eval/score_edit.py

"""Score the edit track: Semantic Consistency (SC) and Presentation Quality (PQ).

Predictions are read from <pred_dir>/edit/<id>.png.

    python eval/score_edit.py --pred_dir outputs/my_model --workers 16

SC = min(editing_success, overediting), judged from (source, edited) slides.
PQ = unweighted mean of clarity, layout, style coherence, professionalism,
     judged from the edited slide alone.
Edit score = sqrt(SC * PQ). One call per prompt, temperature 0.7, 512 tokens.
"""

import argparse
import json
import math
import os
import re
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path

from common import bench_path, chat, get_client, image_part, load_jsonl, load_prompt, mean, write_jsonl


def _numbers(values, n):
    nums = []
    for v in values if isinstance(values, list) else []:
        if isinstance(v, (int, float)):
            nums.append(float(v))
        elif isinstance(v, str) and re.search(r"-?\d+(?:\.\d+)?", v):
            nums.append(float(re.search(r"-?\d+(?:\.\d+)?", v).group()))
    return (nums + [5.0] * n)[:n]  # missing values fall back to the scale midpoint


def score_case(client, case, img):
    sc_prompt = load_prompt("edit_evaluation_source_control.txt").replace("<instruction>", case["instruction"])
    sc_res = chat(client, "", [image_part(bench_path(case["source_image"])), image_part(img), {"type": "text", "text": sc_prompt}], 0.7, 512)
    pq_res = chat(client, "", [image_part(img), {"type": "text", "text": load_prompt("edit_evaluation_quality.txt")}], 0.7, 512)
    success, preserve = _numbers(sc_res.get("score"), 2)
    pq = mean(_numbers(pq_res.get("score"), 4))
    sc = min(success, preserve)
    return {"id": case["id"], "operation": case["operation"], "editing_success": success, "edit_consistency": preserve,
            "sc": sc, "pq": pq, "edit_score": math.sqrt(max(sc, 0) * max(pq, 0))}


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--pred_dir", required=True)
    ap.add_argument("--out", default=None, help="Defaults to <pred_dir>/scores/edit.jsonl.")
    ap.add_argument("--workers", type=int, default=8)
    args = ap.parse_args()

    client = get_client()
    cases = load_jsonl(bench_path("data/edit.jsonl"))

    def run(case):
        img = Path(args.pred_dir) / "edit" / f"{case['id']}.png"
        if not img.exists():
            return {"id": case["id"], "error": "missing prediction"}
        try:
            return score_case(client, case, str(img))
        except Exception as e:
            return {"id": case["id"], "error": repr(e)}

    with ThreadPoolExecutor(args.workers) as ex:
        rows = list(ex.map(run, cases))
    out = args.out or os.path.join(args.pred_dir, "scores", "edit.jsonl")
    write_jsonl(out, rows)
    ok = [r for r in rows if "sc" in r]
    sc, pq = mean(r["sc"] for r in ok), mean(r["pq"] for r in ok)
    print(json.dumps({"task": "edit", "sc": sc, "pq": pq, "edit": math.sqrt(sc * pq) if ok else None, "n_scored": len(ok)}, indent=1))


if __name__ == "__main__":
    main()