Translator updated

skllm/openai/base_gpt.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, List, Optional, Union
+from typing import Any
 
 import numpy as np
 import pandas as pd
@@ -14,24 +14,21 @@
 from skllm.utils import to_numpy as _to_numpy
 
 
-class BaseZeroShotGPTTransformer(_BaseEstimator, _TransformerMixin, _OAIMixin): 
-
+class BaseZeroShotGPTTransformer(_BaseEstimator, _TransformerMixin, _OAIMixin):
     system_msg = "You are an scikit-learn transformer."
     default_output = "Output is unavailable"
 
     def _get_chat_completion(self, X):
-        """
-        Gets the chat completion for the given input using open ai API.
+        """Gets the chat completion for the given input using open ai API.
 
         Parameters
         ----------
         X : str
             Input string
-        
+
         Returns
         -------
         str
-
         """
         prompt = self._get_prompt(X)
         msgs = []
@@ -45,10 +42,11 @@ def _get_chat_completion(self, X):
         except Exception as e:
             print(f"Skipping a sample due to the following error: {str(e)}")
             return self.default_output
-
-    def fit(self, X: Any = None, y: Any = None, **kwargs: Any) -> BaseZeroShotGPTTransformer:
-        """
-        Fits the model to the data.
+
+    def fit(
+        self, X: Any = None, y: Any = None, **kwargs: Any
+    ) -> BaseZeroShotGPTTransformer:
+        """Fits the model to the data.
 
         Parameters
         ----------
@@ -60,12 +58,13 @@ def fit(self, X: Any = None, y: Any = None, **kwargs: Any) -> BaseZeroShotGPTTra
         -------
         self : BaseZeroShotGPTTransformer
         """
-
         return self
 
-    def transform(self, X: Optional[Union[np.ndarray, pd.Series, List[str]]], **kwargs: Any) -> ndarray:
-        """
-        Converts a list of strings using the open ai API and a predefined prompt.
+    def transform(
+        self, X: np.ndarray | pd.Series | list[str], **kwargs: Any
+    ) -> ndarray:
+        """Converts a list of strings using the open ai API and a predefined
+        prompt.
 
         Parameters
         ----------
@@ -78,23 +77,22 @@ def transform(self, X: Optional[Union[np.ndarray, pd.Series, List[str]]], **kwar
         X = _to_numpy(X)
         transformed = []
         for i in tqdm(range(len(X))):
-            transformed.append(
-                self._get_chat_completion(X[i])
-            )
-        transformed = np.asarray(transformed, dtype = object)
+            transformed.append(self._get_chat_completion(X[i]))
+        transformed = np.asarray(transformed, dtype=object)
         return transformed
 
-    def fit_transform(self, X: Optional[Union[np.ndarray, pd.Series, List[str]]], y=None, **fit_params) -> ndarray:
-        """
-        Fits and transforms a list of strings using the transform method.
-        This is modelled to function as the sklearn fit_transform method
+    def fit_transform(
+        self, X: np.ndarray | pd.Series | list[str], y=None, **fit_params
+    ) -> ndarray:
+        """Fits and transforms a list of strings using the transform method.
+        This is modelled to function as the sklearn fit_transform method.
 
         Parameters
         ----------
         X : np.ndarray, pd.Series, or list
 
         Returns
         -------
-        ndarray       
+        ndarray
         """
-        return self.fit(X, y).transform(X)
+        return self.fit(X, y).transform(X)

skllm/preprocessing/gpt_translator.py

@@ -1,4 +1,8 @@
-from typing import Optional
+from typing import Any, List, Optional, Union
+
+import numpy as np
+from numpy import ndarray
+from pandas import Series
 
 from skllm.openai.base_gpt import BaseZeroShotGPTTransformer as _BaseGPT
 from skllm.prompts.builders import build_translation_prompt
@@ -35,3 +39,11 @@ def _get_prompt(self, X: str) -> str:
             translated sample
         """
         return build_translation_prompt(X, self.output_language)
+
+    def transform(self, X: Union[ndarray, Series, List[str]], **kwargs: Any) -> ndarray:
+        y = super().transform(X, **kwargs)
+        y = np.asarray(
+            [i.replace("[Translated text:]", "").replace("```", "").strip() for i in y],
+            dtype=object,
+        )
+        return y

skllm/prompts/templates.py

@@ -81,16 +81,9 @@
 """
 
 TRANSLATION_PROMPT_TEMPLATE = """
-You will be provided with an arbitrary text sample, delimited by triple backticks.
-Your task is to translate this text to {output_language} language and output the translated text.
+If the original text, delimited by triple backticks, is already in {output_language} language, output the original text.
+Otherwise, translate the original text, delimited by triple backticks, to {output_language} language, and output the translated text only. Do not output any additional information except the translated text.
 
-Perform the following actions:
-1. Determine the language of the text sample.
-2. If the language is not {output_language}, translate the text sample to {output_language} language.
-3. Output the translated text.
-If the text sample provided is not in a recognizable language, output "No translation available".
-Do not output any additional information except the translated text.
-
-Text sample: ```{x}```
-Translated text:
+Original text: ```{x}```
+Output:
 """