Fu DaiandClaude Opus 4.8
1a585693be
FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM: legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint, a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual example outputs, and quantization/concurrency benchmarks. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
import os
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import click
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import json
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import time
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from collections import defaultdict
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from surya.inference import SuryaInferenceManager
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from surya.logging import configure_logging, get_logger
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from surya.recognition import RecognitionPredictor
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from surya.scripts.config import CLILoader
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configure_logging()
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logger = get_logger()
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@click.command(help="OCR text — full-page OCR (one VLM call per page).")
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@CLILoader.common_options
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def ocr_text_cli(input_path: str, **kwargs):
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# Full-page OCR is the default path: one VLM call per page returns layout
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# + content together. Pages whose full-page output fails to parse fall
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# back to layout + per-block OCR automatically (see RecognitionPredictor).
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loader = CLILoader(input_path, kwargs, highres=True)
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manager = SuryaInferenceManager()
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rec_predictor = RecognitionPredictor(manager)
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start = time.time()
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page_results = rec_predictor(loader.highres_images, full_page=True)
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if loader.debug:
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logger.debug(f"OCR took {time.time() - start:.2f} seconds")
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out_preds = defaultdict(list)
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for name, page in zip(loader.names, page_results):
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out_pred = page.model_dump()
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out_pred["page"] = len(out_preds[name]) + 1
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out_preds[name].append(out_pred)
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with open(
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os.path.join(loader.result_path, "results.json"), "w+", encoding="utf-8"
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) as f:
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json.dump(out_preds, f, ensure_ascii=False)
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logger.info(f"Wrote results to {loader.result_path}")
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if __name__ == "__main__":
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ocr_text_cli()
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