fix process

LLM/openai_api_language_model.py

+from openai import OpenAI
+from LLM.chat import Chat
+from baseHandler import BaseHandler
+from rich.console import Console
+import logging
+import time
+logger = logging.getLogger(__name__)
+
+console = Console()
+from nltk import sent_tokenize
+
+class OpenApiModelHandler(BaseHandler):
+    """
+    Handles the language model part.
+    """
+    def setup(
+        self,
+        model_name="deepseek-chat",
+        device="cuda",
+        gen_kwargs={},
+        base_url =None,
+        api_key=None,
+        stream=False,
+        user_role="user",
+        chat_size=1,
+        init_chat_role="system",
+        init_chat_prompt="You are a helpful AI assistant.",
+    ):
+        self.model_name = model_name
+        self.stream = stream
+        self.chat = Chat(chat_size)
+        if init_chat_role:
+            if not init_chat_prompt:
+                raise ValueError(
+                    "An initial promt needs to be specified when setting init_chat_role."
+                )
+            self.chat.init_chat({"role": init_chat_role, "content": init_chat_prompt})
+        self.user_role = user_role
+        self.client = OpenAI(api_key=api_key, base_url=base_url)
+        self.warmup()
+
+    def warmup(self):
+        logger.info(f"Warming up {self.__class__.__name__}")
+        start = time.time()
+        response = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant"},
+                {"role": "user", "content": "Hello"},
+            ],
+            stream=self.stream
+        )
+        end = time.time()
+        logger.info(
+            f"{self.__class__.__name__}:  warmed up! time: {(end - start):.3f} s"
+        )
+
+    def process(self, prompt):
+            logger.debug("call api language model...")
+            self.chat.append({"role": self.user_role, "content": prompt})
+
+            language_code = None
+            if isinstance(prompt, tuple):
+                prompt, language_code = prompt
+
+            response = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=[
+                    {"role": self.user_role, "content": prompt},
+                ],
+                stream=self.stream
+            )
+            if self.stream:
+                generated_text, printable_text = "", ""
+                for chunk in response:
+                    new_text = chunk.choices[0].delta.content or ""
+                    generated_text += new_text
+                    printable_text += new_text
+                    sentences = sent_tokenize(printable_text)
+                    if len(sentences) > 1:
+                        yield sentences[0], language_code
+                        printable_text = new_text
+                self.chat.append({"role": "assistant", "content": generated_text})
+                # don't forget last sentence
+                yield printable_text, language_code
+            else:
+                generated_text = response.choices[0].message.content
+                self.chat.append({"role": "assistant", "content": generated_text})
+                yield generated_text, language_code