import os, requests, time
from collections import defaultdict
GOOG = os.environ["GOOGLE_ACCESS_TOKEN"]
ACCT = os.environ["GOOGLE_ACCOUNT_ID"]
MV = os.environ["MAVERA_API_KEY"]
GB_BASE = "https://mybusiness.googleapis.com/v4"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}
GB_H = {"Authorization": f"Bearer {GOOG}"}
# 1. List all locations
locations = requests.get(f"{GB_BASE}/{ACCT}/locations",
headers=GB_H,
params={"pageSize": 100}).json().get("locations", [])
print(f"Found {len(locations)} locations")
# 2. Pull reviews for each location
location_data = []
for loc in locations:
loc_id = loc.get("name", "")
loc_name = loc.get("locationName", loc.get("title", "Unknown"))
address = loc.get("address", {})
city = address.get("locality", "Unknown")
state = address.get("administrativeArea", "")
reviews = []
page_token = None
while len(reviews) < 100:
params = {"pageSize": 50}
if page_token:
params["pageToken"] = page_token
r = requests.get(f"{GB_BASE}/{loc_id}/reviews",
headers=GB_H, params=params)
if r.status_code == 429:
time.sleep(2)
continue
if r.status_code != 200:
break
data = r.json()
reviews.extend(data.get("reviews", []))
page_token = data.get("nextPageToken")
if not page_token:
break
time.sleep(0.3)
ratings = [rev.get("starRating", "FIVE") for rev in reviews]
star_map = {"ONE": 1, "TWO": 2, "THREE": 3, "FOUR": 4, "FIVE": 5}
numeric_ratings = [star_map.get(r, 3) for r in ratings]
avg_rating = sum(numeric_ratings) / len(numeric_ratings) if numeric_ratings else 0
review_texts = []
for rev in reviews:
comment = rev.get("comment", "")
if comment:
stars = star_map.get(rev.get("starRating", "FIVE"), 5)
review_texts.append(f"[{stars}★] {comment[:200]}")
location_data.append({
"name": loc_name,
"city": city,
"state": state,
"review_count": len(reviews),
"avg_rating": round(avg_rating, 1),
"reviews": review_texts,
})
time.sleep(0.5)
# 3. Mave cross-location analysis
loc_block = "\n\n".join(
f"## {ld['name']} ({ld['city']}, {ld['state']})\n"
f"Reviews: {ld['review_count']} | Avg: {ld['avg_rating']}/5\n"
f"Sample reviews:\n" + "\n".join(ld["reviews"][:5])
for ld in location_data
)
analysis = requests.post("https://app.mavera.io/api/v1/mave/chat",
headers=MV_H,
json={"message": f"""Compare customer feedback across these {len(location_data)} business locations.
{loc_block}
Produce:
1. Location ranking (best to worst by customer satisfaction)
2. Per-location strengths (what each does well)
3. Per-location weaknesses (what each needs to fix)
4. Cross-location patterns (issues affecting multiple locations)
5. Location-specific operational recommendations
6. Best practices from top-rated locations to apply elsewhere"""}).json()
print("=== Multi-Location Review Intelligence ===")
print(analysis.get("content", "")[:2000])
