Create handler.py

handler.py

@@ -0,0 +1,38 @@
+import torch
+from typing import  Dict, List, Any
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+# check for GPU
+device = 0 if torch.cuda.is_available() else -1
+
+# multi-model list
+multi_model_list = [
+    {"model_id": "omarabb315/gemma-2B-2nd_filtered_3_full", "task": "text-generation"},
+    {"model_id": "omarabb315/gemma-2B-2nd_filtered_3_16bit", "task": "text-generation"},
+    {"model_id": "omarabb315/Gemma-2-9B-filtered_3_4bits", "task": "text-generation"},
+]
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.multi_model={}
+        # load all the models onto device
+        for model in multi_model_list:
+            self.multi_model[model["model_id"]] = pipeline(model["task"], model=model["model_id"], device=device)
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        # deserialize incomin request
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+        model_id = data.pop("model_id", None)
+        
+        # check if model_id is in the list of models
+        if model_id is None or model_id not in self.multi_model:
+            raise ValueError(f"model_id: {model_id} is not valid. Available models are: {list(self.multi_model.keys())}")
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            prediction = self.multi_model[model_id](inputs, **parameters)
+        else:
+            prediction = self.multi_model[model_id](inputs)
+        # postprocess the prediction
+        return prediction