gpt4free/g4f/api/stubs.py

from __future__ import annotations

from pydantic import BaseModel, Field, model_validator
from typing import Union, Optional

from ..typing import Messages


class RequestConfig(BaseModel):
    model: str = Field(default="")
    provider: Optional[str] = None
    media: Optional[list[tuple[str, str]]] = None
    modalities: Optional[list[str]] = None
    temperature: Optional[float] = None
    presence_penalty: Optional[float] = None
    frequency_penalty: Optional[float] = None
    top_p: Optional[float] = None
    max_tokens: Optional[int] = None
    stop: Union[list[str], str, None] = None
    api_key: Optional[Union[str, dict[str, str]]] = None
    base_url: Optional[str] = None
    web_search: Optional[bool] = None
    proxy: Optional[str] = None
    conversation: Optional[dict] = None
    timeout: Optional[int] = None
    stream_timeout: Optional[int] = None
    tool_calls: list = Field(
        default=[],
        examples=[
            [
                {
                    "function": {
                        "arguments": {
                            "query": "search query",
                            "max_results": 5,
                            "max_words": 2500,
                            "backend": "auto",
                            "add_text": True,
                            "timeout": 5,
                        },
                        "name": "search_tool",
                    },
                    "type": "function",
                }
            ]
        ],
    )
    reasoning_effort: Optional[str] = None
    logit_bias: Optional[dict] = None
    modalities: Optional[list[str]] = None
    audio: Optional[dict] = None
    response_format: Optional[dict] = None
    download_media: bool = False
    raw: bool = False
    extra_body: Optional[dict] = None
    # When set (or when env G4F_TOOL_EMULATION=1), the server will attempt to
    # emulate OpenAI tool_calls for providers that don't support tools natively.
    tool_emulation: Optional[bool] = None


class ChatCompletionsConfig(RequestConfig):
    messages: Messages = Field(
        examples=[[{"role": "system", "content": ""}, {"role": "user", "content": ""}]]
    )
    stream: bool = False
    image: Optional[str] = None
    image_name: Optional[str] = None
    images: Optional[list[tuple[str, str]]] = None
    tools: Optional[list] = None
    parallel_tool_calls: Optional[bool] = None
    tool_choice: Optional[str] = None
    conversation_id: Optional[str] = None


class ResponsesConfig(RequestConfig):
    input: Union[Messages, str]


class ImageGenerationConfig(BaseModel):
    prompt: str
    model: Optional[str] = None
    provider: Optional[str] = None
    response_format: Optional[str] = None
    api_key: Optional[str] = None
    proxy: Optional[str] = None
    width: Optional[int] = None
    height: Optional[int] = None
    num_inference_steps: Optional[int] = None
    seed: Optional[int] = None
    guidance_scale: Optional[int] = None
    aspect_ratio: Optional[str] = None
    n: Optional[int] = None
    negative_prompt: Optional[str] = None
    resolution: Optional[str] = None
    audio: Optional[dict] = None
    download_media: bool = True

    @model_validator(mode="before")
    def parse_size(cls, values):
        if values.get("width") is not None and values.get("height") is not None:
            return values

        size = values.get("size")
        if size:
            try:
                width, height = map(int, size.split("x"))
                values["width"] = width
                values["height"] = height
            except (ValueError, AttributeError):
                pass  # If the format is incorrect, we simply ignore it.
        return values


class ProviderResponseModel(BaseModel):
    id: str
    object: str = "provider"
    created: int
    url: Optional[str]
    label: Optional[str]


class ProviderResponseDetailModel(ProviderResponseModel):
    models: list[str]
    image_models: list[str]
    vision_models: list[str]
    params: list[str]


class ModelResponseModel(BaseModel):
    id: str
    object: str = "model"
    created: int
    owned_by: Optional[str]


class UploadResponseModel(BaseModel):
    bucket_id: str
    url: str


class ErrorResponseModel(BaseModel):
    error: ErrorResponseMessageModel
    model: Optional[str] = None
    provider: Optional[str] = None


class ErrorResponseMessageModel(BaseModel):
    message: str


class FileResponseModel(BaseModel):
    filename: str


class TranscriptionResponseModel(BaseModel):
    text: str
    model: str
    provider: str


class AudioSpeechConfig(BaseModel):
    input: str
    model: Optional[str] = None
    provider: Optional[str] = None
    voice: Optional[str] = None
    instrcutions: str = "Speech this text in a natural way."
    response_format: Optional[str] = None
    language: Optional[str] = None
    download_media: bool = True