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Merged
merged 7 commits into from
Jun 22, 2023
Merged

Add edit, audio, and moderation APIs to client abstraction #5

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a21caf6
add edit apis
kristapratico Jun 20, 2023
ed0ff4d
add moderation apis
kristapratico Jun 20, 2023
673eaad
add audio apis
kristapratico Jun 20, 2023
21ac49e
Update openai/client.py
kristapratico Jun 22, 2023
755bcaa
Update openai/client.py
kristapratico Jun 22, 2023
0326584
Update openai/client.py
kristapratico Jun 22, 2023
5343a5f
Update openai/client.py
kristapratico Jun 22, 2023
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147 changes: 146 additions & 1 deletion openai/client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -371,8 +371,153 @@ async def aimage_edit(
response_format=response_format,
user=user,
)
return typing.cast(openai.Image, await openai.Image.acreate_edit(**kwargs))
return cast(openai.Image, await openai.Image.acreate_edit(**kwargs))

def edit(
self,
instruction: str,
*,
input: str = ...,
n: int = ...,
temperature: float = ...,
top_p: float = ...,
**kwargs,
):
self._populate_args(
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It is a bit of an annoying pattern to have to do this dance. But any "magic" (e.g. decorator) would not be significantly simpler. It might be possible to inspect the decorated function and use annotations to avoid the duplication of signatures across, but that does feel pri3:ish.

kwargs,
instruction=instruction,
input=input,
n=n,
temperature=temperature,
top_p=top_p,
)
self._normalize_model(kwargs)
return cast(openai.Edit, openai.Edit.create(**kwargs))

async def aedit(
self,
instruction: str,
*,
input: str = ...,
n: int = ...,
temperature: float = ...,
top_p: float = ...,
**kwargs,
):
self._populate_args(
kwargs,
instruction=instruction,
input=input,
n=n,
temperature=temperature,
top_p=top_p,
)
self._normalize_model(kwargs)
return cast(openai.Edit, await openai.Edit.acreate(**kwargs))

def moderation(
self,
input: Union[str, Iterable[str]],
**kwargs,
):
self._populate_args(
kwargs,
input=input,
)
self._normalize_model(kwargs)
return cast(openai.Moderation, openai.Moderation.create(**kwargs))

async def amoderation(
self,
input: Union[str, Iterable[str]],
**kwargs,
):
self._populate_args(
kwargs,
input=input,
)
self._normalize_model(kwargs)
return cast(openai.Moderation, await openai.Moderation.acreate(**kwargs))

def transcribe_audio(
self,
file: Union[bytes, BinaryIO],
*,
prompt: str = ...,
response_format: str = ...,
temperature: float = ...,
language: str = ...,
**kwargs,
):
self._populate_args(
kwargs,
file=file,
prompt=prompt,
response_format=response_format,
temperature=temperature,
language=language
)
self._normalize_model(kwargs)
return cast(openai.Audio, openai.Audio.transcribe(**kwargs))

async def atranscribe_audio(
self,
file: Union[bytes, BinaryIO],
*,
prompt: str = ...,
response_format: str = ...,
temperature: float = ...,
language: str = ...,
**kwargs,
):
self._populate_args(
kwargs,
file=file,
prompt=prompt,
response_format=response_format,
temperature=temperature,
language=language
)
self._normalize_model(kwargs)
return cast(openai.Audio, await openai.Audio.atranscribe(**kwargs))

def translate_audio(
self,
file: Union[bytes, BinaryIO],
*,
prompt: str = ...,
response_format: str = ...,
temperature: float = ...,
**kwargs,
):
self._populate_args(
kwargs,
file=file,
prompt=prompt,
response_format=response_format,
temperature=temperature,
)
self._normalize_model(kwargs)
return cast(openai.Audio, openai.Audio.translate(**kwargs))

async def atranslate_audio(
self,
file: Union[bytes, BinaryIO],
*,
prompt: str = ...,
response_format: str = ...,
temperature: float = ...,
**kwargs,
):
self._populate_args(
kwargs,
file=file,
prompt=prompt,
response_format=response_format,
temperature=temperature,
)
self._normalize_model(kwargs)
return cast(openai.Audio, await openai.Audio.atranslate(**kwargs))

if __name__ == "__main__":
client = OpenAIClient(
Expand Down