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DINOv2 image embedding handler for HF Inference Endpoints.
Accepts base64-encoded images via {"inputs": "<base64_string>"}.
Returns a 768-dim CLS token embedding as a flat list of floats.
"""
import base64
import os
from io import BytesIO
import torch
from PIL import Image
from transformers import AutoImageProcessor, AutoModel
class EndpointHandler:
def __init__(self, path=""):
# If the repo contains model weights, use them; otherwise load from HF Hub.
has_weights = path and os.path.exists(os.path.join(path, "config.json"))
model_id = path if has_weights else "facebook/dinov2-base"
self.processor = AutoImageProcessor.from_pretrained(model_id)
self.model = AutoModel.from_pretrained(model_id)
self.model.eval()
def __call__(self, data: dict) -> list:
"""
data: {"inputs": "<base64_string>"}
Returns: [float x 768] — DINOv2 CLS token embedding
"""
raw = data.get("inputs", "")
if isinstance(raw, str):
img_bytes = base64.b64decode(raw)
elif isinstance(raw, bytes):
img_bytes = raw
else:
raise ValueError(f"Unexpected input type: {type(raw)}")
image = Image.open(BytesIO(img_bytes)).convert("RGB")
with torch.no_grad():
inputs = self.processor(images=image, return_tensors="pt")
outputs = self.model(**inputs)
# last_hidden_state: [1, seq_len, 768] — take CLS token at index 0
embedding = outputs.last_hidden_state[:, 0, :].squeeze().tolist()
return embedding
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