mirror of
https://github.com/xtekky/gpt4free.git
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182 lines
6.1 KiB
Python
182 lines
6.1 KiB
Python
from __future__ import annotations
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import json
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import base64
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from aiohttp import ClientSession
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from typing import AsyncGenerator
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from ..typing import AsyncResult, Messages
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from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
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from ..image import ImageResponse
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from .helper import format_prompt
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class Nexra(AsyncGeneratorProvider, ProviderModelMixin):
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url = "https://nexra.aryahcr.cc"
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api_endpoint_text = "https://nexra.aryahcr.cc/api/chat/gpt"
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api_endpoint_image = "https://nexra.aryahcr.cc/api/image/complements"
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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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models = [
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# Text models
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'gpt-4', 'gpt-4-0613', 'gpt-4-32k', 'gpt-4-0314', 'gpt-4-32k-0314',
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'gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0301',
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'gpt-3', 'text-davinci-003', 'text-davinci-002', 'code-davinci-002',
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'text-curie-001', 'text-babbage-001', 'text-ada-001',
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'davinci', 'curie', 'babbage', 'ada', 'babbage-002', 'davinci-002',
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# Image models
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'dalle', 'dalle-mini', 'emi'
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]
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image_models = {"dalle", "dalle-mini", "emi"}
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text_models = set(models) - image_models
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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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}
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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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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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) -> AsyncGenerator[str | ImageResponse, None]:
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model = cls.get_model(model)
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if model in cls.image_models:
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async for result in cls.create_image_async_generator(model, messages, proxy, **kwargs):
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yield result
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else:
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async for result in cls.create_text_async_generator(model, messages, proxy, **kwargs):
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yield result
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@classmethod
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async def create_text_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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) -> AsyncGenerator[str, None]:
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headers = {
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"Content-Type": "application/json",
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}
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async with ClientSession(headers=headers) as session:
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data = {
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"messages": messages,
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"prompt": format_prompt(messages),
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"model": model,
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"markdown": False,
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"stream": False,
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}
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async with session.post(cls.api_endpoint_text, json=data, proxy=proxy) as response:
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response.raise_for_status()
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result = await response.text()
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json_result = json.loads(result)
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yield json_result["gpt"]
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@classmethod
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async def create_image_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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) -> AsyncGenerator[ImageResponse | str, None]:
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headers = {
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"Content-Type": "application/json"
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}
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prompt = messages[-1]['content'] if messages else ""
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data = {
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"prompt": prompt,
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"model": model
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}
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async def process_response(response_text: str) -> ImageResponse | None:
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json_start = response_text.find('{')
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if json_start != -1:
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json_data = response_text[json_start:]
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try:
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response_data = json.loads(json_data)
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image_data = response_data.get('images', [])[0]
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if image_data.startswith('data:image/'):
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return ImageResponse([image_data], "Generated image")
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try:
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base64.b64decode(image_data)
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data_uri = f"data:image/jpeg;base64,{image_data}"
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return ImageResponse([data_uri], "Generated image")
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except:
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print("Invalid base64 data")
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return None
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except json.JSONDecodeError:
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print("Failed to parse JSON.")
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else:
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print("No JSON data found in the response.")
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return None
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async with ClientSession(headers=headers) as session:
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async with session.post(cls.api_endpoint_image, json=data, proxy=proxy) as response:
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response.raise_for_status()
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response_text = await response.text()
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image_response = await process_response(response_text)
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if image_response:
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yield image_response
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else:
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yield "Failed to process image data."
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@classmethod
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async def create_async(
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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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) -> str:
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async for response in cls.create_async_generator(model, messages, proxy, **kwargs):
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if isinstance(response, ImageResponse):
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return response.images[0]
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return response
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