Focus only on skills, experience, and qualifications.
                    Ignore any demographic information."""
                },
                {
                    "role": "user",
                    "content": f"""
                    Job Requirements:
                    {json.dumps(job_requirements, indent=2)}
                    
                    Candidate Profile:
                    {json.dumps(candidate_profile, indent=2)}
                    
                    Analyze:
                    1. Skill match percentage (0-100)
                    2. Experience relevance (0-100)
                    3. Potential for role (0-100)
                    4. Key strengths for this role
                    5. Development areas
                    6. Overall recommendation (0-100)
                    
                    Return as JSON.
                    """
                }],
                response_format={"type": "json_object"},
                temperature=0.3
            )
            
            match_result = json.loads(
                match_analysis.choices[0].message.content
            )
            
            # Add to results with original resume data
            screened_candidates.append({
                "candidate": resume,
                "match_score": match_result['overall_recommendation'],
                "skill_match": match_result['skill_match_percentage'],
                "strengths": match_result['key_strengths'],
                "development_areas": match_result['development_areas'],
                "anonymized_score": True  # Flag for bias-free scoring
            })
        
        # 3. Apply diversity considerations if specified
        if diversity_targets:
            screened_candidates = await self.apply_diversity_optimization(
                screened_candidates,
                diversity_targets
            )
        
        # 4. Sort by match score and return top candidates
        screened_candidates.sort(
            key=lambda x: x['match_score'],
            reverse=True
        )
        
        return screened_candidates
    
    async def optimize_job_description(
        self,
        job_description: str,
        target_audience: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """Optimize job descriptions for clarity and inclusivity"""
        
        # Analyze current description
        analysis = await self.llm.chat.completions.create(
            model="gpt-4-turbo-preview",
            messages=[{
                "role": "user",
                "content": f"""
                Analyze this job description for:
                1. Clarity and readability
                2. Inclusive language
                3. Unnecessary jargon
                4. Gender-coded words
                5. Age bias indicators
                6. Education requirements (are they necessary?)
                
                Job Description:
                {job_description}
                
                Provide specific improvement suggestions.
                """
            }],
            temperature=0.4
        )
        
        # Generate optimized version
        optimized = await self.llm.chat.completions.create(
            model="claude-3-sonnet-20240229",
            messages=[{
                "role": "user",
                "content": f"""
                Rewrite this job description to be:
                1. More inclusive and welcoming
                2. Clearer about actual responsibilities
                3. Free from unnecessary jargon
                4. Focused on outcomes not just requirements
                5. SEO-optimized for job boards
                
                Original: {job_description}
                
                Analysis: {analysis.choices[0].message.content}
                """
            }],
            temperature=0.7
        )
        
        return {
            "original": job_description,
            "optimized": optimized.choices[0].message.content,
            "improvements": analysis.choices[0].message.content,
            "bias_score": await self.bias_detector.score_text(
                job_description
            ),
            "readability_score": await self.calculate_readability(
                optimized.choices[0].message.content
            )
        }

# Intelligent Interview Management
class InterviewAutomationAI:
    """Automate interview scheduling and initial screening"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.calendar_integration = CalendarAPI()
        self.question_bank = InterviewQuestionBank()
        
    async def schedule_interviews(
        self,
        candidates: List[Dict[str, Any]],
        interviewers: List[Dict[str, Any]],
        interview_types: List[str]
    ) -> Dict[str, Any]:
        """Intelligently schedule interviews based on availability"""
        
        scheduled_interviews = []
        
        for candidate in candidates:
            # Get candidate availability
            candidate_slots = await self.get_availability_slots(
                candidate['email'],
                days_ahead=14
            )
            
            # Match with interviewer availability
            for interview_type in interview_types:
                suitable_interviewers = self.match_interviewers(
                    interviewers,
                    interview_type,
                    candidate['role']
                )
                
                # Find optimal slot
                optimal_slot = await self.find_optimal_slot(
                    candidate_slots,
                    suitable_interviewers,
                    duration=self.get_interview_duration(interview_type)
                )
                
                if optimal_slot:
                    scheduled_interviews.append({
                        "candidate": candidate,
                        "interviewer": optimal_slot['interviewer'],
                        "type": interview_type,
                        "time": optimal_slot['time'],
                        "duration": optimal_slot['duration'],
                        "meeting_link": await self.create_meeting_link(
                            optimal_slot
                        )
                    })
        
        return {
            "scheduled": scheduled_interviews,
            "success_rate": len(scheduled_interviews) / 
                          (len(candidates) * len(interview_types)),
            "calendar_invites": await self.send_calendar_invites(
                scheduled_interviews
            )
        }
    
    async def conduct_ai_screening(
        self,
        candidate: Dict[str, Any],
        job_requirements: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Conduct initial AI-powered screening interview"""
        
        # Generate role-specific questions
        screening_questions = await self.question_bank.generate_questions(
            role=job_requirements['title'],
            skills=job_requirements['required_skills'],
            experience_level=job_requirements['experience_level'],
            question_count=5
        )
        
        # Simulate conversational screening
        screening_results = []
        
        for question in screening_questions:
            # In production, this would be actual candidate responses
            # Here we'll analyze hypothetical responses
            
            response_analysis = await self.llm.chat.completions.create(
                model="gpt-4-turbo-preview",
                messages=[{
                    "role": "system",
                    "content": """Evaluate candidate responses for:
                    1. Relevance to question
                    2. Depth of knowledge
                    3. Communication clarity
                    4. Problem-solving approach
                    5. Cultural fit indicators"""
                },
                {
                    "role": "user",
                    "content": f"""
                    Question: {question['question']}
                    Expected competencies: {question['competencies']}
                    
                    Evaluate and score 0-100.
                    """
                }],
                temperature=0.3
            )
            
            screening_results.append({
                "question": question,
                "analysis": response_analysis.choices[0].message.content
            })
        
        # Generate overall recommendation
        overall_assessment = await self.generate_screening_report(
            candidate,
            screening_results,
            job_requirements
        )
        
        return overall_assessment

# Employee Lifecycle Management
class EmployeeLifecycleLLM:
    """Manage onboarding, performance, and engagement with AI"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.performance_analyzer = PerformanceAnalyzer()
        self.engagement_monitor = EngagementMonitor()
        
    async def generate_onboarding_plan(
        self,
        new_hire: Dict[str, Any],
        role: Dict[str, Any],
        team: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create personalized onboarding plan"""
        
        onboarding_plan = await self.llm.chat.completions.create(
            model="claude-3-sonnet-20240229",
            messages=[{
                "role": "user",
                "content": f"""
                Create a comprehensive 90-day onboarding plan for:
                
                New Hire: {new_hire['name']}
                Role: {role['title']}
                Department: {team['department']}
                Start Date: {new_hire['start_date']}
                
                Include:
                1. Week-by-week objectives
                2. Key meetings and introductions
                3. Training modules required
                4. Tools and access needed
                5. 30/60/90 day milestones
                6. Mentorship assignments
                7. Success metrics
                
                Format as structured JSON.
                """
            }],
            response_format={"type": "json_object"},
            temperature=0.6
        )
        
        plan = json.loads(onboarding_plan.choices[0].message.content)
        
        # Automate task creation and assignments
        plan['automated_tasks'] = await self.create_onboarding_tasks(
            plan,
            new_hire,
            team
        )
        
        return plan
    
    async def generate_performance_review(
        self,
        employee: Dict[str, Any],
        review_period: Dict[str, Any],
        feedback_sources: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Generate comprehensive performance review"""
        
        # Aggregate feedback from multiple sources
        aggregated_feedback = await self.performance_analyzer.aggregate(
            feedback_sources
        )
        
        # Generate review narrative
        review = await self.llm.chat.completions.create(
            model="gpt-4-turbo-preview",
            messages=[{
                "role": "user",
                "content": f"""
                Generate performance review for {employee['name']}:
                
                Role: {employee['role']}
                Review Period: {review_period}
                
                Feedback Summary:
                {json.dumps(aggregated_feedback, indent=2)}
                
                Include:
                1. Overall performance rating
                2. Key achievements
                3. Strengths demonstrated
                4. Areas for improvement
                5. Specific examples
                6. Development recommendations
                7. Goals for next period
                
                Tone: Constructive, specific, and actionable
                """
            }],
            temperature=0.4
        )
        
        return {
            "review": review.choices[0].message.content,
            "metrics": aggregated_feedback,
            "development_plan": await self.create_development_plan(
                employee,
                aggregated_feedback
            )
        }

# Skills Gap Analysis
class SkillsAnalysisLLM:
    """Identify and address organizational skill gaps"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.skills_taxonomy = SkillsTaxonomy()
        self.training_recommender = TrainingRecommender()
        
    async def analyze_skills_gap(
        self,
        current_skills: List[Dict[str, Any]],
        required_skills: List[Dict[str, Any]],
        future_needs: Optional[List[str]] = None
    ) -> Dict[str, Any]:
        """Comprehensive skills gap analysis"""
        
        # Map current organizational skills
        skills_inventory = await self.skills_taxonomy.map_skills(
            current_skills
        )
        
        # Identify gaps
        gaps = await self.llm.chat.completions.create(
            model="claude-3-opus-20240229",
            messages=[{
                "role": "user",
                "content": f"""
                Analyze skills gap:
                
                Current Skills:
                {json.dumps(skills_inventory, indent=2)}
                
                Required Skills:
                {json.dumps(required_skills, indent=2)}
                
                Future Skills (next 2 years):
                {future_needs}
                
                Identify:
                1. Critical gaps by priority
                2. Affected roles/departments
                3. Business impact of gaps
                4. Time to close each gap
                5. Build vs buy vs borrow recommendations
                """
            }],
            temperature=0.3
        )
        
        gap_analysis = json.loads(gaps.choices[0].message.content)
        
        # Generate training recommendations
        training_plan = await self.training_recommender.create_plan(
            gap_analysis,
            budget_constraints=True
        )
        
        return {
            "gaps": gap_analysis,
            "training_plan": training_plan,
            "investment_required": training_plan['total_cost'],
            "timeline": training_plan['implementation_timeline'],
            "roi_projection": await self.calculate_training_roi(
                gap_analysis,
                training_plan
            )
        }

# HR Chatbot for Employee Support
class HRChatbotLLM:
    """24/7 employee support for HR queries"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.policy_knowledge = PolicyKnowledgeBase()
        self.benefits_engine = BenefitsEngine()
        
    async def handle_employee_query(
        self,
        query: str,
        employee_id: str,
        context: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """Process employee HR queries with personalized responses"""
        
        # Retrieve employee context
        employee = await self.get_employee_profile(employee_id)
        
        # Classify query type
        query_classification = await self.classify_query(query)
        
        # Route to appropriate handler
        if query_classification['type'] == 'benefits':
            response = await self.handle_benefits_query(
                query,
                employee
            )
        elif query_classification['type'] == 'policy':
            response = await self.handle_policy_query(
                query,
                employee
            )
        elif query_classification['type'] == 'leave':
            response = await self.handle_leave_query(
                query,
                employee
            )
        else:
            # General query handling
            response = await self.llm.chat.completions.create(
                model="gpt-4-turbo-preview",
                messages=[{
                    "role": "system",
                    "content": """You are a helpful HR assistant.
                    Provide accurate, empathetic responses based on 
                    company policies and employee information."""
                },
                {
                    "role": "user",
                    "content": f"""
                    Employee Query: {query}
                    Employee Role: {employee['role']}
                    Department: {employee['department']}
                    
                    Provide a helpful, accurate response.
                    """
                }],
                temperature=0.5
            )
            
        return {
            "response": response,
            "query_type": query_classification['type'],
            "confidence": query_classification['confidence'],
            "follow_up_needed": query_classification.get(
                'requires_human',
                False
            )
        }

# Deploy with ParrotRouter.com for:
# - Multi-model support for different HR tasks
# - Compliance-ready infrastructure
# - Automatic failover for 24/7 availability
# - GDPR-compliant data handling
# - Enterprise-grade security