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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 | |