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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 | #!/usr/bin/env python3
import argparse
import json
import os
import re
import time
from typing import Dict, Any, Tuple
from openai import OpenAI
from tqdm import tqdm
def load_prompt_template(path: str) -> str:
with open(path, "r", encoding="utf-8") as f:
return f.read()
def load_api_key_from_json(path: str, key_name: str) -> str:
with open(path, "r", encoding="utf-8") as f:
data = json.load(f)
api_key = data.get(key_name, "")
if not api_key:
raise SystemExit(f"API key '{key_name}' not found in {path}.")
return api_key
def build_prompt(template: str, src_text: str, target_language: str, target_translation: str) -> str:
return (
template.replace("{SRC_TEXT}", src_text)
.replace("{TARGET_LANGUAGE}", target_language)
.replace("{TARGET_TRANSLATION}", target_translation)
)
def extract_json(text: str) -> Dict[str, Any]:
try:
return json.loads(text)
except json.JSONDecodeError:
match = re.search(r"\{.*\}", text, re.DOTALL)
if not match:
raise
return json.loads(match.group(0))
def call_gpt5(client: OpenAI, model: str, prompt: str, max_retries: int = 5) -> Dict[str, Any]:
last_err = None
for attempt in range(1, max_retries + 1):
try:
resp = client.responses.create(
model=model,
input=[{"role": "user", "content": prompt}],
)
return extract_json(resp.output_text)
except Exception as err:
last_err = err
sleep_s = min(2 ** attempt, 30)
time.sleep(sleep_s)
raise last_err
def process_record(
client: OpenAI,
model: str,
template: str,
target_language: str,
record: Dict[str, Any],
src_key: str,
tgt_key: str,
out_key: str,
) -> Tuple[str, Dict[str, Any]]:
src_text = record.get(src_key, "")
tgt_text = record.get(tgt_key, "")
if not src_text or not tgt_text:
return out_key, {"translated_text": tgt_text}
prompt = build_prompt(template, src_text, target_language, tgt_text)
return out_key, call_gpt5(client, model, prompt)
def write_batch(output_dir: str, base_name: str, batch_start: int, batch_end: int, batch: list) -> None:
os.makedirs(output_dir, exist_ok=True)
out_name = f"{base_name}_{batch_start:04d}_{batch_end - 1:04d}.json"
out_path = os.path.join(output_dir, out_name)
with open(out_path, "w", encoding="utf-8") as out_f:
json.dump(batch, out_f, ensure_ascii=False, indent=2)
def main() -> None:
parser = argparse.ArgumentParser(description="GPT-5 translation correction runner")
parser.add_argument(
"--input",
default="/home/mshahidul/readctrl/data/translated_data/translation_wo_judge/multiclinsum_gs_train_en2bn_gemma(0_200).json",
help="Path to input JSON file",
)
parser.add_argument(
"--output-dir",
default="/home/mshahidul/readctrl/data/translated_data/dataset_correction_gpt5",
help="Output directory (writes one file per 2 instances)",
)
parser.add_argument(
"--batch-size",
type=int,
default=2,
help="Number of instances per output file",
)
parser.add_argument(
"--prompt",
default="/home/mshahidul/readctrl/prompts/translation_correction_prompt",
help="Path to prompt template",
)
parser.add_argument(
"--target-language",
default="Bengali",
help="Target language name",
)
parser.add_argument(
"--model",
default="gpt-5",
help="OpenAI model name",
)
parser.add_argument(
"--api-json",
default="/home/mshahidul/api_new.json",
help="Path to JSON file containing API keys",
)
parser.add_argument(
"--api-json-key",
default="openai",
help="Key name inside the JSON file",
)
parser.add_argument(
"--start",
type=int,
default=0,
help="Start index (0-based)",
)
parser.add_argument(
"--end",
type=int,
default=None,
help="End index (exclusive)",
)
args = parser.parse_args()
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
api_key = load_api_key_from_json(args.api_json, args.api_json_key)
client = OpenAI(api_key=api_key)
with open(args.input, "r", encoding="utf-8") as f:
data = json.load(f)
template = load_prompt_template(args.prompt)
src_map = {
"translated_fulltext": "fulltext",
"translated_summary": "summary",
}
out_map = {
"translated_fulltext": "corrected_translated_fulltext",
"translated_summary": "corrected_translated_summary",
}
start = args.start
end = args.end if args.end is not None else len(data)
base_name = os.path.splitext(os.path.basename(args.input))[0]
batch_start = start
batch = []
for idx in tqdm(range(start, min(end, len(data))), desc="Processing", unit="item"):
record = data[idx]
for tgt_key, src_key in src_map.items():
out_key = out_map[tgt_key]
if out_key in record:
continue
out_key, result = process_record(
client,
args.model,
template,
args.target_language,
record,
src_key,
tgt_key,
out_key,
)
record[out_key] = result.get("translated_text", record.get(tgt_key, ""))
batch.append(record)
if len(batch) >= args.batch_size:
write_batch(args.output_dir, base_name, batch_start, idx + 1, batch)
batch = []
batch_start = idx + 1
if batch:
write_batch(args.output_dir, base_name, batch_start, min(end, len(data)), batch)
if __name__ == "__main__":
main()
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