Create handler.py

handler.py

@@ -0,0 +1,68 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from langchain.llms import HuggingFacePipeline
+from langchain import PromptTemplate, LLMChain
+
+template = """{char_name}'s Persona: {char_persona}
+<START>
+{chat_history}
+{char_name}: {char_greeting}
+<END>
+{user_name}: {user_input}
+{char_name}: """
+
+class EndpointHandler():
+
+    def __init__(self, path=""):
+        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):
+        inputs = data.pop("inputs", data)
+        try:
+            response = 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 {
+                "inputs": inputs,
+                "text": response
+            }
+        except Exception as e:
+            return {
+                "inputs": inputs,
+                "error": str(e)
+            }