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https://github.com/tiennm99/DocsGPT.git
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Alex
ad201f8318
A deflate-compressed TIFF under 1 MB can declare 144 million pixels and take 1.2 GB to convert to PNG, and Pillow only warns below 179 million. Read the dimensions from the header and refuse images over 40 million pixels before any pixel data is decoded. Pillow's DecompressionBombError is now raised as DocumentParseError, so the upload fails once instead of being retried.
83 lines
3.2 KiB
Python
83 lines
3.2 KiB
Python
"""Image parser.
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Contains the parser for image files (.png, .jpg, .jpeg, .tiff, .tif, .bmp,
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.webp) and the PNG re-encoding for the formats model providers reject.
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"""
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import io
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from pathlib import Path
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from typing import BinaryIO, Dict, Tuple, Union
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import requests
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from docsgpt.core.settings import settings
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from docsgpt.parser.file.base_parser import BaseParser, DocumentParseError
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# Image types the vision APIs refuse: OpenAI and Anthropic take png, jpeg,
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# webp and gif only. A chat attachment in one of these formats is re-encoded
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# to PNG before it is stored for the model.
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VISION_CONVERTIBLE_MIME_TYPES = frozenset({"image/tiff", "image/bmp", "image/x-ms-bmp"})
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# Largest image re-encoded to PNG. A deflate TIFF under 1 MB can declare
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# 144 million pixels and take 1.2 GB to convert, while Pillow only warns below
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# 179 million. Vision models downscale far below this cap (Anthropic refuses
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# more than 8000 px per side), so a larger image gains nothing.
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MAX_CONVERTIBLE_PIXELS = 40_000_000
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def convert_image_to_png(file_obj: BinaryIO) -> Tuple[bytes, int]:
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"""Re-encode the first frame of an image as PNG.
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Args:
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file_obj: Readable binary stream of the source image.
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Returns:
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Tuple[bytes, int]: The PNG bytes, and how many frames (pages) the
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source had. Only the first is kept.
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Raises:
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DocumentParseError: If the bytes are not an image Pillow can decode,
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or the image is larger than ``MAX_CONVERTIBLE_PIXELS``.
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"""
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from PIL import Image, UnidentifiedImageError
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try:
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with Image.open(file_obj) as image:
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# Opening reads only the header; refuse before any pixels decode.
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width, height = image.size
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if width * height > MAX_CONVERTIBLE_PIXELS:
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raise DocumentParseError(
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f"The image is too large to convert ({width}×{height} pixels; the limit is "
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f"{MAX_CONVERTIBLE_PIXELS // 1_000_000} million pixels). Resize it and upload it again."
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)
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frames = getattr(image, "n_frames", 1)
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image.seek(0)
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frame = image
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if frame.mode not in ("RGB", "RGBA", "L", "LA"):
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frame = frame.convert("RGBA" if "A" in frame.getbands() else "RGB")
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out = io.BytesIO()
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frame.save(out, format="PNG")
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except (UnidentifiedImageError, OSError, Image.DecompressionBombError) as exc:
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raise DocumentParseError(f"Could not read the image: {exc}") from exc
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return out.getvalue(), frames
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class ImageParser(BaseParser):
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"""Image parser."""
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def _init_parser(self) -> Dict:
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"""Init parser."""
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return {}
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def parse_file(self, file: Path, errors: str = "ignore") -> Union[str, list[str]]:
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if settings.PARSE_IMAGE_REMOTE:
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doc2md_service = "https://llm.arc53.com/doc2md"
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# alternatively you can use local vision capable LLM
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with open(file, "rb") as file_loaded:
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files = {'file': file_loaded}
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response = requests.post(doc2md_service, files=files, timeout=100)
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data = response.json()["markdown"]
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else:
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data = ""
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return data
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