# =============================================================================
# 📊 SEM 분석기 (빈도분석 / 기술통계 / 신뢰도 / CFA / 상관관계 / SEM)
# 실행: python -m streamlit run sem.py
# =============================================================================
import streamlit as st
import pandas as pd
import numpy as np
import re
from datetime import datetime
from collections import defaultdict

st.set_page_config(page_title="📊 SEM 분석기",
                   page_icon="📊", layout="wide")

# ── 비밀번호 잠금 ─────────────────────────────────────────────────────────────
def _check_password():
    try:
        correct_pw = st.secrets.get("PASSWORD", "9400")
    except Exception:
        correct_pw = "9400"

    def _submit():
        if st.session_state.get("_pw_input") == correct_pw:
            st.session_state["_pw_ok"] = True
        else:
            st.session_state["_pw_ok"] = False

    if st.session_state.get("_pw_ok"):
        return True

    st.markdown("## 🔒 SEM 분석기")
    st.text_input("비밀번호를 입력하세요", type="password",
                  key="_pw_input", on_change=_submit)
    if "_pw_ok" in st.session_state and not st.session_state["_pw_ok"]:
        st.error("비밀번호가 틀렸습니다.")
    st.stop()

_check_password()

st.markdown("""
<style>
  .main-title{font-size:2rem;font-weight:700;color:#2F5496}
  .sub-title{font-size:.95rem;color:#666;margin-bottom:1rem}
  .card{background:#F3F6FB;border-left:4px solid #2F5496;
        padding:8px 14px;border-radius:4px;margin:3px 0}
</style>""", unsafe_allow_html=True)

# ── 모듈 임포트 ───────────────────────────────────────────────────────────────
try:
    from modules.utils import detect_scale, cronbach_alpha, calc_ave_cr, fmt_p, sig_stars, build_excel
except Exception as e:
    st.error(f"모듈 로드 오류: {e}\n\n앱과 같은 폴더에 modules/ 폴더가 있는지 확인하세요.")
    st.stop()


# ══════════════════════════════════════════════════════════════════════════════
# 유틸 함수
# ══════════════════════════════════════════════════════════════════════════════

def detect_outliers_mahalanobis(df, cols, p_threshold=0.001):
    from scipy import stats as sc
    data = df[cols].dropna()
    n, k = data.shape
    if n < k + 2:
        return None, None, []
    try:
        mean    = data.mean().values
        cov_mat = np.cov(data.values.T, ddof=1)
        try:
            cov_inv = np.linalg.inv(cov_mat)
        except np.linalg.LinAlgError:
            cov_inv = np.linalg.pinv(cov_mat)
        diffs  = data.values - mean
        mahal  = np.array([float(d @ cov_inv @ d) for d in diffs])
        pvals  = sc.chi2.sf(mahal, df=k)
        cutoff = sc.chi2.ppf(1 - p_threshold, df=k)
        result = pd.DataFrame({
            "원본 행번호":      data.index + 1,
            "마할라노비스 거리": np.round(mahal, 3),
            "p값":              np.round(pvals, 4),
            "이상치":           mahal > cutoff,
        }).reset_index(drop=True)
        outlier_idx = data.index[mahal > cutoff].tolist()
        return result, round(cutoff, 3), outlier_idx
    except Exception:
        return None, None, []


def compute_smc(df, cols):
    data = df[cols].dropna()
    if len(data) < len(cols) + 2:
        return pd.DataFrame({"변수": cols, "SMC": [np.nan]*len(cols),
                             "판정": ["사례 수 부족"]*len(cols)})
    rows = []
    for col in cols:
        others = [c for c in cols if c != col]
        if not others:
            rows.append({"변수": col, "SMC": np.nan, "판정": "-"})
            continue
        try:
            X  = data[others].values.astype(float)
            y  = data[col].values.astype(float)
            Xc = np.column_stack([np.ones(len(X)), X])
            b, _, _, _ = np.linalg.lstsq(Xc, y, rcond=None)
            yh     = Xc @ b
            ss_res = float(np.sum((y - yh) ** 2))
            ss_tot = float(np.sum((y - y.mean()) ** 2))
            r2 = (1.0 - ss_res / ss_tot) if ss_tot > 0 else np.nan
            rows.append({
                "변수": col,
                "SMC": round(float(r2), 3),
                "판정": "⚠️ 제거 고려 (SMC < .20)"
                        if (not np.isnan(r2) and r2 < 0.2) else "✅ 정상"
            })
        except Exception:
            rows.append({"변수": col, "SMC": np.nan, "판정": "계산 오류"})
    return pd.DataFrame(rows)


def auto_detect_constructs(df):
    likert = [c for c in df.columns if detect_scale(df[c]) == "likert"]
    groups = defaultdict(list)
    for col in likert:
        m = re.match(r'^([A-Za-z]+\d*)(\d)$', col)
        groups[m.group(1) if m else col].append(col)
    return {k: v for k, v in groups.items() if len(v) >= 2}


def _adequate_check(fit):
    return (fit.get("NFI",0)>=.90 and fit.get("RFI",0)>=.90 and
            fit.get("IFI",0)>=.90 and fit.get("TLI",0)>=.90 and
            fit.get("CFI",0)>=.90 and fit.get("RMSEA",1)<.049)


def get_composite_scores(df, constructs):
    return pd.DataFrame(
        {lv: df[items].mean(axis=1) for lv, items in constructs.items()})


# ══════════════════════════════════════════════════════════════════════════════
# semopy 기반 CFA (lavaan 폴백용)
# ══════════════════════════════════════════════════════════════════════════════

def calc_mi_approx(model, obs_df, obs_vars):
    try:
        n   = len(obs_df)
        S   = np.cov(obs_df[obs_vars].values.T, ddof=1)
        Sig, _ = model.calc_sigma()
        Sig = Sig + np.eye(len(obs_vars)) * 1e-8
        Si  = np.linalg.inv(Sig)
        G   = Si @ (S - Sig) @ Si
        rows = []
        p = len(obs_vars)
        for i in range(p):
            for j in range(i + 1, p):
                denom = 2 * Si[i, i] * Si[j, j] + 2 * Si[i, j] ** 2
                mi = (n - 1) * G[i, j] ** 2 / max(denom, 1e-8)
                rows.append({"변수1": obs_vars[i], "변수2": obs_vars[j],
                              "MI": round(float(mi), 3)})
        return pd.DataFrame(rows).sort_values("MI", ascending=False).reset_index(drop=True)
    except Exception:
        return pd.DataFrame()


def run_cfa_with_mi(df, constructs, mi_threshold=3.84, max_mods=200):
    try:
        from semopy import Model, calc_stats
    except ImportError:
        return None, "semopy 패키지가 필요합니다."
    try:
        item_to_lv = {item: lv for lv, items in constructs.items() for item in items}
        all_items  = [i for items in constructs.values() for i in items]
        data       = df[all_items].dropna()

        meas_lines = [f"  {lv} =~ {' + '.join(items)}" for lv, items in constructs.items()]
        meas_str   = "\n".join(meas_lines)
        lv_keys    = list(constructs.keys())
        lv_cov_lines = [f"  {lv_keys[i]} ~~ {lv_keys[j]}"
                        for i in range(len(lv_keys))
                        for j in range(i + 1, len(lv_keys))]
        meas_str = meas_str + ("\n" + "\n".join(lv_cov_lines) if lv_cov_lines else "")

        def _fit(model_str):
            try:
                m = Model(model_str); m.fit(data); return m
            except Exception:
                return None

        base_str = meas_str
        for _var_syntax in [
            "\n".join(f"  {lv} ~~ 1*{lv}"   for lv in constructs.keys()),
            "\n".join(f"  {lv} ~~ 1 * {lv}" for lv in constructs.keys()),
            "\n".join(f"  {lv} ~~ 1@{lv}"   for lv in constructs.keys()),
        ]:
            _candidate = meas_str + "\n" + _var_syntax
            if _fit(_candidate) is not None:
                base_str = _candidate
                break

        def _extract_fit(m):
            try:
                sd      = calc_stats(m).iloc[0].to_dict()
                chi2    = float(sd.get("chi2", 0))
                dof     = float(sd.get("DoF", 1))
                chi2_bl = float(sd.get("chi2 Baseline", 0))
                dof_bl  = float(sd.get("DoF Baseline", 1))
                pval    = float(sd.get("chi2 p-value", 1))
                cfi     = float(sd.get("CFI", 0))
                tli     = float(sd.get("TLI", 0))
                nfi     = float(sd.get("NFI", 0))
                rmsea   = float(sd.get("RMSEA", 1))
                rfi = ((chi2_bl/dof_bl - chi2/dof) / (chi2_bl/dof_bl)
                       if chi2_bl>0 and dof_bl>0 and dof>0 else 0.0)
                ifi = ((chi2_bl - chi2) / (chi2_bl - dof)
                       if (chi2_bl - dof) > 0 else 0.0)
                return {"χ²": round(chi2,3), "df": int(dof),
                        "χ²/df": round(chi2/dof,3) if dof>0 else "-",
                        "p": round(pval,3),
                        "NFI": round(nfi,3), "RFI": round(rfi,3),
                        "IFI": round(ifi,3), "TLI": round(tli,3),
                        "CFI": round(cfi,3), "RMSEA": round(rmsea,3)}
            except Exception:
                return {}

        m0 = _fit(base_str)
        if m0 is None:
            return None, "초기 CFA 추정 실패"
        fit0   = _extract_fit(m0)
        mi_df0 = calc_mi_approx(m0, data, all_items)

        extra_cov   = []
        added_pairs = set()
        mod_log     = []
        m_cur, fit_cur, mi_cur = m0, fit0.copy(), mi_df0.copy()

        for step in range(max_mods):
            if _adequate_check(fit_cur):
                break
            high_mi = mi_cur[mi_cur["MI"] > mi_threshold]
            if high_mi.empty:
                break
            avail = high_mi[
                high_mi.apply(
                    lambda r: tuple(sorted([r["변수1"], r["변수2"]])) not in added_pairs,
                    axis=1)]
            if avail.empty:
                break
            row    = avail.iloc[0]
            v1, v2 = row["변수1"], row["변수2"]
            pair   = tuple(sorted([v1, v2]))
            new_cov = extra_cov + [(v1, v2)]
            new_str = (base_str + "\n" +
                       "\n".join(f"  {a} ~~ {b}" for a, b in new_cov))
            m_new = _fit(new_str)
            if m_new is None:
                added_pairs.add(pair); continue
            fit_new = _extract_fit(m_new)
            lv1 = item_to_lv.get(v1, v1)
            lv2 = item_to_lv.get(v2, v2)
            mod_log.append({
                "단계":        step + 1,
                "수정 경로":   f"{v1} ~~ {v2}",
                "소속 요인":   lv1 if lv1 == lv2 else f"{lv1}↔{lv2}",
                "MI":          round(row["MI"], 3),
                "CFI 전→후":   f"{fit_cur.get('CFI','-')} → {fit_new.get('CFI','-')}",
                "RMSEA 전→후": f"{fit_cur.get('RMSEA','-')} → {fit_new.get('RMSEA','-')}",
            })
            extra_cov = new_cov
            m_cur, fit_cur = m_new, fit_new
            added_pairs.add(pair)
            mi_cur = calc_mi_approx(m_cur, data, all_items)

        ins     = m_cur.inspect(std_est=True)
        std_col = next((c for c in ins.columns if "std" in c.lower()), ins.columns[-1])
        final_mi = calc_mi_approx(m_cur, data, all_items)
        return {
            "init_model": m0, "mod_model": m_cur,
            "init_fit":   fit0, "mod_fit":  fit_cur,
            "init_mi":    mi_df0, "final_mi": final_mi,
            "mod_log":    pd.DataFrame(mod_log),
            "extra_cov":  extra_cov,
            "was_modified": len(extra_cov) > 0,
            "ins": ins, "std_col": std_col,
            "data": data, "all_items": all_items,
            "base_str": base_str,
        }, None
    except Exception as e:
        return None, f"CFA MI 오류: {e}"


def run_cfa_sem(df, constructs, hypotheses=None):
    try:
        from semopy import Model, calc_stats
    except ImportError:
        return None, None, None, None
    try:
        mm = "\n".join(f"  {lv} =~ {' + '.join(items)}" for lv, items in constructs.items())
        if hypotheses:
            deps = defaultdict(list)
            for s, t in hypotheses:
                deps[t].append(s)
            mm += "\n" + "\n".join(f"  {t} ~ {' + '.join(ss)}" for t, ss in deps.items())
        all_items = [i for items in constructs.values() for i in items]
        data = df[all_items].dropna()
        model = Model(mm); model.fit(data)
        ins = model.inspect(std_est=True)
        std_col = next((c for c in ins.columns if "std" in c.lower()), ins.columns[-1])
        stats = calc_stats(model)
        try:
            stats_dict = stats.iloc[0].to_dict() if hasattr(stats, "iloc") else dict(stats)
        except Exception:
            stats_dict = {}
        try:
            chi2    = float(stats_dict.get("chi2", 0))
            dof     = float(stats_dict.get("DoF", 1))
            chi2_bl = float(stats_dict.get("chi2 Baseline", 0))
            dof_bl  = float(stats_dict.get("DoF Baseline", 1))
            pval    = float(stats_dict.get("chi2 p-value", 1))
            cfi     = float(stats_dict.get("CFI", 0))
            tli     = float(stats_dict.get("TLI", 0))
            nfi     = float(stats_dict.get("NFI", 0))
            rmsea   = float(stats_dict.get("RMSEA", 1))
            rfi = ((chi2_bl/dof_bl - chi2/dof)/(chi2_bl/dof_bl)
                   if chi2_bl>0 and dof_bl>0 and dof>0 else 0.0)
            ifi = ((chi2_bl - chi2)/(chi2_bl - dof)
                   if (chi2_bl - dof) > 0 else 0.0)
            fit = {"χ²": round(chi2,3), "df": int(dof),
                   "χ²/df": round(chi2/dof,3) if dof>0 else "-", "p": round(pval,3),
                   "NFI": round(nfi,3), "RFI": round(rfi,3),
                   "IFI": round(ifi,3), "TLI": round(tli,3),
                   "CFI": round(cfi,3), "RMSEA": round(rmsea,3)}
        except Exception:
            fit = {}
        return ins, std_col, stats_dict, fit
    except Exception:
        return None, None, None, None


def _extract_load_df(ins, std_col, constructs):
    lv_names  = set(constructs.keys())
    all_items = set(i for v in constructs.values() for i in v)
    if (ins["op"] == "=~").any():
        load_df         = ins[ins["op"] == "=~"].copy()
        lv_col, ind_col = "lval", "rval"
    else:
        load_df         = ins[ins["lval"].isin(all_items) & ins["rval"].isin(lv_names)].copy()
        lv_col, ind_col = "rval", "lval"
    cols_needed = [c for c in [lv_col, ind_col, std_col, "Std. Err", "z-value", "p-value"]
                   if c in load_df.columns]
    load_df = load_df[cols_needed].copy()
    rename_map = {lv_col: "잠재변수", ind_col: "측정변수", std_col: "표준화계수",
                  "Std. Err": "SE", "z-value": "t값", "p-value": "p값_raw"}
    load_df = load_df.rename(columns=rename_map)
    if "p값_raw" in load_df.columns:
        load_df["p값"] = load_df["p값_raw"].apply(
            lambda p: fmt_p(p) if str(p).strip() not in ("-", "") else "-")
        load_df = load_df.drop(columns=["p값_raw"])
    load_df["표준화계수"] = pd.to_numeric(load_df["표준화계수"], errors="coerce").round(3)
    for col in ["SE", "t값"]:
        if col in load_df.columns:
            load_df[col] = load_df[col].apply(
                lambda v: round(float(v), 3)
                if pd.notna(v) and str(v).strip() not in ("None", "", "nan", "-") else "-")
    return load_df.reset_index(drop=True), lv_col


# ══════════════════════════════════════════════════════════════════════════════
# lavaan (R) 기반 CFA / SEM
# ══════════════════════════════════════════════════════════════════════════════

def _get_rscript_path():
    import subprocess, os, glob
    try:
        r = subprocess.run(['Rscript', '--version'], capture_output=True, timeout=10)
        if r.returncode == 0:
            return 'Rscript'
    except Exception:
        pass
    candidates = glob.glob(r'C:\Program Files\R\R-*\bin\Rscript.exe')
    candidates += glob.glob(r'C:\Program Files (x86)\R\R-*\bin\Rscript.exe')
    if candidates:
        candidates.sort(reverse=True)
        return candidates[0]
    return None


def _rscript_available():
    return _get_rscript_path() is not None


def _parse_fit_csv(path):
    df = pd.read_csv(path)
    if df.empty:
        return {}
    row  = df.iloc[0]
    chi2 = float(row.get('chisq', 0))
    dof  = float(row.get('df', 1))
    return {
        'χ²':    round(chi2, 3),
        'df':    int(dof),
        'χ²/df': round(chi2 / dof, 3) if dof > 0 else '-',
        'p':     round(float(row.get('pvalue', 1)), 6),
        'NFI':   round(float(row.get('nfi',  0)), 3),
        'RFI':   round(float(row.get('rfi',  0)), 3),
        'IFI':   round(float(row.get('ifi',  0)), 3),
        'TLI':   round(float(row.get('tli',  0)), 3),
        'CFI':   round(float(row.get('cfi',  0)), 3),
        'RMSEA': round(float(row.get('rmsea', 1)), 3),
    }


def _fmt_mi_df(df):
    if df is None or df.empty:
        return pd.DataFrame(columns=['변수1', '변수2', 'MI'])
    df = df.copy()
    df.columns = ['변수1', '변수2', 'MI']
    df['MI'] = pd.to_numeric(df['MI'], errors='coerce').round(3)
    return df.sort_values('MI', ascending=False).reset_index(drop=True)


def run_lavaan_cfa_with_mi(df, constructs, mi_threshold=3.84, max_mods=200,
                           manual_extra_cov=None):
    import subprocess, tempfile, os

    if not _rscript_available():
        return None, "Rscript를 찾을 수 없음. R 설치 후 PATH 등록 필요."

    all_items  = [i for items in constructs.values() for i in items]
    item_to_lv = {item: lv for lv, items in constructs.items() for item in items}
    lv_names   = list(constructs.keys())
    data       = df[all_items].dropna()

    meas_lines   = [f"{lv} =~ {' + '.join(items)}" for lv, items in constructs.items()]
    manual_pairs = list(manual_extra_cov) if manual_extra_cov else []
    extra_lines  = [f"{v1} ~~ {v2}" for v1, v2 in manual_pairs]
    model_base   = "\n".join(meas_lines + extra_lines)

    with tempfile.TemporaryDirectory() as tmpdir:
        def p(name):
            return os.path.join(tmpdir, name).replace('\\', '/')

        data.to_csv(p('data.csv'), index=False)
        with open(p('model.txt'), 'w', encoding='utf-8') as f:
            f.write(model_base)
        with open(p('lvnames.txt'), 'w', encoding='utf-8') as f:
            f.write('\n'.join(lv_names))

        r_script = f'''
suppressPackageStartupMessages(library(lavaan))
options(warn=-1)

out      <- "{p("")}"
data     <- read.csv(file.path(out,"data.csv"))
mdl_base <- paste(readLines(file.path(out,"model.txt"), encoding="UTF-8"), collapse="\\n")
lv_names <- readLines(file.path(out,"lvnames.txt"), encoding="UTF-8")
mi_thr   <- {mi_threshold}
max_mods <- {max_mods}

get_fit <- function(fit) {{
  fm <- fitMeasures(fit, c("chisq","df","pvalue","cfi","tli","nfi","rfi","ifi","rmsea"))
  data.frame(as.list(fm))
}}
is_ok <- function(fit) {{
  fm <- fitMeasures(fit, c("cfi","rmsea"))
  unname(fm["cfi"]) >= 0.95 && unname(fm["rmsea"]) < 0.049
}}
get_mi_cov <- function(fit) {{
  m <- tryCatch(modindices(fit, sort.=TRUE, maximum.number=500), error=function(e) NULL)
  if (is.null(m)) return(data.frame(lhs=character(),rhs=character(),mi=numeric()))
  m <- m[m$op=="~~", c("lhs","rhs","mi")]
  m[!(m$lhs %in% lv_names & m$rhs %in% lv_names), ]
}}

fit0 <- tryCatch(cfa(mdl_base, data=data, estimator="ML"), error=function(e) NULL)
if (is.null(fit0)) {{
  writeLines("ERROR: CFA failed", file.path(out,"status.txt")); quit(status=1)
}}
write.csv(get_fit(fit0), file.path(out,"fit_init.csv"), row.names=FALSE)
mi0 <- get_mi_cov(fit0)
write.csv(mi0, file.path(out,"mi_init.csv"), row.names=FALSE)

added <- character(0); extra <- character(0); log_rows <- list()
fit_cur <- fit0; mdl_cur <- mdl_base

for (step in seq_len(max_mods)) {{
  if (is_ok(fit_cur)) break
  mi_c <- get_mi_cov(fit_cur)
  mi_c <- mi_c[mi_c$mi > mi_thr, ]
  if (nrow(mi_c)==0) break
  ok <- sapply(seq_len(nrow(mi_c)), function(i) {{
    pk <- paste(sort(c(mi_c$lhs[i], mi_c$rhs[i])), collapse="~~")
    !(pk %in% added)
  }})
  mi_c <- mi_c[ok, ]
  if (nrow(mi_c)==0) break

  v1 <- mi_c$lhs[1]; v2 <- mi_c$rhs[1]; mv <- mi_c$mi[1]
  pk <- paste(sort(c(v1,v2)), collapse="~~")
  mdl_new  <- paste0(mdl_cur,"\\n  ",v1," ~~ ",v2)
  fit_new  <- tryCatch(cfa(mdl_new, data=data, estimator="ML"), error=function(e) NULL)
  added    <- c(added, pk)
  if (is.null(fit_new)) next

  fm_b <- fitMeasures(fit_cur, c("cfi","rmsea","rfi","ifi"))
  fm_a <- fitMeasures(fit_new, c("cfi","rmsea","rfi","ifi"))
  log_rows[[length(log_rows)+1]] <- data.frame(
    step=step, v1=v1, v2=v2, mi=round(mv,3),
    cfi_b=round(unname(fm_b["cfi"]),3),   cfi_a=round(unname(fm_a["cfi"]),3),
    rmsea_b=round(unname(fm_b["rmsea"]),3),rmsea_a=round(unname(fm_a["rmsea"]),3),
    rfi_b=round(unname(fm_b["rfi"]),3),   rfi_a=round(unname(fm_a["rfi"]),3),
    ifi_b=round(unname(fm_b["ifi"]),3),   ifi_a=round(unname(fm_a["ifi"]),3),
    stringsAsFactors=FALSE)
  extra   <- c(extra, paste(v1, v2, sep=","))
  fit_cur <- fit_new; mdl_cur <- mdl_new
}}

write.csv(get_fit(fit_cur),                        file.path(out,"fit_mod.csv"),  row.names=FALSE)
write.csv(as.data.frame(standardizedSolution(fit_cur)), file.path(out,"std_sol.csv"), row.names=FALSE)
write.csv(get_mi_cov(fit_cur),                     file.path(out,"mi_final.csv"), row.names=FALSE)
if (length(extra)>0)     writeLines(extra, file.path(out,"extra_cov.txt"))
if (length(log_rows)>0) {{
  write.csv(do.call(rbind, log_rows), file.path(out,"mod_log.csv"), row.names=FALSE)
}}
writeLines("SUCCESS", file.path(out,"status.txt"))
'''
        with open(p('cfa.R'), 'w', encoding='utf-8') as f:
            f.write(r_script)

        rscript = _get_rscript_path()
        try:
            res = subprocess.run([rscript, '--vanilla', p('cfa.R')],
                                 capture_output=True, text=True, timeout=300)
        except subprocess.TimeoutExpired:
            return None, "R 실행 시간 초과 (5분)"
        except Exception as ex:
            return None, f"Rscript 실행 오류: {ex}"

        if not os.path.exists(os.path.join(tmpdir, 'status.txt')):
            err = res.stderr[-2000:] if res.stderr else "알 수 없는 오류"
            return None, f"R 오류:\n{err}"

        try:
            fit0     = _parse_fit_csv(os.path.join(tmpdir, 'fit_init.csv'))
            fit_mod  = _parse_fit_csv(os.path.join(tmpdir, 'fit_mod.csv'))
            std_sol  = pd.read_csv(os.path.join(tmpdir, 'std_sol.csv'))
            mi_init  = _fmt_mi_df(pd.read_csv(os.path.join(tmpdir, 'mi_init.csv')))
            mf_path  = os.path.join(tmpdir, 'mi_final.csv')
            mi_final = _fmt_mi_df(pd.read_csv(mf_path)) if os.path.exists(mf_path) else pd.DataFrame()
        except Exception as ex:
            return None, f"결과 파싱 오류: {ex}"

        extra_cov = list(manual_pairs)
        ec_path = os.path.join(tmpdir, 'extra_cov.txt')
        if os.path.exists(ec_path):
            with open(ec_path, encoding='utf-8') as f:
                for line in f:
                    parts = line.strip().split(',')
                    if len(parts) == 2:
                        t = tuple(parts)
                        if t not in extra_cov:
                            extra_cov.append(t)

        mod_log_records = []
        ml_path = os.path.join(tmpdir, 'mod_log.csv')
        if os.path.exists(ml_path):
            log_df = pd.read_csv(ml_path)
            for _, row in log_df.iterrows():
                lv1 = item_to_lv.get(str(row['v1']), str(row['v1']))
                lv2 = item_to_lv.get(str(row['v2']), str(row['v2']))
                mod_log_records.append({
                    '단계':        int(row['step']),
                    '수정 경로':   f"{row['v1']} ~~ {row['v2']}",
                    '소속 요인':   lv1 if lv1 == lv2 else f'{lv1}↔{lv2}',
                    'MI':          row['mi'],
                    'CFI 전→후':   f"{row['cfi_b']} → {row['cfi_a']}",
                    'RMSEA 전→후': f"{row['rmsea_b']} → {row['rmsea_a']}",
                    'RFI 전→후':   f"{row.get('rfi_b', '-')} → {row.get('rfi_a', '-')}",
                    'IFI 전→후':   f"{row.get('ifi_b', '-')} → {row.get('ifi_a', '-')}",
                })

        return {
            'init_fit':     fit0,
            'mod_fit':      fit_mod,
            'init_mi':      mi_init,
            'final_mi':     mi_final,
            'mod_log':      pd.DataFrame(mod_log_records),
            'extra_cov':    extra_cov,
            'was_modified': len(extra_cov) > 0,
            'std_sol':      std_sol,
            'data':         data,
            'all_items':    all_items,
            'lv_names':     lv_names,
        }, None


def _extract_load_df_lavaan(std_sol, constructs):
    try:
        loads = std_sol[std_sol['op'] == '=~'].copy()
        cols  = [c for c in ['lhs', 'rhs', 'est.std', 'se', 'z', 'pvalue'] if c in loads.columns]
        loads = loads[cols].copy()
        rename = {'lhs': '잠재변수', 'rhs': '측정변수',
                  'est.std': '표준화계수', 'se': 'SE', 'z': 't값', 'pvalue': 'p값_raw'}
        loads = loads.rename(columns=rename)
        loads['표준화계수'] = pd.to_numeric(loads['표준화계수'], errors='coerce').round(3)
        loads['SE']  = pd.to_numeric(loads['SE'],  errors='coerce').round(3)
        loads['t값'] = pd.to_numeric(loads['t값'], errors='coerce').round(3)
        if 'p값_raw' in loads.columns:
            loads['p값'] = loads['p값_raw'].apply(
                lambda p: fmt_p(float(p))
                if pd.notna(p) and str(p).strip() not in ('NA', '', 'nan') else '-')
            loads = loads.drop(columns=['p값_raw'])
        return loads.reset_index(drop=True)
    except Exception:
        return pd.DataFrame()


def build_cfa_tables(df, constructs, mi_threshold=3.84, max_mods=200,
                     manual_extra_cov=None):
    result, err = run_lavaan_cfa_with_mi(df, constructs, mi_threshold, max_mods,
                                          manual_extra_cov=manual_extra_cov)
    use_lavaan = (result is not None)

    if not use_lavaan:
        st.warning(f"⚠️ lavaan 실패 → semopy 사용\n\n원인: {err}")
        result, err = run_cfa_with_mi(df, constructs, mi_threshold, max_mods)

    if result is None:
        st.error(err or "CFA 실행 실패")
        return None, None, None, None, None, None, None, False, []

    try:
        if use_lavaan:
            std_sol = result["std_sol"]
            load_df = _extract_load_df_lavaan(std_sol, constructs)
            rel_rows = []
            for lv, items in constructs.items():
                lv_loads = std_sol[(std_sol['op'] == '=~') & (std_sol['lhs'] == lv)]
                lam = pd.to_numeric(lv_loads['est.std'], errors='coerce').dropna().values
                ave, cr = calc_ave_cr(lam)
                alpha   = cronbach_alpha(df[items])
                rel_rows.append({"잠재변수": lv, "AVE": ave, "CR": cr, "Cronbach α": alpha})
        else:
            ins     = result["ins"]
            std_col = result["std_col"]
            load_df, lv_col = _extract_load_df(ins, std_col, constructs)
            rel_rows = []
            for lv, items in constructs.items():
                raw = ins[ins[lv_col] == lv][std_col].values
                lam = np.array([v for v in raw
                                if str(v).strip() not in ("-","","nan","None")], dtype=float)
                ave, cr = calc_ave_cr(lam)
                alpha   = cronbach_alpha(df[items])
                rel_rows.append({"잠재변수": lv, "AVE": ave, "CR": cr, "Cronbach α": alpha})

        return (load_df, pd.DataFrame(rel_rows),
                result["init_fit"], result["mod_fit"],
                result["init_mi"],  result.get("final_mi", pd.DataFrame()),
                result["mod_log"],
                result["was_modified"], result["extra_cov"])
    except Exception as e:
        st.error(f"CFA 결과 처리 오류: {e}")
        return None, None, None, None, None, None, None, False, []


def build_lavaan_sem_table(df, constructs, hypotheses,
                            cfa_extra_cov=None, mi_threshold=3.84, max_mods=200):
    import subprocess, tempfile, os

    if not _rscript_available():
        return _build_sem_semopy(df, constructs, hypotheses, cfa_extra_cov, mi_threshold, max_mods)

    all_items  = [i for items in constructs.values() for i in items]
    lv_names   = list(constructs.keys())
    lv_set     = set(lv_names)
    item_to_lv = {item: lv for lv, items in constructs.items() for item in items}
    data       = df[all_items].dropna()

    cfa_cov_pairs = (cfa_extra_cov
                     if isinstance(cfa_extra_cov, list) and
                        (not cfa_extra_cov or isinstance(cfa_extra_cov[0], tuple))
                     else [])

    deps = defaultdict(list)
    for s, t in hypotheses:
        deps[t].append(s)

    with tempfile.TemporaryDirectory() as tmpdir:
        def p(name):
            return os.path.join(tmpdir, name).replace('\\', '/')

        data.to_csv(p('data.csv'), index=False)
        with open(p('meas.txt'), 'w', encoding='utf-8') as f:
            f.write('\n'.join(f"{lv} =~ {' + '.join(items)}" for lv, items in constructs.items()))
        with open(p('struct.txt'), 'w', encoding='utf-8') as f:
            f.write('\n'.join(f"{t} ~ {' + '.join(ss)}" for t, ss in deps.items()))
        with open(p('lvnames.txt'), 'w', encoding='utf-8') as f:
            f.write('\n'.join(lv_names))
        with open(p('cfa_cov.txt'), 'w', encoding='utf-8') as f:
            for v1, v2 in cfa_cov_pairs:
                f.write(f'{v1},{v2}\n')

        r_script = f'''
suppressPackageStartupMessages(library(lavaan))
options(warn=-1)

out          <- "{p("")}"
data         <- read.csv(file.path(out,"data.csv"))
meas_lines   <- readLines(file.path(out,"meas.txt"),   encoding="UTF-8")
struct_lines <- readLines(file.path(out,"struct.txt"), encoding="UTF-8")
lv_names     <- readLines(file.path(out,"lvnames.txt"),encoding="UTF-8")
cfa_cov_raw  <- tryCatch(readLines(file.path(out,"cfa_cov.txt"),encoding="UTF-8"),
                         error=function(e) character(0))
mi_thr   <- {mi_threshold}
max_mods <- {max_mods}

build_model <- function(extra_str=character(0)) {{
  cov_lines <- character(0)
  all_covs  <- c(cfa_cov_raw, extra_str)
  for (cv in all_covs) {{
    pts <- strsplit(trimws(cv),",")[[1]]
    if (length(pts)==2) {{
      ln <- paste0("  ",pts[1]," ~~ ",pts[2])
      if (!(ln %in% cov_lines)) cov_lines <- c(cov_lines, ln)
    }}
  }}
  paste(c(paste0("  ",meas_lines), cov_lines, paste0("  ",struct_lines)), collapse="\\n")
}}

get_fit <- function(fit) {{
  fm <- fitMeasures(fit, c("chisq","df","pvalue","cfi","tli","nfi","rfi","ifi","rmsea"))
  data.frame(as.list(fm))
}}
is_ok <- function(fit) {{
  fm <- fitMeasures(fit, c("cfi","rmsea"))
  unname(fm["cfi"]) >= 0.95 && unname(fm["rmsea"]) < 0.049
}}
get_mi_cov <- function(fit) {{
  m <- tryCatch(modindices(fit,sort.=TRUE,maximum.number=500),error=function(e) NULL)
  if (is.null(m)) return(data.frame(lhs=character(),rhs=character(),mi=numeric()))
  m <- m[m$op=="~~", c("lhs","rhs","mi")]
  m[!(m$lhs %in% lv_names & m$rhs %in% lv_names), ]
}}

fit0 <- tryCatch(sem(build_model(), data=data, estimator="ML"), error=function(e) NULL)
if (is.null(fit0)) {{
  writeLines("ERROR: SEM initial fit failed", file.path(out,"status.txt")); quit(status=1)
}}
write.csv(get_fit(fit0), file.path(out,"fit_init.csv"), row.names=FALSE)
mi0 <- get_mi_cov(fit0)
write.csv(mi0, file.path(out,"mi_init.csv"), row.names=FALSE)

added <- character(0); extra <- character(0); log_rows <- list()
fit_cur <- fit0

for (step in seq_len(max_mods)) {{
  if (is_ok(fit_cur)) break
  mi_c <- get_mi_cov(fit_cur)
  mi_c <- mi_c[mi_c$mi > mi_thr, ]
  if (nrow(mi_c)==0) break
  ok <- sapply(seq_len(nrow(mi_c)), function(i) {{
    pk <- paste(sort(c(mi_c$lhs[i],mi_c$rhs[i])),collapse="~~")
    !(pk %in% added)
  }})
  mi_c <- mi_c[ok,]
  if (nrow(mi_c)==0) break

  v1<-mi_c$lhs[1]; v2<-mi_c$rhs[1]; mv<-mi_c$mi[1]
  pk <- paste(sort(c(v1,v2)),collapse="~~")
  extra_new <- c(extra, paste(v1,v2,sep=","))
  fit_new   <- tryCatch(sem(build_model(extra_new),data=data,estimator="ML"),error=function(e) NULL)
  added <- c(added, pk)
  if (is.null(fit_new)) next

  fm_b <- fitMeasures(fit_cur,c("cfi","rmsea","rfi","ifi"))
  fm_a <- fitMeasures(fit_new,c("cfi","rmsea","rfi","ifi"))
  log_rows[[length(log_rows)+1]] <- data.frame(
    step=step, v1=v1, v2=v2, mi=round(mv,3),
    cfi_b=round(unname(fm_b["cfi"]),3),   cfi_a=round(unname(fm_a["cfi"]),3),
    rmsea_b=round(unname(fm_b["rmsea"]),3),rmsea_a=round(unname(fm_a["rmsea"]),3),
    rfi_b=round(unname(fm_b["rfi"]),3),   rfi_a=round(unname(fm_a["rfi"]),3),
    stringsAsFactors=FALSE)
  extra   <- extra_new
  fit_cur <- fit_new
}}

write.csv(get_fit(fit_cur),                              file.path(out,"fit_mod.csv"),  row.names=FALSE)
write.csv(as.data.frame(standardizedSolution(fit_cur)), file.path(out,"std_sol.csv"),  row.names=FALSE)
write.csv(get_mi_cov(fit_cur),                          file.path(out,"mi_final.csv"), row.names=FALSE)
if (length(extra)>0)    writeLines(extra, file.path(out,"extra_cov.txt"))
if (length(log_rows)>0) write.csv(do.call(rbind,log_rows), file.path(out,"mod_log.csv"), row.names=FALSE)
writeLines("SUCCESS", file.path(out,"status.txt"))
'''
        with open(p('sem.R'), 'w', encoding='utf-8') as f:
            f.write(r_script)

        rscript = _get_rscript_path()
        try:
            res = subprocess.run([rscript, '--vanilla', p('sem.R')],
                                 capture_output=True, text=True, timeout=300)
        except Exception:
            return _build_sem_semopy(df, constructs, hypotheses, cfa_extra_cov, mi_threshold, max_mods)

        if not os.path.exists(os.path.join(tmpdir, 'status.txt')):
            return _build_sem_semopy(df, constructs, hypotheses, cfa_extra_cov, mi_threshold, max_mods)

        try:
            fit0     = _parse_fit_csv(os.path.join(tmpdir, 'fit_init.csv'))
            fit_mod  = _parse_fit_csv(os.path.join(tmpdir, 'fit_mod.csv'))
            std_sol  = pd.read_csv(os.path.join(tmpdir, 'std_sol.csv'))
            mi_init  = _fmt_mi_df(pd.read_csv(os.path.join(tmpdir, 'mi_init.csv')))
            mf_path  = os.path.join(tmpdir, 'mi_final.csv')
            mi_final = _fmt_mi_df(pd.read_csv(mf_path)) if os.path.exists(mf_path) else pd.DataFrame()
        except Exception:
            return _build_sem_semopy(df, constructs, hypotheses, cfa_extra_cov, mi_threshold, max_mods)

        extra_cov = list(cfa_cov_pairs)
        ec_path = os.path.join(tmpdir, 'extra_cov.txt')
        if os.path.exists(ec_path):
            with open(ec_path, encoding='utf-8') as f:
                for line in f:
                    parts = line.strip().split(',')
                    if len(parts) == 2:
                        t = tuple(parts)
                        if t not in extra_cov:
                            extra_cov.append(t)

        mod_log_records = []
        ml_path = os.path.join(tmpdir, 'mod_log.csv')
        if os.path.exists(ml_path):
            log_df = pd.read_csv(ml_path)
            for _, row in log_df.iterrows():
                lv1 = item_to_lv.get(str(row['v1']), str(row['v1']))
                lv2 = item_to_lv.get(str(row['v2']), str(row['v2']))
                mod_log_records.append({
                    '단계':        int(row['step']),
                    '수정 경로':   f"{row['v1']} ~~ {row['v2']}",
                    '소속 요인':   lv1 if lv1 == lv2 else f'{lv1}↔{lv2}',
                    'MI':          row['mi'],
                    'CFI 전→후':   f"{row['cfi_b']} → {row['cfi_a']}",
                    'RMSEA 전→후': f"{row['rmsea_b']} → {row['rmsea_a']}",
                })

        def _sp(v):
            try:
                return float(v) if str(v).strip() not in ('NA', '', 'nan') else np.nan
            except Exception:
                return np.nan

        struct = std_sol[(std_sol['op'] == '~') &
                         std_sol['lhs'].isin(lv_set) &
                         std_sol['rhs'].isin(lv_set)].copy()
        hyp_map  = {(s, t): f"H{i+1}" for i, (s, t) in enumerate(hypotheses)}
        cols_need = [c for c in ['lhs','rhs','est.std','se','z','pvalue'] if c in struct.columns]
        struct = struct[cols_need].copy()
        struct.columns = ['종속변수','독립변수','표준화β','SE','t값','p값_raw'][:len(cols_need)]
        struct['가설']    = struct.apply(lambda r: hyp_map.get((r['독립변수'], r['종속변수']), '-'), axis=1)
        struct['경로']    = struct['독립변수'] + ' → ' + struct['종속변수']
        struct['유의성']  = struct['p값_raw'].apply(lambda v: sig_stars(_sp(v)))
        struct['채택여부'] = struct['p값_raw'].apply(
            lambda v: ('채택' if _sp(v) < 0.05 else '기각') if not np.isnan(_sp(v)) else '-')
        struct['p값'] = struct['p값_raw'].apply(
            lambda v: fmt_p(_sp(v)) if not np.isnan(_sp(v)) else '-')
        for col in ['표준화β', 'SE', 't값']:
            struct[col] = pd.to_numeric(struct[col], errors='coerce').round(3)
        struct = struct.drop(columns=['p값_raw'])
        out_cols = ['가설','경로','표준화β','SE','t값','p값','유의성','채택여부']
        path_df = struct[[c for c in out_cols if c in struct.columns]].reset_index(drop=True)

        was_mod = len(mod_log_records) > 0
        return (path_df, fit0, fit_mod,
                pd.DataFrame(mod_log_records), extra_cov, was_mod,
                mi_init, mi_final)


def _build_sem_semopy(df, constructs, hypotheses,
                      cfa_extra_cov=None, mi_threshold=3.84, max_mods=200):
    """semopy 기반 SEM (lavaan 폴백)"""
    try:
        from semopy import Model, calc_stats
    except ImportError:
        st.error("semopy 패키지가 필요합니다.")
        return None, None, None, None, [], False, pd.DataFrame(), pd.DataFrame()

    try:
        item_to_lv = {item: lv for lv, items in constructs.items() for item in items}
        all_items  = [i for items in constructs.values() for i in items]
        lv_names   = set(constructs.keys())
        data       = df[all_items].dropna()

        cfa_cov_pairs = (cfa_extra_cov
                         if isinstance(cfa_extra_cov, list) and
                            (not cfa_extra_cov or isinstance(cfa_extra_cov[0], tuple))
                         else [])

        meas_lines = [f"  {lv} =~ {' + '.join(items)}" for lv, items in constructs.items()]
        meas_str   = "\n".join(meas_lines)
        lv_keys = list(constructs.keys())
        lv_cov_lines = [f"  {lv_keys[i]} ~~ {lv_keys[j]}"
                        for i in range(len(lv_keys)) for j in range(i+1, len(lv_keys))]
        meas_str = meas_str + ("\n" + "\n".join(lv_cov_lines) if lv_cov_lines else "")
        var_str  = "\n".join(f"  {lv} ~~ 1*{lv}" for lv in constructs.keys())

        deps = defaultdict(list)
        for s, t in hypotheses:
            deps[t].append(s)
        struct_str = "\n".join(f"  {t} ~ {' + '.join(ss)}" for t, ss in deps.items())

        cov_part = ("\n" + "\n".join(f"  {a} ~~ {b}" for a, b in cfa_cov_pairs)
                    if cfa_cov_pairs else "")

        def _fit_model(model_str):
            try:
                m = Model(model_str); m.fit(data); return m
            except Exception:
                return None

        def _extract_fit(m):
            try:
                sd      = calc_stats(m).iloc[0].to_dict()
                chi2    = float(sd.get("chi2", 0))
                dof     = float(sd.get("DoF", 1))
                chi2_bl = float(sd.get("chi2 Baseline", 0))
                dof_bl  = float(sd.get("DoF Baseline", 1))
                pval    = float(sd.get("chi2 p-value", 1))
                cfi     = float(sd.get("CFI", 0))
                tli     = float(sd.get("TLI", 0))
                nfi     = float(sd.get("NFI", 0))
                rmsea   = float(sd.get("RMSEA", 1))
                rfi = ((chi2_bl/dof_bl - chi2/dof)/(chi2_bl/dof_bl)
                       if chi2_bl>0 and dof_bl>0 and dof>0 else 0.0)
                ifi = ((chi2_bl - chi2)/(chi2_bl - dof)
                       if (chi2_bl - dof) > 0 else 0.0)
                return {"χ²": round(chi2,3), "df": int(dof),
                        "χ²/df": round(chi2/dof,3) if dof>0 else "-", "p": round(pval,3),
                        "NFI": round(nfi,3), "RFI": round(rfi,3),
                        "IFI": round(ifi,3), "TLI": round(tli,3),
                        "CFI": round(cfi,3), "RMSEA": round(rmsea,3)}
            except Exception:
                return {}

        def _extract_paths(m):
            ins     = m.inspect(std_est=True)
            std_col = next((c for c in ins.columns if "std" in c.lower()), ins.columns[-1])
            struct  = ins[ins["lval"].isin(lv_names) & ins["rval"].isin(lv_names)].copy()
            cols    = [c for c in ["lval","rval",std_col,"Std. Err","z-value","p-value"]
                       if c in struct.columns]
            struct  = struct[cols].copy()
            rm = {"lval":"종속변수","rval":"독립변수", std_col:"표준화β",
                  "Std. Err":"SE", "z-value":"t값", "p-value":"p값_raw"}
            struct  = struct.rename(columns=rm)
            hyp_map = {(s,t): f"H{i+1}" for i,(s,t) in enumerate(hypotheses)}
            struct["가설"] = struct.apply(
                lambda r: hyp_map.get((r["독립변수"],r["종속변수"]),"-"), axis=1)
            struct["경로"] = struct["독립변수"] + " → " + struct["종속변수"]
            def _sp(p):
                try: return float(p) if str(p).strip() not in("-","","nan") else np.nan
                except: return np.nan
            struct["유의성"]  = struct["p값_raw"].apply(lambda p: sig_stars(_sp(p)))
            struct["채택여부"] = struct["p값_raw"].apply(
                lambda p: ("채택" if _sp(p)<0.05 else "기각") if not np.isnan(_sp(p)) else "-")
            struct["p값"] = struct["p값_raw"].apply(
                lambda p: fmt_p(_sp(p)) if not np.isnan(_sp(p)) else "-")
            for col in [c for c in ["표준화β","SE","t값"] if c in struct.columns]:
                struct[col] = pd.to_numeric(struct[col], errors="coerce").round(3)
            out = ["가설","경로","표준화β","SE","t값","p값","유의성","채택여부"]
            return struct[[c for c in out if c in struct.columns]].reset_index(drop=True)

        base_str = meas_str + "\n" + var_str + cov_part + "\n" + struct_str
        base_no_var = meas_str + cov_part + "\n" + struct_str
        m0 = _fit_model(base_str) or _fit_model(base_no_var)
        if m0 is None:
            return None, None, None, None, [], False, pd.DataFrame(), pd.DataFrame()

        fit0  = _extract_fit(m0)
        path0 = _extract_paths(m0)
        extra_cov   = list(cfa_cov_pairs)
        added_pairs = {tuple(sorted(p)) for p in cfa_cov_pairs}
        mod_log = []
        m_cur, fit_cur = m0, fit0.copy()

        for step in range(max_mods):
            if _adequate_check(fit_cur): break
            mi_cur = calc_mi_approx(m_cur, data, all_items)
            avail  = mi_cur[(mi_cur["MI"] > mi_threshold) &
                            (~mi_cur.apply(
                                lambda r: tuple(sorted([r["변수1"], r["변수2"]])) in added_pairs,
                                axis=1))]
            if avail.empty: break
            row  = avail.iloc[0]
            v1, v2 = row["변수1"], row["변수2"]
            pair = tuple(sorted([v1, v2]))
            new_cov = extra_cov + [(v1, v2)]
            new_cov_str = "\n".join(f"  {a} ~~ {b}" for a, b in new_cov)
            new_str = (meas_str + "\n" + var_str + "\n" + new_cov_str + "\n" + struct_str
                       if "\n" + var_str in base_str
                       else meas_str + "\n" + new_cov_str + "\n" + struct_str)
            m_new = _fit_model(new_str)
            if m_new is None:
                added_pairs.add(pair); continue
            fit_new = _extract_fit(m_new)
            lv1 = item_to_lv.get(v1, v1); lv2 = item_to_lv.get(v2, v2)
            mod_log.append({
                "단계":        step+1,
                "수정 경로":   f"{v1} ~~ {v2}",
                "소속 요인":   lv1 if lv1==lv2 else f"{lv1}↔{lv2}",
                "MI":          round(row["MI"],3),
                "CFI 전→후":   f"{fit_cur.get('CFI','-')}→{fit_new.get('CFI','-')}",
                "RMSEA 전→후": f"{fit_cur.get('RMSEA','-')}→{fit_new.get('RMSEA','-')}",
            })
            extra_cov = new_cov; m_cur = m_new; fit_cur = fit_new
            added_pairs.add(pair)

        path_final   = _extract_paths(m_cur)
        was_mod      = len(mod_log) > 0
        init_mi_sem  = calc_mi_approx(m0,   data, all_items)
        final_mi_sem = calc_mi_approx(m_cur, data, all_items)
        return (path_final, fit0, fit_cur,
                pd.DataFrame(mod_log), extra_cov, was_mod,
                init_mi_sem, final_mi_sem)
    except Exception as e:
        st.error(f"SEM 오류: {e}")
        return None, None, None, None, [], False, pd.DataFrame(), pd.DataFrame()


def run_correlation(df, constructs):
    try:
        comp = {lv: df[items].mean(axis=1) for lv, items in constructs.items()}
        corr = pd.DataFrame(comp).corr().round(3)
        sqrt_ave = {}
        try:
            ins, std_col, _, _ = run_cfa_sem(df, constructs)
            if ins is not None:
                lv_names  = set(constructs.keys())
                all_items = set(i for v in constructs.values() for i in v)
                use_rval  = not (ins["op"] == "=~").any()
                lv_col    = "rval" if use_rval else "lval"
                for lv, items in constructs.items():
                    raw = ins[ins[lv_col] == lv][std_col].values
                    lam = np.array([v for v in raw
                                    if str(v).strip() not in ("-","","nan","None")], dtype=float)
                    ave, _ = calc_ave_cr(lam)
                    sqrt_ave[lv] = round(float(np.sqrt(ave)), 3)
        except Exception:
            pass

        tbl = corr.copy().astype(object)
        for lv in constructs:
            if lv in tbl.columns:
                tbl.loc[lv, lv] = sqrt_ave.get(lv, "-")
        for i in range(len(tbl.columns)):
            for j in range(i + 1, len(tbl.columns)):
                tbl.iloc[i, j] = ""
        return tbl.reset_index().rename(columns={"index": "변수"})
    except Exception as e:
        st.error(f"상관관계 분석 오류: {e}")
        return pd.DataFrame()


# ══════════════════════════════════════════════════════════════════════════════
# Excel 생성
# ══════════════════════════════════════════════════════════════════════════════

def build_single_sheet_excel(sheets: dict) -> bytes:
    try:
        from openpyxl import Workbook
        from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
        import io as _io

        wb = Workbook()
        ws = wb.active
        ws.title = "분석결과"

        _hdr_font  = Font(name="Arial", bold=True, color="FFFFFF", size=10)
        _hdr_fill  = PatternFill("solid", fgColor="2F5496")
        _ttl_font  = Font(name="Arial", bold=True, size=12, color="2F5496")
        _note_font = Font(name="Arial", size=9, color="595959", italic=True)
        _ctr = Alignment(horizontal="center", vertical="center", wrap_text=True)
        _lft = Alignment(horizontal="left",   vertical="center")
        _bd  = Border(left=Side(style="thin"), right=Side(style="thin"),
                      top=Side(style="thin"),  bottom=Side(style="thin"))
        _adopt_fill_y = PatternFill("solid", fgColor="E2EFDA")
        _adopt_fill_n = PatternFill("solid", fgColor="FFC7CE")
        _adopt_font_y = Font(name="Arial", size=10, color="375623")
        _adopt_font_n = Font(name="Arial", size=10, color="9C0006")

        cur_row = 1
        for name, payload in sheets.items():
            try:
                title     = payload[0]
                df_data   = payload[1]
                note      = payload[2] if len(payload) > 2 else ""
                adopt_col = payload[3] if len(payload) > 3 else None
                if df_data is None or len(df_data) == 0:
                    continue
                cols = list(df_data.columns)
                nc   = max(len(cols), 1)
                adopt_idx = (cols.index(adopt_col) + 1
                             if adopt_col and adopt_col in cols else None)
                ws.merge_cells(start_row=cur_row, start_column=1,
                               end_row=cur_row, end_column=nc)
                cell = ws.cell(row=cur_row, column=1, value=title)
                cell.font = _ttl_font
                cur_row += 1
                for ci, col in enumerate(cols, 1):
                    c = ws.cell(row=cur_row, column=ci, value=str(col))
                    c.font = _hdr_font; c.fill = _hdr_fill
                    c.alignment = _ctr; c.border = _bd
                cur_row += 1
                for row in df_data.itertuples(index=False):
                    for ci, val in enumerate(row, 1):
                        safe_val = ("" if isinstance(val, float) and (val != val) else val)
                        cell = ws.cell(row=cur_row, column=ci, value=safe_val)
                        cell.alignment = _lft if ci <= 2 else _ctr
                        cell.border = _bd
                        if adopt_idx and ci == adopt_idx:
                            is_y = str(val) == "채택"
                            cell.fill = _adopt_fill_y if is_y else _adopt_fill_n
                            cell.font = _adopt_font_y if is_y else _adopt_font_n
                    cur_row += 1
                if note:
                    nc_cell = ws.cell(row=cur_row, column=1, value=note)
                    nc_cell.font = _note_font
                    cur_row += 1
                cur_row += 2
            except Exception:
                pass

        for col_cells in ws.columns:
            try:
                w = max((len(str(c.value)) if c.value else 0) for c in col_cells)
                ws.column_dimensions[col_cells[0].column_letter].width = min(w + 4, 45)
            except Exception:
                pass

        buf = _io.BytesIO()
        wb.save(buf)
        return buf.getvalue()
    except Exception:
        from openpyxl import Workbook
        import io as _io
        wb = Workbook(); buf = _io.BytesIO(); wb.save(buf); return buf.getvalue()


# ══════════════════════════════════════════════════════════════════════════════
# UI
# ══════════════════════════════════════════════════════════════════════════════
st.markdown('<p class="main-title">📊 SEM 분석기</p>', unsafe_allow_html=True)
st.markdown('<p class="sub-title">빈도분석 · 기술통계 · 신뢰도 · CFA · 상관관계 · SEM</p>',
            unsafe_allow_html=True)

# ── STEP 1: 파일 업로드 ───────────────────────────────────────────────────────
st.markdown("### 📁 STEP 1. 데이터 업로드")
uploaded = st.file_uploader("Excel(.xlsx/.xls) 또는 CSV 파일", type=["xlsx","xls","csv"])
if not uploaded:
    st.info("파일을 업로드하면 분석이 시작됩니다.")
    st.stop()

try:
    if uploaded.name.lower().endswith(".csv"):
        df = pd.read_csv(uploaded)
    else:
        df = pd.read_excel(uploaded)
except Exception as e:
    st.error(f"파일 읽기 오류: {e}")
    st.stop()

for c in df.columns:
    try:
        converted = pd.to_numeric(df[c], errors="coerce")
        if converted.notna().sum() == df[c].notna().sum() and df[c].notna().sum() > 0:
            df[c] = converted
    except Exception:
        pass

st.success(f"✅ 파일 로드 완료 — {len(df)}행 × {len(df.columns)}열")
with st.expander("📋 데이터 미리보기 (상위 10행)"):
    st.dataframe(df.head(10), use_container_width=True)

# ── STEP 1-5: 이상치 탐지 ────────────────────────────────────────────────────
st.markdown("---")
st.markdown("### 🔎 STEP 1-5. 이상치 탐지 (마할라노비스 거리 + SMC)")

num_cols_all = [c for c in df.columns if pd.api.types.is_numeric_dtype(df[c])]
tab_maha, tab_smc = st.tabs(["📍 마할라노비스 거리", "📐 SMC (다중상관자승)"])

with tab_maha:
    st.caption("χ² 분포 기준으로 마할라노비스 거리가 임계값을 초과하는 **표본(행)**을 이상치로 탐지합니다.")
    col_a, col_b = st.columns([3, 1])
    outlier_cols = col_a.multiselect("분석 변수 선택", num_cols_all, default=num_cols_all, key="oc_cols")
    p_thr = col_b.selectbox("유의수준", [0.001, 0.005, 0.01, 0.05], index=0, key="oc_pthr")
    if outlier_cols and st.button("🔍 마할라노비스 탐지 실행", key="oc_run"):
        oc_result, oc_cutoff, oc_idx = detect_outliers_mahalanobis(df, outlier_cols, p_threshold=p_thr)
        st.session_state["oc_result"] = oc_result
        st.session_state["oc_cutoff"] = oc_cutoff
        st.session_state["oc_idx"]    = oc_idx
        st.session_state.pop("oc_remove_rows", None)
    if "oc_result" in st.session_state and st.session_state["oc_result"] is not None:
        oc_result = st.session_state["oc_result"]
        oc_cutoff = st.session_state["oc_cutoff"]
        oc_idx    = st.session_state["oc_idx"]
        n_out     = len(oc_idx)
        st.markdown(f"**χ² 임계값:** `{oc_cutoff}`")
        col_r1, col_r2, col_r3 = st.columns(3)
        col_r1.metric("전체 표본", len(df))
        col_r2.metric("탐지된 이상치", n_out, delta=f"-{n_out}" if n_out > 0 else "없음", delta_color="inverse")
        col_r3.metric("제거 후 표본", len(df) - n_out)
        disp_all = oc_result.sort_values("마할라노비스 거리", ascending=False).copy()
        disp_all["판정"] = disp_all["이상치"].map({True: "⚠️ 이상치", False: "✅ 정상"})
        disp_all = disp_all.drop(columns=["이상치"])
        with st.expander("📋 마할라노비스 전체 결과", expanded=(n_out > 0)):
            st.dataframe(disp_all, use_container_width=True, hide_index=True)
        if n_out > 0:
            outlier_row_nums = (oc_result[oc_result["이상치"] == True]["원본 행번호"].tolist()
                                if "이상치" in oc_result.columns else [])
            _default_rows = [r for r in st.session_state.get("oc_remove_rows", outlier_row_nums)
                             if r in outlier_row_nums]
            st.multiselect(f"🗑️ 제거할 표본 선택 (총 {n_out}개 탐지)", options=outlier_row_nums,
                           default=_default_rows, key="oc_remove_rows")
        else:
            st.success("✅ 이상치가 탐지되지 않았습니다.")

with tab_smc:
    st.caption("각 변수를 나머지 변수로 회귀분석한 R²(SMC) < .20 이면 변수 제거를 고려합니다.")
    smc_cols = st.multiselect("SMC 분석 변수 선택", num_cols_all, default=num_cols_all, key="smc_cols")
    if smc_cols and st.button("🔍 SMC 탐지 실행", key="smc_run"):
        with st.spinner("SMC 계산 중..."):
            smc_result = compute_smc(df, smc_cols)
        st.session_state["smc_result"] = smc_result
        st.session_state.pop("smc_drop_vars", None)
    if "smc_result" in st.session_state and st.session_state["smc_result"] is not None:
        smc_result = st.session_state["smc_result"]
        low_smc    = smc_result[smc_result["SMC"].notna() & (smc_result["SMC"] < 0.2)]
        n_low      = len(low_smc)
        col_s1, col_s2 = st.columns(2)
        col_s1.metric("분석 변수 수", len(smc_result))
        col_s2.metric("SMC < .20 변수", n_low)
        with st.expander("📋 SMC 분석 결과", expanded=(n_low > 0)):
            st.dataframe(smc_result.sort_values("SMC").reset_index(drop=True),
                         use_container_width=True, hide_index=True)
        if n_low > 0:
            low_vars = low_smc["변수"].tolist()
            _default_vars = [v for v in st.session_state.get("smc_drop_vars", low_vars)
                             if v in smc_result["변수"].tolist()]
            st.multiselect(f"🗑️ 제거할 변수 선택 (SMC < .20, 총 {n_low}개)",
                           options=smc_result["변수"].tolist(),
                           default=_default_vars, key="smc_drop_vars")
        else:
            st.success("✅ SMC < .20 변수가 없습니다.")

# ── 이상치/변수 제거 적용 ─────────────────────────────────────────────────────
_removed_rows = st.session_state.get("oc_remove_rows", [])
_dropped_vars = st.session_state.get("smc_drop_vars", [])
if _removed_rows:
    _remove_idx = [int(r) - 1 for r in _removed_rows]
    _valid_idx  = [i for i in _remove_idx if i in df.index]
    if _valid_idx:
        df = df.drop(index=_valid_idx).reset_index(drop=True)
        st.info(f"🗑️ 이상치 **{len(_valid_idx)}개 표본** 제거 → 분석 표본: **{len(df)}개**")
if _dropped_vars:
    _actual_drop = [v for v in _dropped_vars if v in df.columns]
    if _actual_drop:
        df = df.drop(columns=_actual_drop)
        st.info(f"🗑️ SMC 변수 **{len(_actual_drop)}개** 제거: `{', '.join(_actual_drop)}`")

# ── STEP 2: 구성개념 탐지 ─────────────────────────────────────────────────────
st.markdown("---")
st.markdown("### 🔍 STEP 2. 구성개념 자동 탐지")
constructs_auto = auto_detect_constructs(df)
c1, c2, c3 = st.columns(3)
c1.metric("전체 변수", len(df.columns))
c2.metric("유효 사례", len(df.dropna()))
c3.metric("탐지된 구성개념", len(constructs_auto))

if constructs_auto:
    for lv, items in constructs_auto.items():
        st.markdown(f"- **{lv}** ({len(items)}문항): {', '.join(items)}")
else:
    st.warning("자동 탐지된 구성개념이 없습니다. 아래에서 직접 설정해 주세요.")

with st.expander("✏️ 구성개념 직접 편집 (필요 시)"):
    n_c = st.number_input("구성개념 수", min_value=1, max_value=20,
                           value=max(len(constructs_auto), 1), key="n_constructs")
    auto_k = list(constructs_auto.keys())
    auto_v = list(constructs_auto.values())
    constructs_edit = {}
    for i in range(int(n_c)):
        ca, cb = st.columns([1, 3])
        default_name  = auto_k[i] if i < len(auto_k) else f"LV{i+1}"
        default_items = auto_v[i] if i < len(auto_v) else []
        nm = ca.text_input(f"이름 {i+1}", value=default_name, key=f"cn_{i}")
        it = cb.multiselect(f"문항 {i+1}", df.columns.tolist(), default=default_items, key=f"ci_{i}")
        if nm and len(it) >= 2:
            constructs_edit[nm] = it
    constructs = constructs_edit if constructs_edit else constructs_auto

if not constructs:
    constructs = constructs_auto

# ── STEP 3: 분석 방법 선택 ────────────────────────────────────────────────────
st.markdown("---")
st.markdown("### ✅ STEP 3. 분석 방법 선택")

num_cols = [c for c in df.columns if pd.api.types.is_numeric_dtype(df[c])]
cat_cols = [c for c in df.columns if detect_scale(df[c]) in ("binary","categorical")]
lv_list  = list(constructs.keys())
n_valid  = len(df.dropna())

suggestions = {
    "빈도분석":              {"ok": len(cat_cols) > 0,
                              "reason": f"범주형 변수 {len(cat_cols)}개 감지"},
    "기술통계":              {"ok": len(num_cols) > 0,
                              "reason": f"수치형 변수 {len(num_cols)}개"},
    "신뢰도 (Cronbach's α)": {"ok": len(constructs) > 0,
                              "reason": f"구성개념 {len(constructs)}개 탐지"},
    "확인적 요인분석 (CFA)": {"ok": len(constructs) >= 2 and n_valid >= 100,
                              "reason": f"구성개념 {len(constructs)}개, N={n_valid}"},
    "상관관계 분석":         {"ok": len(constructs) >= 2,
                              "reason": f"잠재변수 {len(constructs)}개"},
    "구조방정식 (SEM)":      {"ok": len(constructs) >= 3 and n_valid >= 200,
                              "reason": f"구성개념 {len(constructs)}개, N={n_valid}"},
}

selected = {}
for method, info in suggestions.items():
    icon = "✅" if info["ok"] else "⚠️"
    col_a, col_b = st.columns([1, 10])
    selected[method] = col_a.checkbox(f"{icon}", value=info["ok"], key=f"sel_{method}")
    col_b.markdown(
        f'<div class="card"><b>{method}</b>'
        f'&nbsp;<span style="color:#888;font-size:.83rem">{info["reason"]}</span></div>',
        unsafe_allow_html=True)

# ── SEM 가설 설정 ─────────────────────────────────────────────────────────────
hypotheses = []
if selected.get("구조방정식 (SEM)") and len(lv_list) >= 2:
    st.markdown("---\n#### 🔗 SEM 가설 설정")
    if "hyps" not in st.session_state:
        st.session_state.hyps = [("", "")]
    if st.button("＋ 가설 추가"):
        st.session_state.hyps.append(("", ""))
    to_delete = None
    for i, (sd, td) in enumerate(st.session_state.hyps):
        ca, cb, cc = st.columns([3, 3, 1])
        s = ca.selectbox(f"H{i+1} 독립변수", lv_list,
                         index=lv_list.index(sd) if sd in lv_list else 0, key=f"hs_{i}")
        t = cb.selectbox(f"H{i+1} 종속변수", lv_list,
                         index=lv_list.index(td) if td in lv_list else 0, key=f"ht_{i}")
        if cc.button("삭제", key=f"hd_{i}"):
            to_delete = i
        if s != t:
            hypotheses.append((s, t))
        st.session_state.hyps[i] = (s, t)
    if to_delete is not None and len(st.session_state.hyps) > 1:
        st.session_state.hyps.pop(to_delete)
        st.rerun()

# ── STEP 4: 실행 ──────────────────────────────────────────────────────────────
st.markdown("---")
_run_btn = st.button("🚀 분석 실행", type="primary", use_container_width=True)

if _run_btn:
    for _k in ["_sem_applied", "_sem_init", "_sem_manual_pairs",
               "_sem_df", "_sem_constructs", "_sem_hypotheses", "_sem_cfa_cov",
               "_cfa_applied", "_cfa_init", "_cfa_df", "_cfa_constructs",
               "_cfa_manual_pairs"]:
        st.session_state.pop(_k, None)

if not _run_btn and "_tab_data" not in st.session_state:
    st.stop()

if not _run_btn and "_tab_data" in st.session_state:
    tab_data    = st.session_state["_tab_data"]
    tab_labels  = st.session_state["_tab_labels"]
    xlsx_sheets = st.session_state.get("_xlsx_sheets", {})
    if "_cfa_applied" in st.session_state and "CFA" in tab_data:
        tab_data["CFA"] = st.session_state["_cfa_applied"]
        _lds, _rd, _fi, _fm, _im, _fmi, _ml, _wm, _ec = tab_data["CFA"]
        if _lds is not None:
            xlsx_sheets["표2_CFA"]    = ("CFA — 요인부하량", _lds, "※ 표준화계수≥.50, AVE≥.50, CR≥.70")
            xlsx_sheets["표2_신뢰도"] = ("CFA — AVE/CR/α", _rd, "")
            if _wm and not _ml.empty:
                xlsx_sheets["CFA_수정과정"] = ("CFA — MI 기반 수정 과정", _ml, "※ MI>3.84 기준")
    if "_sem_applied" in st.session_state and "SEM" in tab_data:
        tab_data["SEM"] = st.session_state["_sem_applied"]
        _sp, _fi, _fm, _ml, _ec, _wm, _im, _fm2 = tab_data["SEM"]
        if _sp is not None:
            xlsx_sheets["표4_가설검증"] = (
                "SEM 가설 검증", _sp,
                "※ ***p<.001 **p<.01 *p<.05 n.s.=유의하지않음", "채택여부")
            if _wm and not _ml.empty:
                xlsx_sheets["SEM_수정과정"] = ("SEM — MI 기반 수정 과정", _ml, "※ MI>3.84 기준")
else:
    xlsx_sheets   = {}
    tab_labels    = []
    tab_data      = {}
    cfa_extra_cov = []

    # 빈도분석
    if selected.get("빈도분석") and cat_cols:
        try:
            freq_res = {}
            for c in cat_cols[:10]:
                f   = df[c].value_counts().sort_index()
                pct = (f / f.sum() * 100).round(1)
                fdf = pd.DataFrame({"빈도": f, "백분율(%)": pct, "누적(%)": pct.cumsum().round(1)})
                fdf.index.name = c
                freq_res[c] = fdf
            tab_data["빈도분석"] = freq_res
            tab_labels.append("빈도분석")
            for cn, fdf in freq_res.items():
                xlsx_sheets[f"빈도_{cn}"[:31]] = (f"빈도분석 — {cn}", fdf.reset_index(), "")
        except Exception as e:
            st.warning(f"빈도분석 오류: {e}")

    # 기술통계
    if selected.get("기술통계") and num_cols:
        try:
            desc = df[num_cols].describe().T[["count","mean","std","min","max"]].copy()
            desc.columns = ["N","평균","표준편차","최솟값","최댓값"]
            desc["왜도"] = df[num_cols].skew()
            desc["첨도"] = df[num_cols].kurt()
            desc = desc.round(3)
            tab_data["기술통계"] = desc
            tab_labels.append("기술통계")
            xlsx_sheets["기술통계"] = ("기술통계", desc.reset_index().rename(columns={"index":"변수"}), "")
        except Exception as e:
            st.warning(f"기술통계 오류: {e}")

    # 신뢰도
    if selected.get("신뢰도 (Cronbach's α)") and constructs:
        try:
            rel_rows = []
            for lv, items in constructs.items():
                alpha = cronbach_alpha(df[items])
                rel_rows.append({"구성개념": lv, "문항 수": len(items),
                                  "Cronbach α": alpha,
                                  "판정": "충족 ✓" if (not np.isnan(alpha) and alpha >= 0.7) else "미충족 ✗"})
            rel_df = pd.DataFrame(rel_rows)
            tab_data["신뢰도"] = rel_df
            tab_labels.append("신뢰도")
            xlsx_sheets["신뢰도분석"] = ("신뢰도 분석 (Cronbach's α)", rel_df, "※ α ≥ .70 권장")
        except Exception as e:
            st.warning(f"신뢰도 분석 오류: {e}")

    # CFA
    if selected.get("확인적 요인분석 (CFA)") and constructs:
        with st.spinner("CFA 초기 분석 중..."):
            try:
                loads, rel_df, fit_init, fit_mod, init_mi, final_mi, mod_log, was_mod, extra_cov = \
                    build_cfa_tables(df, constructs, max_mods=0)
                if loads is not None:
                    st.session_state["_cfa_init"] = (loads, rel_df, fit_init, fit_mod,
                                                      init_mi, final_mi, mod_log, was_mod, extra_cov)
                    st.session_state["_cfa_df"]         = df
                    st.session_state["_cfa_constructs"] = constructs
                    _cfa_display = st.session_state.get("_cfa_applied", st.session_state["_cfa_init"])
                    tab_data["CFA"] = _cfa_display
                    tab_labels.append("CFA")
                    _lds, _rd, _fi, _fm, _im, _fmi, _ml, _wm, _ec = _cfa_display
                    cfa_extra_cov = list(_ec) if _ec else []
                    if _lds is not None:
                        xlsx_sheets["표2_CFA"]    = ("CFA — 요인부하량", _lds,
                                                      "※ 표준화계수≥.50, AVE≥.50, CR≥.70")
                        xlsx_sheets["표2_신뢰도"] = ("CFA — AVE/CR/α", _rd, "")
                        if _wm and not _ml.empty:
                            xlsx_sheets["CFA_수정과정"] = ("CFA — MI 기반 수정 과정", _ml, "※ MI>3.84 기준")
                        if not _im.empty:
                            xlsx_sheets["CFA_MI초기"] = ("CFA — 초기 수정지수(MI)", _im.head(20), "")
                        if not _fmi.empty:
                            xlsx_sheets["CFA_MI최종"] = ("CFA — 최종 수정지수(MI)", _fmi.head(20), "")
                else:
                    st.warning("⚠️ CFA 결과 생성 실패.")
            except Exception as e:
                st.warning(f"CFA 오류: {e}")

    # 상관관계
    if selected.get("상관관계 분석") and constructs:
        try:
            corr_tbl = run_correlation(df, constructs)
            if not corr_tbl.empty:
                tab_data["상관관계"] = corr_tbl
                tab_labels.append("상관관계")
                xlsx_sheets["표3_상관관계"] = (
                    "상관관계 분석 (대각선=√AVE)", corr_tbl,
                    "※ 대각선: √AVE | 판별타당도 기준: √AVE > 타 변수 상관계수")
        except Exception as e:
            st.warning(f"상관관계 오류: {e}")

    # SEM
    if selected.get("구조방정식 (SEM)") and constructs and hypotheses:
        with st.spinner("SEM 초기 분석 중..."):
            try:
                sem_paths, fit_init_sem, fit_mod_sem, mod_log_sem, extra_cov_sem, was_mod_sem, \
                    init_mi_sem, final_mi_sem = \
                    build_lavaan_sem_table(df, constructs, hypotheses,
                                          cfa_extra_cov=cfa_extra_cov, max_mods=0)
                if sem_paths is not None:
                    st.session_state["_sem_init"] = (
                        sem_paths, fit_init_sem, fit_mod_sem,
                        mod_log_sem, extra_cov_sem, was_mod_sem,
                        init_mi_sem, final_mi_sem)
                    st.session_state["_sem_df"]         = df
                    st.session_state["_sem_constructs"] = constructs
                    st.session_state["_sem_hypotheses"] = hypotheses
                    st.session_state["_sem_cfa_cov"]    = cfa_extra_cov
                    _sem_display = st.session_state.get("_sem_applied", st.session_state["_sem_init"])
                    tab_data["SEM"] = _sem_display
                    tab_labels.append("SEM")
                    _sp, _fi, _fm, _ml, _ec, _wm, _im, _fm2 = _sem_display
                    if _sp is not None:
                        xlsx_sheets["표4_가설검증"] = (
                            "SEM 가설 검증", _sp,
                            "※ ***p<.001 **p<.01 *p<.05 n.s.=유의하지않음", "채택여부")
                        if _wm and not _ml.empty:
                            xlsx_sheets["SEM_수정과정"] = (
                                "SEM — MI 기반 수정 과정", _ml, "※ MI>3.84 기준")
                else:
                    st.warning("⚠️ SEM 결과를 생성하지 못했습니다.")
            except Exception as e:
                st.warning(f"SEM 오류: {e}")

    st.session_state["_tab_data"]    = tab_data
    st.session_state["_tab_labels"]  = tab_labels
    st.session_state["_xlsx_sheets"] = xlsx_sheets

# ── 결과 출력 ──────────────────────────────────────────────────────────────────
st.markdown("---")
st.markdown("## 📋 분석 결과")

if not tab_labels:
    st.warning("실행된 분석이 없습니다.")
    st.stop()

FIT_NOTE = "권장 기준: NFI·RFI·IFI·TLI·CFI ≥ .90 | RMSEA < .049 | SRMR ≤ .08"
tabs = st.tabs(tab_labels)

for tab, lbl in zip(tabs, tab_labels):
    with tab:
        c = tab_data[lbl]

        if lbl == "빈도분석":
            for col, fdf in c.items():
                st.markdown(f"**{col}**")
                st.dataframe(fdf, use_container_width=True)

        elif lbl == "기술통계":
            st.dataframe(c, use_container_width=True)

        elif lbl == "신뢰도":
            st.dataframe(c, use_container_width=True)
            st.caption("α ≥ .70: 내적 일관성 확보")

        elif lbl == "CFA":
            loads, rel_df, fit_init, fit_mod, init_mi, final_mi, mod_log, was_mod, extra_cov = c

            crit_df = pd.DataFrame([
                {"지수": "NFI",   "권장기준": "≥ .90"},
                {"지수": "RFI",   "권장기준": "≥ .90"},
                {"지수": "IFI",   "권장기준": "≥ .90"},
                {"지수": "TLI",   "권장기준": "≥ .90"},
                {"지수": "CFI",   "권장기준": "≥ .90"},
                {"지수": "RMSEA", "권장기준": "< .049"},
                {"지수": "SRMR",  "권장기준": "≤ .08"},
            ])
            with st.expander("📌 적합도 권장 기준"):
                st.dataframe(crit_df, use_container_width=True, hide_index=True)

            st.markdown("**모델 적합도 비교**")
            fit_col_order = ["χ²", "df", "χ²/df", "p", "NFI", "RFI", "IFI", "TLI", "CFI", "RMSEA"]

            def _fit_row(label, fit):
                row = {"모형": label}
                for k in fit_col_order:
                    row[k] = fit.get(k, "-")
                return row

            if was_mod:
                fit_cmp = pd.DataFrame([_fit_row("초기 모형", fit_init), _fit_row("수정 모형", fit_mod)])
            else:
                fit_cmp = pd.DataFrame([_fit_row("초기 모형", fit_init)])

            def _hl_fit(row):
                colors = [""] * len(row)
                checks = {"NFI":.90,"RFI":.90,"IFI":.90,"TLI":.90,"CFI":.90}
                cols = list(row.index)
                for idx, col in enumerate(cols):
                    v = row[col]
                    try:
                        fv = float(v)
                        if col in checks and fv < checks[col]:
                            colors[idx] = "background-color:#FFE0E0"
                        elif col == "RMSEA" and fv >= 0.049:
                            colors[idx] = "background-color:#FFE0E0"
                        elif col in checks and fv >= checks[col]:
                            colors[idx] = "background-color:#E8F5E9"
                        elif col == "RMSEA" and fv < 0.049:
                            colors[idx] = "background-color:#E8F5E9"
                    except Exception:
                        pass
                return colors

            st.dataframe(fit_cmp.style.apply(_hl_fit, axis=1), use_container_width=True)
            st.caption("🟢 기준 충족 | 🔴 기준 미충족 | RMSEA < .049 권장")

            if was_mod and extra_cov:
                st.success(f"✅ MI 기반 모형수정 {len(extra_cov)}회 적용")
                paths_str = " / ".join(f"{a} ~~ {b}" for a, b in extra_cov)
                st.markdown(f"**수정된 경로:** `{paths_str}`")
                with st.expander("📋 수정 과정 단계별 상세"):
                    st.dataframe(mod_log, use_container_width=True)
            elif not was_mod:
                all_ok = _adequate_check(fit_init)
                if all_ok:
                    st.success("✅ 초기 모형 적합도 양호 — 수정 불필요")
                else:
                    st.info("ℹ️ 적합도 미충족이나 MI > 3.84 쌍이 없어 더 이상 수정 불가")

            # CFA MI 수동 선택
            _cfa_init_mi = st.session_state.get("_cfa_init", (None,)*9)[4]
            if _cfa_init_mi is not None and not _cfa_init_mi.empty:
                _cfa_mi_over = _cfa_init_mi[_cfa_init_mi["MI"] > 3.84]
                if not _cfa_mi_over.empty:
                    st.markdown("---")
                    st.markdown("#### 🛠️ 잔차공분산 수동 추가")
                    st.caption("MI > 3.84 쌍 중 적용할 항목을 선택한 뒤 **재실행** 버튼을 눌러주세요.")
                    _cfa_opts = [f"{row['변수1']} ~~ {row['변수2']}  (MI = {row['MI']})"
                                 for _, row in _cfa_mi_over.iterrows()]
                    _cfa_cur_pairs = st.session_state.get("_cfa_manual_pairs", [])
                    _cfa_cur_opts  = [o for o in _cfa_opts
                                      if any(f"{v1} ~~ {v2}" in o for v1, v2 in _cfa_cur_pairs)]
                    _cfa_sel = st.multiselect("적용할 잔차공분산 경로 선택",
                                              options=_cfa_opts, default=_cfa_cur_opts,
                                              key="cfa_mi_multisel")
                    if st.button("▶️ 선택 경로 적용하여 CFA 재실행", key="cfa_rerun_btn"):
                        _cfa_new_pairs = []
                        for s in _cfa_sel:
                            part = s.split("  (MI")[0].strip()
                            v1, v2 = [x.strip() for x in part.split("~~")]
                            _cfa_new_pairs.append((v1, v2))
                        st.session_state["_cfa_manual_pairs"] = _cfa_new_pairs
                        with st.spinner("CFA 재분석 중..."):
                            try:
                                _cr = build_cfa_tables(
                                    st.session_state["_cfa_df"],
                                    st.session_state["_cfa_constructs"],
                                    max_mods=0, manual_extra_cov=_cfa_new_pairs)
                                if _cr[0] is not None:
                                    st.session_state["_cfa_applied"] = _cr
                                    st.rerun()
                            except Exception as _ce:
                                st.warning(f"CFA 재실행 오류: {_ce}")

            col_mi1, col_mi2 = st.columns(2)
            with col_mi1:
                if init_mi is not None and not init_mi.empty:
                    with st.expander("🔍 초기 수정지수(MI) 상위 20개"):
                        d = init_mi.head(20).copy()
                        d["판정"] = d["MI"].apply(lambda v: "⚠️ 수정 고려" if v > 3.84 else "")
                        st.dataframe(d, use_container_width=True)
            with col_mi2:
                if final_mi is not None and not final_mi.empty:
                    with st.expander("🔍 최종 수정지수(MI) 상위 20개"):
                        d = final_mi.head(20).copy()
                        d["판정"] = d["MI"].apply(lambda v: "⚠️ 추가 수정 가능" if v > 3.84 else "✅")
                        st.dataframe(d, use_container_width=True)

            st.markdown("---")
            st.markdown("**CFA 결과** (수정모형 기준)")
            try:
                merged = loads.copy()
                merged["AVE"] = ""; merged["CR"] = ""; merged["Cronbach α"] = ""
                rel_map = rel_df.set_index("잠재변수")
                seen = set()
                for idx, row in merged.iterrows():
                    lv = row["잠재변수"]
                    if lv not in seen:
                        seen.add(lv)
                        if lv in rel_map.index:
                            merged.at[idx, "AVE"]        = rel_map.loc[lv, "AVE"]
                            merged.at[idx, "CR"]         = rel_map.loc[lv, "CR"]
                            merged.at[idx, "Cronbach α"] = rel_map.loc[lv, "Cronbach α"]
                disp_cols = ["잠재변수", "측정변수", "표준화계수", "SE", "t값", "p값", "AVE", "CR", "Cronbach α"]
                merged = merged[[c for c in disp_cols if c in merged.columns]]
                st.dataframe(merged, use_container_width=True, hide_index=True)
            except Exception:
                st.dataframe(loads, use_container_width=True)
                st.dataframe(rel_df, use_container_width=True)
            st.caption("권장: 표준화계수 ≥ .50 | AVE ≥ .50 | CR ≥ .70 | Cronbach α ≥ .70")

        elif lbl == "상관관계":
            st.dataframe(c, use_container_width=True)
            st.caption("대각선 = √AVE | 하삼각 = 잠재변수 간 상관계수")

        elif lbl == "SEM":
            paths, fit_init, fit_mod, mod_log, extra_cov, was_mod, init_mi_sem, final_mi_sem = c
            fit_col_order = ["χ²","df","χ²/df","p","NFI","RFI","IFI","TLI","CFI","RMSEA"]

            def _hl_sem(row):
                checks = {"NFI":.90,"RFI":.90,"IFI":.90,"TLI":.90,"CFI":.90}
                colors = []
                for col in row.index:
                    try:
                        fv = float(row[col])
                        if col in checks:
                            colors.append("background-color:#E8F5E9" if fv>=checks[col]
                                          else "background-color:#FFE0E0")
                        elif col == "RMSEA":
                            colors.append("background-color:#E8F5E9" if fv<0.049
                                          else "background-color:#FFE0E0")
                        else:
                            colors.append("")
                    except Exception:
                        colors.append("")
                return colors

            st.markdown("**모델 적합도**")
            if was_mod:
                fit_cmp = pd.DataFrame([
                    {"모형":"초기 모형", **{k: fit_init.get(k,"-") for k in fit_col_order}},
                    {"모형":"수정 모형", **{k: fit_mod.get(k,"-")  for k in fit_col_order}},
                ])
            else:
                fit_cmp = pd.DataFrame([
                    {"모형":"초기 모형", **{k: fit_init.get(k,"-") for k in fit_col_order}}
                ])
            st.dataframe(fit_cmp.style.apply(_hl_sem, axis=1), use_container_width=True)
            st.caption(FIT_NOTE)

            if was_mod and extra_cov:
                st.success(f"✅ MI 기반 수정 {len(mod_log)}회 적용")
                paths_str = " / ".join(f"{a} ~~ {b}" for a, b in extra_cov if isinstance(a, str))
                st.markdown(f"**수정된 경로:** `{paths_str}`")
                with st.expander("📋 수정 과정 상세"):
                    st.dataframe(mod_log, use_container_width=True)
            elif not was_mod:
                if _adequate_check(fit_init):
                    st.success("✅ 초기 모형 적합도 양호 — 수정 불필요")
                else:
                    st.info("ℹ️ 적합도 미충족이나 MI > 3.84 쌍이 없어 더 이상 수정 불가")

            # SEM MI 수동 선택
            _init_mi_for_sel = st.session_state.get("_sem_init", (None,)*8)[6]
            if _init_mi_for_sel is not None and not _init_mi_for_sel.empty:
                mi_over = _init_mi_for_sel[_init_mi_for_sel["MI"] > 3.84]
                if not mi_over.empty:
                    st.markdown("---")
                    st.markdown("#### 🛠️ 잔차공분산 수동 추가")
                    st.caption("MI > 3.84 쌍 중 적용할 항목을 선택한 뒤 **재실행** 버튼을 눌러주세요.")
                    _opts = [f"{row['변수1']} ~~ {row['변수2']}  (MI = {row['MI']})"
                             for _, row in mi_over.iterrows()]
                    _cur_pairs = st.session_state.get("_sem_manual_pairs", [])
                    _cur_opts  = [o for o in _opts
                                  if any(f"{v1} ~~ {v2}" in o for v1, v2 in _cur_pairs)]
                    _sel = st.multiselect("적용할 잔차공분산 경로 선택",
                                          options=_opts, default=_cur_opts,
                                          key="sem_mi_multisel")
                    if st.button("▶️ 선택 경로 적용하여 SEM 재실행", key="sem_rerun_btn"):
                        _new_pairs = []
                        for s in _sel:
                            part = s.split("  (MI")[0].strip()
                            v1, v2 = [x.strip() for x in part.split("~~")]
                            _new_pairs.append((v1, v2))
                        st.session_state["_sem_manual_pairs"] = _new_pairs
                        _cfa_cov  = st.session_state.get("_sem_cfa_cov", [])
                        _all_covs = list(_cfa_cov) + _new_pairs
                        with st.spinner("SEM 재분석 중..."):
                            try:
                                _r = build_lavaan_sem_table(
                                    st.session_state["_sem_df"],
                                    st.session_state["_sem_constructs"],
                                    st.session_state["_sem_hypotheses"],
                                    cfa_extra_cov=_all_covs, max_mods=0)
                                if _r[0] is not None:
                                    _r2 = list(_r)
                                    _r2[4] = _all_covs
                                    _r2[5] = len(_new_pairs) > 0
                                    st.session_state["_sem_applied"] = tuple(_r2)
                                    st.rerun()
                            except Exception as _e:
                                st.warning(f"SEM 재실행 오류: {_e}")

            col_mi1, col_mi2 = st.columns(2)
            with col_mi1:
                if init_mi_sem is not None and not init_mi_sem.empty:
                    with st.expander("🔍 초기 수정지수(MI) 상위 20개"):
                        d = init_mi_sem.head(20).copy()
                        d["판정"] = d["MI"].apply(lambda v: "⚠️ 수정 고려" if v > 3.84 else "")
                        st.dataframe(d, use_container_width=True)
            with col_mi2:
                if final_mi_sem is not None and not final_mi_sem.empty:
                    with st.expander("🔍 최종 수정지수(MI) 상위 20개"):
                        d = final_mi_sem.head(20).copy()
                        d["판정"] = d["MI"].apply(lambda v: "⚠️ 추가 수정 가능" if v > 3.84 else "✅")
                        st.dataframe(d, use_container_width=True)

            st.markdown("---")
            st.markdown("**가설 검증 결과** (수정모형 기준)")
            def hl(row):
                color = "#E8F5E9" if row.get("채택여부") == "채택" else "#FFF3F3"
                return [f"background-color:{color}"] * len(row)
            st.dataframe(paths.style.apply(hl, axis=1), use_container_width=True)
            n_adopt = (paths["채택여부"] == "채택").sum()
            st.markdown(f"**채택: {n_adopt}개 / 기각: {len(paths)-n_adopt}개 / 전체: {len(paths)}개**")

# ── Excel 다운로드 ─────────────────────────────────────────────────────────────
st.markdown("---")
xlsx_sheets = st.session_state.get("_xlsx_sheets", xlsx_sheets)
if xlsx_sheets:
    try:
        _ts        = datetime.now().strftime("%Y%m%d_%H%M%S")
        _fname     = f"SEM_RESULT_{_ts}.xlsx"
        xlsx_bytes = build_single_sheet_excel(xlsx_sheets)
        st.download_button(
            label=f"📥 {_fname} 다운로드 (모든 결과 한 시트)",
            data=xlsx_bytes,
            file_name=_fname,
            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
            use_container_width=True,
            type="primary"
        )
        st.caption("※ 모든 분석 결과가 '분석결과' 시트 하나에 섹션별로 통합되어 저장됩니다.")
    except Exception as e:
        st.error(f"Excel 생성 오류: {e}")