country_by_country/table_extraction/unstructured.py

# MIT License
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# Copyright (c) 2024 dataforgood
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# Standard imports
import logging
import uuid

# External imports
from io import StringIO

import pandas as pd
from unstructured.partition.pdf import partition_pdf


class Unstructured:
    def __init__(self, **kwargs: dict) -> dict:
        """
        Builds a pdf page parser, looking for tables using
        the unstructured library.
        The kwargs given to the constructor are directly propagated
        to the partition_pdf function.
        You are free to define any parameter partition_pdf recognizes
        """
        self.kwargs = kwargs
        self.type = "unstructured"

    def __call__(self, pdf_filepath: str) -> dict:
        logging.info("\nKicking off extraction stage...")
        logging.info(f"Extraction type: {self.type}, with params: {self.kwargs}")

        elements = partition_pdf(
            pdf_filepath,
            infer_table_structure=True,
            strategy="hi_res",
            **self.kwargs,
        )
        tables_list = [el for el in elements if el.category == "Table"]
        tables_list = [
            pd.read_html(StringIO(t.metadata.text_as_html))[0] for t in tables_list
        ]

        # Create asset
        new_asset = {
            "id": uuid.uuid4(),
            "type": "unstructured",
            "params": self.kwargs,
            "tables": tables_list,
        }

        return new_asset