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# MIT License
#
# Copyright (c) 2024 dataforgood
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# Standard imports
import logging
# Local imports
from . import pagefilter, table_extraction
from .utils.utils import keep_pages
class ReportProcessor:
def __init__(self, config: dict) -> None:
# Report filter
self.page_filter = pagefilter.from_config(config["pagefilter"])
self.table_extractors = []
self.table_cleaners = []
# Tables extraction
if "table_extraction" in config:
table_extractors = config["table_extraction"]
self.table_extractors = [
table_extraction.from_config(name) for name in table_extractors
]
# Table cleaning & reformatting
# We can do this step only if we had table extraction algorithms
# otherwise, the assets will not be available
#if "table_cleaning" in config:
# table_cleaners = config["table_cleaning"]
# self.table_cleaners = [
# table_cleaning.from_config(name) for name in table_cleaners
# ]
def process(self, pdf_filepath: str) -> dict:
logging.info(f"Processing {pdf_filepath}")
assets = {
"pagefilter": {},
"table_extractors": [],
"table_cleaners": [],
}
# Identifying the pages to extract
self.page_filter(pdf_filepath, assets)
# Now that we identified the pages to be extracted, we extract them
# Note, in a GUI, we could ask the user to the change the content of
# assets["pagefilter"]["selected_pages"] before selecting the pages
pdf_to_process = keep_pages(
pdf_filepath,
assets["pagefilter"]["selected_pages"],
)
# Process the selected pages to detect the tables and extract
# their contents
for table_extractor in self.table_extractors:
new_asset = table_extractor(pdf_to_process)
assets["table_extractors"].append(new_asset)
# Give the parsed content to the cleaner stage for getting organized data
#for table_cleaner in self.table_cleaners:
# for asset in assets["table_extractors"]:
# new_asset = table_cleaner(asset)
# assets["table_cleaners"].append(new_asset)
return assets
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