118 lines
3.5 KiB
Python
118 lines
3.5 KiB
Python
"""Result Interpretation / Report Generator Router"""
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from fastapi import APIRouter
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from pydantic import BaseModel
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from typing import Optional
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from datetime import datetime
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router = APIRouter()
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class MetricInput(BaseModel):
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name: str
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value: float
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previous_value: Optional[float] = None
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unit: Optional[str] = None
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threshold_warning: Optional[float] = None
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threshold_critical: Optional[float] = None
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class ReportRequest(BaseModel):
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title: str
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metrics: list[MetricInput]
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period: str = "last_30_days"
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audience: str = "executive" # executive, technical, operational
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format: str = "markdown" # markdown, json, html
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class Insight(BaseModel):
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category: str # improvement, decline, stable, anomaly
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metric: str
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description: str
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action: Optional[str] = None
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priority: str # high, medium, low
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class GeneratedReport(BaseModel):
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title: str
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generated_at: datetime
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summary: str
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insights: list[Insight]
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action_items: list[str]
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content: str # Full report content
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@router.post("/generate", response_model=GeneratedReport)
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async def generate_report(request: ReportRequest):
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"""Generate an interpreted report from metrics"""
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# TODO: Implement report generation with LLM
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insights = []
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action_items = []
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for metric in request.metrics:
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# Simple trend analysis
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if metric.previous_value:
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change = ((metric.value - metric.previous_value) / metric.previous_value) * 100
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if change > 10:
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insights.append(Insight(
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category="improvement",
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metric=metric.name,
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description=f"{metric.name} increased by {change:.1f}%",
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priority="medium"
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))
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elif change < -10:
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insights.append(Insight(
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category="decline",
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metric=metric.name,
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description=f"{metric.name} decreased by {abs(change):.1f}%",
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action=f"Investigate cause of {metric.name} decline",
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priority="high"
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))
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return GeneratedReport(
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title=request.title,
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generated_at=datetime.now(),
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summary=f"Report covering {request.period} with {len(request.metrics)} metrics analyzed.",
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insights=insights,
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action_items=action_items,
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content=""
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)
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@router.post("/summarize")
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async def summarize_metrics(metrics: list[MetricInput]):
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"""Generate an executive summary from metrics"""
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# TODO: Implement LLM-based summarization
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return {
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"summary": "Executive summary placeholder",
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"key_points": [],
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"concerns": []
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}
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@router.get("/templates")
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async def list_report_templates():
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"""List available report templates"""
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return {
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"templates": [
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{"name": "weekly_performance", "description": "Weekly AI performance report"},
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{"name": "monthly_costs", "description": "Monthly cost analysis report"},
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{"name": "quarterly_review", "description": "Quarterly business review"},
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{"name": "incident_summary", "description": "Incident and downtime summary"},
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]
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}
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@router.post("/schedule")
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async def schedule_report(
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template: str,
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frequency: str, # daily, weekly, monthly
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recipients: list[str]
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):
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"""Schedule automated report generation"""
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# TODO: Implement report scheduling
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return {
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"message": "Report scheduled",
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"template": template,
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"frequency": frequency,
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"recipients": recipients
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}
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