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from typing import Dict, List, Any
from transformers import pipeline
from PIL import Image
import requests
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration
class EndpointHandler():
def __init__(self, path="."):
self.model = LlavaForConditionalGeneration.from_pretrained(
path,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
self.processor = AutoProcessor.from_pretrained(path)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
date (:obj: `str`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# get inputs
prompt = "USER: <image>\nWhat's in the image\nASSISTANT:"
default_url = "https://cdn.faire.com/fastly/3c335e5c06d3027964ee8351093784c94dfa264e5eb26430c803f4ab3c44da84.jpeg"
url = data.pop("image_url", default_url)
inputs = data.pop("inputs", None)
image = Image.open(requests.get(url, stream=True).raw)
inputs = self.processor(prompt, image, return_tensors='pt').to(0, torch.float16)
# run normal prediction
output = self.model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(output)
return output |