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])