46dea3304f
This commit integrates the backend API for fetching and updating report data. It also includes a normalization function to handle data consistency between the API and local storage. Co-authored-by: anthonymuncher <anthonymuncher@gmail.com>
65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
# app/routes/analytics.py
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from fastapi import APIRouter, Depends
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from sqlalchemy.orm import Session
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from sqlalchemy import func
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from app.database import get_db
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from app.models.ticket_model import Ticket, SeverityLevel, TicketStatus
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from typing import Dict, Any
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router = APIRouter()
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# ----------------------
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# GET /analytics
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# ----------------------
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@router.get("/analytics", response_model=Dict[str, Any])
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def analytics(db: Session = Depends(get_db), cluster_size: float = 0.01):
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"""
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Returns summary statistics for tickets:
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- Total tickets
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- Counts by category
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- Counts by severity
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- Counts by status
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- Optional: location clustering (hotspots) using grid-based approach
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"""
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# Total tickets
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total_tickets = db.query(func.count(Ticket.id)).scalar()
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# Counts by category
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category_counts = dict(
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db.query(Ticket.category, func.count(Ticket.id))
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.group_by(Ticket.category)
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.all()
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)
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# Counts by severity
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severity_counts = dict(
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db.query(Ticket.severity, func.count(Ticket.id))
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.group_by(Ticket.severity)
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.all()
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)
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# Counts by status
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status_counts = dict(
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db.query(Ticket.status, func.count(Ticket.id))
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.group_by(Ticket.status)
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.all()
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)
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# ----------------------
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# Location Clustering
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# ----------------------
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# Simple grid-based clustering: round lat/lon to nearest cluster_size
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tickets = db.query(Ticket.latitude, Ticket.longitude).all()
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location_clusters: Dict[str, int] = {}
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for lat, lon in tickets:
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key = f"{round(lat/cluster_size)*cluster_size:.4f},{round(lon/cluster_size)*cluster_size:.4f}"
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location_clusters[key] = location_clusters.get(key, 0) + 1
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return {
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"total_tickets": total_tickets,
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"category_counts": category_counts,
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"severity_counts": {k.value: v for k, v in severity_counts.items()},
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"status_counts": {k.value: v for k, v in status_counts.items()},
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"location_clusters": location_clusters # format: "lat,lon": count
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}
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