File size: 5,669 Bytes
b837da3 6c2a241 b837da3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
import os
import time
import json
import argparse
from pathlib import Path
import unstructured_client
from unstructured_client.models import operations, shared
from unstructured.staging.base import elements_from_dicts
from utils import read_file_paths, validate_json_save_path, load_json_file
CATEGORY_MAP = {
"NarrativeText": "paragraph",
"ListItem": "paragraph",
"Title": "heading1",
"Address": "paragraph",
"Header": "header",
"Footer": "footer",
"UncategorizedText": "paragraph",
"Formula": "equation",
"FigureCaption": "caption",
"Table": "table",
"PageBreak": "paragraph",
"Image": "figure",
"PageNumber": "paragraph",
"CodeSnippet": "paragraph"
}
class UnstructuredInference:
def __init__(
self,
save_path,
input_formats=[".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".heic"]
):
"""Initialize the UnstructuredInference class
Args:
save_path (str): the json path to save the results
input_formats (list, optional): the supported file formats.
"""
self.formats = input_formats
self.api_key = os.getenv("UNSTRUCTURED_API_KEY") or ""
self.url = os.getenv("UNSTRUCTURED_URL") or ""
self.languages = ["eng", "kor"]
self.get_coordinates = True
self.infer_table_structure = True
# create save basepath
validate_json_save_path(save_path)
self.save_path = save_path
self.processed_data = load_json_file(save_path)
self.client = unstructured_client.UnstructuredClient(
api_key_auth=self.api_key,
server_url=self.url,
)
def post_process(self, data):
processed_dict = {}
for input_key in data.keys():
output_data = data[input_key]
processed_dict[input_key] = {
"elements": []
}
id_counter = 0
for elem in output_data:
transcription = elem
category = CATEGORY_MAP.get(elem.category, "paragraph")
if elem.metadata.coordinates is None:
continue
xy_coord = [{"x": x, "y": y} for x, y in elem.metadata.coordinates.points]
if category == "table":
transcription = elem.metadata.text_as_html
data_dict = {
"coordinates": xy_coord,
"category": category,
"id": id_counter,
"content": {
"text": str(transcription) if category != "table" else "",
"html": transcription if category == "table" else "",
"markdown": ""
}
}
processed_dict[input_key]["elements"].append(data_dict)
id_counter += 1
for key in self.processed_data:
processed_dict[key] = self.processed_data[key]
return processed_dict
def infer(self, file_path):
"""Infer the layout of the documents in the given file path
Args:
file_path (str): the path to the file or directory containing the documents to process
"""
paths = read_file_paths(file_path, supported_formats=self.formats)
error_files = []
result_dict = {}
for filepath in paths:
print("({}/{}) Processing {}".format(paths.index(filepath) + 1, len(paths), filepath))
filename = filepath.name
if filename in self.processed_data.keys():
print(f"'{filename}' is already in the loaded dictionary. Skipping this sample")
continue
with open(filepath, "rb") as f:
data = f.read()
req = operations.PartitionRequest(
partition_parameters=shared.PartitionParameters(
files=shared.Files(
content=data,
file_name=str(filepath),
),
# --- Other partition parameters ---
strategy=shared.Strategy.HI_RES,
pdf_infer_table_structure=self.infer_table_structure,
coordinates=self.get_coordinates,
languages=self.languages,
),
)
try:
res = self.client.general.partition(request=req)
elements = elements_from_dicts(res.elements)
except Exception as e:
print(e)
print("Error processing document..")
error_files.append(filepath)
continue
result_dict[filename] = elements
result_dict = self.post_process(result_dict)
with open(self.save_path, "w") as f:
json.dump(result_dict, f)
if __name__ == "__main__":
args = argparse.ArgumentParser()
args.add_argument(
"--data_path",
type=str, default="", required=True,
help="Path containing the documents to process"
)
args.add_argument(
"--save_path",
type=str, default="", required=True,
help="Path to save the results"
)
args.add_argument(
"--input_formats",
type=list, default=[
".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".heic"
],
help="Supported input file formats"
)
args = args.parse_args()
unstructured_inference = UnstructuredInference(
args.save_path,
input_formats=args.input_formats
)
unstructured_inference.infer(args.data_path)
|