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from typing import Literal
from datetime import datetime
from typing import Annotated, Literal,Optional
from uuid import uuid4
from pydantic import BaseModel, Field, HttpUrl, IPvAnyAddress, PositiveInt,AfterValidator,validate_call
VoicePresets = Literal["v2/en_speaker_1", "v2/en_speaker_9"]
class ModelRequest(BaseModel):
prompt: Annotated[str, Field(min_length=1, max_length=10000)]
class ModelResponse(BaseModel):
request_id: Annotated[str, Field(default_factory=lambda: uuid4().hex)]
ip: Annotated[Optional[IPvAnyAddress], Field(default=None)]
content: Annotated[Optional[str], Field(min_length=0, max_length=10000,default=None)]
created_at: datetime = datetime.now()
class TextModelRequest(ModelRequest):
model: Literal["gpt-3.5-turbo", "gpt-4o"]
temperature: Annotated[float, Field(ge=0.0, le=1.0, default=0.0)]
class TextModelResponse(ModelResponse):
tokens: Annotated[Optional[int], Field(ge=0,default=None)]
ImageSize = Annotated[tuple[PositiveInt, PositiveInt], "Width and height of an image in pixels"]
SupportedModels = Annotated[
Literal["tinysd", "sd1.5"], "Supported Image Generation Models"
]
@validate_call
def is_square_image(value: ImageSize) -> ImageSize:
if value[0] / value[1] != 1:
raise ValueError("Only square images are supported")
if value[0] not in [512, 1024]:
raise ValueError(f"Invalid output size: {value} - expected 512 or 1024")
return value
@validate_call
def is_valid_inference_step(
num_inference_steps: int, model: SupportedModels
) -> int:
if model == "tinysd" and num_inference_steps > 2000:
raise ValueError(
"TinySD model cannot have more than 2000 inference steps"
)
return num_inference_steps
class ImageModelRequest(ModelRequest):
model: SupportedModels
output_size: ImageSize
num_inference_steps: Annotated[int, Field(ge=0, le=2000)] = 200
class ImageModelResponse(ModelResponse):
size: ImageSize
url: Annotated[Optional[HttpUrl], Field(default=None)]
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