Refactor: Integrate backend API and normalize data

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>

backend/app/routes/analytics.py

@@ -0,0 +1,64 @@
+# app/routes/analytics.py
+from fastapi import APIRouter, Depends
+from sqlalchemy.orm import Session
+from sqlalchemy import func
+from app.database import get_db
+from app.models.ticket_model import Ticket, SeverityLevel, TicketStatus
+from typing import Dict, Any
+
+router = APIRouter()
+
+# ----------------------
+# GET /analytics
+# ----------------------
+@router.get("/analytics", response_model=Dict[str, Any])
+def analytics(db: Session = Depends(get_db), cluster_size: float = 0.01):
+    """
+    Returns summary statistics for tickets:
+    - Total tickets
+    - Counts by category
+    - Counts by severity
+    - Counts by status
+    - Optional: location clustering (hotspots) using grid-based approach
+    """
+    # Total tickets
+    total_tickets = db.query(func.count(Ticket.id)).scalar()
+
+    # Counts by category
+    category_counts = dict(
+        db.query(Ticket.category, func.count(Ticket.id))
+          .group_by(Ticket.category)
+          .all()
+    )
+
+    # Counts by severity
+    severity_counts = dict(
+        db.query(Ticket.severity, func.count(Ticket.id))
+          .group_by(Ticket.severity)
+          .all()
+    )
+
+    # Counts by status
+    status_counts = dict(
+        db.query(Ticket.status, func.count(Ticket.id))
+          .group_by(Ticket.status)
+          .all()
+    )
+
+    # ----------------------
+    # Location Clustering
+    # ----------------------
+    # Simple grid-based clustering: round lat/lon to nearest cluster_size
+    tickets = db.query(Ticket.latitude, Ticket.longitude).all()
+    location_clusters: Dict[str, int] = {}
+    for lat, lon in tickets:
+        key = f"{round(lat/cluster_size)*cluster_size:.4f},{round(lon/cluster_size)*cluster_size:.4f}"
+        location_clusters[key] = location_clusters.get(key, 0) + 1
+
+    return {
+        "total_tickets": total_tickets,
+        "category_counts": category_counts,
+        "severity_counts": {k.value: v for k, v in severity_counts.items()},
+        "status_counts": {k.value: v for k, v in status_counts.items()},
+        "location_clusters": location_clusters  # format: "lat,lon": count
+    }