Create handler.py

handler.py

@@ -0,0 +1,31 @@
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
+import torch
+from PIL import Image
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        disable_torch_init()
+        device = torch.cuda_device
+        self.processor = LlavaNextProcessor.from_pretrained(path, use_fast=False)
+        self.model = LlavaNextForConditionalGeneration.from_pretrained(
+            path, 
+            torch_dtype=torch.float16, 
+            low_cpu_mem_usage=True,
+            load_in_4bit=True
+        )
+        self.model.to("cuda:0")
+
+    def __call__(self, data):
+        image_encoded = data.pop("inputs", data)
+        prompt = data["text"]
+
+        image = self.decode_base64_image(image_encoded)
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+
+        inputs = self.processor(prompt, image, return_tensors="pt").to("cuda:0")
+
+        # autoregressively complete prompt
+        output = self.model.generate(**inputs, max_new_tokens=500)
+
+        return processor.decode(output[0], skip_special_tokens=True)