mirror of
https://github.com/xtekky/gpt4free.git
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119 lines
3.7 KiB
Python
119 lines
3.7 KiB
Python
from __future__ import annotations
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from aiohttp import ClientSession
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from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
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from .helper import format_prompt
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from .nexra.NexraBing import NexraBing
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from .nexra.NexraChatGPT import NexraChatGPT
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from .nexra.NexraChatGPT4o import NexraChatGPT4o
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from .nexra.NexraChatGPTWeb import NexraChatGPTWeb
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from .nexra.NexraGeminiPro import NexraGeminiPro
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from .nexra.NexraImageURL import NexraImageURL
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from .nexra.NexraLlama import NexraLlama
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from .nexra.NexraQwen import NexraQwen
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class Nexra(AsyncGeneratorProvider, ProviderModelMixin):
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url = "https://nexra.aryahcr.cc"
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working = True
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supports_gpt_35_turbo = True
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supports_gpt_4 = True
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supports_stream = True
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supports_system_message = True
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supports_message_history = True
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default_model = 'gpt-3.5-turbo'
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image_model = 'sdxl-turbo'
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models = (
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*NexraBing.models,
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*NexraChatGPT.models,
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*NexraChatGPT4o.models,
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*NexraChatGPTWeb.models,
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*NexraGeminiPro.models,
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*NexraImageURL.models,
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*NexraLlama.models,
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*NexraQwen.models,
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)
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model_to_provider = {
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**{model: NexraChatGPT for model in NexraChatGPT.models},
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**{model: NexraChatGPT4o for model in NexraChatGPT4o.models},
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**{model: NexraChatGPTWeb for model in NexraChatGPTWeb.models},
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**{model: NexraGeminiPro for model in NexraGeminiPro.models},
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**{model: NexraImageURL for model in NexraImageURL.models},
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**{model: NexraLlama for model in NexraLlama.models},
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**{model: NexraQwen for model in NexraQwen.models},
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**{model: NexraBing for model in NexraBing.models},
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}
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model_aliases = {
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"gpt-4": "gpt-4-0613",
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"gpt-4": "gpt-4-32k",
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"gpt-4": "gpt-4-0314",
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"gpt-4": "gpt-4-32k-0314",
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"gpt-3.5-turbo": "gpt-3.5-turbo-16k",
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"gpt-3.5-turbo": "gpt-3.5-turbo-0613",
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"gpt-3.5-turbo": "gpt-3.5-turbo-16k-0613",
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"gpt-3.5-turbo": "gpt-3.5-turbo-0301",
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"gpt-3": "text-davinci-003",
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"gpt-3": "text-davinci-002",
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"gpt-3": "code-davinci-002",
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"gpt-3": "text-curie-001",
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"gpt-3": "text-babbage-001",
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"gpt-3": "text-ada-001",
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"gpt-3": "text-ada-001",
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"gpt-3": "davinci",
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"gpt-3": "curie",
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"gpt-3": "babbage",
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"gpt-3": "ada",
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"gpt-3": "babbage-002",
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"gpt-3": "davinci-002",
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"gpt-4": "gptweb",
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"gpt-4": "Bing (Balanced)",
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"gpt-4": "Bing (Creative)",
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"gpt-4": "Bing (Precise)",
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"dalle-2": "dalle2",
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"sdxl": "sdxl-turbo",
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}
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@classmethod
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def get_model(cls, model: str) -> str:
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if model in cls.models:
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return model
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elif model in cls.model_aliases:
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return cls.model_aliases[model]
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else:
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return cls.default_model
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@classmethod
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def get_api_endpoint(cls, model: str) -> str:
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provider_class = cls.model_to_provider.get(model)
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if provider_class:
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return provider_class.api_endpoint
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raise ValueError(f"API endpoint for model {model} not found.")
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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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proxy: str = None,
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**kwargs
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) -> AsyncResult:
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model = cls.get_model(model)
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api_endpoint = cls.get_api_endpoint(model)
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provider_class = cls.model_to_provider.get(model)
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if provider_class:
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async for response in provider_class.create_async_generator(model, messages, proxy, **kwargs):
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yield response
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else:
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raise ValueError(f"Provider for model {model} not found.")
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