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, Kind, Grid, H2, Enter, Markdown, Mermaid,
Metric, Muted, Progress, Ring, Row, Slider, Small, Swap, 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.as we speak()
DATES = (TODAY - timedelta(days=29 - i) for i in vary(30))
REGIONS = ("All", "APAC", "EMEA", "NA", "LATAM")
PIPELINES = (
"Buyer 360 ETL",
"Bill OCR",
"LLM Triage",
"Danger Scoring",
"Forecast Sync",
"Warehouse Load",
)
OWNERS = ("Knowledge Platform", "AI Apps", "Income Ops", "Danger Engineering")
STATES = ("Accomplished", "Accomplished", "Accomplished", "Accomplished", "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,
}(area)
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.selection(PIPELINES)
proprietor = random.selection(OWNERS)
state = random.selections(
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)
),
)
price = 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.selections(PRIORITIES, weights=(1, 3, 7, 10), ok=1)(0)
if state == "Failed":
failures += 1
if state == "Late":
late += 1
total_cost += price
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": price,
"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("precedence"), 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("area"))
b("area") = r("area")
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(area)
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": ok, "rely": v} for ok, v in counts.gadgets())
def make_pipeline_rows(table_rows):
counts = Counter(r("pipeline") for r in table_rows)
return ({"pipeline": ok, "rely": v} for ok, 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)
price = 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(price, 2),
"revenue_k": spherical(income, 1),
"roi": spherical(income / max(1, price), 1),
"open_issues": len(table_rows),
"p0p1": sum(1 for r in table_rows if r("precedence") 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(area) = (r for r in daily_region_rows if r("area") == area)
RUNS_BY_REGION = {
area: (r for r in runs if area == "All" or r("area") == area)
for area in REGIONS
}
STATUS_BY_REGION = {
area: make_status_rows(RUNS_BY_REGION(area))
for area in REGIONS
}
PIPELINE_BY_REGION = {
area: make_pipeline_rows(RUNS_BY_REGION(area))
for area in REGIONS
}
KPI_BY_REGION = {
area: make_kpis(area, DAILY_BY_REGION(area), RUNS_BY_REGION(area))
for area in REGIONS
}
WATCHLIST = sorted(
runs,
key=lambda r: (r("precedence"), r("state") != "Failed", -r("sla_gap"))
)(:8)
SCATTER_ROWS = (
{
"area": r("area"),
"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("area"): r("success_rate") for r in REGION_ROWS}},
{"metric": "ROI", **{r("area"): min(100, r("roi") * 8) for r in REGION_ROWS}},
{"metric": "Latency", **{r("area"): max(0, 100 - r("avg_latency") / 2) for r in REGION_ROWS}},
{"metric": "Value", **{r("area"): max(0, 100 - r("cost_usd") / 20) for r in REGION_ROWS}},
)
REGION_ACTIONS = {
area: (
SetState("selected_region", area),
SetState("line_rows", DAILY_BY_REGION(area)),
SetState("table_rows", RUNS_BY_REGION(area)),
SetState("status_rows", STATUS_BY_REGION(area)),
SetState("pipeline_rows", PIPELINE_BY_REGION(area)),
SetState("region_kpis", KPI_BY_REGION(area)),
SetState("selected_run", None),
ShowToast(f"Area set to {area}", variant="data", period=1800),
)
for area in REGIONS
}
'''