Create handler.py

handler.py

@@ -0,0 +1,28 @@
+from typing import Any, Dict, List
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # We point directly to the original weights
+        self.model_id = "zhihan1996/DNABERT-2-117M"
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_id, trust_remote_code=True)
+        self.model = AutoModel.from_pretrained(self.model_id, trust_remote_code=True)
+        if torch.cuda.is_available():
+            self.model = self.model.to("cuda")
+
+    def __call__(self, data: Dict[str, Any]) -> List[float]:
+        inputs = data.pop("inputs", data)
+        
+        # DNA Tokenization
+        encoded_input = self.tokenizer(inputs, return_tensors='pt')
+        if torch.cuda.is_available():
+            encoded_input = {k: v.to("cuda") for k, v in encoded_input.items()}
+            
+        with torch.no_grad():
+            outputs = self.model(**encoded_input)
+            
+        # Returns a 768-dimensional vector representing the DNA sequence
+        embeddings = outputs[0][0].mean(dim=0).cpu().numpy().tolist()
+        
+        return embeddings