Create handler.py

handler.py

@@ -0,0 +1,36 @@
+from typing import Dict, List, Any
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+        model = PeftModel.from_pretrained(model, "srmorfi/phi3-mini-med-adapter")
+        tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+        self.model = model
+        self.tokenizer = tokenizer
+        self.model.eval()
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        Process input data and generate predictions using the model.
+        
+        Args:
+            data (Dict[str, Any]): Input data containing either an "inputs" key
+                                  or the input directly in the data dictionary.
+        
+        Returns:
+            List[Dict[str, Any]]: Processed model predictions that will be serialized and returned.
+        """
+        inputs = data.pop("inputs", data)
+        inputs = self.tokenizer(inputs, return_tensors="pt")
+
+        print(inputs)
+
+        with torch.no_grad():
+            outputs = self.model.generate(input_ids=inputs["input_ids"], max_new_tokens=20)
+            output = self.tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
+            predictions = [{"generated_text": output}]
+        
+            return predictions