Product Review Analysis

import os, json, requests
from openai import OpenAI

STORE = os.environ["SHOPIFY_STORE"]
TOKEN = os.environ["SHOPIFY_ACCESS_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
SH_B = f"https://{STORE}.myshopify.com/admin/api/2024-10"
SH_H = {"X-Shopify-Access-Token": TOKEN}
client = OpenAI(api_key=MV, base_url="https://app.mavera.io/api/v1")

def fetch_reviews(product_id):
    resp = requests.get(f"{SH_B}/products/{product_id}/metafields.json?namespace=reviews", headers=SH_H)
    resp.raise_for_status()
    for mf in resp.json().get("metafields", []):
        if mf["key"] == "review_list":
            try: return json.loads(mf["value"])
            except (json.JSONDecodeError, TypeError): pass
    jm = requests.get(f"https://judge.me/api/v1/reviews?shop_domain={STORE}.myshopify.com&external_id={product_id}")
    return [{"body": r["body"], "rating": r["rating"]} for r in jm.json().get("reviews", [])] if jm.ok else []

def analyze(title, reviews):
    text = "\n".join(f"- [{r.get('rating','?')}/5] {r.get('body','')}" for r in reviews[:40])
    c = client.responses.create(model="mavera-default", input=[
        {"role": "system", "content": "You are a product review analyst. Return JSON: sentiment_breakdown (positive/neutral/negative counts), top_themes [{theme, count, sample_quote}], overall_score (1-10), summary (2 sentences)."},
        {"role": "user", "content": f"Product: {title}\n\nReviews:\n{text}"},
    ], response_format={"type": "json_object"})
    return json.loads(c.output[0].content[0].text)

products = requests.get(f"{SH_B}/products.json?limit=10&status=active", headers=SH_H).json()["products"]
for p in products:
    reviews = fetch_reviews(p["id"])
    if not reviews: continue
    r = analyze(p["title"], reviews)
    print(f"{p['title']}: score={r['overall_score']}/10, themes={[t['theme'] for t in r['top_themes']]}")

const STORE = process.env.SHOPIFY_STORE, TOKEN = process.env.SHOPIFY_ACCESS_TOKEN, MV = process.env.MAVERA_API_KEY;
const SH_B = `https://${STORE}.myshopify.com/admin/api/2024-10`;

async function fetchReviews(productId) {
  const resp = await fetch(`${SH_B}/products/${productId}/metafields.json?namespace=reviews`, { headers: { "X-Shopify-Access-Token": TOKEN } });
  const mf = ((await resp.json()).metafields || []).find(m => m.key === "review_list");
  if (mf) { try { return JSON.parse(mf.value); } catch {} }
  const jm = await fetch(`https://judge.me/api/v1/reviews?shop_domain=${STORE}.myshopify.com&external_id=${productId}`);
  return jm.ok ? (await jm.json()).reviews?.map(r => ({ body: r.body, rating: r.rating })) || [] : [];
}
async function analyze(title, reviews) {
  const text = reviews.slice(0, 40).map(r => `- [${r.rating ?? "?"}/5] ${r.body ?? ""}`).join("\n");
  const resp = await fetch("https://app.mavera.io/api/v1/responses", { method: "POST",
    headers: { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" },
    body: JSON.stringify({ model: "mavera-default", input: [
      { role: "system", content: "Product review analyst. Return JSON: sentiment_breakdown, top_themes [{theme,count,sample_quote}], overall_score (1-10), summary." },
      { role: "user", content: `Product: ${title}\n\nReviews:\n${text}` }
    ], response_format: { type: "json_object" } })
  });
  return JSON.parse((await resp.json()).output[0].content[0].text);
}
(async () => {
  const products = await (await fetch(`${SH_B}/products.json?limit=10&status=active`, { headers: { "X-Shopify-Access-Token": TOKEN } })).json();
  for (const p of products.products) {
    const reviews = await fetchReviews(p.id);
    if (!reviews.length) continue;
    const r = await analyze(p.title, reviews);
    console.log(`${p.title}: score=${r.overall_score}/10, themes=${r.top_themes.map(t => t.theme)}`);
  }
})();