File size: 1,271 Bytes
236a466 9ace47e 236a466 1775fa1 236a466 9ace47e dba2b08 9ace47e 236a466 9ace47e 236a466 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import torch
from typing import Dict, List, Any
from transformers import pipeline
import base64
from PIL import Image
import io
def base64_to_pil(base64_image):
image_data = base64.b64decode(base64_image)
image_data = io.BytesIO(image_data)
pil_image = Image.open(image_data)
return pil_image
# check for GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def is_base64(s):
try:
return base64.b64encode(base64.b64decode(s)).decode('utf-8') == s
except Exception:
return False
class EndpointHandler():
def __init__(self, path=""):
# Preload all the elements you are going to need at inference.
# pseudo:
self.pipeline= pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", device=device)
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
"""
inputs = data.pop("inputs", data)
if(is_base64(inputs)):
inputs = base64_to_pil(inputs)
return self.pipeline(inputs)
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