File size: 4,254 Bytes
b837da3 6c2a241 b837da3 6c2a241 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 |
import os
import sys
import json
import requests
import argparse
from pathlib import Path
from utils import read_file_paths, validate_json_save_path, load_json_file
class UpstageInference:
def __init__(
self,
save_path,
input_formats=[".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".heic"],
output_formats=["text", "html", "markdown"],
model_name="document-parse-240910",
):
"""Initialize the UpstageInference class
Args:
save_path (str): the json path to save the results
input_formats (list, optional): the supported input file formats.
output_formats (list, optional): the supported output formats.
model_name (str, optional): the model name. Defaults to "document-parse-240910".
"""
self.endpoint = os.getenv("UPSTAGE_ENDPOINT", "")
self.api_key = os.getenv("UPSTAGE_API_KEY", "")
validate_json_save_path(save_path)
self.save_path = save_path
self.processed_data = load_json_file(save_path)
self.input_formats = input_formats
self.output_formats = output_formats
self.headers = {
"Authorization": f"Bearer {self.api_key}",
}
self.data = {
"ocr": "force",
"model": model_name,
"output_formats": f"{self.output_formats}"
}
def infer(self, file_path) -> None:
"""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, self.input_formats)
error_files = []
result_dict = {}
for idx, filepath in enumerate(paths):
print("({}/{}) {}".format(idx+1, len(paths), filepath))
filename = Path(filepath).name
if filename in self.processed_data.keys():
print(f"'{filename}' is already in the loaded dictionary. Skipping this sample")
continue
files = {
"document": open(filepath, "rb"),
}
try:
# The API does not support files exceeding 50MB
# or containing more than 100 pages.
response = requests.post(
self.endpoint,
headers=self.headers,
files=files,
data=self.data
)
json_result = response.json()
result_dict[filename] = json_result
except Exception as e:
print(e)
print("Error processing document..")
error_files.append(filepath)
continue
for key in self.processed_data:
result_dict[key] = self.processed_data[key]
with open(self.save_path, "w", encoding="utf-8") as f:
json.dump(result_dict, f, ensure_ascii=False, indent=4)
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.add_argument(
"--output_formats",
type=list, default=["text", "html", "markdown"],
help="Output formats supported by the API"
)
args = args.parse_args()
upstage_inference = UpstageInference(
args.save_path,
input_formats=args.input_formats,
output_formats=args.output_formats
)
upstage_inference.infer(args.data_path)
|