Update handler.py

handler.py

@@ -6,60 +6,74 @@ 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]:
-        image_input = data.get('image')
-        text_input = data.get('text', "Describe this image.")
+        inputs = data.pop("inputs", data)
+        image_input = inputs.get('image')
+        text_input = inputs.get('text', "Describe this image.")
 
-        if image_input is None:
+        if not image_input:
             return {"error": "No image provided."}
 
         try:
             if image_input.startswith('http'):
-                image = Image.open(requests.get(image_input, stream=True).raw).convert('RGB')
+                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)}"}
 
-        conversation = [
-            {
-                "role": "user",
-                "content": [
-                    {"type": "image"},
-                    {"type": "text", "text": text_input},
-                ],
-            }
-        ]
+        try:
+            conversation = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image"},
+                        {"type": "text", "text": text_input},
+                    ],
+                }
+            ]
 
-        text_prompt = self.processor.apply_chat_template(
-            conversation, add_generation_prompt=True
-        )
+            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 = self.processor(
+                text=[text_prompt],
+                images=[image],
+                padding=True,
+                return_tensors="pt",
+            )
+
+            inputs = inputs.to(device)
 
-        inputs = inputs.to(self.model.device)
+            output_ids = self.model.generate(
+                **inputs, max_new_tokens=128
+            )
 
-        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)
+            ]
 
-        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]
 
-        output_text = self.processor.batch_decode(
-            generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
-        )[0]
+            return {"generated_text": output_text}
 
-        return {"generated_text": output_text}
+        except Exception as e:
+            return {"error": f"Failed during generation. Details: {str(e)}"}