handler.py

handler.py

@@ -0,0 +1,40 @@
+# handler.py
+import torch
+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": "distilbert-base-uncased-finetuned-sst-2-english", "task": "text-classification"},
+    {"model_id": "Helsinki-NLP/opus-mt-en-de", "task": "translation"},
+    {"model_id": "facebook/bart-large-cnn", "task": "summarization"},
+    {"model_id": "dslim/bert-base-NER", "task": "token-classification"},
+    {"model_id": "textattack/bert-base-uncased-ag-news", "task": "text-classification"},
+]
+
+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):
+        # 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