TrustScore Monitoring → Reputation Response

import os, requests, time

TP_KEY = os.environ["TRUSTPILOT_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
BU_ID = os.environ["TRUSTPILOT_BU_ID"]
TP_BASE = "https://api.trustpilot.com/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

# 1. Current TrustScore
bu = requests.get(f"{TP_BASE}/business-units/{BU_ID}",
    params={"apikey": TP_KEY}).json()
current_score = bu.get("score", {}).get("trustScore", 0)
review_count = bu.get("numberOfReviews", {}).get("total", 0)
star_dist = bu.get("score", {}).get("stars", {})

BASELINE_SCORE = 4.5

print(f"Current TrustScore: {current_score} (baseline: {BASELINE_SCORE})")
print(f"Total reviews: {review_count}")
print(f"Star distribution: {star_dist}")

# 2. If score dropped, pull recent negative reviews
if current_score < BASELINE_SCORE:
    drop = BASELINE_SCORE - current_score
    print(f"\n⚠ Score dropped by {drop:.1f} points")

    negative_reviews = []
    for stars in [1, 2, 3]:
        r = requests.get(f"{TP_BASE}/business-units/{BU_ID}/reviews",
            params={"apikey": TP_KEY, "stars": stars, "perPage": 20,
                     "orderBy": "createdat.desc"})
        r.raise_for_status()
        negative_reviews.extend(r.json().get("reviews", []))
        time.sleep(0.2)

    # 3. Build review block for analysis
    review_block = "\n\n".join(
        f"[{r.get('stars',0)}★] {r.get('title','No title')}\n{r.get('text','')[:300]}\nDate: {r.get('createdAt','')[:10]}"
        for r in negative_reviews[:20]
    )

    # 4. Mave root cause analysis
    analysis = requests.post("https://app.mavera.io/api/v1/mave/chat",
        headers=MV_H,
        json={"message": f"""Our Trustpilot TrustScore dropped from {BASELINE_SCORE} to {current_score}.
Total reviews: {review_count}. Star distribution: {star_dist}.

Recent negative reviews:
{review_block}

Analyze:
1. Root causes — what themes drive negative reviews?
2. Frequency — which issues appear most?
3. Severity — which issues cause 1-star vs 3-star?
4. Trend — are issues getting worse or improving?
5. Actionable fixes — what operational changes would address each root cause?
6. Response strategy — how to reply to each theme category"""}).json()

    print("\n=== Root Cause Analysis ===")
    print(analysis.get("content", "")[:1500])

    # 5. Generate response templates
    gen = requests.post("https://app.mavera.io/api/v1/generations",
        headers=MV_H,
        json={"prompt": f"""Based on this root cause analysis, generate 4 review response templates:

{analysis.get('content','')[:1000]}

For each template:
- Category name (e.g., "Shipping Delay", "Product Quality")
- Tone: empathetic, solution-oriented, professional
- Acknowledge the specific issue
- Offer a concrete next step
- Keep under 100 words each
- Never use corporate jargon"""}).json()

    print("\n=== Response Templates ===")
    print(gen.get("output", gen.get("content", gen.get("text", "")))[:1200])

else:
    print(f"✓ Score stable at {current_score}")

const TP_KEY = process.env.TRUSTPILOT_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const BU_ID = process.env.TRUSTPILOT_BU_ID;
const TP_BASE = "https://api.trustpilot.com/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

// 1. TrustScore
const bu = await fetch(`${TP_BASE}/business-units/${BU_ID}?apikey=${TP_KEY}`).then((r) => r.json());
const currentScore = bu.score?.trustScore || 0;
const reviewCount = bu.numberOfReviews?.total || 0;
const starDist = bu.score?.stars || {};
const BASELINE = 4.5;

console.log(`TrustScore: ${currentScore} (baseline: ${BASELINE})`);

// 2. If dropped
if (currentScore < BASELINE) {
  console.log(`Score dropped ${(BASELINE - currentScore).toFixed(1)} pts`);

  const negReviews = [];
  for (const stars of [1, 2, 3]) {
    const res = await fetch(
      `${TP_BASE}/business-units/${BU_ID}/reviews?apikey=${TP_KEY}&stars=${stars}&perPage=20&orderBy=createdat.desc`
    );
    negReviews.push(...((await res.json()).reviews || []));
    await new Promise((r) => setTimeout(r, 200));
  }

  const reviewBlock = negReviews.slice(0, 20)
    .map((r) => `[${r.stars}★] ${r.title || "No title"}\n${(r.text || "").slice(0, 300)}`)
    .join("\n\n");

  // 3. Root cause
  const analysis = await fetch("https://app.mavera.io/api/v1/mave/chat", {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      message: `TrustScore dropped ${BASELINE}→${currentScore}. Reviews: ${reviewCount}.\n\n${reviewBlock}\n\nAnalyze: 1) Root causes 2) Frequency 3) Severity 4) Trend 5) Fixes 6) Response strategy`,
    }),
  }).then((r) => r.json());

  console.log("\n=== Root Cause ===");
  console.log((analysis.content || "").slice(0, 1500));

  // 4. Response templates
  const gen = await fetch("https://app.mavera.io/api/v1/generations", {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      prompt: `Generate 4 review response templates based on:\n\n${(analysis.content || "").slice(0, 1000)}\n\nEmpathetic, solution-oriented, under 100 words each.`,
    }),
  }).then((r) => r.json());

  console.log("\n=== Templates ===");
  console.log(gen.output || gen.content || gen.text || "");
}