|
|
from typing import Dict, Any |
|
|
from PIL import Image |
|
|
import requests |
|
|
import torch |
|
|
import numpy as np |
|
|
from transformers import AutoProcessor, LlavaForConditionalGeneration |
|
|
|
|
|
class EndpointHandler(): |
|
|
def __init__(self, path=""): |
|
|
model_id = path |
|
|
self.model = LlavaForConditionalGeneration.from_pretrained( |
|
|
model_id, |
|
|
torch_dtype=torch.float16, |
|
|
low_cpu_mem_usage=True, |
|
|
).to(0) |
|
|
self.processor = AutoProcessor.from_pretrained(model_id) |
|
|
|
|
|
def __call__(self, data: Dict[str, Any]): |
|
|
parameters = data.pop("inputs", data) |
|
|
if parameters is not None: |
|
|
url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
|
|
prompt = "USER: <image>\nWhat are these?\nASSISTANT:" |
|
|
raw_image = Image.open(requests.get(url, stream=True).raw) |
|
|
inputs = self.processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16) |
|
|
output = self.model.generate(**inputs, max_new_tokens=200, do_sample=False) |
|
|
|
|
|
output = output.cpu().numpy().tolist() |
|
|
return output |
|
|
|
