34edb3bef8
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0155BJMHJyhqqAPGYjGKYhPS
221 lines
10 KiB
Python
221 lines
10 KiB
Python
#!/usr/bin/env python3
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"""Question Bank 120문항 + 2엔진 실측 결과를 엑셀(xlsx)로 내보낸다.
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시트: 요약 / 통합결과 / 병원빈도 / TOP50 / GPT상세 / PPLX상세
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"""
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import json
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import os
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import time
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from openpyxl import Workbook
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from openpyxl.styles import Alignment, Font, PatternFill
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from openpyxl.utils import get_column_letter
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ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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QUESTIONS = os.path.join(ROOT, "scripts", "question_bank_viewclinic.json")
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OUT = os.path.join(ROOT, "docs", "reports", "Viewclinic_QB_Results_TOP50.xlsx")
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INK = "0A1128"
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VIOLET = "4F1DA1"
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LIGHT = "F7F8FC"
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GOOD = "DCFCE7"
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BAD = "FEE2E2"
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WARN = "FEF9C3"
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CAT_LABEL = {
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"A": "브랜드 직접", "B": "위치·운영", "C": "지역 추천·비교",
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"D1": "가슴성형", "D2": "눈성형", "D3": "코성형", "D4": "안면윤곽·양악",
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"D5": "지방성형", "D6": "피부·리프팅", "D7": "재수술",
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"E": "신뢰·안전", "F": "가격·이벤트", "G": "후기·평판",
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}
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def load(engine):
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p = os.path.join(ROOT, "scripts", "out", f"qb_{engine}_results.jsonl")
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rows = {}
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for line in open(p):
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if line.strip():
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r = json.loads(line)
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rows[r["id"]] = r
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return rows
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def style_header(ws, ncols, row=1):
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for c in range(1, ncols + 1):
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cell = ws.cell(row=row, column=c)
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cell.font = Font(bold=True, color="FFFFFF", size=11)
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cell.fill = PatternFill("solid", fgColor=INK)
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cell.alignment = Alignment(vertical="center", wrap_text=True)
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ws.freeze_panes = ws.cell(row=row + 1, column=1)
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def main():
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qs = json.load(open(QUESTIONS))
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oa, px = load("openai"), load("perplexity")
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wb = Workbook()
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# ── 요약 ─────────────────────────────────────────────
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ws = wb.active
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ws.title = "요약"
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ws["A1"] = "뷰성형외과 AI Discovery Question Bank 실측 결과"
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ws["A1"].font = Font(bold=True, size=16, color=INK)
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ws["A2"] = f"측정일 {time.strftime('%Y-%m-%d')} · OpenAI Responses API + web_search(gpt-4o, 서울 고정) / Perplexity API(sonar)"
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ws["A3"] = "답변엔진 소비자 UI 자동 조회는 약관 위반이므로 정식 API로 측정. 문의 o2oteam@o2o.kr"
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ws["A2"].font = ws["A3"].font = Font(size=10, color="666B7A")
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def brand(q):
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return "뷰성형외과" in q["question"]
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ws.append([])
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ws.append(["구분", "문항 수", "ChatGPT 뷰 언급", "ChatGPT 언급률", "Perplexity 뷰 언급", "Perplexity 언급률"])
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hr = ws.max_row
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for label, group in (("브랜드 질문 (질문에 병원명 포함)", [q for q in qs if brand(q)]),
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("논브랜드 질문 (추천·비교·시술 정보)", [q for q in qs if not brand(q)]),
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("전체", qs)):
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om = sum(1 for q in group if oa[q["id"]].get("view_mentioned"))
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pm = sum(1 for q in group if px[q["id"]].get("view_mentioned"))
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ws.append([label, len(group), om, f"{om*100//len(group)}%", pm, f"{pm*100//len(group)}%"])
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style_header(ws, 6, hr)
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ws.append([])
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ws.append(["사전판정 분포", "◯ 인용 기대 5", "△ 불안정 35", "✕ 인용 불가 80"])
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ws.cell(row=ws.max_row, column=1).font = Font(bold=True)
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for col, w in zip("ABCDEF", [34, 10, 16, 14, 16, 14]):
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ws.column_dimensions[col].width = w
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# ── 통합결과 ─────────────────────────────────────────
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ws = wb.create_sheet("통합결과")
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ws.append(["ID", "카테고리", "질문", "퍼널", "브랜드", "사전판정",
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"GPT 언급", "GPT 순위", "GPT 상위 병원 (등장순)",
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"PPLX 언급", "PPLX 순위", "PPLX 상위 병원 (등장순)"])
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style_header(ws, 12)
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for q in qs:
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o, p = oa[q["id"]], px[q["id"]]
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oc = ", ".join([c for c in o.get("clinics", []) if isinstance(c, str)][:5])
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pc = ", ".join([c for c in p.get("clinics", []) if isinstance(c, str)][:5])
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ws.append([q["id"], CAT_LABEL[q["id"].split("-")[0]], q["question"], q["stage"],
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"O" if brand(q) else "", q["prior"],
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"O" if o.get("view_mentioned") else "X", o.get("view_rank") or "",
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oc or "(병원 언급 없음)",
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"O" if p.get("view_mentioned") else "X", p.get("view_rank") or "",
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pc or "(병원 언급 없음)"])
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r = ws.max_row
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for col, eng in ((7, o), (10, p)):
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ws.cell(row=r, column=col).fill = PatternFill(
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"solid", fgColor=GOOD if eng.get("view_mentioned") else BAD)
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for c in range(1, 13):
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ws.cell(row=r, column=c).alignment = Alignment(vertical="top", wrap_text=c in (3, 9, 12))
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for col, w in zip(range(1, 13), [8, 13, 44, 7, 7, 9, 9, 9, 44, 9, 9, 44]):
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ws.column_dimensions[get_column_letter(col)].width = w
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# ── 병원빈도 (논브랜드 한정) ─────────────────────────
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ws = wb.create_sheet("병원빈도")
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ws.append(["엔진", "병원", "언급 질문 수", "1순위 등장 수"])
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style_header(ws, 4)
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def canon(name):
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n = name.strip().lower().replace(" ", "")
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if ("view" in n and ("plastic" in n or "성형" in n)) or n.startswith("뷰성형"):
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return "뷰성형외과"
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for suf in ("성형외과의원", "성형외과", "의원", "클리닉", "clinic", "plasticsurgery"):
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if n.endswith(suf) and len(n) > len(suf):
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return n[: -len(suf)]
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return n
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for eng_label, data in (("ChatGPT(OpenAI)", oa), ("Perplexity", px)):
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freq, label = {}, {}
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for q in qs:
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if brand(q):
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continue
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seen = set()
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for i, c in enumerate([c for c in data[q["id"]].get("clinics", []) if isinstance(c, str)][:5]):
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key = canon(c)
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if key in seen:
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continue
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seen.add(key)
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label.setdefault(key, c)
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freq.setdefault(key, [0, 0])
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freq[key][0] += 1
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if i == 0:
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freq[key][1] += 1
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for key, (cnt, first) in sorted(freq.items(), key=lambda x: -x[1][0]):
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ws.append([eng_label, label[key], cnt, first])
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if key == "뷰성형외과":
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for c in range(1, 5):
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ws.cell(row=ws.max_row, column=c).fill = PatternFill("solid", fgColor=WARN)
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for col, w in zip("ABCD", [16, 30, 13, 13]):
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ws.column_dimensions[col].width = w
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# ── TOP20 (논브랜드 84문항 · 2엔진 합산 랭킹) ─────────
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ws = wb.create_sheet("TOP50")
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ws["A1"] = "성형외과 AI Discovery TOP 50 (논브랜드 84문항 · ChatGPT+Perplexity 합산)"
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ws["A1"].font = Font(bold=True, size=14, color=INK)
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ws["A2"] = "점수 = 답변 내 등장 순위 가중치(1위 5점 ~ 5위 1점)를 질문·엔진 단위로 합산. 브랜드 직접 질문 36문항은 제외."
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ws["A2"].font = Font(size=10, color="666B7A")
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ws.append([])
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ws.append(["순위", "병원", "AI Discovery 점수", "언급 질문 수(합산)", "GPT 언급", "PPLX 언급", "1순위 등장", "주요 등장 카테고리"])
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hr = ws.max_row
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style_header(ws, 8, hr)
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agg = {}
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for eng_key, data in (("gpt", oa), ("pplx", px)):
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for q in qs:
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if brand(q):
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continue
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seen = set()
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for i, c in enumerate([c for c in data[q["id"]].get("clinics", []) if isinstance(c, str)][:5]):
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key = canon(c)
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if key in seen:
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continue
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seen.add(key)
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a = agg.setdefault(key, {"label": "뷰성형외과" if key == "뷰성형외과" else c,
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"score": 0, "gpt": 0, "pplx": 0,
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"first": 0, "cats": {}})
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a["score"] += 5 - i
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a[eng_key] += 1
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if i == 0:
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a["first"] += 1
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cat = CAT_LABEL[q["id"].split("-")[0]]
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a["cats"][cat] = a["cats"].get(cat, 0) + 1
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ranked = sorted(agg.values(), key=lambda a: (-a["score"], -a["first"], -(a["gpt"] + a["pplx"])))
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view_rank = next((i + 1 for i, a in enumerate(ranked) if canon(a["label"]) == "뷰성형외과"), None)
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shown = ranked[:50]
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if view_rank and view_rank > 50:
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shown = ranked[:50] + [ranked[view_rank - 1]]
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for idx, a in enumerate(shown):
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rank = idx + 1 if idx < 50 else view_rank
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cats = " · ".join(k for k, _ in sorted(a["cats"].items(), key=lambda x: -x[1])[:3])
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ws.append([rank, a["label"], a["score"], a["gpt"] + a["pplx"], a["gpt"], a["pplx"], a["first"], cats])
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if canon(a["label"]) == "뷰성형외과":
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for c in range(1, 9):
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ws.cell(row=ws.max_row, column=c).fill = PatternFill("solid", fgColor=WARN)
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ws.cell(row=ws.max_row, column=c).font = Font(bold=True)
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for col, w in zip("ABCDEFGH", [6, 30, 16, 16, 10, 10, 10, 40]):
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ws.column_dimensions[col].width = w
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# ── 엔진별 상세 (답변 전문 + 인용) ────────────────────
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# 논브랜드 84문항만 수록 (브랜드 직접 질문 36개의 원본 답변은 scripts/out/ JSONL 참조)
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for sheet, data in (("GPT상세", oa), ("PPLX상세", px)):
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ws = wb.create_sheet(sheet)
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ws.append(["ID", "질문 (논브랜드 84문항)", "뷰 언급", "순위", "답변 전문", "인용 URL"])
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style_header(ws, 6)
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for q in qs:
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if brand(q):
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continue
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r = data[q["id"]]
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ws.append([q["id"], q["question"],
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"O" if r.get("view_mentioned") else "X", r.get("view_rank") or "",
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r.get("answer", "")[:32000], "\n".join(r.get("citations", [])[:10])])
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row = ws.max_row
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for c in range(1, 7):
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ws.cell(row=row, column=c).alignment = Alignment(vertical="top", wrap_text=c in (2, 5, 6))
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for col, w in zip("ABCDEF", [8, 36, 8, 6, 90, 50]):
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ws.column_dimensions[col].width = w
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wb.save(OUT)
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print("저장:", OUT)
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if __name__ == "__main__":
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main()
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