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import os
import time
import json
import markdown
import requests
import argparse
from pathlib import Path
from bs4 import BeautifulSoup
from utils import read_file_paths, validate_json_save_path, load_json_file
CATEGORY_MAP = {
"text": "paragraph",
"heading": "heading1",
"table": "table"
}
class LlamaParseInference:
def __init__(
self,
save_path,
input_formats=[".pdf", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".heic"]
):
"""Initialize the LlamaParseInference 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("LLAMAPARSE_API_KEY") or ""
self.post_url = os.getenv("LLAMAPARSE_POST_URL") or ""
self.get_url = os.getenv("LLAMAPARSE_GET_URL") or ""
self.headers = {
"Accept": "application/json",
"Authorization": f"Bearer {self.api_key}",
}
validate_json_save_path(save_path)
self.save_path = save_path
self.processed_data = load_json_file(save_path)
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["pages"]:
for item in elem["items"]:
coord = [[0, 0], [0, 0], [0, 0], [0, 0]]
category = item["type"]
if category == "table":
transcription = markdown.markdown(
item["md"],
extensions=["markdown.extensions.tables"]
)
transcription = transcription.replace("\n", "")
else:
transcription = item["value"]
pts = item["bBox"]
if "x" in pts and "y" in pts and \
"w" in pts and "h" in pts:
coord = [
[pts["x"], pts["y"]],
[pts["x"] + pts["w"], pts["y"]],
[pts["x"] + pts["w"], pts["y"] + pts["h"]],
[pts["x"], pts["y"] + pts["h"]],
]
xy_coord = [{"x": x, "y": y} for x, y in coord]
category = CATEGORY_MAP.get(category, "paragraph")
data_dict = {
"coordinates": xy_coord,
"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 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
try:
with open(filepath, "rb") as file_data:
file_data = {
"file": ("dummy.pdf", file_data, "")
}
data = {
"invalidate_cache": True,
"premium_mode": True,
"disable_ocr": False
}
response = requests.post(
self.post_url, headers=self.headers, files=file_data, data=data
)
result_data = response.json()
status = result_data["status"]
id_ = result_data["id"]
while status == "PENDING":
get_url = f"{self.get_url}/{id_}"
response = requests.get(get_url, headers=self.headers)
response_json = response.json()
status = response_json["status"]
if status == "SUCCESS":
get_url = f"{self.get_url}/{id_}/result/json"
response = requests.get(get_url, headers=self.headers)
break
time.sleep(1)
result_dict[filename] = response.json()
except Exception as e:
print(e)
print("Error processing document..")
error_files.append(filepath)
continue
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()
llamaparse_inference = LlamaParseInference(
args.save_path,
input_formats=args.input_formats
)
llamaparse_inference.infer(args.data_path)
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