mirror of
https://github.com/xtekky/gpt4free.git
synced 2025-09-26 20:31:14 +08:00
Qwen Catch error (#3186)
This commit is contained in:
Ammar
@@ -61,6 +61,7 @@ FOLLOWUPS_DEVELOPER_MESSAGE = [{
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"content": "Provide conversation options.",
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}]
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class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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label = "Pollinations AI 🌸"
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url = "https://pollinations.ai"
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@@ -110,6 +111,7 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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elif alias in cls.swap_model_aliases:
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alias = cls.swap_model_aliases[alias]
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return alias.replace("-instruct", "").replace("qwen-", "qwen").replace("qwen", "qwen-")
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if not cls._models_loaded:
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try:
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# Update of image models
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@@ -121,12 +123,12 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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# Combine image models without duplicates
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image_models = cls.image_models.copy() # Start with default model
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# Add extra image models if not already in the list
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for model in new_image_models:
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if model not in image_models:
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image_models.append(model)
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cls.image_models = image_models
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text_response = requests.get("https://g4f.dev/api/pollinations.ai/models")
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@@ -192,36 +194,37 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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@classmethod
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async def create_async_generator(
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cls,
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model: str,
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messages: Messages,
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stream: bool = True,
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proxy: str = None,
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cache: bool = None,
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referrer: str = STATIC_URL,
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api_key: str = None,
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extra_body: dict = None,
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# Image generation parameters
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prompt: str = None,
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aspect_ratio: str = None,
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width: int = None,
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height: int = None,
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seed: Optional[int] = None,
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nologo: bool = True,
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private: bool = False,
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enhance: bool = None,
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safe: bool = False,
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transparent: bool = False,
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n: int = 1,
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# Text generation parameters
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media: MediaListType = None,
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temperature: float = None,
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presence_penalty: float = None,
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top_p: float = None,
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frequency_penalty: float = None,
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response_format: Optional[dict] = None,
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extra_parameters: list[str] = ["tools", "parallel_tool_calls", "tool_choice", "reasoning_effort", "logit_bias", "voice", "modalities", "audio"],
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**kwargs
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cls,
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model: str,
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messages: Messages,
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stream: bool = True,
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proxy: str = None,
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cache: bool = None,
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referrer: str = STATIC_URL,
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api_key: str = None,
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extra_body: dict = None,
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# Image generation parameters
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prompt: str = None,
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aspect_ratio: str = None,
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width: int = None,
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height: int = None,
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seed: Optional[int] = None,
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nologo: bool = True,
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private: bool = False,
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enhance: bool = None,
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safe: bool = False,
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transparent: bool = False,
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n: int = 1,
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# Text generation parameters
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media: MediaListType = None,
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temperature: float = None,
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presence_penalty: float = None,
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top_p: float = None,
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frequency_penalty: float = None,
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response_format: Optional[dict] = None,
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extra_parameters: list[str] = ["tools", "parallel_tool_calls", "tool_choice", "reasoning_effort",
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"logit_bias", "voice", "modalities", "audio"],
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**kwargs
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) -> AsyncResult:
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if cache is None:
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cache = kwargs.get("action") == "next"
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@@ -241,23 +244,23 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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debug.log(f"Using model: {model}")
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if model in cls.image_models:
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async for chunk in cls._generate_image(
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model="gptimage" if model == "transparent" else model,
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prompt=format_media_prompt(messages, prompt),
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media=media,
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proxy=proxy,
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aspect_ratio=aspect_ratio,
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width=width,
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height=height,
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seed=seed,
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cache=cache,
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nologo=nologo,
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private=private,
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enhance=enhance,
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safe=safe,
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transparent=transparent or model == "transparent",
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n=n,
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referrer=referrer,
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api_key=api_key
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model="gptimage" if model == "transparent" else model,
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prompt=format_media_prompt(messages, prompt),
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media=media,
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proxy=proxy,
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aspect_ratio=aspect_ratio,
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width=width,
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height=height,
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seed=seed,
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cache=cache,
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nologo=nologo,
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private=private,
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enhance=enhance,
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safe=safe,
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transparent=transparent or model == "transparent",
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n=n,
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referrer=referrer,
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api_key=api_key
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):
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yield chunk
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else:
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@@ -272,47 +275,47 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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}
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model = cls.default_audio_model
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async for result in cls._generate_text(
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model=model,
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messages=messages,
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media=media,
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proxy=proxy,
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temperature=temperature,
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presence_penalty=presence_penalty,
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top_p=top_p,
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frequency_penalty=frequency_penalty,
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response_format=response_format,
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seed=seed,
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cache=cache,
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stream=stream,
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extra_parameters=extra_parameters,
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referrer=referrer,
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api_key=api_key,
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extra_body=extra_body,
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**kwargs
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model=model,
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messages=messages,
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media=media,
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proxy=proxy,
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temperature=temperature,
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presence_penalty=presence_penalty,
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top_p=top_p,
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frequency_penalty=frequency_penalty,
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response_format=response_format,
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seed=seed,
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cache=cache,
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stream=stream,
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extra_parameters=extra_parameters,
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referrer=referrer,
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api_key=api_key,
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extra_body=extra_body,
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**kwargs
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):
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yield result
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@classmethod
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async def _generate_image(
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cls,
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model: str,
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prompt: str,
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media: MediaListType,
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proxy: str,
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aspect_ratio: str,
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width: int,
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height: int,
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seed: Optional[int],
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cache: bool,
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nologo: bool,
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private: bool,
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enhance: bool,
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safe: bool,
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transparent: bool,
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n: int,
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referrer: str,
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api_key: str,
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timeout: int = 120
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cls,
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model: str,
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prompt: str,
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media: MediaListType,
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proxy: str,
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aspect_ratio: str,
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width: int,
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height: int,
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seed: Optional[int],
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cache: bool,
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nologo: bool,
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private: bool,
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enhance: bool,
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safe: bool,
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transparent: bool,
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n: int,
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referrer: str,
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api_key: str,
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timeout: int = 120
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) -> AsyncResult:
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if enhance is None:
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enhance = True if model == "flux" else False
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@@ -339,37 +342,47 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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encoded_prompt = prompt.strip(". \n")
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if model == "gptimage" and aspect_ratio is not None:
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encoded_prompt = f"{encoded_prompt} aspect-ratio: {aspect_ratio}"
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encoded_prompt = quote_plus(encoded_prompt)[:4096-len(cls.image_api_endpoint)-len(query)-8].rstrip("%")
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encoded_prompt = quote_plus(encoded_prompt)[:4096 - len(cls.image_api_endpoint) - len(query) - 8].rstrip("%")
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url = f"{cls.image_api_endpoint}prompt/{encoded_prompt}?{query}"
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def get_url_with_seed(i: int, seed: Optional[int] = None):
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if model == "gptimage":
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return url
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if i == 0:
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if not cache and seed is None:
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seed = random.randint(0, 2**32)
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seed = random.randint(0, 2 ** 32)
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else:
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seed = random.randint(0, 2**32)
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seed = random.randint(0, 2 ** 32)
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return f"{url}&seed={seed}" if seed else url
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headers = {"referer": referrer}
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if api_key:
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headers["authorization"] = f"Bearer {api_key}"
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async with ClientSession(
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headers=DEFAULT_HEADERS,
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connector=get_connector(proxy=proxy),
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timeout=ClientTimeout(timeout)
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headers=DEFAULT_HEADERS,
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connector=get_connector(proxy=proxy),
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timeout=ClientTimeout(timeout)
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) as session:
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responses = set()
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yield Reasoning(label=f"Generating {n} {'image' if n == 1 else 'images'}")
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finished = 0
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start = time.time()
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async def get_image(responses: set, i: int, seed: Optional[int] = None):
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try:
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async with session.get(get_url_with_seed(i, seed), allow_redirects=False, headers=headers) as response:
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async with session.get(get_url_with_seed(i, seed), allow_redirects=False,
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headers=headers) as response:
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await raise_for_status(response)
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except Exception as e:
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responses.add(e)
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debug.error(f"Error fetching image: {e}")
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responses.add(ImageResponse(str(response.url), prompt, {"headers": headers}))
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if response.headers['content-type'].startswith("image/"):
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responses.add(ImageResponse(str(response.url), prompt, {"headers": headers}))
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else:
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t_ = await response.text()
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debug.error(f"UnHandel Error fetching image: {t_}")
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responses.add(t_)
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tasks: list[asyncio.Task] = []
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for i in range(int(n)):
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tasks.append(asyncio.create_task(get_image(responses, i, seed)))
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@@ -386,8 +399,9 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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raise item
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else:
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finished += 1
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yield Reasoning(label=f"Image {finished}/{n} failed after {time.time() - start:.2f}s: {item}")
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else:
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yield Reasoning(
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label=f"Image {finished}/{n} failed after {time.time() - start:.2f}s: {item}")
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else:
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finished += 1
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yield Reasoning(label=f"Image {finished}/{n} generated in {time.time() - start:.2f}s")
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yield item
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@@ -397,27 +411,27 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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@classmethod
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async def _generate_text(
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cls,
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model: str,
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messages: Messages,
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media: MediaListType,
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proxy: str,
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temperature: float,
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presence_penalty: float,
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top_p: float,
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frequency_penalty: float,
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response_format: Optional[dict],
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seed: Optional[int],
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cache: bool,
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stream: bool,
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extra_parameters: list[str],
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referrer: str,
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api_key: str,
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extra_body: dict,
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**kwargs
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cls,
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model: str,
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messages: Messages,
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media: MediaListType,
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proxy: str,
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temperature: float,
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presence_penalty: float,
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top_p: float,
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frequency_penalty: float,
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response_format: Optional[dict],
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seed: Optional[int],
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cache: bool,
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stream: bool,
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extra_parameters: list[str],
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referrer: str,
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api_key: str,
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extra_body: dict,
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**kwargs
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) -> AsyncResult:
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if not cache and seed is None:
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seed = random.randint(0, 2**32)
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seed = random.randint(0, 2 ** 32)
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async with ClientSession(headers=DEFAULT_HEADERS, connector=get_connector(proxy=proxy)) as session:
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extra_body.update({param: kwargs[param] for param in extra_parameters if param in kwargs})
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@@ -440,7 +454,7 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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frequency_penalty=frequency_penalty,
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response_format=response_format,
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stream=stream,
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seed=None if model =="grok" else seed,
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seed=None if model == "grok" else seed,
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referrer=referrer,
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**extra_body
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)
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@@ -451,7 +465,8 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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if response.status in (400, 500):
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debug.error(f"Error: {response.status} - Bad Request: {data}")
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full_resposne = []
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async for chunk in read_response(response, stream, format_media_prompt(messages), cls.get_dict(), kwargs.get("download_media", True)):
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async for chunk in read_response(response, stream, format_media_prompt(messages), cls.get_dict(),
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kwargs.get("download_media", True)):
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if isinstance(chunk, str):
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full_resposne.append(chunk)
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yield chunk
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@@ -479,7 +494,8 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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async with session.post(cls.openai_endpoint, json=data, headers=headers) as response:
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try:
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await raise_for_status(response)
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tool_calls = (await response.json()).get("choices", [{}])[0].get("message", {}).get("tool_calls", [])
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tool_calls = (await response.json()).get("choices", [{}])[0].get("message", {}).get(
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"tool_calls", [])
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if tool_calls:
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arguments = json.loads(tool_calls.pop().get("function", {}).get("arguments"))
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if arguments.get("title"):
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@@ -487,4 +503,4 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
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if arguments.get("followups"):
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yield SuggestedFollowups(arguments.get("followups"))
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except Exception as e:
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debug.error("Error generating title and followups:", e)
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debug.error("Error generating title and followups:", e)
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@@ -8,7 +8,7 @@ from time import time
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from typing import Literal, Optional
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import aiohttp
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from ..errors import RateLimitError
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from ..errors import RateLimitError, ResponseError
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from ..typing import AsyncResult, Messages, MediaListType
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from ..providers.response import JsonConversation, Reasoning, Usage, ImageResponse, FinishReason
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from ..requests import sse_stream
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@@ -97,20 +97,20 @@ class Qwen(AsyncGeneratorProvider, ProviderModelMixin):
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@classmethod
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async def create_async_generator(
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cls,
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model: str,
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messages: Messages,
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media: MediaListType = None,
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conversation: JsonConversation = None,
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proxy: str = None,
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timeout: int = 120,
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stream: bool = True,
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enable_thinking: bool = True,
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chat_type: Literal[
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"t2t", "search", "artifacts", "web_dev", "deep_research", "t2i", "image_edit", "t2v"
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] = "t2t",
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aspect_ratio: Optional[Literal["1:1", "4:3", "3:4", "16:9", "9:16"]] = None,
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**kwargs
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cls,
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model: str,
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messages: Messages,
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media: MediaListType = None,
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conversation: JsonConversation = None,
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proxy: str = None,
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timeout: int = 120,
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stream: bool = True,
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enable_thinking: bool = True,
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chat_type: Literal[
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"t2t", "search", "artifacts", "web_dev", "deep_research", "t2i", "image_edit", "t2v"
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] = "t2t",
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aspect_ratio: Optional[Literal["1:1", "4:3", "3:4", "16:9", "9:16"]] = None,
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**kwargs
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) -> AsyncResult:
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"""
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chat_type:
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@@ -265,6 +265,9 @@ class Qwen(AsyncGeneratorProvider, ProviderModelMixin):
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usage = None
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async for chunk in sse_stream(resp):
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try:
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error = chunk.get("error", {})
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if error:
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raise ResponseError(f'{error["code"]}: {error["details"]}')
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usage = chunk.get("usage", usage)
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choices = chunk.get("choices", [])
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if not choices: continue
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