Update handler.py

handler.py

@@ -14,55 +14,49 @@ template = """{char_name}'s Persona: {char_persona}
 class EndpointHandler():
 
     def __init__(self, path=""):
-        self.tokenizer = AutoTokenizer.from_pretrained(path)
-        self.model = AutoModelForCausalLM.from_pretrained(path, load_in_8bit = True, device_map = "auto")
-        self.local_llm = HuggingFacePipeline(
-            pipeline = pipeline(
-                "text-generation",
-                model = self.model,
-                tokenizer = self.tokenizer,
-                max_length = 2048,
-                temperature = 0.5,
-                top_p = 0.9,
-                top_k = 0,
-                repetition_penalty = 1.1,
-                pad_token_id = 50256,
-                num_return_sequences = 1
-            )
-        )
-        self.prompt_template = PromptTemplate(
-            template = template,
-            input_variables = [
-                "user_input",
-                "user_name",
-                "char_name",
-                "char_persona",
-                "char_greeting",
-                "chat_history"
-            ],
-            validate_template = True
-        )
-        self.llm_engine = LLMChain(
-            llm = self.local_llm,
-            prompt = self.prompt_template,
-            verbose = True
-        )
+        pass
+        # tokenizer = AutoTokenizer.from_pretrained(path)
+        # model = AutoModelForCausalLM.from_pretrained(path, load_in_8bit = True, device_map = "auto")
+        # local_llm = HuggingFacePipeline(
+        #     pipeline = pipeline(
+        #         "text-generation",
+        #         model = model,
+        #         tokenizer = tokenizer,
+        #         max_length = 2048,
+        #         temperature = 0.5,
+        #         top_p = 0.9,
+        #         top_k = 0,
+        #         repetition_penalty = 1.1,
+        #         pad_token_id = 50256,
+        #         num_return_sequences = 1
+        #     )
+        # )
+        # prompt_template = PromptTemplate(
+        #     template = template,
+        #     input_variables = [
+        #         "user_input",
+        #         "user_name",
+        #         "char_name",
+        #         "char_persona",
+        #         "char_greeting",
+        #         "chat_history"
+        #     ],
+        #     validate_template = True
+        # )
+        # self.llm_engine = LLMChain(
+        #     llm = local_llm,
+        #     prompt = prompt_template
+        # )
 
-    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
-        """
-        data args:
-            inputs (:obj: `str`)
-            date (:obj: `str`)
-        Return:
-            A :obj:`list` | `dict`: will be serialized and returned
-        """
-        inputs = data.pop("inputs", data)
+    def __call__(self, data: Any) -> Any:
+        return data, type(data)
+        # inputs = data.pop("inputs", data)
 
-        return self.llm_engine.predict(
-            user_input = inputs["user_input"],
-            user_name = inputs["user_name"],
-            char_name = inputs["char_name"],
-            char_persona = inputs["char_persona"],
-            char_greeting = inputs["char_greeting"],
-            chat_history = inputs["chat_history"]
-        ).split("\n",1)[0]
+        # return self.llm_engine.predict(
+        #     user_input = inputs["user_input"],
+        #     user_name = inputs["user_name"],
+        #     char_name = inputs["char_name"],
+        #     char_persona = inputs["char_persona"],
+        #     char_greeting = inputs["char_greeting"],
+        #     chat_history = inputs["chat_history"]
+        # ).split("\n",1)[0]