Recommendation Quotes → Social Proof Content

import os, requests, time

G2 = os.environ["G2_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
G2_H = {"Authorization": f"Token token={G2}", "Content-Type": "application/vnd.api+json"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

# 1. Pull reviews with recommendations
reviews = []
page = 1
while len(reviews) < 200:
    r = requests.get(f"https://data.g2.com/api/v1/survey-responses",
        headers=G2_H, params={"page[size]": 50, "page[number]": page})
    if r.status_code == 429:
        time.sleep(1); continue
    r.raise_for_status()
    data = r.json().get("data", [])
    if not data: break
    reviews.extend(data)
    page += 1
    time.sleep(0.1)

# 2. Extract high-rating recommendations
quotes = []
for rev in reviews:
    attrs = rev.get("attributes", {})
    if attrs.get("star_rating", 0) < 4:
        continue
    for key, val in attrs.get("comment_answers", {}).items():
        text = val if isinstance(val, str) else val.get("text", "")
        if ("recommend" in key.lower() or "love" in key.lower()) and len(text) > 30:
            quotes.append({
                "text": text[:300],
                "role": attrs.get("title", "User"),
                "industry": attrs.get("industry", "Technology"),
                "company_size": attrs.get("company_size", "Mid-market"),
                "star": attrs.get("star_rating", 5),
            })

# 3. Generate social proof assets
quote_block = "\n\n".join(
    f'"{q["text"]}" — {q["role"]}, {q["industry"]}, {q["company_size"]}'
    for q in quotes[:15]
)

gen = requests.post(f"https://app.mavera.io/api/v1/generations",
    headers=MV_H,
    json={
        "prompt": f"""Using these real G2 reviewer quotes, generate:

1. Five testimonial card headlines (15 words max each)
2. Three case study header lines pairing the quote with a metric
3. Five email proof-point bullets for a nurture sequence
4. Three social media post variants featuring quotes

QUOTES:
{quote_block}

Rules:
- Keep quotes verbatim or clearly attributed
- Pair quotes with role/industry for credibility
- Focus on outcomes and specificity""",
    }).json()

print("=== Social Proof Assets ===")
print(gen.get("output", gen.get("content", gen.get("text", "")))[:1500])

const G2 = process.env.G2_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const G2_H = { Authorization: `Token token=${G2}`, "Content-Type": "application/vnd.api+json" };
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

// 1. Pull reviews
const reviews = [];
let page = 1;
while (reviews.length < 200) {
  const res = await fetch(
    `https://data.g2.com/api/v1/survey-responses?page[size]=50&page[number]=${page}`,
    { headers: G2_H }
  );
  if (res.status === 429) { await new Promise((r) => setTimeout(r, 1000)); continue; }
  const data = (await res.json()).data || [];
  if (!data.length) break;
  reviews.push(...data);
  page++;
  await new Promise((r) => setTimeout(r, 100));
}

// 2. Extract quotes
const quotes = [];
for (const rev of reviews) {
  const attrs = rev.attributes || {};
  if ((attrs.star_rating || 0) < 4) continue;
  for (const [key, val] of Object.entries(attrs.comment_answers || {})) {
    const text = typeof val === "string" ? val : val?.text || "";
    if ((key.toLowerCase().includes("recommend") || key.toLowerCase().includes("love")) && text.length > 30) {
      quotes.push({ text: text.slice(0, 300), role: attrs.title || "User", industry: attrs.industry || "Technology" });
    }
  }
}

// 3. Generate
const quoteBlock = quotes.slice(0, 15).map((q) => `"${q.text}" — ${q.role}, ${q.industry}`).join("\n\n");
const gen = await fetch("https://app.mavera.io/api/v1/generations", {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    prompt: `From G2 quotes, generate: 1) 5 testimonial headlines 2) 3 case study headers 3) 5 email proof bullets 4) 3 social posts\n\nQUOTES:\n${quoteBlock}`,
  }),
}).then((r) => r.json());
console.log(gen.output || gen.content || gen.text || "");