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from typing import Dict, Any
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
import torch
from PIL import Image
import requests
from io import BytesIO
import json
# Check for GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class EndpointHandler:
def __init__(self, path: str = ""):
"""
Initializes the handler for the Qwen2-VL model.
Args:
path (str): Path to the model weights and processor. Defaults to the current directory.
"""
# 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]:
"""
Processes the input data and returns the model's prediction.
Args:
data (Dict[str, Any]): Input data containing `image_url` and `text`.
Returns:
Dict[str, Any]: The prediction or an error message.
"""
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)}"}
# Prepare the text prompt
text_prompt = self.processor.apply_chat_template(
[{"role": "user", "content": [{"type": "text", "text": text}]}],
add_generation_prompt=True
)
# Preprocess the input
inputs = self.processor(
text=[text_prompt],
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 generated text
output_text = self.processor.batch_decode(
output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)[0]
# Clean and parse the JSON response
cleaned_data = output_text.replace("```json\n", "").replace("```", "").strip()
try:
prediction = json.loads(cleaned_data)
except json.JSONDecodeError as e:
return {"error": f"Failed to parse JSON output: {str(e)}", "raw_output": cleaned_data}
return {"prediction": prediction}
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