import os, requests
from google.analytics.data_v1beta import BetaAnalyticsDataClient
from google.analytics.data_v1beta.types import (
RunReportRequest, Dimension, Metric, DateRange, OrderBy,
)
PROPERTY_ID = os.environ["GA4_PROPERTY_ID"]
MV = os.environ["MAVERA_API_KEY"]
client = BetaAnalyticsDataClient()
device_report = client.run_report(RunReportRequest(
property=f"properties/{PROPERTY_ID}",
dimensions=[
Dimension(name="deviceCategory"),
Dimension(name="screenResolution"),
],
metrics=[
Metric(name="totalUsers"),
Metric(name="sessions"),
Metric(name="engagementRate"),
Metric(name="averageSessionDuration"),
Metric(name="conversions"),
Metric(name="screenPageViewsPerSession"),
],
date_ranges=[DateRange(start_date="30daysAgo", end_date="today")],
order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="totalUsers"), desc=True)],
limit=100,
))
from collections import defaultdict
devices = defaultdict(lambda: {
"users": 0, "sessions": 0, "conversions": 0,
"eng_sum": 0, "dur_sum": 0, "pages_sum": 0,
"resolutions": defaultdict(int),
})
for row in device_report.rows:
cat = row.dimension_values[0].value
res = row.dimension_values[1].value
users = int(row.metric_values[0].value)
sessions = int(row.metric_values[1].value)
engagement = float(row.metric_values[2].value)
duration = float(row.metric_values[3].value)
conversions = int(row.metric_values[4].value)
pages_per = float(row.metric_values[5].value)
devices[cat]["users"] += users
devices[cat]["sessions"] += sessions
devices[cat]["conversions"] += conversions
devices[cat]["eng_sum"] += engagement * users
devices[cat]["dur_sum"] += duration * users
devices[cat]["pages_sum"] += pages_per * sessions
devices[cat]["resolutions"][res] += users
device_block = []
for cat, data in sorted(devices.items(), key=lambda x: -x[1]["users"]):
avg_eng = data["eng_sum"] / max(data["users"], 1)
avg_dur = data["dur_sum"] / max(data["users"], 1)
avg_pages = data["pages_sum"] / max(data["sessions"], 1)
conv_rate = data["conversions"] / max(data["users"], 1)
top_res = sorted(data["resolutions"].items(), key=lambda x: -x[1])[:5]
res_str = ", ".join(f"{r}: {n}" for r, n in top_res)
device_block.append(
f"**{cat.upper()}**\n"
f" Users: {data['users']} | Sessions: {data['sessions']}\n"
f" Engagement: {avg_eng:.0%} | Avg duration: {avg_dur:.0f}s | Pages/session: {avg_pages:.1f}\n"
f" Conversions: {data['conversions']} ({conv_rate:.2%})\n"
f" Top resolutions: {res_str}"
)
device_summary = "\n\n".join(device_block)
mave = requests.post(
"https://app.mavera.io/api/v1/mave/chat",
headers={"Authorization": f"Bearer {MV}", "Content-Type": "application/json"},
json={"message": f"""Recommend creative format adjustments for each device category based on this GA4 behavioral data.
DEVICE BEHAVIORAL PROFILES (last 30 days):
{device_summary}
For each device category, provide:
1. Recommended ad creative dimensions and formats (static, video, carousel, etc.)
2. Optimal content layout (long-form vs. snackable, scroll depth expectations)
3. CTA placement recommendations based on session duration and pages/session
4. Landing page design considerations for the top screen resolutions
5. Content format priorities (video length, image aspect ratio, text density)
6. Specific do's and don'ts for creative on this device
Also provide cross-device recommendations:
- Which messages to keep consistent across devices
- Which elements to adapt per device
- Mobile-first vs. desktop-first content strategy recommendation"""},
).json()
print("--- Device-Specific Creative Recommendations ---")
print(mave.get("content", "")[:3000])
