mirror of
https://github.com/xtekky/gpt4free.git
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81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
from __future__ import annotations
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import requests
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from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
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from ...typing import AsyncResult, Messages
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from ...requests import StreamSession, raise_for_status
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from ...image import ImageResponse
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class DeepInfraImage(AsyncGeneratorProvider, ProviderModelMixin):
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url = "https://deepinfra.com"
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parent = "DeepInfra"
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working = True
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needs_auth = True
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default_model = ''
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image_models = [default_model]
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@classmethod
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def get_models(cls):
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if not cls.models:
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url = 'https://api.deepinfra.com/models/featured'
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models = requests.get(url).json()
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cls.models = [model['model_name'] for model in models if model["reported_type"] == "text-to-image"]
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cls.image_models = cls.models
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return cls.models
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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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**kwargs
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) -> AsyncResult:
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yield await cls.create_async(messages[-1]["content"], model, **kwargs)
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@classmethod
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async def create_async(
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cls,
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prompt: str,
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model: str,
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api_key: str = None,
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api_base: str = "https://api.deepinfra.com/v1/inference",
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proxy: str = None,
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timeout: int = 180,
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extra_data: dict = {},
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**kwargs
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) -> ImageResponse:
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headers = {
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'Accept-Encoding': 'gzip, deflate, br',
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'Accept-Language': 'en-US',
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'Connection': 'keep-alive',
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'Origin': 'https://deepinfra.com',
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'Referer': 'https://deepinfra.com/',
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'Sec-Fetch-Dest': 'empty',
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'Sec-Fetch-Mode': 'cors',
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'Sec-Fetch-Site': 'same-site',
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'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
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'X-Deepinfra-Source': 'web-embed',
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'sec-ch-ua': '"Google Chrome";v="119", "Chromium";v="119", "Not?A_Brand";v="24"',
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'sec-ch-ua-mobile': '?0',
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'sec-ch-ua-platform': '"macOS"',
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}
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if api_key is not None:
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headers["Authorization"] = f"Bearer {api_key}"
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async with StreamSession(
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proxies={"all": proxy},
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headers=headers,
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timeout=timeout
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) as session:
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model = cls.get_model(model)
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data = {"prompt": prompt, **extra_data}
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data = {"input": data} if model == cls.default_model else data
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async with session.post(f"{api_base.rstrip('/')}/{model}", json=data) as response:
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await raise_for_status(response)
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data = await response.json()
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images = data["output"] if "output" in data else data["images"]
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if not images:
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raise RuntimeError(f"Response: {data}")
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images = images[0] if len(images) == 1 else images
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return ImageResponse(images, prompt)
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