Create handler.py

handler.py

@@ -0,0 +1,84 @@
+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}