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1118181 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | """Results viewer — data loading and helpers for OCR bench results."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any
import structlog
from datasets import load_dataset
if TYPE_CHECKING:
from PIL import Image
logger = structlog.get_logger()
def load_results(repo_id: str) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
"""Load leaderboard and comparisons from a Hub results dataset.
Tries the default config first (new repos), then falls back to the
named ``leaderboard`` config (old repos).
Returns:
(leaderboard_rows, comparison_rows)
"""
try:
leaderboard_ds = load_dataset(repo_id, split="train")
leaderboard_rows = [dict(row) for row in leaderboard_ds]
except Exception:
leaderboard_ds = load_dataset(repo_id, name="leaderboard", split="train")
leaderboard_rows = [dict(row) for row in leaderboard_ds]
try:
comparisons_ds = load_dataset(repo_id, name="comparisons", split="train")
except Exception:
logger.warning("no_comparisons_config", repo=repo_id)
return leaderboard_rows, []
comparison_rows = [dict(row) for row in comparisons_ds]
return leaderboard_rows, comparison_rows
def _load_source_metadata(repo_id: str) -> dict[str, Any]:
"""Load metadata config from results repo to find the source dataset."""
try:
meta_ds = load_dataset(repo_id, name="metadata", split="train")
if len(meta_ds) > 0:
return dict(meta_ds[0])
except Exception as exc:
logger.warning("could_not_load_metadata", repo=repo_id, error=str(exc))
return {}
class ImageLoader:
"""Lazy image loader — fetches images from source dataset by sample_idx."""
def __init__(self, source_dataset: str, from_prs: bool = False):
self._source = source_dataset
self._from_prs = from_prs
self._cache: dict[int, Any] = {}
self._image_col: str | None = None
self._pr_revision: str | None = None
self._available = True
self._init_done = False
def _init_source(self) -> None:
"""Lazy init: discover image column and PR revision on first call."""
if self._init_done:
return
self._init_done = True
try:
if self._from_prs:
from ocr_bench.dataset import discover_pr_configs
_, revisions = discover_pr_configs(self._source)
if revisions:
# Use the first PR revision to get images
first_config = next(iter(revisions))
self._pr_revision = revisions[first_config]
# Probe for image column by loading 1 row
kwargs: dict[str, Any] = {"path": self._source, "split": "train[:1]"}
if self._pr_revision:
# Load from the first PR config
first_config = next(iter(revisions))
kwargs["name"] = first_config
kwargs["revision"] = self._pr_revision
probe = load_dataset(**kwargs)
for col in probe.column_names:
if col == "image" or "image" in col.lower():
self._image_col = col
break
if not self._image_col:
logger.info("no_image_column_in_source", source=self._source)
self._available = False
except Exception as exc:
logger.warning("image_loader_init_failed", source=self._source, error=str(exc))
self._available = False
def get(self, sample_idx: int) -> Image.Image | None:
"""Fetch image for a sample index. Returns None on failure."""
self._init_source()
if not self._available or self._image_col is None:
return None
if sample_idx in self._cache:
return self._cache[sample_idx]
try:
kwargs: dict[str, Any] = {
"path": self._source,
"split": f"train[{sample_idx}:{sample_idx + 1}]",
}
if self._pr_revision:
from ocr_bench.dataset import discover_pr_configs
_, revisions = discover_pr_configs(self._source)
if revisions:
first_config = next(iter(revisions))
kwargs["name"] = first_config
kwargs["revision"] = revisions[first_config]
row = load_dataset(**kwargs)
img = row[0][self._image_col]
self._cache[sample_idx] = img
return img
except Exception as exc:
logger.debug("image_load_failed", sample_idx=sample_idx, error=str(exc))
return None
def _filter_comparisons(
comparisons: list[dict[str, Any]],
winner_filter: str,
model_filter: str,
) -> list[dict[str, Any]]:
"""Filter comparison rows by winner and model."""
filtered = comparisons
if winner_filter and winner_filter != "All":
filtered = [c for c in filtered if c.get("winner") == winner_filter]
if model_filter and model_filter != "All":
filtered = [
c
for c in filtered
if c.get("model_a") == model_filter or c.get("model_b") == model_filter
]
return filtered
def _winner_badge(winner: str) -> str:
"""Return a badge string for the winner."""
if winner == "A":
return "Winner: A"
elif winner == "B":
return "Winner: B"
else:
return "Tie"
def _model_label(model: str, col: str) -> str:
"""Format model name with optional column name. Avoids empty parens."""
if col:
return f"{model} ({col})"
return model
def _build_pair_summary(comparisons: list[dict[str, Any]]) -> str:
"""Build a win/loss summary string for each model pair."""
from collections import Counter
pair_counts: dict[tuple[str, str], Counter[str]] = {}
for c in comparisons:
ma = c.get("model_a", "")
mb = c.get("model_b", "")
winner = c.get("winner", "tie")
key = (ma, mb) if ma <= mb else (mb, ma)
if key not in pair_counts:
pair_counts[key] = Counter()
# Track from perspective of first model in sorted pair
if winner == "A":
actual_winner = ma
elif winner == "B":
actual_winner = mb
else:
actual_winner = "tie"
if actual_winner == key[0]:
pair_counts[key]["W"] += 1
elif actual_winner == key[1]:
pair_counts[key]["L"] += 1
else:
pair_counts[key]["T"] += 1
if not pair_counts:
return ""
parts = []
for (ma, mb), counts in sorted(pair_counts.items()):
short_a = ma.split("/")[-1] if "/" in ma else ma
short_b = mb.split("/")[-1] if "/" in mb else mb
wins, losses, ties = counts["W"], counts["L"], counts["T"]
parts.append(f"**{short_a}** vs **{short_b}**: {wins}W {losses}L {ties}T")
return " | ".join(parts)
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