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import os
import time
import json
import argparse
from pathlib import Path
import unstructured_client
from unstructured_client.models import operations, shared
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 ""
if not self.api_key or not self.url:
raise ValueError("Please set the environment variables for Unstructured")
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["text"]
category = CATEGORY_MAP.get(elem["type"], "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 = 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)
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