File size: 3,767 Bytes
df6182e 174cd37 df6182e 174cd37 df6182e d0b1031 174cd37 d0b1031 174cd37 df6182e 174cd37 df6182e d0b1031 174cd37 df6182e 174cd37 df6182e 174cd37 628fe8f df6182e 174cd37 df6182e 174cd37 df6182e 628fe8f 174cd37 df6182e 174cd37 628fe8f 174cd37 df6182e 174cd37 df6182e 174cd37 |
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 |
import argparse
import re
import uuid
from transformers import AutoModel, AutoTokenizer
from concrete.ml.common.serialization.loaders import load
from utils_demo import *
def load_models():
# Load the tokenizer and the embedding model
try:
tokenizer = AutoTokenizer.from_pretrained("obi/deid_roberta_i2b2")
embeddings_model = AutoModel.from_pretrained("obi/deid_roberta_i2b2")
except:
print("Error while loading Roberta")
# Load the CML trained model
with open(LOGREG_MODEL_PATH, "r") as model_file:
cml_ner_model = load(file=model_file)
return embeddings_model, tokenizer, cml_ner_model
def anonymize_with_cml(text, embeddings_model, tokenizer, cml_ner_model):
token_pattern = r"(\b[\w\.\/\-@]+\b|[\s,.!?;:'\"-]+)"
tokens = re.findall(token_pattern, text)
uuid_map = {}
processed_tokens = []
for token in tokens:
if token.strip() and re.match(r"\w+", token): # If the token is a word
x = get_batch_text_representation([token], embeddings_model, tokenizer)
prediction_proba = cml_ner_model.predict_proba(x, fhe="disable")
probability = prediction_proba[0][1]
prediction = probability >= 0.77
if prediction:
if token not in uuid_map:
uuid_map[token] = str(uuid.uuid4())[:8]
processed_tokens.append(uuid_map[token])
else:
processed_tokens.append(token)
else:
processed_tokens.append(token) # Preserve punctuation and spaces as is
anonymized_text = "".join(processed_tokens)
return anonymized_text, uuid_map
def anonymize_text(text, verbose=False, save=False):
# Load models
if verbose:
print("Loading models..")
embeddings_model, tokenizer, cml_ner_model = load_models()
if verbose:
print(f"\nText to process:--------------------\n{text}\n--------------------\n")
# Save the original text to its specified file
if save:
write_txt(ORIGINAL_FILE_PATH, text)
# Anonymize the text
anonymized_text, uuid_map = anonymize_with_cml(text, embeddings_model, tokenizer, cml_ner_model)
# Save the anonymized text to its specified file
if save:
mapping = {o: (i, a) for i, (o, a) in enumerate(zip(text.split("\n\n"), anonymized_text.split("\n\n")))}
write_txt(ANONYMIZED_FILE_PATH, anonymized_text)
write_pickle(MAPPING_SENTENCES_PATH, mapping)
if verbose:
print(f"\nAnonymized text:--------------------\n{anonymized_text}\n--------------------\n")
# Save the UUID mapping to a JSON file
if save:
write_json(MAPPING_UUID_PATH, uuid_map)
if verbose and save:
print(f"Original text saved to :{ORIGINAL_FILE_PATH}")
print(f"Anonymized text saved to :{ANONYMIZED_FILE_PATH}")
print(f"UUID mapping saved to :{MAPPING_UUID_PATH}")
print(f"Sentence mapping saved to :{MAPPING_SENTENCES_PATH}")
return anonymized_text
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Anonymize named entities in a text file and save the mapping to a JSON file."
)
parser.add_argument(
"--file_path",
type=str,
default="files/original_document.txt",
help="The path to the file to be processed.",
)
parser.add_argument(
"--verbose",
type=bool,
default=True,
help="This provides additional details about the program's execution.",
)
parser.add_argument("--save", type=bool, default=True, help="Save the files.")
args = parser.parse_args()
text = read_txt(args.file_path)
anonymize_text(text, verbose=args.verbose, save=args.save)
|