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030876e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | import argparse
import json
import os
import time
from pathlib import Path
from typing import List
import tqdm
from openai import OpenAI
# -----------------------------
# SUBCLAIM EXTRACTION PROMPT
# -----------------------------
def extraction_prompt(medical_text: str) -> str:
prompt = f"""
You are an expert medical annotator. Your task is to extract granular, factual subclaims from medical text.
A subclaim is the smallest standalone factual unit that can be independently verified.
Instructions:
1. Read the provided medical text.
2. Break it into clear, objective, atomic subclaims.
3. Each subclaim must come directly from the text. Do not infer or add information.
4. Keep subclaims short, non-overlapping, and de-duplicated.
5. Preserve numbers, units, and dates exactly as written.
6. If the text is empty, return an empty JSON list.
7. Return ONLY a valid JSON list of strings (no extra text).
Medical Text:
{medical_text}
Return your output in JSON list format:
[
"subclaim 1",
"subclaim 2"
]
"""
return prompt
def _load_openai_client() -> OpenAI:
api_file = "/home/mshahidul/api_new.json"
with open(api_file, "r") as f:
api_keys = json.load(f)
return OpenAI(api_key=api_keys["openai"])
def _parse_json_list(text: str) -> List[str]:
cleaned = text.replace("```json", "").replace("```", "").strip()
start_idx = cleaned.find("[")
end_idx = cleaned.rfind("]") + 1
if start_idx == -1 or end_idx <= start_idx:
raise ValueError("No JSON list found")
parsed = json.loads(cleaned[start_idx:end_idx])
if not isinstance(parsed, list):
raise ValueError("Parsed JSON is not a list")
return parsed
def infer_subclaims(
medical_text: str,
client: OpenAI,
model: str = "gpt-5-mini",
retries: int = 1,
) -> List[str]:
if not medical_text or medical_text.strip() == "":
return []
prompt = extraction_prompt(medical_text)
try:
response = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": "Return only a valid JSON list of strings."},
{"role": "user", "content": prompt},
],
)
output_text = response.choices[0].message.content.strip()
return _parse_json_list(output_text)
except Exception as e:
if retries > 0:
time.sleep(1.5)
return infer_subclaims(
medical_text,
client,
model=model,
retries=retries - 1,
)
return [f"ERROR: {str(e)}"]
# -----------------------------
# MAIN EXECUTION
# -----------------------------
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--input_file",
type=str,
default="/home/mshahidul/readctrl/data/annotators_validate_data_(20_80)/combine/verified_combined_0-80.json",
)
parser.add_argument(
"--save_folder",
type=str,
default="/home/mshahidul/readctrl/data/extracting_subclaim",
)
parser.add_argument("--model", type=str, default="gpt-5-mini")
args = parser.parse_args()
input_file = args.input_file
save_folder = args.save_folder
file_name = os.path.basename(input_file).split(".json")[0]
output_file = os.path.join(save_folder, f"extracted_subclaims_{file_name}.json")
Path(save_folder).mkdir(parents=True, exist_ok=True)
client = _load_openai_client()
with open(input_file, "r") as f:
data = json.load(f)
result = []
if os.path.exists(output_file):
with open(output_file, "r") as f:
result = json.load(f)
def _item_key(obj: dict) -> str:
if obj.get("index") is not None:
return str(obj.get("index"))
if obj.get("id") is not None:
return str(obj.get("id"))
if obj.get("doc_id") is not None and obj.get("label") is not None:
return f"{obj.get('doc_id')}_{obj.get('label')}"
return str(obj.get("doc_id") or obj.get("label") or "")
processed_data = {_item_key(item): item for item in result}
for item in tqdm.tqdm(data):
item_id = _item_key(item)
existing_entry = processed_data.get(item_id)
# 1. Process Fulltext
if not existing_entry or not isinstance(existing_entry.get("fulltext_subclaims"), list):
f_sub = infer_subclaims(
item.get("fulltext", ""),
client,
model=args.model,
retries=2,
)
else:
f_sub = existing_entry["fulltext_subclaims"]
# 2. Process Summary
if not existing_entry or not isinstance(existing_entry.get("summary_subclaims"), list):
s_sub = infer_subclaims(
item.get("summary", ""),
client,
model=args.model,
retries=1,
)
else:
s_sub = existing_entry["summary_subclaims"]
# 3. Process Generated Texts (diff_label_texts)
diff_label_texts = item.get("diff_label_texts", "")
if isinstance(diff_label_texts, dict):
diff_label_subclaims = existing_entry.get("diff_label_subclaims", {}) if existing_entry else {}
for label, text in diff_label_texts.items():
if label not in diff_label_subclaims or not isinstance(diff_label_subclaims[label], list):
diff_label_subclaims[label] = infer_subclaims(
text,
client,
model=args.model,
retries=1,
)
else:
if not existing_entry or not isinstance(existing_entry.get("diff_label_subclaims"), list):
diff_label_subclaims = infer_subclaims(
diff_label_texts,
client,
model=args.model,
retries=1,
)
else:
diff_label_subclaims = existing_entry["diff_label_subclaims"]
# 4. Save
new_entry = {
"doc_id": item.get("doc_id"),
"label": item.get("label"),
"fulltext": item.get("fulltext", ""),
"fulltext_subclaims": f_sub,
"summary": item.get("summary", ""),
"summary_subclaims": s_sub,
"diff_label_texts": diff_label_texts,
"diff_label_subclaims": diff_label_subclaims,
}
processed_data[item_id] = new_entry
if len(processed_data) % 10 == 0:
with open(output_file, "w") as f:
json.dump(list(processed_data.values()), f, indent=4, ensure_ascii=False)
with open(output_file, "w") as f:
json.dump(list(processed_data.values()), f, indent=4, ensure_ascii=False)
print(f"Extraction completed. File saved at: {output_file}")
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