Multi-Location Review Analysis

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])

const GOOG = process.env.GOOGLE_ACCESS_TOKEN;
const ACCT = process.env.GOOGLE_ACCOUNT_ID;
const MV = process.env.MAVERA_API_KEY;
const GB_BASE = "https://mybusiness.googleapis.com/v4";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const GB_H = { Authorization: `Bearer ${GOOG}` };

const STAR_MAP = { ONE: 1, TWO: 2, THREE: 3, FOUR: 4, FIVE: 5 };

// 1. Locations
const locations = (await fetch(`${GB_BASE}/${ACCT}/locations?pageSize=100`, { headers: GB_H })
  .then((r) => r.json())).locations || [];

// 2. Reviews per location
const locationData = [];
for (const loc of locations) {
  const locId = loc.name || "";
  const locName = loc.locationName || loc.title || "Unknown";
  const city = loc.address?.locality || "Unknown";

  const reviews = [];
  let pageToken = null;
  while (reviews.length < 100) {
    const params = new URLSearchParams({ pageSize: "50" });
    if (pageToken) params.set("pageToken", pageToken);
    const res = await fetch(`${GB_BASE}/${locId}/reviews?${params}`, { headers: GB_H });
    if (res.status === 429) { await new Promise((r) => setTimeout(r, 2000)); continue; }
    if (!res.ok) break;
    const data = await res.json();
    reviews.push(...(data.reviews || []));
    pageToken = data.nextPageToken;
    if (!pageToken) break;
    await new Promise((r) => setTimeout(r, 300));
  }

  const ratings = reviews.map((r) => STAR_MAP[r.starRating] || 3);
  const avg = ratings.length ? +(ratings.reduce((s, v) => s + v, 0) / ratings.length).toFixed(1) : 0;
  const texts = reviews.filter((r) => r.comment)
    .map((r) => `[${STAR_MAP[r.starRating] || 3}★] ${r.comment.slice(0, 200)}`);

  locationData.push({ name: locName, city, reviewCount: reviews.length, avg, reviews: texts });
  await new Promise((r) => setTimeout(r, 500));
}

// 3. Mave analysis
const locBlock = locationData.map((ld) =>
  `## ${ld.name} (${ld.city})\nReviews: ${ld.reviewCount} | Avg: ${ld.avg}/5\n${ld.reviews.slice(0, 5).join("\n")}`
).join("\n\n");

const analysis = await fetch("https://app.mavera.io/api/v1/mave/chat", {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    message: `Compare feedback across ${locationData.length} locations:\n\n${locBlock}\n\nProduce: 1) Ranking 2) Per-location strengths 3) Weaknesses 4) Cross-location patterns 5) Recommendations 6) Best practices to share`,
  }),
}).then((r) => r.json());

console.log("=== Multi-Location Intelligence ===");
console.log((analysis.content || "").slice(0, 2000));