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#!/usr/bin/env python3
from typing import Dict, List, Any
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
class EndpointHandler():
def __init__(self, path=""):
# Preload all the elements you are going to need at inference.
# pseudo:
# self.model= load_model(path)
self.model = PaliGemmaForConditionalGeneration.from_pretrained(path)
self.processor = AutoProcessor.from_pretrained(path)
pass
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# pseudo
# self.model(input)
raw_inputs = data.pop("inputs", data)
image_file = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg?download=true"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = self.processor(raw_inputs["prompt"], raw_image, return_tensors="pt")
output = self.model.generate(**inputs, max_new_tokens=20)
response = processor.decode(output[0], skip_special_tokens=True)
return response
# print(processor.decode(output[0], skip_special_tokens=True)[len(prompt):])
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