handler.py

from typing import Dict, Any
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from PIL import Image
import io
import base64
import requests
import torch

class EndpointHandler():
    def __init__(self, path=""):
        self.processor = AutoProcessor.from_pretrained(path)
        self.model = Qwen2VLForConditionalGeneration.from_pretrained(
            path, device_map="auto"
        )

    def __call__(self, data: Any) -> Dict[str, Any]:
        image_input = data.get('image')
        text_input = data.get('text', "Describe this image.")

        if image_input is None:
            return {"error": "No image provided."}

        try:
            if image_input.startswith('http'):
                image = Image.open(requests.get(image_input, stream=True).raw).convert('RGB')
            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)}"}

        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(self.model.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}