Product Review Sentiment → Brand Health

import os, json, requests, time
from openai import OpenAI

STORE = os.environ["BIGCOMMERCE_STORE_HASH"]
BC_TOKEN = os.environ["BIGCOMMERCE_ACCESS_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
BC = f"https://api.bigcommerce.com/stores/{STORE}/v3"
BC_HEADERS = {"X-Auth-Token": BC_TOKEN, "Content-Type": "application/json", "Accept": "application/json"}

products = requests.get(f"{BC}/catalog/products",
    headers=BC_HEADERS,
    params={"sort": "total_sold", "direction": "desc", "limit": 10},
).json().get("data", [])

all_reviews = []
for prod in products:
    page = 1
    while True:
        r = requests.get(f"{BC}/catalog/products/{prod['id']}/reviews",
            headers=BC_HEADERS, params={"page": page, "limit": 50})
        if r.status_code == 429:
            time.sleep(2); continue
        r.raise_for_status()
        batch = r.json().get("data", [])
        for rev in batch:
            all_reviews.append({
                "product": prod["name"], "rating": rev["rating"],
                "title": rev.get("title", ""), "text": rev.get("text", "")[:300],
            })
        if len(batch) < 50: break
        page += 1
        time.sleep(0.2)

mavera = OpenAI(api_key=MV, base_url="https://app.mavera.io/api/v1")
schema = {"type": "json_schema", "json_schema": {"name": "brand_health", "strict": True, "schema": {
    "type": "object", "required": ["overall_sentiment", "themes", "recommendations"],
    "properties": {
        "overall_sentiment": {"type": "number"},
        "themes": {"type": "array", "items": {"type": "object",
            "required": ["theme", "frequency", "avg_sentiment", "sample_quote"],
            "properties": {"theme": {"type": "string"}, "frequency": {"type": "number"},
                "avg_sentiment": {"type": "number"}, "sample_quote": {"type": "string"}}}},
        "recommendations": {"type": "array", "items": {"type": "string"}},
    }}}}

review_block = "\n".join(f"[{r['rating']}/5] {r['product']}: {r['title']} — {r['text']}"
    for r in all_reviews[:80])

result = mavera.responses.create(model="mavera-1",
    input=[{"role": "user", "content": f"Analyze {len(all_reviews)} product reviews for brand health.\n\n{review_block}"}],
    extra_body={"persona_id": os.environ.get("CUSTOMER_PERSONA_ID", ""), "response_format": schema})

health = json.loads(result.output[0].content[0].text)
print(f"Overall sentiment: {health['overall_sentiment']}/10")
for t in health["themes"][:5]:
    print(f"  {t['theme']} (n={t['frequency']}, sentiment={t['avg_sentiment']})")
for rec in health["recommendations"]:
    print(f"  → {rec}")

import OpenAI from "openai";

const STORE = process.env.BIGCOMMERCE_STORE_HASH;
const BC_TOKEN = process.env.BIGCOMMERCE_ACCESS_TOKEN;
const MV = process.env.MAVERA_API_KEY;
const BC = `https://api.bigcommerce.com/stores/${STORE}/v3`;
const bcHeaders = { "X-Auth-Token": BC_TOKEN, "Content-Type": "application/json", Accept: "application/json" };

const products = await fetch(`${BC}/catalog/products?sort=total_sold&direction=desc&limit=10`,
  { headers: bcHeaders }).then(r => r.json()).then(d => d.data || []);

const allReviews = [];
for (const prod of products) {
  let page = 1;
  while (true) {
    const res = await fetch(`${BC}/catalog/products/${prod.id}/reviews?page=${page}&limit=50`,
      { headers: bcHeaders });
    if (res.status === 429) { await new Promise(r => setTimeout(r, 2000)); continue; }
    const batch = (await res.json()).data || [];
    for (const rev of batch) {
      allReviews.push({
        product: prod.name, rating: rev.rating,
        title: rev.title || "", text: (rev.text || "").slice(0, 300),
      });
    }
    if (batch.length < 50) break;
    page++;
    await new Promise(r => setTimeout(r, 200));
  }
}

const mavera = new OpenAI({ apiKey: MV, baseURL: "https://app.mavera.io/api/v1" });
const schema = { type: "json_schema", json_schema: { name: "brand_health", strict: true, schema: {
  type: "object", required: ["overall_sentiment", "themes", "recommendations"],
  properties: {
    overall_sentiment: { type: "number" },
    themes: { type: "array", items: { type: "object",
      required: ["theme", "frequency", "avg_sentiment", "sample_quote"],
      properties: { theme: { type: "string" }, frequency: { type: "number" },
        avg_sentiment: { type: "number" }, sample_quote: { type: "string" } } } },
    recommendations: { type: "array", items: { type: "string" } },
  } } } };

const reviewBlock = allReviews.slice(0, 80)
  .map(r => `[${r.rating}/5] ${r.product}: ${r.title} — ${r.text}`).join("\n");

const result = await mavera.responses.create({
  model: "mavera-1",
  input: [{ role: "user", content: `Analyze ${allReviews.length} product reviews for brand health.\n\n${reviewBlock}` }],
  extra_body: { persona_id: process.env.CUSTOMER_PERSONA_ID || "", response_format: schema },
});

const health = JSON.parse(result.output[0].content[0].text);
console.log(`Overall sentiment: ${health.overall_sentiment}/10`);
health.themes.slice(0, 5).forEach(t =>
  console.log(`  ${t.theme} (n=${t.frequency}, sentiment=${t.avg_sentiment})`));
health.recommendations.forEach(rec => console.log(`  → ${rec}`));