dierk
4a82352391
Model downloads automatically from HuggingFace Hub on first use. No manual download required. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
26 lines
1008 B
Python
26 lines
1008 B
Python
from sentence_transformers import SentenceTransformer
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from PIL import Image
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_model = None
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def _get_model():
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# Lazy load: the CLIP model is ~600 MB and takes several seconds to initialise.
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# Loading on first call avoids the cost at import time and during indexing warmup.
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# Downloaded automatically from Hugging Face Hub on first use:
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# https://huggingface.co/sentence-transformers/clip-ViT-B-32
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# Cached in ~/.cache/huggingface/hub/
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global _model
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if _model is None:
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_model = SentenceTransformer("clip-ViT-B-32")
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return _model
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def embed_image(path: str) -> list[float]:
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# CLIP requires RGB — some JPEGs are stored as CMYK or grayscale.
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img = Image.open(path).convert("RGB")
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return _get_model().encode(img).tolist()
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def embed_text(text: str) -> list[float]:
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# Text and images share the same 512-dimensional vector space in CLIP,
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# so the returned vector is directly comparable to image embeddings.
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return _get_model().encode(text).tolist()
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