Spot tired ads and wasted ad spend

from collections import defaultdict
from clickruntime import on_trigger, credentials, http, ai, notify

G = "https://graph.facebook.com/v21.0"
AD_ACCOUNT = "act_1234567890"

@on_trigger
def run(event):
    token = credentials.connection("facebook", "work")
    base = {"access_token": token, "date_preset": "last_14d", "level": "ad", "limit": 500}
    daily = http.get(f"{G}/{AD_ACCOUNT}/insights", params={**base, "time_increment": 1,
                     "fields": "ad_name,ctr,frequency,spend"}).json()["data"]
    by_ad = defaultdict(list)
    for d in sorted(daily, key=lambda d: d["date_start"]):
        by_ad[d["ad_name"]].append(d)
    fatigued = [(name, sum(float(x["spend"]) for x in rows)) for name, rows in by_ad.items() if len(rows) >= 6
                and float(rows[-1]["ctr"]) < 0.8 * float(rows[0]["ctr"]) and float(rows[-1]["frequency"]) > float(rows[0]["frequency"])]
    places = http.get(f"{G}/{AD_ACCOUNT}/insights", params={**base, "level": "account",
                      "breakdowns": "publisher_platform,platform_position", "fields": "spend,clicks"}).json()["data"]
    avg = sum(float(p["spend"]) for p in places) / max(sum(int(p["clicks"]) for p in places), 1)
    costly = [(f"{p['publisher_platform']}/{p['platform_position']}", float(p["spend"]) - int(p["clicks"]) * avg)
              for p in places if int(p["clicks"]) and float(p["spend"]) / int(p["clicks"]) > 1.5 * avg]
    ranked = sorted(fatigued + costly, key=lambda x: -x[1])
    report = "\n".join(f"${waste:,.0f}  {name}" for name, waste in ranked) or "Nothing flagged."
    note = ai.generate(f"In two sentences, what should I change first?\n{report}", max_output_tokens=120).text
    notify.email_me("Meta ads: waste and fatigue", f"{note}\n\n{report}")
    return {"flagged": len(ranked)}