OSVauco/ml/billing_agent.py

149 lines
5.4 KiB
Python

import os
import datetime
from google.cloud import bigquery
class BillingAgent:
def __init__(self):
self.project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
if not self.project_id:
raise ValueError("GOOGLE_CLOUD_PROJECT environment variable not set.")
self.billing_table = os.environ.get("BILLING_TABLE")
if not self.billing_table:
raise ValueError("BILLING_TABLE environment variable not set.")
self.bq_client = bigquery.Client(project=self.project_id)
def get_summary(self):
"""
Retrieves the billing summary for the last 30 days.
"""
query = f"""
SELECT
service.description as service,
SUM(cost) + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)) as total_cost
FROM `{self.billing_table}`
WHERE _PARTITIONTIME >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 30 DAY)
GROUP BY 1
ORDER BY total_cost DESC
LIMIT 10
"""
query_job = self.bq_client.query(query)
results = query_job.result()
summary = []
for row in results:
summary.append({"service": row.service, "total_cost": row.total_cost})
if not summary:
return {
"onboarding_status": {
"state": "awaiting_data",
"message": "Første fakturaeksport fra BigQuery kan ta 24-48 timer. Data er på vei."
}
}
return {"summary": summary}
def get_forecast(self):
"""
Retrieves a billing forecast based on the last 7 days.
"""
query_last_7_days = f"""
SELECT SUM(cost) + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)) as total_cost
FROM `{self.billing_table}`
WHERE _PARTITIONTIME >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
"""
query_job = self.bq_client.query(query_last_7_days)
results = query_job.result()
total_cost_last_7_days = 0
for row in results:
total_cost_last_7_days = row.total_cost or 0
daily_average = total_cost_last_7_days / 7
today = datetime.date.today()
if today.month == 12:
next_month_first_day = datetime.date(today.year + 1, 1, 1)
else:
next_month_first_day = datetime.date(today.year, today.month + 1, 1)
last_day_of_month = next_month_first_day - datetime.timedelta(days=1)
remaining_days = (last_day_of_month - today).days
forecasted_cost = daily_average * remaining_days
query_mtd = f"""
SELECT SUM(cost) + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)) as total_cost
FROM `{self.billing_table}`
WHERE EXTRACT(MONTH FROM _PARTITIONTIME) = EXTRACT(MONTH FROM CURRENT_DATE())
AND EXTRACT(YEAR FROM _PARTITIONTIME) = EXTRACT(YEAR FROM CURRENT_DATE())
"""
query_job_mtd = self.bq_client.query(query_mtd)
results_mtd = query_job_mtd.result()
mtd_cost = 0
for row in results_mtd:
mtd_cost = row.total_cost or 0
total_forecast = mtd_cost + forecasted_cost
return {
"daily_average_last_7_days": daily_average,
"month_to_date_cost": mtd_cost,
"forecasted_remaining_cost": forecasted_cost,
"total_monthly_forecast": total_forecast,
"data_note": "Prognose basert på siste 7 dager. Fakturadata fra BigQuery kan ha 24-48 timers forsinkelse."
}
def get_anomalies(self):
"""
Detects anomalies in billing data by comparing today's cost to the 7-day average.
"""
query = f"""
WITH daily_costs AS (
SELECT
service.description AS service,
DATE(_PARTITIONTIME) AS usage_date,
SUM(cost) + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)) AS daily_cost
FROM `{self.billing_table}`
WHERE _PARTITIONTIME >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 14 DAY)
GROUP BY 1, 2
),
costs_with_avg AS (
SELECT
service,
usage_date,
daily_cost,
AVG(daily_cost) OVER (PARTITION BY service ORDER BY usage_date ROWS BETWEEN 7 PRECEDING AND 1 PRECEDING) AS avg_7day
FROM daily_costs
)
SELECT
service,
daily_cost AS today_cost,
avg_7day,
(daily_cost / avg_7day) AS ratio
FROM costs_with_avg
WHERE usage_date = CURRENT_DATE()
AND avg_7day > 0
AND daily_cost > (2.0 * avg_7day)
"""
query_job = self.bq_client.query(query)
results = query_job.result()
anomalies = []
for row in results:
anomalies.append({
"service": row.service,
"today_cost": row.today_cost,
"avg_7d": row.avg_7day,
"ratio": row.ratio
})
return {"anomalies": anomalies}
if __name__ == '__main__':
agent = BillingAgent()
print("Summary:", agent.get_summary())
print("Forecast:", agent.get_forecast())
print("Anomalies:", agent.get_anomalies())