#!/usr/bin/env python3
"""Analyse les résultats bench_baseline.json + bench_postfix.json.

Calcule :
- accuracy par dossier (3 runs, vote majoritaire)
- accuracy globale, UHCD, Forfait
- stabilité inter-runs
- score qualité justification (présence CCMU, GEMSA, durée, citations,
  cohérence type_forfait)
- Δ baseline vs postfix par dossier

Sortie : tables markdown sur stdout + JSON brut sauvegardé.
"""
from __future__ import annotations

import json
import re
import sys
from collections import Counter
from pathlib import Path

ROOT = Path(__file__).resolve().parent.parent
RES = ROOT / "tools" / "_bench_t2a_out"

# (ipp, label court, ground truth, type_forfait attendu)
GT = [
    ("25003284", "Pneumo VRS 78a 3h37",       "FORFAIT_URGENCE",                 "Standard"),
    ("25003362", "Intox enfant 3a 4h41",      "FORFAIT_URGENCE",                 "PE2"),
    ("25003364", "Pneumo SLA 71a 7h35",       "REQUALIFICATION_HOSPITALISATION", None),
    ("25003451", "Plaie suturée 3a 2h00",     "FORFAIT_URGENCE",                 "SU2"),
    ("25003475", "Aura migr. 34a 4h03",       "REQUALIFICATION_HOSPITALISATION", None),
    ("25005866", "TC hockey 17a 12h01",       "REQUALIFICATION_HOSPITALISATION", None),
    ("25010621", "Laryngite 5a 2h49",         "FORFAIT_URGENCE",                 "PE2"),
    ("25012257", "Douleur abdo 76a 7h20",     "REQUALIFICATION_HOSPITALISATION", None),
    ("25048485", "CTCG ado 13a 6h50",         "FORFAIT_URGENCE",                 "PE2"),
    ("25056615", "Salpingite 39a 4h30",       "FORFAIT_URGENCE",                 "Standard"),
    ("25151530", "Colique nephr. 58a 6h21",   "FORFAIT_URGENCE",                 "Standard"),
]

LITIGIEUX = {"25003475", "25012257", "25048485", "25056615"}  # cas borderline cf. audit DIM


def short(d: str | None) -> str:
    if d is None: return "?"
    if d == "REQUALIFICATION_HOSPITALISATION": return "UHCD"
    if d == "FORFAIT_URGENCE": return "Forf"
    return d[:8]


def majority(decisions: list[str]) -> str | None:
    decisions = [d for d in decisions if d]
    if not decisions:
        return None
    c = Counter(decisions).most_common(1)
    return c[0][0]


def quality_score(raw: dict, ipp: str, gt: str, mode: str) -> tuple[int, list[str]]:
    """Score qualité justif sur 5, retourne aussi la liste des points marqués/manqués."""
    notes = []
    score = 0

    # Concaténation de tous les textes pour grep
    blob_parts = []
    for k, v in raw.items():
        if k.startswith("_"):
            continue
        if isinstance(v, str):
            blob_parts.append(v)
        elif isinstance(v, dict):
            blob_parts.extend(str(x) for x in v.values() if isinstance(x, str))
        elif isinstance(v, list):
            for x in v:
                if isinstance(x, str):
                    blob_parts.append(x)
                elif isinstance(x, dict):
                    blob_parts.extend(str(y) for y in x.values() if isinstance(y, str))
    blob = " ".join(blob_parts).lower()

    # 1. Mention CCMU ?
    if "ccmu" in blob:
        score += 1; notes.append("+CCMU")
    else:
        notes.append("-CCMU")

    # 2. Mention GEMSA ?
    if "gemsa" in blob:
        score += 1; notes.append("+GEMSA")
    else:
        notes.append("-GEMSA")

    # 3. Mention durée passage ?
    duree = raw.get("duree_passage_heures")
    if duree is not None and "duree" in str(raw) or re.search(r"\d+\s*h\s*\d+|h(?:eure|rs)", blob):
        if duree is not None:
            score += 1; notes.append(f"+durée({duree}h)")
        else:
            notes.append("-durée")
    else:
        notes.append("-durée")

    # 4. Mention mode de sortie / décision médicale ?
    if any(w in blob for w in ("retour à domicile", "domicile", "consultation externe",
                                "hospitalisation", "transfert", "mutation")):
        score += 1; notes.append("+mode_sortie")
    else:
        notes.append("-mode_sortie")

    # 5. Présence de citations littérales (« » ou guillemets droits) avec contenu non-vide ?
    has_citation = (
        bool(re.search(r"«\s*[^»]{6,}\s*»", " ".join(blob_parts)))
        or bool(re.search(r'"[^"]{8,}"', " ".join(blob_parts)))
    )
    if has_citation:
        score += 1; notes.append("+citation")
    else:
        notes.append("-citation")

    return score, notes


def hallucination_check(raw: dict, dpi: str) -> list[str]:
    """Liste de citations « ... » présentes dans la sortie LLM mais ABSENTES du DPI."""
    out = []
    blob_parts = []
    for k, v in raw.items():
        if k.startswith("_"):
            continue
        if isinstance(v, str):
            blob_parts.append(v)
        elif isinstance(v, dict):
            for x in v.values():
                if isinstance(x, str):
                    blob_parts.append(x)
    full = " ".join(blob_parts)
    citations = re.findall(r"«\s*([^»]{6,80})\s*»", full)
    dpi_lower = dpi.lower()
    for c in citations[:20]:  # limite
        # tolérance : on cherche un sous-fragment de 8+ caractères
        if not any(c.lower()[i:i+12] in dpi_lower for i in range(0, max(1, len(c) - 12), 4)):
            out.append(c.strip())
    return out


def analyze(mode_label: str, path: Path, dpis: dict[str, str]) -> dict:
    if not path.is_file():
        print(f"⚠ Fichier manquant : {path}")
        return {}
    data = json.loads(path.read_text(encoding="utf-8"))
    results = data["results"]
    model = data["model"]
    n_runs = data["runs"]

    rows = []
    correct_total = 0; total_runs = 0
    for ipp, label, gt, ftype in GT:
        runs = results.get(ipp, [])
        decisions = [r.get("decision") for r in runs]
        type_forfaits = [r.get("type_forfait") for r in runs]
        match = sum(1 for r in runs if r.get("match"))
        total_runs += len(runs)
        correct_total += match
        maj = majority(decisions)
        # type_forfait majoritaire (ignoré si UHCD attendu)
        type_maj = Counter([t for t in type_forfaits if t]).most_common(1)
        type_maj_str = type_maj[0][0] if type_maj else "—"

        # Qualité moyenne sur les 3 runs
        qscores = []
        all_notes = []
        halluc_total = []
        for r in runs:
            raw = r.get("raw", {})
            s, notes = quality_score(raw, ipp, gt, mode_label)
            qscores.append(s)
            all_notes.append(notes)
            halluc = hallucination_check(raw, dpis.get(ipp, ""))
            halluc_total.extend(halluc)

        rows.append({
            "ipp": ipp,
            "label": label,
            "gt": gt,
            "gt_short": short(gt),
            "ftype": ftype,
            "decisions": decisions,
            "decisions_short": [short(d) for d in decisions],
            "majority": short(maj),
            "majority_match": maj == gt,
            "type_forfait_maj": type_maj_str,
            "type_forfait_match": (gt == "REQUALIFICATION_HOSPITALISATION") or (type_maj_str == ftype),
            "stable": len(set(decisions)) == 1,
            "match_runs": match,
            "litigieux": ipp in LITIGIEUX,
            "quality_avg": round(sum(qscores) / max(1, len(qscores)), 1),
            "quality_max": max(qscores) if qscores else 0,
            "quality_notes_first": all_notes[0] if all_notes else [],
            "hallucinations": halluc_total[:5],
        })

    # Stats globales
    n_dossiers = len(rows)
    accuracy_runs = correct_total / max(1, total_runs)
    accuracy_majority = sum(1 for r in rows if r["majority_match"]) / n_dossiers
    uhcd_rows = [r for r in rows if r["gt"] == "REQUALIFICATION_HOSPITALISATION"]
    forf_rows = [r for r in rows if r["gt"] == "FORFAIT_URGENCE"]
    uhcd_acc_majority = sum(1 for r in uhcd_rows if r["majority_match"]) / max(1, len(uhcd_rows))
    forf_acc_majority = sum(1 for r in forf_rows if r["majority_match"]) / max(1, len(forf_rows))
    stability = sum(1 for r in rows if r["stable"]) / n_dossiers
    litigieux_acc = sum(1 for r in rows if r["litigieux"] and r["majority_match"]) / max(1, len([r for r in rows if r["litigieux"]]))
    type_forfait_acc = sum(1 for r in rows if r["gt"] == "FORFAIT_URGENCE" and r["type_forfait_match"]) / max(1, len(forf_rows))
    avg_quality = round(sum(r["quality_avg"] for r in rows) / n_dossiers, 2)
    n_halluc = sum(len(r["hallucinations"]) for r in rows)

    return {
        "mode": mode_label,
        "model": model,
        "n_runs": n_runs,
        "rows": rows,
        "accuracy_runs": round(accuracy_runs, 3),
        "accuracy_majority": round(accuracy_majority, 3),
        "uhcd_acc_majority": round(uhcd_acc_majority, 3),
        "forfait_acc_majority": round(forf_acc_majority, 3),
        "type_forfait_acc": round(type_forfait_acc, 3),
        "stability": round(stability, 3),
        "litigieux_acc": round(litigieux_acc, 3),
        "avg_quality": avg_quality,
        "n_hallucinations": n_halluc,
    }


def print_table(report: dict):
    print(f"\n## {report['mode']} (model={report['model']}, {report['n_runs']} runs/dossier)\n")
    print(f"- Accuracy runs (3×11=33 inférences) : **{report['accuracy_runs']*100:.0f}%**")
    print(f"- Accuracy vote majoritaire (sur 11 dossiers) : **{report['accuracy_majority']*100:.0f}%**")
    print(f"- Accuracy UHCD (majoritaire) : {report['uhcd_acc_majority']*100:.0f}%")
    print(f"- Accuracy Forfait (majoritaire) : {report['forfait_acc_majority']*100:.0f}%")
    print(f"- Type forfait correct (parmi forfaits OK) : {report['type_forfait_acc']*100:.0f}%")
    print(f"- Stabilité inter-runs : {report['stability']*100:.0f}%")
    print(f"- Cas litigieux OK : {report['litigieux_acc']*100:.0f}%")
    print(f"- Qualité justification moyenne : **{report['avg_quality']}/5**")
    print(f"- Hallucinations citations : {report['n_hallucinations']}")
    print()
    print("| IPP | Cas | GT | Run1 | Run2 | Run3 | Maj | Stable | Type | Qual |")
    print("|---|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|")
    for r in report["rows"]:
        runs = r["decisions_short"] + ["—"] * (3 - len(r["decisions_short"]))
        stable = "✓" if r["stable"] else " "
        ftype = r["type_forfait_maj"] if r["gt"] == "FORFAIT_URGENCE" else "—"
        ftype_mark = "" if r["gt"] == "REQUALIFICATION_HOSPITALISATION" else (" ✓" if r["type_forfait_match"] else " ✗")
        flag = "✓" if r["majority_match"] else "✗"
        litig = " 🔴" if r["litigieux"] else ""
        print(f"| {r['ipp']} | {r['label']}{litig} | {r['gt_short']} | "
              f"{runs[0]} | {runs[1]} | {runs[2]} | {flag} {r['majority']} | {stable} | "
              f"{ftype}{ftype_mark} | {r['quality_avg']}/5 |")


def print_delta(baseline: dict, postfix: dict):
    print("\n## Δ Baseline → Post-fix\n")
    print("| IPP | Cas | GT | Baseline | Post-fix | Δ |")
    print("|---|---|:---:|:---:|:---:|:---:|")
    for b, p in zip(baseline["rows"], postfix["rows"]):
        b_flag = "✓" if b["majority_match"] else "✗"
        p_flag = "✓" if p["majority_match"] else "✗"
        if b["majority_match"] and p["majority_match"]:
            delta = "= ✓"
        elif not b["majority_match"] and p["majority_match"]:
            delta = "🟢 +1"
        elif b["majority_match"] and not p["majority_match"]:
            delta = "🔴 -1"
        else:
            delta = "= ✗"
        litig = " 🔴" if b["litigieux"] else ""
        print(f"| {b['ipp']} | {b['label']}{litig} | {b['gt_short']} | {b_flag} {b['majority']} | {p_flag} {p['majority']} | {delta} |")

    # Headlines
    print()
    print(f"**Synthèse Δ** :")
    print(f"- Baseline : {sum(1 for r in baseline['rows'] if r['majority_match'])}/11 → {baseline['accuracy_majority']*100:.0f}%")
    print(f"- Post-fix : {sum(1 for r in postfix['rows'] if r['majority_match'])}/11 → {postfix['accuracy_majority']*100:.0f}%")
    print(f"- Gain absolu : {(postfix['accuracy_majority'] - baseline['accuracy_majority'])*100:+.0f} points")
    print(f"- Stabilité : {baseline['stability']*100:.0f}% → {postfix['stability']*100:.0f}%")
    print(f"- Qualité justification : {baseline['avg_quality']}/5 → {postfix['avg_quality']}/5")


def main():
    dpis = json.loads((RES / "dpis.json").read_text(encoding="utf-8"))
    baseline = analyze("Baseline", RES / "bench_baseline.json", dpis)
    postfix = analyze("Post-fix", RES / "bench_postfix.json", dpis)
    if baseline:
        print_table(baseline)
    if postfix:
        print_table(postfix)
    if baseline and postfix:
        print_delta(baseline, postfix)
        # Sauve l'analyse complète
        out = RES / "analysis.json"
        out.write_text(json.dumps({"baseline": baseline, "postfix": postfix}, ensure_ascii=False, indent=2), encoding="utf-8")
        print(f"\n📁 {out}")


if __name__ == "__main__":
    main()