import random
from collections import Counter, defaultdict
from datetime import date, timedelta
from prefab_ui.actions import AppendState, OpenLink, PopState, SetState, ShowToast, ToggleState
from prefab_ui.app import PrefabApp
from prefab_ui.parts import (
Alert, AlertDescription, AlertTitle, Badge, Button, Card, CardContent,
CardDescription, CardFooter, CardHeader, CardTitle, Code, Column,
DataTable, DataTableColumn, Type, Grid, H2, Enter, Markdown, Mermaid,
Metric, Muted, Progress, Ring, Row, Slider, Small, Change, Tab, Tabs,
Textual content
)
from prefab_ui.parts.charts import (
BarChart, ChartSeries, LineChart, PieChart, RadarChart, ScatterChart,
Sparkline
)
from prefab_ui.parts.control_flow import Else, ForEach, If
from prefab_ui.rx import EVENT, STATE
random.seed(42)
TODAY = date.right now()
DATES = [TODAY - timedelta(days=29 - i) for i in range(30)]
REGIONS = ["All", "APAC", "EMEA", "NA", "LATAM"]
PIPELINES = [
"Customer 360 ETL",
"Invoice OCR",
"LLM Triage",
"Risk Scoring",
"Forecast Sync",
"Warehouse Load",
]
OWNERS = ["Data Platform", "AI Apps", "Revenue Ops", "Risk Engineering"]
STATES = ["Completed", "Completed", "Completed", "Completed", "Late", "Failed"]
PRIORITIES = ["P0", "P1", "P2", "P3"]
runs = []
daily_region_rows = []
for d in DATES:
for area in REGIONS[1:]:
region_bias = {
"APAC": 0.96,
"EMEA": 0.94,
"NA": 0.97,
"LATAM": 0.91,
}[region]
quantity = random.randint(32, 78)
failures = 0
late = 0
total_cost = 0.0
total_latency = 0.0
total_revenue = 0.0
for i in vary(quantity):
pipeline = random.alternative(PIPELINES)
proprietor = random.alternative(OWNERS)
state = random.decisions(
STATES,
weights=[
region_bias * 10,
6,
4,
3,
1.2,
max(0.2, (1 - region_bias) * 16),
],
ok=1,
)[0]
period = max(
12,
int(
random.gauss(95, 35)
+ (20 if state == "Late" else 0)
+ (45 if state == "Failed" else 0)
),
)
value = spherical(max(0.09, random.lognormvariate(-1.15, 0.55) + period / 1800), 2)
income = spherical(random.uniform(1.2, 8.5) * (1.3 if state == "Accomplished" else 0.6), 2)
precedence = random.decisions(PRIORITIES, weights=[1, 3, 7, 10], ok=1)[0]
if state == "Failed":
failures += 1
if state == "Late":
late += 1
total_cost += value
total_latency += period
total_revenue += income
if d >= TODAY - timedelta(days=10) and (state in {"Failed", "Late"} or random.random() < 0.05):
runs.append({
"run_id": f"{d.strftime('%mpercentd')}-{area[:2]}-{len(runs)+1:04d}",
"date": d.strftime("%Y-%m-%d"),
"pipeline": pipeline,
"proprietor": proprietor,
"area": area,
"state": state,
"precedence": precedence,
"duration_s": period,
"cost_usd": value,
"revenue_k": income,
"sla_gap": spherical(max(0, period - 120) / 60, 1),
})
daily_region_rows.append({
"date": d.strftime("%b %d"),
"area": area,
"runs": quantity,
"failures": failures,
"late": late,
"success_rate": spherical(100 * (quantity - failures - late * 0.35) / quantity, 1),
"avg_latency": spherical(total_latency / quantity, 1),
"cost_usd": spherical(total_cost, 2),
"revenue_k": spherical(total_revenue, 1),
})
runs = sorted(
runs,
key=lambda r: (r["priority"], r["state"] != "Failed", -r["duration_s"])
)[:80]
def aggregate_daily(rows):
by_date = defaultdict(lambda: {
"date": "",
"runs": 0,
"failures": 0,
"late": 0,
"cost_usd": 0.0,
"revenue_k": 0.0,
"latency_weighted": 0.0,
})
for r in rows:
bucket = by_date[r["date"]]
bucket["date"] = r["date"]
bucket["runs"] += r["runs"]
bucket["failures"] += r["failures"]
bucket["late"] += r["late"]
bucket["cost_usd"] += r["cost_usd"]
bucket["revenue_k"] += r["revenue_k"]
bucket["latency_weighted"] += r["avg_latency"] * r["runs"]
out = []
for d in [x.strftime("%b %d") for x in DATES]:
b = by_date[d]
if b["runs"]:
b["success_rate"] = spherical(100 * (b["runs"] - b["failures"] - b["late"] * 0.35) / b["runs"], 1)
b["avg_latency"] = spherical(b["latency_weighted"] / b["runs"], 1)
b["cost_usd"] = spherical(b["cost_usd"], 2)
b["revenue_k"] = spherical(b["revenue_k"], 1)
del b["latency_weighted"]
out.append(dict(b))
return out
def aggregate_regions(rows):
by_region = defaultdict(lambda: {
"area": "",
"runs": 0,
"failures": 0,
"late": 0,
"cost_usd": 0.0,
"revenue_k": 0.0,
"latency_weighted": 0.0,
})
for r in rows:
b = by_region[r["region"]]
b["region"] = r["region"]
b["runs"] += r["runs"]
b["failures"] += r["failures"]
b["late"] += r["late"]
b["cost_usd"] += r["cost_usd"]
b["revenue_k"] += r["revenue_k"]
b["latency_weighted"] += r["avg_latency"] * r["runs"]
out = []
for area in REGIONS[1:]:
b = by_region[region]
b["success_rate"] = spherical(100 * (b["runs"] - b["failures"] - b["late"] * 0.35) / b["runs"], 1)
b["avg_latency"] = spherical(b["latency_weighted"] / b["runs"], 1)
b["cost_usd"] = spherical(b["cost_usd"], 2)
b["revenue_k"] = spherical(b["revenue_k"], 1)
b["roi"] = spherical(b["revenue_k"] / max(1, b["cost_usd"]), 1)
del b["latency_weighted"]
out.append(dict(b))
return out
def make_status_rows(table_rows):
counts = Counter(r["state"] for r in table_rows)
return [{"state": k, "count": v} for k, v in counts.items()]
def make_pipeline_rows(table_rows):
counts = Counter(r["pipeline"] for r in table_rows)
return [{"pipeline": k, "count": v} for k, v in counts.most_common()]
def make_kpis(area, daily_rows, table_rows):
runs_count = sum(r["runs"] for r in daily_rows)
failures = sum(r["failures"] for r in daily_rows)
late = sum(r["late"] for r in daily_rows)
value = sum(r["cost_usd"] for r in daily_rows)
income = sum(r["revenue_k"] for r in daily_rows)
return {
"area": area,
"runs": runs_count,
"success_rate": spherical(100 * (runs_count - failures - late * 0.35) / max(1, runs_count), 1),
"avg_latency": spherical(sum(r["avg_latency"] * r["runs"] for r in daily_rows) / max(1, runs_count), 1),
"cost_usd": spherical(value, 2),
"revenue_k": spherical(income, 1),
"roi": spherical(income / max(1, value), 1),
"open_issues": len(table_rows),
"p0p1": sum(1 for r in table_rows if r["priority"] in {"P0", "P1"}),
"failure_rate": spherical(100 * failures / max(1, runs_count), 2),
"spark": [r["success_rate"] for r in daily_rows[-14:]],
}
DAILY_BY_REGION = {"All": aggregate_daily(daily_region_rows)}
REGION_ROWS = aggregate_regions(daily_region_rows)
for area in REGIONS[1:]:
DAILY_BY_REGION[region] = [r for r in daily_region_rows if r["region"] == area]
RUNS_BY_REGION = {
area: [r for r in runs if region == "All" or r["region"] == area]
for area in REGIONS
}
STATUS_BY_REGION = {
area: make_status_rows(RUNS_BY_REGION[region])
for area in REGIONS
}
PIPELINE_BY_REGION = {
area: make_pipeline_rows(RUNS_BY_REGION[region])
for area in REGIONS
}
KPI_BY_REGION = {
area: make_kpis(area, DAILY_BY_REGION[region], RUNS_BY_REGION[region])
for area in REGIONS
}
WATCHLIST = sorted(
runs,
key=lambda r: (r["priority"], r["state"] != "Failed", -r["sla_gap"])
)[:8]
SCATTER_ROWS = [
{
"region": r["region"],
"success_rate": r["success_rate"],
"cost_usd": r["cost_usd"],
"avg_latency": r["avg_latency"],
}
for r in REGION_ROWS
]
RADAR_ROWS = [
{"metric": "Success", **{r["region"]: r["success_rate"] for r in REGION_ROWS}},
{"metric": "ROI", **{r["region"]: min(100, r["roi"] * 8) for r in REGION_ROWS}},
{"metric": "Latency", **{r["region"]: max(0, 100 - r["avg_latency"] / 2) for r in REGION_ROWS}},
{"metric": "Price", **{r["region"]: max(0, 100 - r["cost_usd"] / 20) for r in REGION_ROWS}},
]
REGION_ACTIONS = {
area: [
SetState("selected_region", region),
SetState("line_rows", DAILY_BY_REGION[region]),
SetState("table_rows", RUNS_BY_REGION[region]),
SetState("status_rows", STATUS_BY_REGION[region]),
SetState("pipeline_rows", PIPELINE_BY_REGION[region]),
SetState("region_kpis", KPI_BY_REGION[region]),
SetState("selected_run", None),
ShowToast(f"Area set to {area}", variant="information", period=1800),
]
for area in REGIONS
}
'''
