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f5ef9fb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | """Docling document parsing with figure extraction and markdown export."""
from __future__ import annotations
import os
import uuid
from collections.abc import Callable
from pathlib import Path
from typing import Any
from storage import get_temp_dir
def _top_class(figure: Any) -> str | None:
"""Return the highest-confidence Docling classification label for a picture item.
Tries ``figure.meta.classification.predictions`` first (docling-core ≥2.5),
then falls back to the deprecated ``figure.annotations`` list.
Returns ``None`` if classification is unavailable or fails.
"""
try:
meta = getattr(figure, "meta", None)
if meta is not None:
classification = getattr(meta, "classification", None)
if classification is not None:
preds = getattr(classification, "predictions", None)
if preds:
best = max(preds, key=lambda p: p.confidence)
return best.class_name
except Exception: # noqa: BLE001
pass
try:
for ann in getattr(figure, "annotations", []):
preds = getattr(ann, "predicted_classes", None)
if preds:
best = max(preds, key=lambda p: p.confidence)
return best.class_name
except Exception: # noqa: BLE001
pass
return None
_EXT_TO_INPUT_FORMAT = {
".pdf": "PDF",
".docx": "DOCX",
".xlsx": "XLSX",
".pptx": "PPTX",
}
_converter: Any = None
def _is_zerogpu_space() -> bool:
"""Detect HuggingFace ZeroGPU Spaces where CUDA init must happen inside @spaces.GPU."""
return bool(os.environ.get("SPACE_ID"))
def _pick_accelerator_device() -> Any:
"""Return the best AcceleratorDevice for Docling, or None if unsupported."""
try:
from docling.datamodel.pipeline_options import AcceleratorDevice
except ImportError:
return None
if _is_zerogpu_space():
return AcceleratorDevice.CPU
try:
import torch
if torch.cuda.is_available():
return AcceleratorDevice.CUDA
if torch.backends.mps.is_available():
return AcceleratorDevice.MPS
except ImportError:
pass
return AcceleratorDevice.CPU
def get_converter() -> Any:
"""Return a shared DocumentConverter instance, creating it on first call."""
global _converter # noqa: PLW0603
if _converter is not None:
return _converter
from docling.datamodel.base_models import InputFormat
from docling.document_converter import DocumentConverter, PdfFormatOption
pdf_format_option = PdfFormatOption()
pdf_format_option.pipeline_options.generate_picture_images = True
pdf_format_option.pipeline_options.images_scale = 2.0
pdf_format_option.pipeline_options.do_picture_classification = True
device = _pick_accelerator_device()
if device is not None:
try:
from docling.datamodel.pipeline_options import AcceleratorOptions
pdf_format_option.pipeline_options.accelerator_options = AcceleratorOptions(
device=device,
)
except Exception: # noqa: BLE001
pass
# On ZeroGPU, hide CUDA from sub-libraries (RapidOCR, etc.) that do their
# own torch.cuda detection and trigger real CUDA init.
hide_cuda = _is_zerogpu_space()
old_val = os.environ.get("CUDA_VISIBLE_DEVICES")
if hide_cuda:
os.environ["CUDA_VISIBLE_DEVICES"] = ""
try:
_converter = DocumentConverter(format_options={InputFormat.PDF: pdf_format_option})
finally:
if hide_cuda:
if old_val is None:
os.environ.pop("CUDA_VISIBLE_DEVICES", None)
else:
os.environ["CUDA_VISIBLE_DEVICES"] = old_val
return _converter
def parse_document(
file_bytes: bytes,
file_ext: str = ".pdf",
on_progress: Callable[[str], None] | None = None,
) -> dict[str, Any]:
"""Parse a document with Docling and extract markdown, text, and figure regions.
Args:
file_bytes: Document file content as bytes.
file_ext: File extension (e.g. ``".pdf"``, ``".docx"``, ``".xlsx"``, ``".pptx"``).
on_progress: Optional callback ``(phase_message) -> None`` for progress reporting.
Returns:
Dictionary with keys:
- ``html``: HTML-wrapped markdown representation of the document.
- ``text``: Full extracted plain text.
- ``figures``: List of figure dicts with ``bbox``, ``page``, ``caption``, ``image``,
and ``figure_type`` (top Docling classification label or ``None``).
"""
def _report(msg: str) -> None:
if on_progress:
on_progress(msg)
try:
from docling.datamodel.base_models import InputFormat
ext = file_ext.lower()
tmp_path = str(get_temp_dir() / f"{uuid.uuid4().hex}{ext}")
Path(tmp_path).write_bytes(file_bytes)
try:
format_name = _EXT_TO_INPUT_FORMAT.get(ext, "PDF")
input_format = getattr(InputFormat, format_name)
_report("Initializing document converter...")
converter = get_converter()
_report("Converting document (this may take a moment)...")
result = converter.convert(tmp_path)
doc = result.document
_report("Exporting document content...")
markdown_text = doc.export_to_markdown()
html = markdown_text
text = doc.export_to_text()
_report("Processing figures...")
figures: list[dict[str, Any]] = []
try:
if hasattr(doc, "pictures"):
for figure in doc.pictures:
if figure.content_layer.value != "body":
continue
page_num = 0
bbox_list = None
if figure.prov:
page_num = figure.prov[0].page_no - 1 # Docling is 1-based
bbox = figure.prov[0].bbox
bbox_list = [bbox.l, bbox.t, bbox.width, bbox.height]
caption = ""
if figure.captions:
for cap_ref in figure.captions:
try:
if hasattr(cap_ref, "cref") and cap_ref.cref.startswith("#/texts/"):
idx = int(cap_ref.cref.split("/")[-1])
if idx < len(doc.texts):
caption = doc.texts[idx].text
break
except Exception: # noqa: BLE001
pass
if figure.image:
try:
pil_image = figure.image.pil_image
figures.append({
"bbox": bbox_list,
"page": page_num,
"caption": caption,
"image": pil_image,
"figure_type": _top_class(figure),
})
except Exception: # noqa: BLE001
pass
except Exception: # noqa: BLE001
figures = []
return {"html": html, "text": text, "figures": figures}
finally:
if os.path.exists(tmp_path):
os.unlink(tmp_path)
except ImportError as e:
print(f"Docling import error: {e}, using placeholder")
return {
"html": "<h1>Sample Document</h1><p>Docling not available - using placeholder.</p>",
"text": "Sample text from PDF.\n\nDocling not available - using placeholder.",
"figures": [],
}
except Exception as e: # noqa: BLE001
import traceback
print(f"Docling parse error: {e}")
traceback.print_exc()
return {
"html": f"<h1>Error</h1><pre>{e!s}</pre>",
"text": f"Error: {e!s}",
"figures": [],
}
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