Love/Hate → Brand Voice Refinement

import os, requests, time, re
from collections import Counter

G2 = os.environ["G2_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
G2_BASE = "https://data.g2.com/api/v1"
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 own product reviews
reviews = []
page = 1
while len(reviews) < 200:
    r = requests.get(f"{G2_BASE}/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 love and hate phrases
love_texts = []
hate_texts = []
for rev in reviews:
    attrs = rev.get("attributes", {})
    for key, val in attrs.get("comment_answers", {}).items():
        text = val if isinstance(val, str) else val.get("text", "")
        if not text.strip():
            continue
        k = key.lower()
        if "love" in k or "best" in k or "like most" in k:
            love_texts.append(text)
        elif "dislike" in k or "hate" in k or "don't like" in k or "cons" in k:
            hate_texts.append(text)

# 3. Extract key phrases
def extract_phrases(texts, min_count=2):
    words = Counter()
    bigrams = Counter()
    for t in texts:
        clean = re.sub(r'[^\w\s]', '', t.lower())
        tokens = clean.split()
        words.update(tokens)
        for i in range(len(tokens) - 1):
            bigrams[f"{tokens[i]} {tokens[i+1]}"] += 1
    stop = {"the", "a", "an", "is", "it", "to", "and", "of", "in", "for", "that", "this", "with", "on", "i", "we", "our"}
    phrases = [(p, c) for p, c in bigrams.items() if c >= min_count and not all(w in stop for w in p.split())]
    return sorted(phrases, key=lambda x: -x[1])[:20]

love_phrases = extract_phrases(love_texts)
hate_phrases = extract_phrases(hate_texts)

preferred_terms = [p for p, _ in love_phrases[:15]]
avoid_terms = [p for p, _ in hate_phrases[:15]]

# 4. Create Brand Voice
love_samples = "\n\n---\n\n".join(love_texts[:15])

bv = requests.post(f"{MV_BASE}/brand-voices",
    headers=MV_H,
    json={
        "name": "G2 Customer Voice",
        "samples": [love_samples],
        "preferred_terms": preferred_terms,
        "avoid_terms": avoid_terms,
    }).json()

print(f"Brand Voice: {bv['id']}")
print(f"\nPreferred terms ({len(preferred_terms)}):")
for p, c in love_phrases[:10]:
    print(f"  ✓ '{p}' ({c}x)")
print(f"\nAvoid terms ({len(avoid_terms)}):")
for p, c in hate_phrases[:10]:
    print(f"  ✗ '{p}' ({c}x)")

# 5. Test with a generation
from openai import OpenAI
mavera = OpenAI(api_key=MV, base_url=MV_BASE)
test = mavera.responses.create(model="mavera-1",
    input=[{"role": "user", "content": "Write a 100-word product description for our landing page."}],
    extra_body={"brand_voice_id": bv["id"]})
print(f"\n--- Test Generation ---\n{test.output[0].content[0].text}")

const G2 = process.env.G2_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const G2_BASE = "https://data.g2.com/api/v1";
const MV_BASE = "https://app.mavera.io/api/v1";
const G2_H = { Authorization: `Token token=${G2}`, "Content-Type": "application/vnd.api+json" };
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const OpenAI = require("openai").default;

// 1. Pull reviews
const reviews = [];
let page = 1;
while (reviews.length < 200) {
  const res = await fetch(`${G2_BASE}/survey-responses?page[size]=50&page[number]=${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 love/hate
const loveTexts = [], hateTexts = [];
for (const rev of reviews) {
  for (const [key, val] of Object.entries(rev.attributes?.comment_answers || {})) {
    const text = typeof val === "string" ? val : val?.text || "";
    if (!text.trim()) continue;
    const k = key.toLowerCase();
    if (k.includes("love") || k.includes("best")) loveTexts.push(text);
    else if (k.includes("dislike") || k.includes("hate") || k.includes("cons")) hateTexts.push(text);
  }
}

// 3. Extract phrases
function extractPhrases(texts, minCount = 2) {
  const bigrams = {};
  const stop = new Set(["the", "a", "an", "is", "it", "to", "and", "of", "in", "for", "that", "this", "with", "on", "i", "we"]);
  for (const t of texts) {
    const tokens = t.toLowerCase().replace(/[^\w\s]/g, "").split(/\s+/);
    for (let i = 0; i < tokens.length - 1; i++) {
      const bg = `${tokens[i]} ${tokens[i + 1]}`;
      bigrams[bg] = (bigrams[bg] || 0) + 1;
    }
  }
  return Object.entries(bigrams)
    .filter(([p, c]) => c >= minCount && !p.split(" ").every((w) => stop.has(w)))
    .sort(([, a], [, b]) => b - a).slice(0, 20);
}

const lovePhrases = extractPhrases(loveTexts);
const hatePhrases = extractPhrases(hateTexts);

// 4. Brand Voice
const bv = await fetch(`${MV_BASE}/brand-voices`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    name: "G2 Customer Voice",
    samples: [loveTexts.slice(0, 15).join("\n\n---\n\n")],
    preferred_terms: lovePhrases.slice(0, 15).map(([p]) => p),
    avoid_terms: hatePhrases.slice(0, 15).map(([p]) => p),
  }),
}).then((r) => r.json());

console.log(`Brand Voice: ${bv.id}`);
lovePhrases.slice(0, 10).forEach(([p, c]) => console.log(`  ✓ '${p}' (${c}x)`));
hatePhrases.slice(0, 10).forEach(([p, c]) => console.log(`  ✗ '${p}' (${c}x)`));

// 5. Test generation
const mavera = new OpenAI({ apiKey: MV, baseURL: MV_BASE });
const test = await mavera.responses.create({
  model: "mavera-1",
  input: [{ role: "user", content: "Write a 100-word landing page description." }],
  extra_body: { brand_voice_id: bv.id },
});
console.log(`\n--- Test ---\n${test.output[0].content[0].text}`);