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import os
import json
import google
import argparse
from glob import glob
from typing import Optional, Sequence
from google.api_core.client_options import ClientOptions
from google.cloud import documentai
from utils import read_file_paths, validate_json_save_path, load_json_file
CATEGORY_MAP = {
"paragraph": "paragraph",
"footer": "footer",
"header": "header",
"heading-1": "heading1",
"heading-2": "heading1",
"heading-3": "heading1",
"table": "table",
"title": "heading1"
}
class GoogleInference:
def __init__(
self,
save_path,
input_formats=[".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".heic"]
):
"""Initialize the GoogleInference class
Args:
save_path (str): the json path to save the results
input_formats (list, optional): the supported file formats.
"""
self.project_id = os.getenv("GOOGLE_PROJECT_ID") or ""
self.processor_id = os.getenv("GOOGLE_PROCESSOR_ID") or ""
self.location = os.getenv("GOOGLE_LOCATION") or ""
self.endpoint = os.getenv("GOOGLE_ENDPOINT") or ""
self.processor_version = "rc"
validate_json_save_path(save_path)
self.save_path = save_path
self.processed_data = load_json_file(save_path)
self.formats = input_formats
@staticmethod
def generate_html_table(table_data):
html = "<table border='1'>\n"
# Process body rows
for row in table_data["bodyRows"]:
html += " <tr>\n"
for cell in row["cells"]:
text = cell["blocks"][0]["textBlock"]["text"] if cell["blocks"] else ""
row_span = f" rowspan='{cell['rowSpan']}'" if cell["rowSpan"] > 1 else ""
col_span = f" colspan='{cell['colSpan']}'" if cell["colSpan"] > 1 else ""
html += f" <td{row_span}{col_span}>{text}</td>\n"
html += " </tr>\n"
html += "</table>"
return html
@staticmethod
def iterate_blocks(data):
block_sequence = []
def recurse_blocks(blocks):
for block in blocks:
block_id = block.get("blockId", "")
block_type = block.get("textBlock", {}).get("type", "")
block_text = block.get("textBlock", {}).get("text", "")
if block_type:
# Append block information as a tuple to the sequence list
block_sequence.append((block_id, block_type, block_text))
block_id = block.get("blockId", "")
block_table = block.get("tableBlock", {})
if block_table:
block_table_html = GoogleInference.generate_html_table(block_table)
block_sequence.append((block_id, "table", block_table_html))
# If the block contains sub-blocks, recurse through them
if block.get("textBlock", {}).get("blocks", []):
recurse_blocks(block["textBlock"]["blocks"])
if "documentLayout" in data:
recurse_blocks(data["documentLayout"].get("blocks", []))
return block_sequence
def post_process(self, data):
processed_dict = {}
for input_key in data.keys():
output_data = data[input_key]
processed_dict[input_key] = {
"elements": []
}
blocks = self.iterate_blocks(output_data)
id_counter = 0
for _, category, transcription in blocks:
category = CATEGORY_MAP.get(category, "paragraph")
data_dict = {
"coordinates": [[0, 0], [0, 0], [0, 0], [0, 0]],
"category": category,
"id": id_counter,
"content": {
"text": 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 process_document_layout_sample(self, file_path, mime_type, chunk_size=1000) -> None:
process_options = documentai.ProcessOptions(
layout_config=documentai.ProcessOptions.LayoutConfig(
chunking_config=documentai.ProcessOptions.LayoutConfig.ChunkingConfig(
chunk_size=chunk_size,
include_ancestor_headings=True,
)
)
)
document = self.process_document(
file_path,
mime_type,
process_options=process_options,
)
document_dict = json.loads(google.cloud.documentai_v1.Document.to_json(document))
return document_dict
def process_document(
self, file_path,
mime_type: str,
process_options: Optional[documentai.ProcessOptions] = None,
) -> documentai.Document:
client = documentai.DocumentProcessorServiceClient(
client_options=ClientOptions(
api_endpoint=f"{self.endpoint}"
)
)
with open(file_path, "rb") as image:
image_content = image.read()
name = client.processor_version_path(
self.project_id,
self.location,
self.processor_id,
self.processor_version
)
request = documentai.ProcessRequest(
name=name,
raw_document=documentai.RawDocument(
content=image_content, mime_type=mime_type
),
process_options=process_options,
)
result = client.process_document(request=request)
return result.document
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 idx, filepath in enumerate(paths):
print("({}/{}) {}".format(idx+1, len(paths), filepath))
if filepath.suffix == ".pdf":
mime_type = "application/pdf"
elif filepath.suffix == ".jpg" or filepath.suffix == ".jpeg":
mime_type = "image/jpeg"
elif filepath.suffix == ".png":
mime_type = "image/png"
else:
raise NotImplementedError
filename = filepath.name
if filename in self.processed_data.keys():
print(f"'{filename}' is already in the loaded dictionary. Skipping this sample")
continue
try:
document_dict = self.process_document_layout_sample(filepath, mime_type)
except Exception as e:
print(e)
print("Error processing document..")
error_files.append(filepath)
continue
result_dict[filename] = document_dict
result_dict = self.post_process(result_dict)
with open(self.save_path, "w") as f:
json.dump(result_dict, f)
for error_file in error_files:
print(f"Error processing file: {error_file}")
print("Finished processing all documents")
print("Results saved to: {}".format(self.save_path))
print("Number of errors: {}".format(len(error_files)))
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()
google_inference = GoogleInference(
args.save_path,
input_formats=args.input_formats
)
google_inference.infer(args.data_path)
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