Delete handler.py

handler.py

@@ -1,52 +0,0 @@
-from typing import Dict, List, Any
-from transformers import AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList,StoppingCriteria
-import torch
-
-
-class EndpointHandler():
-    def __init__(self, path="."):
-        # load model and processor from path
-        self.model =  AutoModelForCausalLM.from_pretrained(path)
-        self.tokenizer = AutoTokenizer.from_pretrained(path)
-
-        # Create a stopping criteria class
-        class KeywordsStoppingCriteria(StoppingCriteria):
-            def __init__(self, keywords_ids: list, occurrences: int):
-                super().__init__()
-                self.keywords = keywords_ids
-                self.occurrences = occurrences
-                self.count = 0
-            
-            def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-                if input_ids[0][-1] in self.keywords:
-                    self.count += 1
-                    if self.count == self.occurrences:
-                        return True
-                return False
-
-    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
-
-        # process input
-        inputs = data["inputs"]
-
-        stop_words = ['.']
-        stop_ids = [self.tokenizer.encode(w)[1] for w in stop_words]
-        gen_outputs = []
-        gen_outputs_no_input = []
-        gen_input = self.tokenizer(input, return_tensors="pt")
-        for _ in range(5):
-            stop_criteria = KeywordsStoppingCriteria(stop_ids, occurrences=2)
-            gen_output = self.model.generate(gen_input.input_ids, do_sample=True,
-                                            top_k=10,
-                                            top_p=0.95,
-                                            max_new_tokens=100,
-                                            penalty_alpha=0.6,
-                                            stopping_criteria=StoppingCriteriaList([stop_criteria])
-                                            )
-            gen_outputs.append(gen_output)
-            gen_outputs_no_input.append(gen_output[0][len(gen_input.input_ids[0]):])
-
-        gen_outputs_decoded = [self.tokenizer.decode(gen_output[0], skip_special_tokens=True) for gen_output in gen_outputs]
-        gen_outputs_no_input_decoded = [self.tokenizer.decode(gen_output_no_input, skip_special_tokens=True) for gen_output_no_input in gen_outputs_no_input]
-
-        return {"gen_outputs_decoded": gen_outputs_decoded, "gen_outputs_no_input_decoded": gen_outputs_no_input_decoded}