File size: 1,669 Bytes
192a99b | 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 | from typing import Dict, List, Any
from transformers import BlipProcessor, BlipForConditionalGeneration
from PIL import Image
import requests
import torch
class EndpointHandler():
def __init__(self, path="./"):
# Load the processor and model, and move to CUDA if available
self.processor = BlipProcessor.from_pretrained(path)
self.model = BlipForConditionalGeneration.from_pretrained(path).to("cuda" if torch.cuda.is_available() else "cpu")
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
image_url (:obj: `str`): URL of the image to caption
prompt (:obj: `str`, optional): Text prompt for conditional captioning
Return:
A :obj:`list` with caption as `dict`
"""
# Get inputs from the data
image_url = data.get("image_url")
prompt = data.get("prompt", "") # Optional prompt for conditional captioning
# Load image from URL and ensure RGB format
image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
# Conditional or Unconditional Captioning
if prompt:
# Conditional captioning
inputs = self.processor(image, prompt, return_tensors="pt").to(self.model.device)
else:
# Unconditional captioning
inputs = self.processor(image, return_tensors="pt").to(self.model.device)
# Generate caption
out = self.model.generate(**inputs)
caption = self.processor.decode(out[0], skip_special_tokens=True)
# Return the generated caption
return [{"caption": caption}]
|