models/intelligence_models.py

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


class DeliveryHealthMetrics(BaseModel):
    """Metrics for delivery health analysis"""
    sprint_id: Optional[str] = None
    sprint_name: Optional[str] = None
    period_start: date
    period_end: date
    
    # Velocity Metrics
    planned_story_points: float = 0
    completed_story_points: float = 0
    velocity: float = 0
    velocity_trend: float = 0  # percentage change from previous period
    
    # Completion Metrics
    total_issues: int = 0
    completed_issues: int = 0
    completion_rate: float = 0
    
    # Time Metrics
    avg_cycle_time_hours: float = 0
    avg_lead_time_hours: float = 0
    
    # Quality Indicators
    blocked_issues_count: int = 0
    overdue_issues_count: int = 0
    reopened_issues_count: int = 0
    
    # Risk Indicators
    at_risk_issues: int = 0
    health_score: float = 0  # 0-100


class ProductivityMetrics(BaseModel):
    """Workforce productivity metrics"""
    team_member_id: str
    team_member_name: str
    period_start: date
    period_end: date
    
    # Activity Metrics
    issues_completed: int = 0
    story_points_completed: float = 0
    code_commits: int = 0  # from GitHub
    pull_requests: int = 0  # from GitHub
    
    # Time Metrics
    total_hours_logged: float = 0
    avg_hours_per_day: float = 0
    
    # Efficiency Metrics
    avg_issue_completion_time_hours: float = 0
    productivity_score: float = 0  # 0-100
    
    # Workload Metrics
    current_assigned_issues: int = 0
    current_story_points: float = 0
    utilization_rate: float = 0  # percentage


class CostEfficiencyMetrics(BaseModel):
    """Cost and efficiency analysis"""
    period_start: date
    period_end: date
    project_key: Optional[str] = None
    
    # Resource Metrics
    total_team_members: int = 0
    total_hours_logged: float = 0
    estimated_cost: float = 0  # based on hours
    
    # Output Metrics
    features_delivered: int = 0
    story_points_delivered: float = 0
    
    # Efficiency Ratios
    cost_per_feature: float = 0
    cost_per_story_point: float = 0
    hours_per_story_point: float = 0
    
    # Waste Indicators
    blocked_time_hours: float = 0
    rework_hours: float = 0
    waste_percentage: float = 0


class TeamCapacityMetrics(BaseModel):
    """Team capacity and utilization"""
    team_id: Optional[str] = None
    team_name: str
    period_start: date
    period_end: date
    
    # Capacity Metrics
    total_capacity_hours: float = 0
    allocated_hours: float = 0
    available_hours: float = 0
    utilization_rate: float = 0
    
    # Workload Distribution
    team_members: List[Dict] = Field(default_factory=list)
    overloaded_members: int = 0
    underutilized_members: int = 0
    
    # Sprint Metrics
    current_sprint_load: float = 0
    forecasted_capacity: float = 0


class RiskAlert(BaseModel):
    """Risk and alert model"""
    alert_id: str
    alert_type: str  # delivery_delay, cost_overrun, resource_shortage, quality_issue
    severity: str  # critical, high, medium, low
    title: str
    description: str
    affected_entity: str  # sprint, project, team_member
    entity_id: str
    detected_at: datetime
    suggested_action: Optional[str] = None
    metrics: Dict = Field(default_factory=dict)


class InsightRecommendation(BaseModel):
    """AI-generated insights and recommendations"""
    insight_id: str
    category: str  # delivery, productivity, cost, resource
    title: str
    description: str
    confidence_score: float = 0  # 0-1
    impact_level: str  # high, medium, low
    recommendations: List[str] = Field(default_factory=list)
    supporting_data: Dict = Field(default_factory=dict)
    generated_at: datetime


class KanbanFlowMetrics(BaseModel):
    """Kanban flow efficiency metrics"""
    board_id: int
    board_name: str
    period_start: date
    period_end: date
    
    # Flow Metrics
    throughput: int = 0  # Issues completed in period
    avg_cycle_time_days: float = 0
    avg_lead_time_days: float = 0
    flow_efficiency: float = 0  # 0-100
    
    # WIP Metrics
    current_wip: int = 0
    avg_wip: float = 0
    wip_violations: int = 0  # Number of times WIP limits were exceeded
    
    # Column Metrics
    bottleneck_column: Optional[str] = None
    bottleneck_score: float = 0
    
    # Predictability
    throughput_variance: float = 0
    cycle_time_variance: float = 0
    
    # Health Score
    flow_health_score: float = 0  # 0-100


class KanbanColumnAnalysis(BaseModel):
    """Analysis for a specific Kanban column"""
    column_name: str
    statuses: List[str]
    
    # Current State
    current_issue_count: int = 0
    wip_limit_min: Optional[int] = None
    wip_limit_max: Optional[int] = None
    is_over_wip_limit: bool = False
    
    # Flow Metrics
    avg_time_in_column_days: float = 0
    throughput: int = 0  # Issues exited this column
    
    # Efficiency
    utilization_rate: float = 0  # current / max limit
    is_bottleneck: bool = False
    bottleneck_score: float = 0  # Higher = more of a bottleneck


class KanbanCumulativeFlow(BaseModel):
    """Cumulative flow diagram data"""
    board_id: int
    period_start: date
    period_end: date
    
    # Daily snapshots
    data_points: List[Dict] = Field(default_factory=list)
    # Each data point: {date, column_name, issue_count}


class WIPLimitRecommendation(BaseModel):
    """WIP limit optimization recommendations"""
    column_name: str
    current_limit: Optional[int]
    recommended_min: int
    recommended_max: int
    reasoning: str
    confidence_score: float = 0  # 0-1