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dabc20d 01d3c03 dabc20d 01d3c03 dabc20d 01d3c03 dabc20d 01d3c03 dabc20d 01d3c03 dabc20d 01d3c03 dabc20d 01d3c03 dabc20d 01d3c03 dabc20d 01d3c03 dabc20d | 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 46 47 48 49 50 51 52 53 54 55 56 57 | from typing import Dict, Any
from PIL import Image
import torch
import requests
from io import BytesIO
from transformers import BlipForConditionalGeneration, BlipProcessor
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class EndpointHandler():
def __init__(self, path=""):
# Load the processor and model
self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
self.model = BlipForConditionalGeneration.from_pretrained(
"Salesforce/blip-image-captioning-base"
).to(device)
self.model.eval()
def __call__(self, data: Any) -> Dict[str, Any]:
"""
Args:
data (:obj:`dict`):
Includes the input data and the parameters for the inference.
Return:
A :obj:`dict`. The object returned contains:
- "caption": A string corresponding to the generated caption.
"""
# Extract image URL and parameters
image_url = data.get("image")
parameters = data.get("parameters", {})
if not image_url:
return {"error": "Missing 'image' field in request body."}
try:
# Download the image
response = requests.get(image_url)
response.raise_for_status()
raw_image = Image.open(BytesIO(response.content)).convert("RGB")
except Exception as e:
return {"error": f"Failed to fetch image from URL: {str(e)}"}
# Preprocess the image
processed_image = self.processor(images=raw_image, return_tensors="pt")
processed_image["pixel_values"] = processed_image["pixel_values"].to(device)
# Merge parameters if needed
processed_image = {**processed_image, **parameters}
with torch.no_grad():
out = self.model.generate(**processed_image)
# Decode the output
caption = self.processor.decode(out[0], skip_special_tokens=True)
return {"caption": caption}
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