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from typing import Dict, Any
import torch
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from PIL import Image
import requests
from io import BytesIO
# Check for GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class EndpointHandler:
def __init__(self, path: str = "morthens/qwen2-vl-infer"):
# Load the processor and model
self.processor = AutoProcessor.from_pretrained(path)
self.model = Qwen2VLForConditionalGeneration.from_pretrained(
path,
torch_dtype="auto",
device_map="auto"
)
# Move the model to the appropriate device
self.model.to(device)
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
# Extract the input data
image_url = data.get("image_url", "")
text = data.get("text", "")
# Load the image from the URL
try:
response = requests.get(image_url)
response.raise_for_status()
image = Image.open(BytesIO(response.content))
except Exception as e:
return {"error": f"Failed to fetch or process image: {str(e)}"}
# Preprocess the input
inputs = self.processor(
text=[text],
images=[image],
padding=True,
return_tensors="pt"
)
# Move inputs to the correct device
inputs = {key: value.to(device) for key, value in inputs.items()}
# Perform inference
output_ids = self.model.generate(
**inputs,
max_new_tokens=128
)
# Decode the output
output_text = self.processor.batch_decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=True
)[0]
# Return the raw prediction
return {"prediction": output_text}
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