49 lines
No EOL
1.6 KiB
Python
49 lines
No EOL
1.6 KiB
Python
# main.py
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import os
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import sys
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# 1. Block hardware-level multi-device discovery loops
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os.environ["CUDA_VISIBLE_DEVICES"] = ""
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import torch
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# 2. Hard monkeypatch PyTorch's MPS detection before ANY machine learning imports occur
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# This stops underlying huggingface models from forcing device="mps" internally
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torch.backends.mps.is_available = lambda: False
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torch.backends.mps.is_built = lambda: False
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# Force standard CPU allocation for safety
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torch.set_default_device("cpu")
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import time
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from docling.document_converter import DocumentConverter
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def main():
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source = "https://arxiv.org/pdf/2408.09869"
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print("🚀 Initializing Docling engine with hardware-isolated CPU routing...")
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start_time = time.time()
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# This will now natively spin up on standard CPU threads
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converter = DocumentConverter()
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print("⏳ Processing document locally (Parsing layout, OCR, and tables)...")
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result = converter.convert(source)
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print("✨ Conversion finished! Compiling Markdown structure...")
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markdown_output = result.document.export_to_markdown()
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# Save output cleanly to project directory
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output_filename = "parsed_paper.md"
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with open(output_filename, "w", encoding="utf-8") as f:
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f.write(markdown_output)
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elapsed_time = time.time() - start_time
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print(f"✅ Success! Processing complete in {elapsed_time:.2f} seconds.")
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print(f"📁 Structured output saved cleanly to: {os.path.abspath(output_filename)}")
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print("\n--- Preview of First 300 Characters: ---")
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print(markdown_output[:300])
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if __name__ == "__main__":
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main() |