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from typing import Dict, List, Any
from PIL import Image
from io import BytesIO
from transformers import AutoProcessor, OmDetTurboForObjectDetection
import base64
import logging
class EndpointHandler():
def __init__(self, path=""):
self.processor = AutoProcessor.from_pretrained("Blueway/inference-endpoint-for-omdet-turbo-swin-tiny-hf")
self.model = OmDetTurboForObjectDetection.from_pretrained("Blueway/inference-endpoint-for-omdet-turbo-swin-tiny-hf")
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
image (:obj:`string`)
candidates (:obj:`list`)
Return:
A :obj:`list`:. The list contains items that are dicts should be liked {"label": "XXX", "score": 0.82}
"""
inputs_request = data.pop("inputs", data)
# decode base64 image to PIL
image = Image.open(BytesIO(base64.b64decode(inputs_request['image'])))
# run prediction one image wit provided candiates
inputs = self.processor(image, text=inputs_request["candidates"], return_tensors="pt")
outputs = self.model(**inputs)
results = self.processor.post_process_grounded_object_detection(
outputs,
classes=inputs_request["candidates"],
target_sizes=[image.size[::-1]],
score_threshold=0.3,
nms_threshold=0.3,
)[0]
# Convert tensors to lists
serializable_results = {
'boxes': results['boxes'].tolist(),
'scores': results['scores'].tolist(),
'candidates': results['classes'] # Already serializable
}
return serializable_results
#prediction = self.pipeline(image=[image], candidate_labels=inputs["candidates"])
#return prediction[0]
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