"""Attach clinical captions / dx to the manifest for FILE-labeled cohorts (json/jsonl/xlsx).
Folder-labeled cohorts (eryuan/eryuan_t17/tongren_95disease/dual/eyfrd6) are already labeled by
build_manifest (diagnosis_raw=parent dir). Join keys verified by the 2026-06-30 label-schema recon.

PII discipline: ONLY clinical findings/impression/dx text -> caption. Names/IDs/DOB/exam-dates/
doctor-signatures are NEVER read into caption (separate fields, dropped); embedded scheduling/
signature/boilerplate tails in free text are stripped by clean_caption().

All joins are STUDY/scan-level except data0410 (image-level 1:1) and hcw (image-level) -> a scan's
caption is fanned out to all its frames.

Output: adds `caption` + `diagnosis_group` columns -> <manifest>_labeled.parquet
Usage: /data/team/lisicheng/octdata_venv/bin/python attach_labels.py --manifest .../new_data_manifest.parquet [--only k1,k2]
"""
import argparse, json, re, os
from collections import Counter
from pathlib import Path
import pandas as pd
import cohorts as C

NFS = "/nfs01/datasetsBackup"
LBL = {
    "handan":       f"{NFS}/ffa/handan_ffa/data.json",
    "chengdu_ffa":  f"{NFS}/ffa/chengdu_ffa/visionoph4.jsonl",
    "yinhai_ffa":   f"{NFS}/ffa/yinhai_ffa/output2.jsonl",
    "yinhai_bscan": f"{NFS}/pretrainNewDS/BUltrasound/YinhaiBscan/YinhaiBscan.json",       # RAW (has findings)
    "xiangya_bscan":f"{NFS}/pretrainNewDS/BUltrasound/XiangyaBscan/xiangyaBUltrasound_cleanedv3.json",
    "chengdu_ubm":  f"{NFS}/pretrainNewDS/ubm/ChengduShiyiUBM/ChengdushiyiUBM_cleanedv3.json",
    "yinhai_ubm":   f"{NFS}/pretrainNewDS/ubm/YinhaiUBM/YinHaiUBM_cleanedv3.json",
}

_TAIL = re.compile(r'(建议[:：]|门诊时间[:：]|医[师生]签[名字]|签[名字][:：]|医师[:：]|检查者|报告医[师生]|TOPCON|Corporation).*$', re.S)
def clean_caption(t):
    if t is None: return None
    t = str(t).strip()
    if not t or t == "18": return None                      # chengdu_ubm degenerate
    t = _TAIL.split(t)[0]                                    # drop scheduling/signature tail (PII)
    t = re.sub(r'请结合临床[。.]?\s*$', '', t)                 # UBM boilerplate
    t = t.replace("&&", " ").replace("\n", " ")             # yinhai_ubm per-eye joiner / newlines
    t = re.sub(r'\s+', ' ', t).strip(" >]")
    return t or None

def _basename(p): return os.path.basename(str(p))
def _stem(p): return Path(str(p)).stem
def _load_array(path, tolerant=False):
    s = open(path, encoding="utf-8", errors="replace").read()
    if tolerant: s = re.sub(r',(\s*[}\]])', r'\1', s)       # xiangya malformed trailing commas
    return json.loads(s)

# each builder returns (lookup_dict, key_fn) ; lookup value = caption_str OR (caption, dx)
def b_handan():
    recs = json.load(open(LBL["handan"]))["data"]
    dup = {k for k, c in Counter(r.get("ID号", "").strip() for r in recs if r.get("ID号", "").strip()).items() if c > 1}
    m = {}
    for r in recs:
        i = r.get("ID号", "").strip()
        if not i or i in dup: continue                       # drop ambiguous/empty
        m[i] = clean_caption(" ".join(x for x in [r.get("影像所见"), r.get("印象")] if x))
    def key(p):
        idp = _basename(p).rsplit("_", 1)[0]
        return idp if idp in m else re.sub(r'\D+$', '', idp)  # strip trailing non-digit (e.g. 1809243Coats)
    return m, key

def b_chengdu_ffa():
    m = {}
    for r in _load_array(LBL["chengdu_ffa"]):
        cap = clean_caption(r.get("description"))
        dx = ";".join(dict.fromkeys(d for d in (r.get("diagnosis") or []) if d)) or None
        for im in r.get("images", []):
            b = re.split(r'[\\/]', im.get("image_path", ""))[-1]   # basename, NO cleanup (mangled names are real)
            if b and b not in m: m[b] = (cap, dx)
    return m, _basename

def b_yinhai_ffa():
    recs = [json.loads(l) for l in open(LBL["yinhai_ffa"]) if l.strip()]
    m = {}
    for r in recs:
        f = r.get("file", ""); stem = f[:-4] if f.endswith(".csv") else f
        if stem: m[stem] = clean_caption(r.get("second_column2"))
    return m, lambda p: _basename(p).rsplit("-", 1)[0]          # <stem>-<frame>.bmp

def b_by_images(path, tolerant=False, ubm_clean=False):
    m = {}
    for r in _load_array(path, tolerant=tolerant):
        cap = r.get("description")
        if ubm_clean and cap and not (cap[:1].isdigit() or ("一" <= cap[:1] <= "鿿")):
            cap = cap[1:]                                       # strip 1 leading junk char (chengdu_ubm)
        cap = clean_caption(cap)
        for im in r.get("images", []):
            b = _basename(re.split(r'[\\/]', im.get("image_path", ""))[-1])
            if b and b not in m: m[b] = cap
    return m, _basename

def b_hcw():
    rp = Path(C.EXTRACT_ROOT) / "hcw_fundus" / "fundus" / "fundus_csv"
    frames = [pd.read_excel(rp / x) for x in ["dramdrvo_cleaned.xlsx", "glaucoma_cleaned.xlsx"] if (rp / x).exists()]
    if not frames: print("  [hcw] xlsx missing -> run extract.sh (needs fundus_csv glob)"); return {}, _stem
    lab = pd.concat(frames, ignore_index=True)
    lab["stem"] = lab["image_name"].map(_stem)
    m = {}
    for stem, g in lab.groupby("stem"):
        prim = g[g["is_primary"] == "是"]
        names = ";".join(dict.fromkeys(g["diag_name"].dropna().astype(str)))
        main = (prim.iloc[0] if len(prim) else g.iloc[0]).get("diag_name")
        m[stem] = (clean_caption(names), str(main) if pd.notna(main) else None)
    return m, _stem

def b_data0410():
    fp = Path(C.EXTRACT_ROOT) / "data0410_master_fundus" / "data" / "reports_2020_2025_cleaned.xlsx"
    if not fp.exists(): print("  [data0410] xlsx missing -> run extract.sh"); return {}, _basename
    df = pd.read_excel(fp)
    return {str(n): clean_caption(c) for n, c in zip(df["ImageName"], df["Notes_clean"])}, _basename

BUILDERS = {
    "handan_fundus": b_handan, "handan_ffa": b_handan,
    "chengdu_ffa": b_chengdu_ffa, "yinhai_ffa": b_yinhai_ffa,
    "yinhai_bscan": lambda: b_by_images(LBL["yinhai_bscan"]),
    "xiangya_bscan": lambda: b_by_images(LBL["xiangya_bscan"], tolerant=True),
    "chengdu_shiyi_ubm": lambda: b_by_images(LBL["chengdu_ubm"], ubm_clean=True),
    "yinhai_ubm": lambda: b_by_images(LBL["yinhai_ubm"], ubm_clean=True),
    "hcw_fundus": b_hcw, "data0410_master_fundus": b_data0410,
}


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--manifest", required=True)
    ap.add_argument("--only", default=None)
    args = ap.parse_args()
    df = pd.read_parquet(args.manifest)
    for col in ("caption", "diagnosis_group"):
        if col not in df: df[col] = None
    only = set(args.only.split(",")) if args.only else None

    for cohort, builder in BUILDERS.items():
        if only and cohort not in only: continue
        sub = df[df["cohort"] == cohort]
        if not len(sub): continue
        try:
            lookup, key_fn = builder()
        except Exception as e:
            print(f"[{cohort}] builder FAILED: {e}"); continue
        # vectorized: build aligned Series on sub.index (preserves df index) -> O(N), not O(N^2)
        caps, dxs = [], []
        for k in sub["file_path"].map(key_fn):
            v = lookup.get(k)
            if v is None: caps.append(None); dxs.append(None); continue
            c, d = v if isinstance(v, tuple) else (v, None)
            caps.append(c); dxs.append(d)
        caps = pd.Series(caps, index=sub.index); dxs = pd.Series(dxs, index=sub.index)
        df.loc[sub.index, "caption"] = caps
        df.loc[sub.index, "diagnosis_group"] = dxs
        nhit = int((caps.notna() | dxs.notna()).sum())
        print(f"[{cohort}] {len(sub)} imgs -> {nhit} captioned ({100*nhit/max(1,len(sub)):.0f}%)")

    outp = args.manifest.replace(".parquet", "_labeled.parquet")
    df.to_parquet(outp, index=False)
    cov = df["caption"].notna().sum()
    print(f"\n=== {len(df)} rows, {cov} with caption -> {outp} ===")


if __name__ == "__main__":
    main()