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import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration
from PIL import Image
import base64
import io
class EndpointHandler():
def __init__(self, model_path=""):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.processor = AutoProcessor.from_pretrained(model_path)
self.model = LlavaForConditionalGeneration.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto" if torch.cuda.is_available() else None
)
self.model.eval()
def __call__(self, data):
inputs = data.get("inputs", {})
prompt = inputs.get("prompt", "Generate a caption for this image.")
images_b64 = inputs.get("images")
# Handle both single image and list of images
if isinstance(images_b64, str):
images_b64 = [images_b64]
if not images_b64:
return {"error": "No images provided in the payload."}
try:
images = [
Image.open(io.BytesIO(base64.b64decode(img_b64))).convert("RGB")
for img_b64 in images_b64
]
except Exception as e:
return {"error": f"Failed to decode image: {str(e)}"}
# Build the conversation template for captioning
conversation = [
{"role": "system", "content": "You are a helpful image captioner."},
{"role": "user", "content": prompt}
]
convo_string = self.processor.apply_chat_template(
conversation,
tokenize=False,
add_generation_prompt=True
)
if not isinstance(convo_string, str):
return {"error": "Failed to create conversation string."}
# Prepare the inputs for the model - process all images at once
model_inputs = self.processor(
text=[convo_string],
images=images,
return_tensors="pt"
)
model_inputs = {k: v.to(self.device) for k, v in model_inputs.items()}
if "pixel_values" in model_inputs:
model_inputs["pixel_values"] = model_inputs["pixel_values"].to(torch.bfloat16)
# Generate caption tokens for all images at once
generate_ids = self.model.generate(
**model_inputs,
max_new_tokens=300,
do_sample=True,
temperature=0.6,
top_p=0.9
)
# Trim off the prompt tokens and decode all captions
generate_ids = generate_ids[:, model_inputs["input_ids"].shape[1]:]
captions = [
self.processor.tokenizer.decode(
ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
).strip()
for ids in generate_ids
]
return {"captions": captions if len(captions) > 1 else captions[0]}
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