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https://github.com/xtekky/gpt4free.git
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1296b3f64f
- Remove the unused `language`, `locale`, and `extra_parameters` parameters from the `EdgeTTS` function signature in `g4f/Provider/audio/EdgeTTS.py`. - Update voice selection logic to check for `"locale"` and `"language"` keys in the `audio` dictionary, defaulting to `cls.default_locale` when neither is provided, and modify the error message accordingly. - Refactor extraction of extra parameters by building a dict from the `audio` dictionary for keys `"rate"`, `"volume"`, and `"pitch"`. - In `g4f/api/stubs.py`, remove the try/except block for importing `Annotated` and import `Messages` from `..typing` instead. - Add an optional `audio: Optional[dict] = None` field to the `ImageGenerationConfig` model.
116 lines
No EOL
3.6 KiB
Python
116 lines
No EOL
3.6 KiB
Python
from __future__ import annotations
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from pydantic import BaseModel, Field, model_validator
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from typing import Union, Optional
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from ..typing import Messages
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class ChatCompletionsConfig(BaseModel):
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messages: Messages = Field(examples=[[{"role": "system", "content": ""}, {"role": "user", "content": ""}]])
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model: str = Field(default="")
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provider: Optional[str] = None
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stream: bool = False
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image: Optional[str] = None
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image_name: Optional[str] = None
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images: Optional[list[tuple[str, str]]] = None
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media: Optional[list[tuple[str, str]]] = None
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modalities: Optional[list[str]] = ["text", "audio"]
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temperature: Optional[float] = None
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presence_penalty: Optional[float] = None
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frequency_penalty: Optional[float] = None
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top_p: Optional[float] = None
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max_tokens: Optional[int] = None
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stop: Union[list[str], str, None] = None
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api_key: Optional[str] = None
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api_base: str = None
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web_search: Optional[bool] = None
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proxy: Optional[str] = None
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conversation_id: Optional[str] = None
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conversation: Optional[dict] = None
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return_conversation: Optional[bool] = None
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history_disabled: Optional[bool] = None
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timeout: Optional[int] = None
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tool_calls: list = Field(default=[], examples=[[
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{
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"function": {
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"arguments": {"query":"search query", "max_results":5, "max_words": 2500, "backend": "auto", "add_text": True, "timeout": 5},
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"name": "search_tool"
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},
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"type": "function"
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}
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]])
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tools: list = None
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parallel_tool_calls: bool = None
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tool_choice: Optional[str] = None
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reasoning_effort: Optional[str] = None
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logit_bias: Optional[dict] = None
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modalities: Optional[list[str]] = None
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audio: Optional[dict] = None
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response_format: Optional[dict] = None
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extra_data: Optional[dict] = None
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class ImageGenerationConfig(BaseModel):
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prompt: str
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model: Optional[str] = None
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provider: Optional[str] = None
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response_format: Optional[str] = None
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api_key: Optional[str] = None
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proxy: Optional[str] = None
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width: Optional[int] = None
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height: Optional[int] = None
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num_inference_steps: Optional[int] = None
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seed: Optional[int] = None
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guidance_scale: Optional[int] = None
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aspect_ratio: Optional[str] = None
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n: Optional[int] = None
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negative_prompt: Optional[str] = None
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resolution: Optional[str] = None
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audio: Optional[dict] = None
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@model_validator(mode='before')
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def parse_size(cls, values):
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if values.get('width') is not None and values.get('height') is not None:
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return values
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size = values.get('size')
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if size:
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try:
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width, height = map(int, size.split('x'))
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values['width'] = width
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values['height'] = height
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except (ValueError, AttributeError): pass # If the format is incorrect, we simply ignore it.
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return values
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class ProviderResponseModel(BaseModel):
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id: str
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object: str = "provider"
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created: int
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url: Optional[str]
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label: Optional[str]
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class ProviderResponseDetailModel(ProviderResponseModel):
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models: list[str]
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image_models: list[str]
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vision_models: list[str]
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params: list[str]
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class ModelResponseModel(BaseModel):
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id: str
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object: str = "model"
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created: int
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owned_by: Optional[str]
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class UploadResponseModel(BaseModel):
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bucket_id: str
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url: str
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class ErrorResponseModel(BaseModel):
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error: ErrorResponseMessageModel
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model: Optional[str] = None
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provider: Optional[str] = None
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class ErrorResponseMessageModel(BaseModel):
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message: str
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class FileResponseModel(BaseModel):
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filename: str |