Bulk Response Analysis

import os, json, 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}", "Content-Type": "application/json"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

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

# 1. Get survey structure
survey = requests.get(f"{SM_BASE}/surveys/{SURVEY_ID}/details",
    headers=SM_H).json()
print(f"Survey: {survey.get('title', 'Untitled')} ({survey.get('response_count', 0)} responses)")

# Build question/answer lookup
questions = {}
choice_labels = {}
for page in survey.get("pages", []):
    for q in page.get("questions", []):
        qid = q["id"]
        questions[qid] = {
            "title": q.get("headings", [{}])[0].get("heading", ""),
            "type": q.get("family", ""),
            "subtype": q.get("subtype", ""),
        }
        for row in q.get("answers", {}).get("rows", []):
            choice_labels[row["id"]] = row.get("text", "")
        for choice in q.get("answers", {}).get("choices", []):
            choice_labels[choice["id"]] = choice.get("text", "")
        for col in q.get("answers", {}).get("columns", []):
            choice_labels[col["id"]] = col.get("text", col.get("label", ""))

# 2. Pull all responses (paginated)
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:
        retry = int(r.headers.get("X-Ratelimit-App-Global-Day-Reset", 60))
        print(f"Rate limited — daily limit. Retry in {retry}s")
        time.sleep(min(retry, 60))
        continue
    r.raise_for_status()
    data = r.json()
    batch = data.get("data", [])
    all_responses.extend(batch)

    total = data.get("total", 0)
    if len(all_responses) >= total or not batch:
        break
    page_num += 1
    time.sleep(0.6)

print(f"Fetched {len(all_responses)} responses")

# 3. Aggregate answers by question
qa_data = defaultdict(list)
for resp in all_responses:
    for page_data in resp.get("pages", []):
        for q_data in page_data.get("questions", []):
            qid = q_data["id"]
            for ans in q_data.get("answers", []):
                if "text" in ans:
                    qa_data[qid].append(ans["text"])
                elif "choice_id" in ans:
                    label = choice_labels.get(ans["choice_id"], ans["choice_id"])
                    qa_data[qid].append(label)
                    if "row_id" in ans:
                        row = choice_labels.get(ans["row_id"], "")
                        qa_data[qid][-1] = f"{row}: {label}"

# 4. Build analysis summary
summary_parts = []
for qid, answers in qa_data.items():
    q_info = questions.get(qid, {})
    title = q_info.get("title", qid)
    q_type = q_info.get("type", "")

    if q_type in ("single_choice", "multiple_choice", "matrix"):
        counts = Counter(answers).most_common(10)
        dist = ", ".join(f"{label}: {count} ({count/len(answers)*100:.0f}%)"
                        for label, count in counts)
        summary_parts.append(f"**{title}** (n={len(answers)})\n  {dist}")
    elif q_type == "open_ended":
        sample = "; ".join(answers[:15])[:500]
        summary_parts.append(f"**{title}** (n={len(answers)}, open-ended)\n  Samples: {sample}")
    else:
        try:
            nums = [float(a) for a in answers if a.replace(".", "").replace("-", "").isdigit()]
            if nums:
                avg = sum(nums) / len(nums)
                summary_parts.append(f"**{title}** (n={len(nums)}, avg={avg:.1f})")
        except ValueError:
            summary_parts.append(f"**{title}** (n={len(answers)})")

summary = "\n\n".join(summary_parts)

# 5. Mave analysis
analysis = requests.post(f"{MB}/mave/chat", headers=MV_H, json={
    "message": f"""Analyze {len(all_responses)} survey responses from "{survey.get('title', 'Survey')}".

QUESTION-BY-QUESTION DATA:
{summary}

Tasks:
1) Statistical patterns: Correlations between questions, unexpected distributions
2) Sentiment trends: Overall and per-question sentiment analysis
3) Key findings: Top 5 insights ranked by impact
4) Segment differences: Any visible subgroups in the data
5) Red flags: Responses that indicate problems or risks
6) Recommendations: 5 actionable next steps based on the data
7) Suggested follow-up questions: What should you ask next?"""
}).json()

print(f"\n{'='*60}")
print(f"ANALYSIS: {survey.get('title', 'Survey')} ({len(all_responses)} responses)")
print(f"{'='*60}")
print(analysis.get("content", "")[:3000])
print(f"\nSources: {len(analysis.get('sources', []))}")

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}`, "Content-Type": "application/json" };
const mvH = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const SURVEY_ID = process.env.SURVEY_ID || "your_survey_id";

// 1. Survey structure
const survey = await fetch(`${SM_BASE}/surveys/${SURVEY_ID}/details`,
  { headers: smH }).then(r => r.json());
console.log(`Survey: ${survey.title} (${survey.response_count} responses)`);

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 || "",
      subtype: q.subtype || "" };
    for (const r of q.answers?.rows || []) choiceLabels[r.id] = r.text || "";
    for (const c of q.answers?.choices || []) choiceLabels[c.id] = c.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) {
    const retry = parseInt(res.headers.get("X-Ratelimit-App-Global-Day-Reset") || "60");
    console.log(`Rate limited. Waiting ${Math.min(retry, 60)}s`);
    await new Promise(r => setTimeout(r, Math.min(retry, 60) * 1000));
    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));
}
console.log(`Fetched ${allResponses.length} responses`);

// 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] || ans.choice_id;
          if (ans.row_id) label = `${choiceLabels[ans.row_id] || ""}: ${label}`;
          qaData[q.id].push(label);
        }
      }
    }
  }
}

// 4. Build summary
const summaryParts = [];
for (const [qid, answers] of Object.entries(qaData)) {
  const q = questions[qid] || {};
  const title = q.title || qid;
  if (["single_choice", "multiple_choice", "matrix"].includes(q.type)) {
    const counts = {};
    answers.forEach(a => { counts[a] = (counts[a] || 0) + 1; });
    const dist = Object.entries(counts).sort(([, a], [, b]) => b - a).slice(0, 10)
      .map(([l, c]) => `${l}: ${c} (${(c / answers.length * 100).toFixed(0)}%)`).join(", ");
    summaryParts.push(`**${title}** (n=${answers.length})\n  ${dist}`);
  } else if (q.type === "open_ended") {
    summaryParts.push(`**${title}** (n=${answers.length}, open-ended)\n  Samples: ${answers.slice(0, 15).join("; ").slice(0, 500)}`);
  } else {
    const nums = answers.map(Number).filter(n => !isNaN(n));
    if (nums.length) summaryParts.push(`**${title}** (n=${nums.length}, avg=${(nums.reduce((s, n) => s + n, 0) / nums.length).toFixed(1)})`);
    else summaryParts.push(`**${title}** (n=${answers.length})`);
  }
}

// 5. Mave analysis
const analysis = await fetch(`${MB}/mave/chat`, { method: "POST", headers: mvH,
  body: JSON.stringify({
    message: `Analyze ${allResponses.length} responses from "${survey.title}".

QUESTION DATA:
${summaryParts.join("\n\n")}

Tasks: 1) Statistical patterns 2) Sentiment trends 3) Top 5 insights 4) Segment differences 5) Red flags 6) 5 recommendations 7) Follow-up questions`,
  }),
}).then(r => r.json());

console.log(`\n${"=".repeat(60)}`);
console.log(`ANALYSIS: ${survey.title} (${allResponses.length} responses)`);
console.log(`${"=".repeat(60)}`);
console.log((analysis.content || "").slice(0, 3000));