Response Data → Generate Reports

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

SM = os.environ["SURVEYMONKEY_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
SM_BASE = "https://api.surveymonkey.com/v3"
MB = "https://app.mavera.io/api/v1"
SM_H = {"Authorization": f"Bearer {SM}"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

SURVEY_ID = os.environ.get("SURVEY_ID", "your_survey_id")
BRAND_VOICE_ID = os.environ.get("BRAND_VOICE_ID", "")

# 1. Survey structure
survey = requests.get(f"{SM_BASE}/surveys/{SURVEY_ID}/details",
    headers=SM_H).json()

questions = {}
choice_labels = {}
for page in survey.get("pages", []):
    for q in page.get("questions", []):
        qid = q["id"]
        heading = q.get("headings", [{}])[0].get("heading", "")
        questions[qid] = {"title": heading, "type": q.get("family", "")}
        for c in q.get("answers", {}).get("choices", []):
            choice_labels[c["id"]] = c.get("text", "")
        for r in q.get("answers", {}).get("rows", []):
            choice_labels[r["id"]] = r.get("text", "")
        for c in q.get("answers", {}).get("columns", []):
            choice_labels[c["id"]] = c.get("text", c.get("label", ""))

# 2. Pull responses
all_responses = []
page_num = 1
while True:
    r = requests.get(f"{SM_BASE}/surveys/{SURVEY_ID}/responses/bulk",
        headers=SM_H, params={"page": page_num, "per_page": 100})
    if r.status_code == 429:
        time.sleep(60); continue
    r.raise_for_status()
    data = r.json()
    all_responses.extend(data.get("data", []))
    if len(all_responses) >= data.get("total", 0) or not data.get("data"):
        break
    page_num += 1
    time.sleep(0.6)

# 3. Statistical summary
qa_data = defaultdict(list)
for resp in all_responses:
    for pg in resp.get("pages", []):
        for q in pg.get("questions", []):
            for ans in q.get("answers", []):
                if "text" in ans:
                    qa_data[q["id"]].append(ans["text"])
                elif "choice_id" in ans:
                    label = choice_labels.get(ans["choice_id"], "?")
                    if "row_id" in ans:
                        label = f"{choice_labels.get(ans['row_id'], '')}: {label}"
                    qa_data[q["id"]].append(label)

report_data = []
for qid, answers in qa_data.items():
    q = questions.get(qid, {})
    title = q["title"]
    q_type = q["type"]

    if q_type in ("single_choice", "multiple_choice"):
        counts = Counter(answers)
        total = len(answers)
        dist = {k: f"{v} ({v/total*100:.0f}%)" for k, v in counts.most_common(10)}
        report_data.append({"question": title, "type": q_type,
                           "n": total, "distribution": dist})
    elif q_type == "open_ended":
        report_data.append({"question": title, "type": "open_ended",
                           "n": len(answers), "samples": answers[:10]})
    else:
        nums = []
        for a in answers:
            try: nums.append(float(a))
            except ValueError: pass
        if nums:
            report_data.append({"question": title, "type": "numeric",
                               "n": len(nums), "mean": sum(nums)/len(nums),
                               "min": min(nums), "max": max(nums)})

# 4. Ensure brand voice
if not BRAND_VOICE_ID:
    bv = requests.post(f"{MB}/brand-voices", headers=MV_H, json={
        "name": "Research Report Voice",
        "samples": [
            "Executive Summary: Our Q4 customer satisfaction survey reveals "
            "strong product-market fit with critical friction points in onboarding. "
            "Key finding: 78% of respondents report daily usage, yet 34% cite "
            "documentation gaps as their primary frustration."
        ],
    }).json()
    BRAND_VOICE_ID = bv["id"]
    print(f"Created brand voice: {BRAND_VOICE_ID}")
    time.sleep(2)

# 5. Generate report
import json
data_block = json.dumps(report_data, indent=2)[:5000]

gen = requests.post(f"{MB}/generations", headers=MV_H, json={
    "brand_voice_id": BRAND_VOICE_ID,
    "prompt": f"""Create an executive summary report for this survey.

SURVEY: {survey.get('title', 'Survey')}
RESPONSES: {len(all_responses)}
DATE: {survey.get('date_modified', 'N/A')}

DATA:
{data_block}

Structure:
1. Executive Summary (3-4 sentences)
2. Methodology (survey type, sample size, collection period)
3. Key Findings (top 5, each with data point + insight)
4. Detailed Results (per-question analysis)
5. Recommendations (5 actionable items)
6. Appendix: Response distribution tables

Use a professional research report tone. Include specific numbers.
Lead each finding with the data, then the implication.""",
}).json()

report = gen.get("output", gen.get("content", gen.get("text", "")))
print(f"\n{'='*60}")
print(f"EXECUTIVE SUMMARY: {survey.get('title', 'Survey')}")
print(f"{'='*60}")
print(report[:3000])

const SM = process.env.SURVEYMONKEY_TOKEN;
const MV = process.env.MAVERA_API_KEY;
const SM_BASE = "https://api.surveymonkey.com/v3";
const MB = "https://app.mavera.io/api/v1";
const smH = { Authorization: `Bearer ${SM}` };
const mvH = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const SURVEY_ID = process.env.SURVEY_ID || "your_survey_id";
let brandVoiceId = process.env.BRAND_VOICE_ID || "";

// 1. Survey structure
const survey = await fetch(`${SM_BASE}/surveys/${SURVEY_ID}/details`,
  { headers: smH }).then(r => r.json());
const questions = {};
const choiceLabels = {};
for (const page of survey.pages || []) {
  for (const q of page.questions || []) {
    questions[q.id] = { title: q.headings?.[0]?.heading || "", type: q.family || "" };
    for (const c of q.answers?.choices || []) choiceLabels[c.id] = c.text || "";
    for (const r of q.answers?.rows || []) choiceLabels[r.id] = r.text || "";
    for (const c of q.answers?.columns || []) choiceLabels[c.id] = c.text || c.label || "";
  }
}

// 2. Pull responses
const allResponses = [];
let pageNum = 1;
while (true) {
  let res = await fetch(`${SM_BASE}/surveys/${SURVEY_ID}/responses/bulk?page=${pageNum}&per_page=100`,
    { headers: smH });
  if (res.status === 429) { await new Promise(r => setTimeout(r, 60000)); continue; }
  const data = await res.json();
  allResponses.push(...(data.data || []));
  if (allResponses.length >= (data.total || 0) || !(data.data || []).length) break;
  pageNum++;
  await new Promise(r => setTimeout(r, 600));
}

// 3. Aggregate
const qaData = {};
for (const resp of allResponses) {
  for (const pg of resp.pages || []) {
    for (const q of pg.questions || []) {
      (qaData[q.id] ??= []);
      for (const ans of q.answers || []) {
        if (ans.text) qaData[q.id].push(ans.text);
        else if (ans.choice_id) {
          let label = choiceLabels[ans.choice_id] || "?";
          if (ans.row_id) label = `${choiceLabels[ans.row_id] || ""}: ${label}`;
          qaData[q.id].push(label);
        }
      }
    }
  }
}

// 4. Report data
const reportData = [];
for (const [qid, answers] of Object.entries(qaData)) {
  const q = questions[qid] || {};
  if (["single_choice", "multiple_choice"].includes(q.type)) {
    const counts = {};
    answers.forEach(a => { counts[a] = (counts[a] || 0) + 1; });
    const dist = Object.fromEntries(
      Object.entries(counts).sort(([, a], [, b]) => b - a).slice(0, 10)
        .map(([k, v]) => [k, `${v} (${(v / answers.length * 100).toFixed(0)}%)`]));
    reportData.push({ question: q.title, type: q.type, n: answers.length, distribution: dist });
  } else if (q.type === "open_ended") {
    reportData.push({ question: q.title, type: "open_ended", n: answers.length, samples: answers.slice(0, 10) });
  } else {
    const nums = answers.map(Number).filter(n => !isNaN(n));
    if (nums.length) reportData.push({ question: q.title, type: "numeric", n: nums.length,
      mean: nums.reduce((s, n) => s + n, 0) / nums.length, min: Math.min(...nums), max: Math.max(...nums) });
  }
}

// 5. Ensure brand voice
if (!brandVoiceId) {
  const bv = await fetch(`${MB}/brand-voices`, { method: "POST", headers: mvH,
    body: JSON.stringify({ name: "Research Report Voice",
      samples: ["Executive Summary: Our Q4 survey reveals strong product-market fit with friction in onboarding. 78% daily usage, 34% cite documentation gaps."] }),
  }).then(r => r.json());
  brandVoiceId = bv.id;
  await new Promise(r => setTimeout(r, 2000));
}

// 6. Generate report
const gen = await fetch(`${MB}/generations`, { method: "POST", headers: mvH,
  body: JSON.stringify({
    brand_voice_id: brandVoiceId,
    prompt: `Create executive summary for this survey.
SURVEY: ${survey.title} | RESPONSES: ${allResponses.length}
DATA:\n${JSON.stringify(reportData, null, 2).slice(0, 5000)}

Structure: 1) Executive Summary 2) Methodology 3) Key Findings (top 5) 4) Detailed Results 5) Recommendations 6) Appendix
Professional tone. Specific numbers. Data first, then implication.`,
  }),
}).then(r => r.json());

console.log(`\n${"=".repeat(60)}\nEXECUTIVE SUMMARY: ${survey.title}\n${"=".repeat(60)}`);
console.log((gen.output || gen.content || gen.text || "").slice(0, 3000));