sdg_predict/cli_predict.py

# sdg_predict/cli_predict.py
import argparse
import json
from pathlib import Path
from tqdm import tqdm
import sys
import torch
from sdg_predict.inference import load_model, predict

def main():
    parser = argparse.ArgumentParser(description="Batch inference using Hugging Face model.")
    parser.add_argument("input", type=Path, help="Input JSONL file")
    parser.add_argument("--key", type=str, required=True, help="JSON key with text input")
    parser.add_argument("--batch_size", type=int, default=8, help="Batch size")
    parser.add_argument("--model", type=str, default="simon-clmtd/sdg-scibert-zo_up", help="Model name on the Hub")
    parser.add_argument("--top1", action="store_true", help="Return only top prediction")
    parser.add_argument("--output", type=Path, help="Output file (optional, otherwise stdout)")
    args = parser.parse_args()

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    tokenizer, model = load_model(args.model, device)

    with args.input.open() as f:
        texts = []
        rows = []
        for line in f:
            row = json.loads(line)
            if args.key not in row:
                continue
            texts.append(row[args.key])
            rows.append(row)

    predictions = predict(
        texts,
        tokenizer,
        model,
        device,
        batch_size=args.batch_size,
        return_all_scores=not args.top1
    )

    output_stream = args.output.open("w") if args.output else sys.stdout
    for row, pred in zip(rows, predictions):
        row["prediction"] = pred
        print(json.dumps(row, ensure_ascii=False), file=output_stream)
    if args.output:
        output_stream.close()