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import os |
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import sys |
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import json |
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import requests |
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import argparse |
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from pathlib import Path |
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from utils import read_file_paths, validate_json_save_path, load_json_file |
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class UpstageInference: |
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def __init__( |
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self, |
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save_path, |
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input_formats=[".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".heic"] |
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output_formats=["text", "html", "markdown"], |
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model_name="document-parse-240910", |
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): |
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"""Initialize the UpstageInference class |
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Args: |
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save_path (str): the json path to save the results |
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input_formats (list, optional): the supported input file formats. |
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output_formats (list, optional): the supported output formats. |
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model_name (str, optional): the model name. Defaults to "document-parse-240910". |
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""" |
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self.endpoint = os.getenv("UPSTAGE_ENDPOINT", "") |
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self.api_key = os.getenv("UPSTAGE_API_KEY", "") |
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validate_json_save_path(save_path) |
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self.save_path = save_path |
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self.processed_data = load_json_file(save_path) |
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self.input_formats = input_formats |
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self.output_formats = output_formats |
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self.headers = { |
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"Authorization": f"Bearer {self.api_key}", |
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} |
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self.data = { |
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"ocr": "force", |
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"model": model_name, |
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"output_formats": f"{self.output_formats}" |
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} |
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def infer(self, file_path) -> None: |
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"""Infer the layout of the documents in the given file path |
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Args: |
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file_path (str): the path to the file or directory containing the documents to process |
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""" |
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paths = read_file_paths(file_path, self.input_formats) |
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error_files = [] |
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result_dict = {} |
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for idx, filepath in enumerate(paths): |
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print("({}/{}) {}".format(idx+1, len(paths), filepath)) |
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filename = Path(filepath).name |
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if filename in self.processed_data.keys(): |
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print(f"'{filename}' is already in the loaded dictionary. Skipping this sample") |
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continue |
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files = { |
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"document": open(filepath, "rb"), |
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} |
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try: |
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response = requests.post( |
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self.endpoint, |
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headers=self.headers, |
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files=files, |
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data=self.data |
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) |
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json_result = response.json() |
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result_dict[filename] = json_result |
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except Exception as e: |
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print(e) |
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print("Error processing document..") |
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error_files.append(filepath) |
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continue |
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for key in self.processed_data: |
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result_dict[key] = self.processed_data[key] |
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with open(self.save_path, "w", encoding="utf-8") as f: |
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json.dump(result_dict, f, ensure_ascii=False, indent=4) |
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for error_file in error_files: |
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print(f"Error processing file: {error_file}") |
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print("Finished processing all documents") |
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print("Results saved to: {}".format(self.save_path)) |
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print("Number of errors: {}".format(len(error_files))) |
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if __name__ == "__main__": |
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args = argparse.ArgumentParser() |
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args.add_argument( |
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"--data_path", |
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type=str, default="", required=True, |
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help="Path containing the documents to process" |
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) |
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args.add_argument( |
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"--save_path", |
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type=str, default="", required=True, |
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help="Path to save the results" |
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) |
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args.add_argument( |
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"--input_formats", |
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type=str, default=[ |
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".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".heic" |
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], |
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help="Supported input file formats" |
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) |
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args.add_argument( |
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"--output_formats", |
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type=str, default=["text", "html", "markdown"], |
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help="Output formats supported by the API" |
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) |
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args = args.parse_args() |
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upstage_inference = UpstageInference( |
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args.save_path, |
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input_formats=args.input_formats, |
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output_formats=args.output_formats |
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) |
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upstage_inference.infer(args.data_path) |
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