refactor code and resolve conflicts with upstream

book_maker/translator/chatgptapi_translator.py

@@ -1,10 +1,10 @@
 import time
 
 import openai
-from ..utils import num_tokens_from_messages
 from os import environ
 
 from .base_translator import Base
+from ..utils import num_tokens_from_messages
 
 
 PROMPT_ENV_MAP = {
@@ -59,8 +59,9 @@ def get_translation(self, text):
                     text=text, language=self.language
                 ),
             }
-        ]
-        count_tokens = num_tokens_from_messages(message_log)
+        )
+
+        count_tokens = num_tokens_from_messages(messages)
         consumed_tokens = 0
         t_text = ""
         if count_tokens > 4000:
@@ -75,71 +76,67 @@ def get_translation(self, text):
                 text_segment = text_list[n * splits : (n + 1) * splits]
                 sub_text = ".".join(text_segment)
                 print(sub_text)
+                message_log = []
+
+                if self.prompt_sys_msg:
+                    message_log.append(
+                        {"role": "system", "content": self.prompt_sys_msg},
+                    )
+
+                message_log.append(
+                    {
+                        "role": "user",
+                        "content": self.prompt_template.format(
+                            text=sub_text, language=self.language
+                        ),
+                    }
+                )
+
+                t_sub_text, completion = self.call_chatgpt(message_log)
+                print(t_sub_text)
+                consumed_tokens += completion["usage"]["prompt_tokens"]
+
+                t_text = t_text + t_sub_text
+
+        else:
+
+            t_sub_text, completion = self.call_chatgpt(messages)
+            consumed_tokens += completion["usage"]["prompt_tokens"]
+
+        print(f"{consumed_tokens} prompt tokens used.")
+        return t_text
 
+    def call_chatgpt(self, message_log):
         completion = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=messages,
+            model="gpt-3.5-turbo", messages=message_log
         )
-        t_text = (
+        t_sub_text = (
             completion["choices"][0]
             .get("message")
             .get("content")
             .encode("utf8")
             .decode()
         )
-        return t_text
-                consumed_tokens += completion["usage"]["prompt_tokens"]
+
+        return t_sub_text, completion
 
     def translate(self, text):
         # todo: Determine whether to print according to the cli option
         print(text)
 
-            else:
-                try:
-                    completion = openai.ChatCompletion.create(
-                        model="gpt-3.5-turbo",
-                        messages=[
-                            {
-                                "role": "user",
-                                # english prompt here to save tokens
-                                "content": f"Please help me to translate,`{text}` to {self.language}, please return only translated content not include the origin text",
-                            }
-                        ],
-                    )
-                    t_text = (
-                        completion["choices"][0]
-                        .get("message")
-                        .get("content")
-                        .encode("utf8")
-                        .decode()
-                    )
-                    consumed_tokens += completion["usage"]["prompt_tokens"]
-
-                except Exception as e:
-                    # TIME LIMIT for open api please pay
-                    key_len = self.key.count(",") + 1
-                    sleep_time = int(60 / key_len)
-                    time.sleep(sleep_time)
-                    print(e, f"will sleep  {sleep_time} seconds")
-                    self.rotate_key()
-                    completion = openai.ChatCompletion.create(
-                        model="gpt-3.5-turbo",
-                        messages=[
-                            {
-                                "role": "user",
-                                "content": f"Please help me to translate,`{text}` to {self.language}, please return only translated content not include the origin text",
-                            }
-                        ],
-                    )
-                    t_text = (
-                        completion["choices"][0]
-                        .get("message")
-                        .get("content")
-                        .encode("utf8")
-                        .decode()
-                    )
-                    consumed_tokens += completion["usage"]["prompt_tokens"]
+        try:
+            t_text = self.get_translation(text)
+        except Exception as e:
+            # todo: better sleep time? why sleep alawys about key_len
+            # 1. openai server error or own network interruption, sleep for a fixed time
+            # 2. an apikey has no money or reach limit, don’t sleep, just replace it with another apikey
+            # 3. all apikey reach limit, then use current sleep
+            sleep_time = int(60 / self.key_len)
+            print(e, f"will sleep {sleep_time} seconds")
+            time.sleep(sleep_time)
 
-        print(t_text)
-        print(f"{consumed_tokens} prompt tokens used.")
+            t_text = self.get_translation(text)
+
+        # todo: Determine whether to print according to the cli option
+        print(t_text.strip())
         return t_text

book_maker/utils.py

@@ -140,7 +140,7 @@ def num_tokens_from_messages(messages, model="gpt-3.5-turbo"):
         num_tokens += 2  # every reply is primed with <im_start>assistant
         return num_tokens
     else:
-        raise NotImplementedError(
+        print(
             f"""num_tokens_from_messages() is not presently implemented for model {model}.
 See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
         )