Create handler.py

handler.py

@@ -0,0 +1,40 @@
+from typing import Dict, List, Any
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
+import torch
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # Load model and processor from path
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(path, device_map="auto", load_in_8bit=True)
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        """
+        Args:
+            data (:obj:):
+                Includes the deserialized image file as PIL.Image
+        """
+        # Process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+
+        # Preprocess
+        input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids
+
+        # Modify parameters to increase max_length
+        if parameters is None:
+            parameters = {}
+        parameters['max_length'] = 1012,  # Set your desired max_length here
+        parameters['min_length'] = 250,
+        parameters['length_penalty'] = 2.0,
+        parameters['num_beams'] = 4,
+        parameters['early_stopping'] = True,
+        
+
+        # Generate output
+        outputs = self.model.generate(input_ids, **parameters)
+        
+        # Postprocess the prediction
+        prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        return [{"generated_text": prediction}]