Make SubFox production-ready with parallel translation and UI controls

.venv/lib/python3.10/site-packages/deep_translator/chatgpt.py

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+__copyright__ = "Copyright (C) 2020 Nidhal Baccouri"
+
+import os
+from typing import List, Optional
+
+from deep_translator.base import BaseTranslator
+from deep_translator.constants import OPEN_AI_ENV_VAR
+from deep_translator.exceptions import ApiKeyException
+
+
+class ChatGptTranslator(BaseTranslator):
+    """
+    class that wraps functions, which use the DeeplTranslator translator
+    under the hood to translate word(s)
+    """
+
+    def __init__(
+        self,
+        source: str = "auto",
+        target: str = "english",
+        api_key: Optional[str] = os.getenv(OPEN_AI_ENV_VAR, None),
+        model: Optional[str] = "gpt-3.5-turbo",
+        **kwargs,
+    ):
+        """
+        @param api_key: your openai api key.
+        @param source: source language
+        @param target: target language
+        """
+        if not api_key:
+            raise ApiKeyException(env_var=OPEN_AI_ENV_VAR)
+
+        self.api_key = api_key
+        self.model = model
+
+        super().__init__(source=source, target=target, **kwargs)
+
+    def translate(self, text: str, **kwargs) -> str:
+        """
+        @param text: text to translate
+        @return: translated text
+        """
+        import openai
+
+        openai.api_key = self.api_key
+
+        prompt = f"Translate the text below into {self.target}.\n"
+        prompt += f'Text: "{text}"'
+
+        response = openai.ChatCompletion.create(
+            model=self.model,
+            messages=[
+                {
+                    "role": "user",
+                    "content": prompt,
+                }
+            ],
+        )
+
+        return response.choices[0].message.content
+
+    def translate_file(self, path: str, **kwargs) -> str:
+        return self._translate_file(path, **kwargs)
+
+    def translate_batch(self, batch: List[str], **kwargs) -> List[str]:
+        """
+        @param batch: list of texts to translate
+        @return: list of translations
+        """
+        return self._translate_batch(batch, **kwargs)