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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 pathlib
import tempfile
import pandas as pd
# External imports
import pypdf
def keep_pages(pdf_filepath: str, selected_pages: list[int]) -> str:
"""
Function to extract the selected pages from a source pdf
It returns the path to the PDF created by keeping only the
selected pages
"""
reader = pypdf.PdfReader(pdf_filepath)
writer = pypdf.PdfWriter()
for pi in selected_pages:
writer.add_page(reader.pages[pi])
# We add the original pdf name without extension
# in the prefix of the temporary file
# in order to keep a trace of this name so that the next modules, from table
# extraction can make use of this name.
# For example, FromCSV makes use of this name to determine the name of the
# CSV to load
pdf_stem = pathlib.Path(pdf_filepath).stem
filename = tempfile.NamedTemporaryFile(
prefix=f"{pdf_stem}____",
suffix=".pdf",
delete=False,
).name
writer.write(filename)
return filename
def gather_tables(
assets: dict,
) -> dict:
tables_by_name = {}
for asset in assets["table_extractors"]:
tables = asset["tables"]
for i in range(len(tables)):
for label, _content in tables[i].items():
if isinstance(tables[i][label], pd.DataFrame):
tables[i].columns = [
"No Extract " + str(i + 1) for i in range(tables[i].shape[1])
]
break
tables_by_name[asset["type"] + "_" + str(i)] = tables[i]
return tables_by_name
def check_if_many(assets: dict) -> bool:
for asset in assets["table_extractors"]:
tables = asset["tables"]
if len(tables) > 1:
return True
return False
def filled_table_extractors(assets: dict) -> list:
tables_by_name = []
for asset in assets["table_extractors"]:
tables = asset["tables"]
if len(tables) > 0:
tables_by_name.append(asset["type"])
return tables_by_name
def gather_tables_with_merge(
assets: dict,
new_tables: pd.DataFrame,
table_extractor: str,
) -> dict:
tables_by_name = {}
for asset in assets["table_extractors"]:
if asset["type"] == table_extractor:
tables_by_name[table_extractor] = new_tables
else:
tables = asset["tables"]
if len(tables) == 1:
tables_by_name[asset["type"]] = tables[0]
elif len(tables) > 1:
for i in range(len(tables)):
tables_by_name[asset["type"] + "_" + str(i)] = tables[i]
return tables_by_name
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