pdf-extractor/pdf_extractor.py

from typing import List, Union, Optional
import json
from indexify_extractor_sdk import Content, Extractor, Feature
from pydantic import BaseModel, Field
from .utils.tt_module import get_tables
import fitz
import tempfile

class PDFExtractorConfig(BaseModel):
    output_types: List[str] = Field(default_factory=lambda: ["text", "image", "table"])

class PDFExtractor(Extractor):
    name = "tensorlake/pdf-extractor"
    description = "PDF Extractor for Texts, Images & Tables"
    system_dependencies = ["poppler-utils"]
    input_mime_types = ["application/pdf"]

    def __init__(self):
        super(PDFExtractor, self).__init__()

    def extract(self, content: Content, params: PDFExtractorConfig) -> List[Union[Feature, Content]]:
        contents = []
        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as inputtmpfile:
            inputtmpfile.write(content.data)
            inputtmpfile.flush()
            doc = fitz.open(inputtmpfile.name)

            for i in range(len(doc)):
                page = doc[i]

                if "text" in params.output_types:
                    page_text = page.get_text()
                    feature = Feature.metadata(value={"type": "text", "page": i+1})
                    contents.append(Content.from_text(page_text, features=[feature]))

                if "image" in params.output_types:
                    image_list = page.get_images()
                    for img in image_list:
                        xref = img[0]
                        pix = fitz.Pixmap(doc, xref)
                        if not pix.colorspace.name in (fitz.csGRAY.name, fitz.csRGB.name):
                            pix = fitz.Pixmap(fitz.csRGB, pix)
                        feature = Feature.metadata({"type": "image", "page": i+1})
                        contents.append(Content(content_type="image/png", data=pix.tobytes(), features=[feature]))

            if "table" in params.output_types:
                tables = get_tables(content.data)
                for page, content in tables.items():
                    feature = Feature.metadata({"type": "table", "page": int(page)})
                    contents.append(Content(content_type="application/json", data=json.dumps(content), features=[feature]))

        return contents