"""Result Interpretation / Report Generator Router"""
from fastapi import APIRouter
from pydantic import BaseModel
from typing import Optional
from datetime import datetime

router = APIRouter()


class MetricInput(BaseModel):
    name: str
    value: float
    previous_value: Optional[float] = None
    unit: Optional[str] = None
    threshold_warning: Optional[float] = None
    threshold_critical: Optional[float] = None


class ReportRequest(BaseModel):
    title: str
    metrics: list[MetricInput]
    period: str = "last_30_days"
    audience: str = "executive"  # executive, technical, operational
    format: str = "markdown"  # markdown, json, html


class Insight(BaseModel):
    category: str  # improvement, decline, stable, anomaly
    metric: str
    description: str
    action: Optional[str] = None
    priority: str  # high, medium, low


class GeneratedReport(BaseModel):
    title: str
    generated_at: datetime
    summary: str
    insights: list[Insight]
    action_items: list[str]
    content: str  # Full report content


@router.post("/generate", response_model=GeneratedReport)
async def generate_report(request: ReportRequest):
    """Generate an interpreted report from metrics"""
    # TODO: Implement report generation with LLM
    insights = []
    action_items = []

    for metric in request.metrics:
        # Simple trend analysis
        if metric.previous_value:
            change = ((metric.value - metric.previous_value) / metric.previous_value) * 100
            if change > 10:
                insights.append(Insight(
                    category="improvement",
                    metric=metric.name,
                    description=f"{metric.name} increased by {change:.1f}%",
                    priority="medium"
                ))
            elif change < -10:
                insights.append(Insight(
                    category="decline",
                    metric=metric.name,
                    description=f"{metric.name} decreased by {abs(change):.1f}%",
                    action=f"Investigate cause of {metric.name} decline",
                    priority="high"
                ))

    return GeneratedReport(
        title=request.title,
        generated_at=datetime.now(),
        summary=f"Report covering {request.period} with {len(request.metrics)} metrics analyzed.",
        insights=insights,
        action_items=action_items,
        content=""
    )


@router.post("/summarize")
async def summarize_metrics(metrics: list[MetricInput]):
    """Generate an executive summary from metrics"""
    # TODO: Implement LLM-based summarization
    return {
        "summary": "Executive summary placeholder",
        "key_points": [],
        "concerns": []
    }


@router.get("/templates")
async def list_report_templates():
    """List available report templates"""
    return {
        "templates": [
            {"name": "weekly_performance", "description": "Weekly AI performance report"},
            {"name": "monthly_costs", "description": "Monthly cost analysis report"},
            {"name": "quarterly_review", "description": "Quarterly business review"},
            {"name": "incident_summary", "description": "Incident and downtime summary"},
        ]
    }


@router.post("/schedule")
async def schedule_report(
    template: str,
    frequency: str,  # daily, weekly, monthly
    recipients: list[str]
):
    """Schedule automated report generation"""
    # TODO: Implement report scheduling
    return {
        "message": "Report scheduled",
        "template": template,
        "frequency": frequency,
        "recipients": recipients
    }
