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import os
from typing import List, Tuple
import gradio as gr
import httpx
from transformers import AutoProcessor
DATA_PATH = (
"/home/mshahidul/readctrl/data/translated_data/translation_wo_judge/"
"multiclinsum_gs_train_en2bn_gemma(0_200).json"
)
TRANSLATE_URL = "http://172.16.34.29:8081/v1/chat/completions"
SOURCE_LANG = "en"
TARGET_LANG = "bn"
MODEL_ID = "google/translategemma-27b-it"
SERVER_MODEL_NAME = "translate_gemma"
MAX_INSTANCES = 80
processor = AutoProcessor.from_pretrained(MODEL_ID)
def load_data(path: str) -> List[dict]:
with open(path, "r", encoding="utf-8") as f:
return json.load(f)
def save_data(path: str, data: List[dict]) -> None:
os.makedirs(os.path.dirname(path), exist_ok=True)
with open(path, "w", encoding="utf-8") as f:
json.dump(data, f, ensure_ascii=False, indent=4)
def build_gemma_prompt(text: str, source_lang: str, target_lang: str) -> List[dict]:
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"source_lang_code": source_lang,
"target_lang_code": target_lang,
"text": text,
}
],
}
]
prompt = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
return [{"role": "user", "content": prompt}]
def call_llm(
text: str,
temperature: float = 0.1,
max_tokens: int | None = None,
source_lang: str = SOURCE_LANG,
target_lang: str = TARGET_LANG,
) -> Tuple[str | None, str | None]:
if not text:
return None, "Empty source text."
messages = build_gemma_prompt(text, source_lang=source_lang, target_lang=target_lang)
payload = {
"model": SERVER_MODEL_NAME,
"messages": messages,
"temperature": float(temperature),
}
if max_tokens is not None:
payload["max_tokens"] = int(max_tokens)
try:
response = httpx.post(TRANSLATE_URL, json=payload, timeout=60.0)
result = response.json()
content = result["choices"][0]["message"]["content"].strip()
return content, None
except Exception as exc:
return None, f"LLM call failed: {exc}"
data = load_data(DATA_PATH)
limit = min(MAX_INSTANCES, len(data))
options = [(f"{i:03d} | {data[i].get('id', 'no-id')}", i) for i in range(limit)]
def get_record(idx: int) -> dict:
return data[idx]
def record_to_fields(idx: int):
rec = get_record(idx)
return (
idx,
rec.get("id", ""),
rec.get("fulltext", ""),
rec.get("summary", ""),
rec.get("translated_fulltext") or "",
rec.get("translated_summary") or "",
f"Loaded index {idx}.",
)
def goto_index(idx: int):
return record_to_fields(int(idx))
def step_index(idx: int, delta: int):
new_idx = max(0, min(limit - 1, int(idx) + delta))
return record_to_fields(new_idx)
def regenerate_fulltext(idx: int, temperature: float, max_tokens: int):
rec = get_record(int(idx))
translated, error = call_llm(
rec.get("fulltext", ""),
temperature=temperature,
max_tokens=max_tokens,
)
if translated is not None:
rec["translated_fulltext"] = translated
return translated, f"Regenerated fulltext at index {idx}."
return rec.get("translated_fulltext") or "", error or "Regenerate failed."
def regenerate_summary(idx: int, temperature: float, max_tokens: int):
rec = get_record(int(idx))
translated, error = call_llm(
rec.get("summary", ""),
temperature=temperature,
max_tokens=max_tokens,
)
if translated is not None:
rec["translated_summary"] = translated
return translated, f"Regenerated summary at index {idx}."
return rec.get("translated_summary") or "", error or "Regenerate failed."
def regenerate_both(idx: int, temperature: float, max_tokens_full: int, max_tokens_sum: int):
fulltext, full_error = regenerate_fulltext(idx, temperature, max_tokens_full)
summary, sum_error = regenerate_summary(idx, temperature, max_tokens_sum)
status = "Regenerated fulltext and summary."
if full_error or sum_error:
errors = "; ".join([e for e in [full_error, sum_error] if e])
status = f"Partial regenerate: {errors}"
return fulltext, summary, status
def save_record(idx: int, translated_fulltext: str, translated_summary: str):
rec = get_record(int(idx))
rec["translated_fulltext"] = translated_fulltext or None
rec["translated_summary"] = translated_summary or None
save_data(DATA_PATH, data)
gr.Info(f"Saved index {idx} to file.")
return f"Saved index {idx} to file."
with gr.Blocks(title="Translation Review") as demo:
gr.Markdown("## Translation review for first 80 instances")
with gr.Row():
record_select = gr.Dropdown(
label="Record",
choices=options,
value=0,
interactive=True,
)
status = gr.Textbox(label="Status", value="Ready.", interactive=False)
with gr.Row():
prev_btn = gr.Button("Prev")
next_btn = gr.Button("Next")
record_id = gr.Textbox(label="Record ID", interactive=False)
fulltext = gr.Textbox(label="Fulltext (source)", lines=8, interactive=False)
summary = gr.Textbox(label="Summary (source)", lines=6, interactive=False)
with gr.Row():
temperature = gr.Slider(
minimum=0.0,
maximum=1.5,
value=0.2,
step=0.05,
label="Temperature",
)
max_tokens_full = gr.Number(value=2048, precision=0, label="Max tokens (fulltext)")
max_tokens_sum = gr.Number(value=1024, precision=0, label="Max tokens (summary)")
translated_fulltext = gr.Textbox(label="Translated fulltext", lines=8)
translated_summary = gr.Textbox(label="Translated summary", lines=6)
with gr.Row():
regen_full_btn = gr.Button("Regenerate Fulltext")
regen_sum_btn = gr.Button("Regenerate Summary")
regen_both_btn = gr.Button("Regenerate Both")
save_btn = gr.Button("Save to file")
record_select.change(
goto_index,
inputs=[record_select],
outputs=[
record_select,
record_id,
fulltext,
summary,
translated_fulltext,
translated_summary,
status,
],
)
prev_btn.click(
lambda idx: step_index(idx, -1),
inputs=[record_select],
outputs=[
record_select,
record_id,
fulltext,
summary,
translated_fulltext,
translated_summary,
status,
],
)
next_btn.click(
lambda idx: step_index(idx, 1),
inputs=[record_select],
outputs=[
record_select,
record_id,
fulltext,
summary,
translated_fulltext,
translated_summary,
status,
],
)
regen_full_btn.click(
regenerate_fulltext,
inputs=[record_select, temperature, max_tokens_full],
outputs=[translated_fulltext, status],
)
regen_sum_btn.click(
regenerate_summary,
inputs=[record_select, temperature, max_tokens_sum],
outputs=[translated_summary, status],
)
regen_both_btn.click(
regenerate_both,
inputs=[record_select, temperature, max_tokens_full, max_tokens_sum],
outputs=[translated_fulltext, translated_summary, status],
)
save_btn.click(
save_record,
inputs=[record_select, translated_fulltext, translated_summary],
outputs=[status],
)
demo.load(
goto_index,
inputs=[record_select],
outputs=[
record_select,
record_id,
fulltext,
summary,
translated_fulltext,
translated_summary,
status,
],
)
if __name__ == "__main__":
demo.launch(share=True)
|