from typing import List, Dict, Optional, Any
from pydantic import BaseModel, Field
from datetime import datetime

class JobPosting(BaseModel):
    id: str = Field(..., description="Unique job identifier, e.g. JOB-BLR-101")
    title: str = Field(..., description="Job title, e.g., Senior Backend Engineer (Go/Distributed Systems)")
    company: str = Field(..., description="Company name")
    tech_domain: str = Field(default="Backend Engineering", description="Domain: Backend, Frontend, Full Stack, DevOps/Cloud, Data Engineering, AI/ML & GenAI, Mobile, Cybersecurity")
    city: str = Field(default="Bengaluru", description="City location: Bengaluru, Pune, Hyderabad, Gurgaon, Mumbai, Chennai, Remote")
    area: str = Field(default="Indiranagar", description="Locality or Tech Park, e.g. Outer Ring Road, Hinjawadi, HITEC City, Cyber City")
    salary_min_lpa: float = Field(..., description="Minimum salary in Lakhs Per Annum (LPA)")
    salary_max_lpa: float = Field(..., description="Maximum salary in Lakhs Per Annum (LPA)")
    experience_min_years: int = Field(..., description="Minimum experience required in years")
    experience_max_years: int = Field(..., description="Maximum experience requested in years")
    tech_stack: List[str] = Field(default_factory=list, description="Extracted tech stack, e.g., ['Go', 'Kubernetes', 'gRPC', 'PostgreSQL', 'Redis']")
    requirements: str = Field(..., description="Full text job description and responsibilities")
    work_mode: str = Field(default="Hybrid", description="Hybrid, On-site, or Remote")
    company_tier: str = Field(default="Product Unicorn / Enterprise", description="Company tier")
    posted_date: str = Field(default_factory=lambda: datetime.now().strftime("%Y-%m-%d"))
    url: Optional[str] = Field(default="https://techradar.ai/jobs", description="Job posting URL")
    semantic_score: Optional[float] = Field(default=None, description="Relevance score from semantic vector search")

class SkillGapReport(BaseModel):
    target_job_id: str
    job_title: str
    company: str
    city: str
    tech_domain: str
    match_percentage: float = Field(..., description="Overall candidate match percentage (0-100%)")
    matched_skills: List[str] = Field(default_factory=list, description="Skills present in candidate profile & JD")
    missing_skills: List[str] = Field(default_factory=list, description="Critical skills in JD missing from candidate profile")
    high_priority_gaps: List[str] = Field(default_factory=list, description="Top deal-breaker missing skills for this role")
    recommended_action_plan: List[Dict[str, str]] = Field(default_factory=list, description="Actionable micro-projects to bridge gaps")
    estimated_learning_hours: int = Field(default=20, description="Estimated effort to reach 90%+ match")

class ResumePatch(BaseModel):
    target_job_id: str
    job_title: str
    company: str
    ats_compatibility_score: float = Field(..., description="Score out of 100 for ATS parsing")
    tailored_bullets: List[Dict[str, str]] = Field(
        ...,
        description="List of dicts with 'original', 'tailored', and 'rationale'"
    )
    added_keywords: List[str] = Field(default_factory=list, description="Keywords injected for ATS optimization")

class CityMarketInsights(BaseModel):
    city: str
    tech_domain: str
    total_active_jobs: int
    avg_salary_lpa: float
    salary_range: str
    top_demanded_frameworks: List[Dict[str, Any]]
    top_hiring_hubs: List[Dict[str, Any]]
    top_employers: List[str]
    growth_trend: str

class InterviewQuestion(BaseModel):
    question: str
    category: str
    difficulty: str
    ideal_answer_points: List[str]
    company_context: Optional[str] = None

class InterviewPrepKit(BaseModel):
    job_id: str
    job_title: str
    company: str
    city: str
    tech_domain: str
    technical_questions: List[InterviewQuestion]
    system_design_challenge: Dict[str, Any]
    prep_tips: List[str]