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handler.py

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+import torch
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+import requests
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        model_id = "PULSE-ECG/PULSE-7B"
+        self.processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            torch_dtype=torch.float16,
+            device_map="auto",
+            trust_remote_code=True
+        )
+
+    def __call__(self, data: dict) -> dict:
+        image_url = data.get("image_url")
+        text = data.get("text", "Interpret this ECG image.")
+
+        if not image_url:
+            return {"error": "No image_url provided"}
+
+        image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
+        inputs = self.processor(text=text, images=image, return_tensors="pt").to(self.model.device)
+
+        with torch.no_grad():
+            outputs = self.model.generate(**inputs, max_new_tokens=512)
+
+        result = self.processor.decode(outputs[0], skip_special_tokens=True)
+        return {"result": result}