#!/usr/bin/env python3
"""
rag_eval_metrics.py

Evaluate RAG retrieval quality by comparing app logs (JSONL) with a gold file (CSV).

Extended to also evaluate answer quality using:
- Lexical similarity: BLEU, ROUGE-1/2/L
- Semantic similarity: BERTScore (Recall, F1)

If nltk / rouge-score / bert-score are missing, the script still runs and
returns NaN for these metrics instead of crashing.

Also uses robust CSV reading to handle non-UTF8 encodings (cp1252/latin1).
"""

import argparse
import json
import os
import sys
from pathlib import Path
from typing import Dict, List, Tuple, Any, Optional

import pandas as pd
import numpy as np

# ----------------------------- Small Utils ----------------------------- #

def filename_key(s: str) -> str:
    s = (s or "").strip().replace("\\", "/").split("/")[-1]
    return s.casefold()

def re_split_sc(s: str) -> List[str]:
    import re
    return re.split(r"[;,]", s)

def _pick_last_non_empty(hit_lists) -> List[dict]:
    """
    Robustly select the last non-empty hits list from a pandas Series or iterable.

    This fixes the KeyError that happens when using reversed() directly on a Series
    with a non-range index.
    """
    # Convert pandas Series or other iterables to a plain Python list
    try:
        values = list(hit_lists.tolist())
    except AttributeError:
        values = list(hit_lists)

    # Walk from last to first, return first non-empty list-like
    for lst in reversed(values):
        if isinstance(lst, (list, tuple)) and len(lst) > 0:
            return lst

    # If everything was empty / NaN
    return []

def _read_csv_robust(path: Path) -> pd.DataFrame:
    """
    Try multiple encodings so we don't crash on Windows-1252 / Latin-1 CSVs.
    """
    encodings = ["utf-8", "utf-8-sig", "cp1252", "latin1"]
    last_err = None
    for enc in encodings:
        try:
            return pd.read_csv(path, encoding=enc)
        except UnicodeDecodeError as e:
            last_err = e
            continue
    # If all fail, re-raise the last error
    raise last_err if last_err is not None else ValueError(
        "Failed to read CSV with fallback encodings."
    )

# ----------------------------- IO Helpers ----------------------------- #

def read_logs(jsonl_path: Path) -> pd.DataFrame:
    """
    Read RAG JSONL logs and aggregate by question.

    Returns a DataFrame with columns:
      - question: original question text (last occurrence)
      - hits:     list of dicts {doc, page} for retrieval
      - answer:   final answer text logged for that question
    """
    rows = []
    if (not jsonl_path.exists()) or jsonl_path.stat().st_size == 0:
        return pd.DataFrame(columns=["question", "hits", "answer"])

    with open(jsonl_path, "r", encoding="utf-8") as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            try:
                rec = json.loads(line)
            except Exception:
                continue

            # Extract question
            q = (((rec.get("inputs") or {}).get("question")) or "").strip()

            # Extract retrieval hits (if present)
            retr = (rec.get("retrieval") or {})
            hits = retr.get("hits", [])
            norm_hits = []
            for h in hits or []:
                doc = (h.get("doc") or "").strip()
                page = str(h.get("page") or "").strip()

                # Normalize page to int or None
                try:
                    page_int = int(page)
                except Exception:
                    page_int = None

                norm_hits.append({"doc": doc, "page": page_int})

            # Extract final answer text (if present)
            out = (rec.get("output") or {})
            ans = ((out.get("final_answer") or "")).strip()

            rows.append({"question": q, "hits": norm_hits, "answer": ans})

    df = pd.DataFrame(rows)
    if df.empty:
        return pd.DataFrame(columns=["question", "hits", "answer"])

    # Group by normalized question text and keep last non-empty hits list and answer per question
    df = (
        df.groupby(df["question"].astype(str).str.casefold().str.strip(), as_index=False)
          .agg({
              "question": "last",
              "hits": _pick_last_non_empty,
              "answer": "last"
          })
    )
    return df

def read_gold(csv_path: Path) -> Tuple[pd.DataFrame, Dict[str, str]]:
    """
    Read gold CSV with retrieval labels and optional reference answers.

    Returns:
      - gold_df: rows with columns ['question', 'doc', 'page', 'answer', ...]
                 where 'question' is normalized (casefold+strip)
      - gold_answers: dict mapping normalized question -> reference answer text
    """
    df = _read_csv_robust(csv_path)
    cols = {c.lower().strip(): c for c in df.columns}

    # --- question column ---
    q_col = None
    for cand in ["question", "query", "q"]:
        if cand in cols:
            q_col = cols[cand]
            break
    if q_col is None:
        raise ValueError("Gold CSV must contain a 'question' column (case-insensitive).")

    # --- possible relevant_docs (list-in-cell) column ---
    rel_list_col = None
    for cand in ["relevant_docs", "relevant", "docs"]:
        if cand in cols:
            rel_list_col = cols[cand]
            break

    # --- single-doc-per-row column ---
    doc_col = None
    for cand in ["doc", "document", "file", "doc_name"]:
        if cand in cols:
            doc_col = cols[cand]
            break

    # --- optional page column ---
    page_col = None
    for cand in ["page", "page_num", "page_number"]:
        if cand in cols:
            page_col = cols[cand]
            break

    # --- optional answer column (for QA metrics) ---
    ans_col = None
    for cand in ["answer", "reference_answer", "gold_answer"]:
        if cand in cols:
            ans_col = cols[cand]
            break

    rows = []

    # Case 1: relevant_docs list column (no explicit doc_col)
    if rel_list_col and doc_col is None:
        for _, r in df.iterrows():
            q_raw = str(r[q_col]).strip()
            q_norm = q_raw.casefold().strip()
            ans_raw = str(r[ans_col]).strip() if (ans_col and pd.notna(r[ans_col])) else ""

            rel_val = str(r[rel_list_col]) if pd.notna(r[rel_list_col]) else ""
            if not rel_val:
                rows.append({
                    "question_raw": q_raw,
                    "question": q_norm,
                    "doc": None,
                    "page": np.nan,
                    "answer": ans_raw
                })
                continue

            parts = [p.strip() for p in re_split_sc(rel_val)]
            for d in parts:
                rows.append({
                    "question_raw": q_raw,
                    "question": q_norm,
                    "doc": filename_key(d),
                    "page": np.nan,
                    "answer": ans_raw
                })

    # Case 2: doc/page columns (one relevant doc per row)
    elif doc_col:
        for _, r in df.iterrows():
            q_raw = str(r[q_col]).strip()
            q_norm = q_raw.casefold().strip()
            ans_raw = str(r[ans_col]).strip() if (ans_col and pd.notna(r[ans_col])) else ""

            d = str(r[doc_col]).strip() if pd.notna(r[doc_col]) else ""
            p = r[page_col] if (page_col and pd.notna(r[page_col])) else np.nan

            try:
                p = int(p)
            except Exception:
                p = np.nan

            rows.append({
                "question_raw": q_raw,
                "question": q_norm,
                "doc": filename_key(d),
                "page": p,
                "answer": ans_raw
            })

    else:
        raise ValueError("Gold CSV must contain either a 'doc' column or a 'relevant_docs' column.")

    gold = pd.DataFrame(rows)

    # Keep only rows with a valid doc (when docs exist)
    gold["has_doc"] = gold["doc"].apply(lambda x: isinstance(x, str) and len(x) > 0)
    if gold["has_doc"].any():
        gold = gold[gold["has_doc"]].copy()
    gold.drop(columns=["has_doc"], inplace=True, errors="ignore")

    # Remove duplicates
    gold = gold.drop_duplicates(subset=["question", "doc", "page"])

    # Build question -> gold_answer map (normalized questions)
    gold_answers: Dict[str, str] = {}
    if "answer" in gold.columns:
        tmp = (
            gold[["question", "answer"]]
            .dropna(subset=["answer"])
            .drop_duplicates(subset=["question"])
        )
        gold_answers = dict(zip(tmp["question"], tmp["answer"]))

    return gold, gold_answers

# ----------------------------- Retrieval Metric Core ----------------------------- #

def dcg_at_k(relevances: List[int]) -> float:
    dcg = 0.0
    for i, rel in enumerate(relevances, start=1):
        if rel > 0:
            dcg += 1.0 / np.log2(i + 1.0)
    return float(dcg)

def ndcg_at_k(relevances: List[int]) -> float:
    dcg = dcg_at_k(relevances)
    ideal = sorted(relevances, reverse=True)
    idcg = dcg_at_k(ideal)
    if idcg == 0.0:
        return 0.0
    return float(dcg / idcg)

def compute_metrics_for_question(gold_docs, gold_pages, hits, k):
    top = hits[:k] if hits else []
    pred_docs = [filename_key(h.get("doc", "")) for h in top]
    pred_pairs = [(filename_key(h.get("doc", "")), h.get("page", None)) for h in top]

    # --- Doc-level metrics ---
    gold_doc_set = set([d for d in gold_docs if isinstance(d, str) and d])

    rel_bin_doc = [1 if d in gold_doc_set else 0 for d in pred_docs]
    hitk_doc = 1 if any(rel_bin_doc) else 0
    prec_doc = (sum(rel_bin_doc) / max(1, len(pred_docs))) if pred_docs else 0.0
    rec_doc = (sum(rel_bin_doc) / max(1, len(gold_doc_set))) if gold_doc_set else 0.0
    ndcg_doc = ndcg_at_k(rel_bin_doc)

    # --- Page-level metrics (only if gold has page labels) ---
    gold_pairs = set()
    for d, p in zip(gold_docs, gold_pages):
        if isinstance(d, str) and d and (p is not None) and (not (isinstance(p, float) and np.isnan(p))):
            try:
                p_int = int(p)
            except Exception:
                continue
            gold_pairs.add((d, p_int))

    if gold_pairs:
        rel_bin_page = []
        for (d, p) in pred_pairs:
            if p is None or not isinstance(p, int):
                rel_bin_page.append(0)
            else:
                rel_bin_page.append(1 if (d, p) in gold_pairs else 0)

        hitk_page = 1 if any(rel_bin_page) else 0
        prec_page = (sum(rel_bin_page) / max(1, len(pred_pairs))) if pred_pairs else 0.0
        rec_page = (sum(rel_bin_page) / max(1, len(gold_pairs))) if gold_pairs else 0.0
        ndcg_page = ndcg_at_k(rel_bin_page)
    else:
        hitk_page = prec_page = rec_page = ndcg_page = np.nan

    return {
        "hit@k_doc": hitk_doc,
        "precision@k_doc": prec_doc,
        "recall@k_doc": rec_doc,
        "ndcg@k_doc": ndcg_doc,
        "hit@k_page": hitk_page,
        "precision@k_page": prec_page,
        "recall@k_page": rec_page,
        "ndcg@k_page": ndcg_page,
        "n_gold_docs": int(len(gold_doc_set)),
        "n_gold_doc_pages": int(len(gold_pairs)),
        "n_pred": int(len(pred_docs))
    }

# ---------------------- Answer Quality Metrics (with fallbacks) ---------------------- #

# Try to import optional libraries; if missing, we fall back to NaN metrics
try:
    from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
    HAVE_NLTK = True
except Exception:
    sentence_bleu = None
    SmoothingFunction = None
    HAVE_NLTK = False

try:
    from rouge_score import rouge_scorer
    HAVE_ROUGE = True
except Exception:
    rouge_scorer = None
    HAVE_ROUGE = False

try:
    from bert_score import score as bert_score
    HAVE_BERT = True
except Exception:
    bert_score = None
    HAVE_BERT = False

if HAVE_NLTK:
    _SMOOTH = SmoothingFunction().method1
else:
    _SMOOTH = None

if HAVE_ROUGE:
    _ROUGE_SCORER = rouge_scorer.RougeScorer(
        ["rouge1", "rouge2", "rougeL"], use_stemmer=True
    )
else:
    _ROUGE_SCORER = None

def _normalize_text_for_metrics(s: str) -> str:
    import re
    s = (s or "").strip().lower()
    # remove simple markdown markers
    s = re.sub(r"\*\*|\*", "", s)
    # drop inline citations like (Doc.pdf, p.X)
    s = re.sub(r"\([^)]*\)", " ", s)
    s = re.sub(r"\s+", " ", s)
    return s.strip()

def compute_text_metrics(pred: str, ref: str) -> Dict[str, float]:
    """
    Compute lexical and semantic similarity metrics between prediction and reference:
      - BLEU
      - ROUGE-1/2/L (F-measure)
      - BERTScore Recall, F1

    If the required libraries (nltk, rouge-score, bert-score) are not installed,
    returns NaN for all metrics.
    """
    # If any of the libraries is missing, skip answer metrics
    if not (HAVE_NLTK and HAVE_ROUGE and HAVE_BERT):
        return {
            "bleu": np.nan,
            "rouge1": np.nan,
            "rouge2": np.nan,
            "rougeL": np.nan,
            "bert_recall": np.nan,
            "bert_f1": np.nan,
        }

    pred_n = _normalize_text_for_metrics(pred)
    ref_n  = _normalize_text_for_metrics(ref)

    if not pred_n or not ref_n:
        return {
            "bleu": np.nan,
            "rouge1": np.nan,
            "rouge2": np.nan,
            "rougeL": np.nan,
            "bert_recall": np.nan,
            "bert_f1": np.nan,
        }

    pred_tokens = pred_n.split()
    ref_tokens  = ref_n.split()

    # BLEU (sentence-level with smoothing)
    bleu = float(
        sentence_bleu([ref_tokens], pred_tokens, smoothing_function=_SMOOTH)
    )

    # ROUGE via rouge-score (F-measure)
    rs = _ROUGE_SCORER.score(ref_n, pred_n)
    rouge1 = float(rs["rouge1"].fmeasure)
    rouge2 = float(rs["rouge2"].fmeasure)
    rougeL = float(rs["rougeL"].fmeasure)

    # BERTScore (semantic similarity)
    P, R, F1 = bert_score([pred_n], [ref_n], lang="en", rescale_with_baseline=True)
    bert_recall = float(R.mean().item())
    bert_f1     = float(F1.mean().item())

    return {
        "bleu": bleu,
        "rouge1": rouge1,
        "rouge2": rouge2,
        "rougeL": rougeL,
        "bert_recall": bert_recall,
        "bert_f1": bert_f1,
    }

# ----------------------------- Orchestration ----------------------------- #

# === Dark blue and accent colors ===
COLOR_TITLE = "\033[94m"     # light blue for titles
COLOR_TEXT = "\033[34m"      # dark blue
COLOR_ACCENT = "\033[36m"    # cyan for metrics
COLOR_RESET = "\033[0m"

def _fmt(x: Any) -> str:
    try:
        return f"{float(x):.3f}"
    except Exception:
        return "-"

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--gold_csv", required=True, type=str)
    ap.add_argument("--logs_jsonl", required=True, type=str)
    ap.add_argument("--k", type=int, default=8)
    ap.add_argument("--out_dir", type=str, default="rag_artifacts")
    args = ap.parse_args()

    out_dir = Path(args.out_dir)
    out_dir.mkdir(parents=True, exist_ok=True)

    gold_path = Path(args.gold_csv)
    logs_path = Path(args.logs_jsonl)

    if not gold_path.exists():
        print(
            f"{COLOR_TEXT}❌ gold.csv not found at {gold_path}{COLOR_RESET}",
            file=sys.stderr,
        )
        sys.exit(0)
    if not logs_path.exists() or logs_path.stat().st_size == 0:
        print(
            f"{COLOR_TEXT}❌ logs JSONL not found or empty at {logs_path}{COLOR_RESET}",
            file=sys.stderr,
        )
        sys.exit(0)

    # Read gold (retrieval + QA answers)
    try:
        gold, gold_answers = read_gold(gold_path)
    except Exception as e:
        print(
            f"{COLOR_TEXT}❌ Failed to read gold: {e}{COLOR_RESET}",
            file=sys.stderr,
        )
        sys.exit(0)

    # Read logs (with robust aggregation)
    try:
        logs = read_logs(logs_path)
    except Exception as e:
        print(
            f"{COLOR_TEXT}❌ Failed to read logs: {e}{COLOR_RESET}",
            file=sys.stderr,
        )
        sys.exit(0)

    if gold.empty:
        print(
            f"{COLOR_TEXT}❌ Gold file contains no usable rows.{COLOR_RESET}",
            file=sys.stderr,
        )
        sys.exit(0)
    if logs.empty:
        print(
            f"{COLOR_TEXT}❌ Logs file contains no usable entries.{COLOR_RESET}",
            file=sys.stderr,
        )
        sys.exit(0)

    # Build gold dict: normalized_question -> list of (doc, page)
    gdict: Dict[str, List[Tuple[str, Optional[int]]]] = {}
    for _, r in gold.iterrows():
        q = str(r["question"]).strip()  # already normalized in read_gold
        d = r["doc"]
        p = r["page"] if "page" in r else np.nan
        gdict.setdefault(q, []).append((d, p))

    # Normalize log questions for join
    logs["q_norm"] = logs["question"].astype(str).str.casefold().str.strip()

    perq_rows = []
    not_in_logs, not_in_gold = [], []

    # For each gold question, compute metrics using logs
    for q_norm, pairs in gdict.items():
        row = logs[logs["q_norm"] == q_norm]
        gdocs = [d for (d, _) in pairs]
        gpages = [p for (_, p) in pairs]

        if row.empty:
            # No logs for this gold question → zero retrieval and no answer metrics
            not_in_logs.append(q_norm)
            base_metrics = {
                "hit@k_doc": 0,
                "precision@k_doc": 0.0,
                "recall@k_doc": 0.0,
                "ndcg@k_doc": 0.0,
                "hit@k_page": np.nan,
                "precision@k_page": np.nan,
                "recall@k_page": np.nan,
                "ndcg@k_page": np.nan,
                "n_gold_docs": int(len(set([d for d in gdocs if isinstance(d, str) and d]))),
                "n_gold_doc_pages": int(
                    len(
                        [
                            (d, p)
                            for (d, p) in zip(gdocs, gpages)
                            if isinstance(d, str) and d and pd.notna(p)
                        ]
                    )
                ),
                "n_pred": 0,
            }

            txt_metrics = {
                "bleu": np.nan,
                "rouge1": np.nan,
                "rouge2": np.nan,
                "rougeL": np.nan,
                "bert_recall": np.nan,
                "bert_f1": np.nan,
            }

            perq_rows.append(
                {
                    "question": q_norm,
                    "covered_in_logs": 0,
                    **base_metrics,
                    **txt_metrics,
                }
            )
            continue

        # Use aggregated hits from read_logs
        hits = row.iloc[0]["hits"] or []
        base_metrics = compute_metrics_for_question(gdocs, gpages, hits, args.k)

        # Answer text: predicted vs. gold
        pred_answer = str(row.iloc[0].get("answer", "")).strip()
        gold_answer = str(gold_answers.get(q_norm, "")).strip()

        if gold_answer and pred_answer:
            txt_metrics = compute_text_metrics(pred_answer, gold_answer)
        else:
            txt_metrics = {
                "bleu": np.nan,
                "rouge1": np.nan,
                "rouge2": np.nan,
                "rougeL": np.nan,
                "bert_recall": np.nan,
                "bert_f1": np.nan,
            }

        perq_rows.append(
            {
                "question": q_norm,
                "covered_in_logs": 1,
                **base_metrics,
                **txt_metrics,
            }
        )

    # Any log questions not in gold
    gold_qs = set(gdict.keys())
    for qn in logs["q_norm"].tolist():
        if qn not in gold_qs:
            not_in_gold.append(qn)

    perq = pd.DataFrame(perq_rows)
    covered = perq[perq["covered_in_logs"] == 1].copy()

    agg = {
        "questions_total_gold": int(len(gdict)),
        "questions_covered_in_logs": int(covered.shape[0]),
        "questions_missing_in_logs": int(len(not_in_logs)),
        "questions_in_logs_not_in_gold": int(len(set(not_in_gold))),
        "k": int(args.k),
        "mean_hit@k_doc": float(covered["hit@k_doc"].mean()) if not covered.empty else 0.0,
        "mean_precision@k_doc": float(covered["precision@k_doc"].mean()) if not covered.empty else 0.0,
        "mean_recall@k_doc": float(covered["recall@k_doc"].mean()) if not covered.empty else 0.0,
        "mean_ndcg@k_doc": float(covered["ndcg@k_doc"].mean()) if not covered.empty else 0.0,
        "mean_hit@k_page": float(covered["hit@k_page"].dropna().mean())
        if covered["hit@k_page"].notna().any()
        else None,
        "mean_precision@k_page": float(covered["precision@k_page"].dropna().mean())
        if covered["precision@k_page"].notna().any()
        else None,
        "mean_recall@k_page": float(covered["recall@k_page"].dropna().mean())
        if covered["recall@k_page"].notna().any()
        else None,
        "mean_ndcg@k_page": float(covered["ndcg@k_page"].dropna().mean())
        if covered["ndcg@k_page"].notna().any()
        else None,
        "avg_gold_docs_per_q": float(perq["n_gold_docs"].mean()) if not perq.empty else 0.0,
        "avg_preds_per_q": float(perq["n_pred"].mean()) if not perq.empty else 0.0,
        "examples_missing_in_logs": list(not_in_logs[:10]),
        "examples_in_logs_not_in_gold": list(dict.fromkeys(not_in_gold))[:10],
    }

    # Aggregate answer-quality metrics (lexical + semantic)
    if "bleu" in covered.columns:
        agg["mean_bleu"] = float(covered["bleu"].mean(skipna=True))
        agg["mean_rouge1"] = float(covered["rouge1"].mean(skipna=True))
        agg["mean_rouge2"] = float(covered["rouge2"].mean(skipna=True))
        agg["mean_rougeL"] = float(covered["rougeL"].mean(skipna=True))
        agg["mean_bert_recall"] = float(covered["bert_recall"].mean(skipna=True))
        agg["mean_bert_f1"] = float(covered["bert_f1"].mean(skipna=True))

    perq_path = out_dir / "metrics_per_question.csv"
    agg_path = out_dir / "metrics_aggregate.json"

    perq.to_csv(perq_path, index=False)
    with open(agg_path, "w", encoding="utf-8") as f:
        json.dump(agg, f, ensure_ascii=False, indent=2)

    # === Console summary with color ===
    print(f"{COLOR_TITLE}RAG Evaluation Summary{COLOR_RESET}")
    print(f"{COLOR_TITLE}----------------------{COLOR_RESET}")
    print(f"{COLOR_TEXT}Gold questions: {COLOR_ACCENT}{agg['questions_total_gold']}{COLOR_RESET}")
    print(f"{COLOR_TEXT}Covered in logs: {COLOR_ACCENT}{agg['questions_covered_in_logs']}{COLOR_RESET}")
    print(f"{COLOR_TEXT}Missing in logs: {COLOR_ACCENT}{agg['questions_missing_in_logs']}{COLOR_RESET}")
    print(
        f"{COLOR_TEXT}In logs but not in gold: "
        f"{COLOR_ACCENT}{agg['questions_in_logs_not_in_gold']}{COLOR_RESET}"
    )
    print(f"{COLOR_TEXT}k = {COLOR_ACCENT}{agg['k']}{COLOR_RESET}\n")

    print(
        f"{COLOR_TEXT}Doc-level:{COLOR_RESET}  "
        f"{COLOR_ACCENT}Hit@k={_fmt(agg['mean_hit@k_doc'])}  "
        f"Precision@k={_fmt(agg['mean_precision@k_doc'])}  "
        f"Recall@k={_fmt(agg['mean_recall@k_doc'])}  "
        f"nDCG@k={_fmt(agg['mean_ndcg@k_doc'])}{COLOR_RESET}"
    )

    if agg.get("mean_hit@k_page") is not None:
        print(
            f"{COLOR_TEXT}Page-level:{COLOR_RESET} "
            f"{COLOR_ACCENT}Hit@k={_fmt(agg['mean_hit@k_page'])}  "
            f"Precision@k={_fmt(agg['mean_precision@k_page'])}  "
            f"Recall={_fmt(agg['mean_recall@k_page'])}  "
            f"nDCG@k={_fmt(agg['mean_ndcg@k_page'])}{COLOR_RESET}"
        )
    else:
        print(f"{COLOR_TEXT}Page-level: (no page labels in gold){COLOR_RESET}")

    # Lexical metrics summary
    if "mean_bleu" in agg:
        print(
            f"{COLOR_TEXT}Lexical (answer quality):{COLOR_RESET} "
            f"{COLOR_ACCENT}BLEU={_fmt(agg.get('mean_bleu'))}  "
            f"ROUGE-1={_fmt(agg.get('mean_rouge1'))}  "
            f"ROUGE-2={_fmt(agg.get('mean_rouge2'))}  "
            f"ROUGE-L={_fmt(agg.get('mean_rougeL'))}{COLOR_RESET}"
        )

    # Semantic metrics summary
    if "mean_bert_f1" in agg:
        print(
            f"{COLOR_TEXT}Semantic (BERTScore):{COLOR_RESET} "
            f"{COLOR_ACCENT}Recall={_fmt(agg.get('mean_bert_recall'))}  "
            f"F1={_fmt(agg.get('mean_bert_f1'))}{COLOR_RESET}"
        )

    print()
    print(
        f"{COLOR_TEXT}Wrote per-question CSV → "
        f"{COLOR_ACCENT}{perq_path}{COLOR_RESET}"
    )
    print(
        f"{COLOR_TEXT}Wrote aggregate JSON   → "
        f"{COLOR_ACCENT}{agg_path}{COLOR_RESET}"
    )

if __name__ == "__main__":
    main()