Update handler.py

handler.py

@@ -1,33 +1,22 @@
 import torch
-
-from typing import Any, Dict
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-
-class EndpointHandler:
-    def __init__(self, path=""):
-        # load model and tokenizer from path
-        self.tokenizer = AutoTokenizer.from_pretrained(path)
-        self.model = AutoModelForCausalLM.from_pretrained(
-            path, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True
-        )
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
-        # process input
-        inputs = data.pop("inputs", data)
-        parameters = data.pop("parameters", None)
-
-        # preprocess
-        inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)
-
-        # pass inputs with all kwargs in data
-        if parameters is not None:
-            outputs = self.model.generate(**inputs, **parameters)
-        else:
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+class ModelHandler:
+    def __init__(self):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = AutoModelForSeq2SeqLM.from_pretrained("shaheerzk/text_to_sql")
+        self.tokenizer = AutoTokenizer.from_pretrained("shaheerzk/text_to_sql")
+        self.model.to(self.device)
+
+    def handle(self, inputs):
+        # Preprocess input
+        text = inputs.get("text", "")
+        inputs = self.tokenizer(text, return_tensors="pt").to(self.device)
+
+        # Inference
+        with torch.no_grad():
             outputs = self.model.generate(**inputs)
 
-        # postprocess the prediction
-        prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-        return [{"generated_text": prediction}]
+        # Post-process output
+        generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return {"generated_text": generated_text}