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from typing import Dict, Any
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from PIL import Image
import io
import base64
import requests
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class EndpointHandler():
def __init__(self, path=""):
self.processor = AutoProcessor.from_pretrained(path)
self.model = Qwen2VLForConditionalGeneration.from_pretrained(
path, device_map="auto"
)
self.model.to(device)
def __call__(self, data: Any) -> Dict[str, Any]:
inputs = data.pop("inputs", data)
image_input = inputs.get('image')
text_input = inputs.get('text', "Describe this image.")
if not image_input:
return {"error": "No image provided."}
try:
if image_input.startswith('http'):
response = requests.get(image_input, stream=True)
if response.status_code == 200:
image = Image.open(response.raw).convert('RGB')
else:
return {"error": f"Failed to fetch image. Status code: {response.status_code}"}
else:
image_data = base64.b64decode(image_input)
image = Image.open(io.BytesIO(image_data)).convert('RGB')
except Exception as e:
return {"error": f"Failed to process the image. Details: {str(e)}"}
try:
conversation = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": text_input},
],
}
]
text_prompt = self.processor.apply_chat_template(
conversation, add_generation_prompt=True
)
inputs = self.processor(
text=[text_prompt],
images=[image],
padding=True,
return_tensors="pt",
)
inputs = inputs.to(device)
output_ids = self.model.generate(
**inputs, max_new_tokens=128
)
generated_ids = [
output_id[len(input_id):] for input_id, output_id in zip(inputs.input_ids, output_ids)
]
output_text = self.processor.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)[0]
return {"generated_text": output_text}
except Exception as e:
return {"error": f"Failed during generation. Details: {str(e)}"}
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