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from __future__ import annotations
import json
from aiohttp import ClientSession
from ..typing import AsyncResult, Messages
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from .helper import format_prompt
from ..image import ImageResponse
class Nexra(AsyncGeneratorProvider, ProviderModelMixin):
url = "https://nexra.aryahcr.cc"
chat_api_endpoint = "https://nexra.aryahcr.cc/api/chat/gpt"
image_api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
working = True
supports_gpt_35_turbo = True
supports_gpt_4 = True
supports_system_message = True
supports_message_history = True
default_model = 'gpt-3.5-turbo'
text_models = [
'gpt-4', 'gpt-4-0613', 'gpt-4-32k', 'gpt-4-0314', 'gpt-4-32k-0314',
'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',
'gpt-3', 'text-davinci-003', 'text-davinci-002', 'code-davinci-002',
'text-curie-001', 'text-babbage-001', 'text-ada-001',
'davinci', 'curie', 'babbage', 'ada', 'babbage-002', 'davinci-002',
]
image_models = ['dalle', 'dalle2', 'dalle-mini', 'emi']
models = [*text_models, *image_models]
model_aliases = {
"gpt-4": "gpt-4-0613",
"gpt-4": "gpt-4-32k",
"gpt-4": "gpt-4-0314",
"gpt-4": "gpt-4-32k-0314",
"gpt-3.5-turbo": "gpt-3.5-turbo-16k",
"gpt-3.5-turbo": "gpt-3.5-turbo-0613",
"gpt-3.5-turbo": "gpt-3.5-turbo-16k-0613",
"gpt-3.5-turbo": "gpt-3.5-turbo-0301",
"gpt-3": "text-davinci-003",
"gpt-3": "text-davinci-002",
"gpt-3": "code-davinci-002",
"gpt-3": "text-curie-001",
"gpt-3": "text-babbage-001",
"gpt-3": "text-ada-001",
"gpt-3": "text-ada-001",
"gpt-3": "davinci",
"gpt-3": "curie",
"gpt-3": "babbage",
"gpt-3": "ada",
"gpt-3": "babbage-002",
"gpt-3": "davinci-002",
"dalle-2": "dalle2",
}
@classmethod
def get_model(cls, model: str) -> str:
if model in cls.text_models or model in cls.image_models:
return model
elif model in cls.model_aliases:
return cls.model_aliases[model]
elif model in cls.image_models:
return cls.default_image_model
else:
return cls.default_chat_model
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
headers = {
"Content-Type": "application/json",
}
async with ClientSession(headers=headers) as session:
if model in cls.image_models:
# Image generation
prompt = messages[-1]['content'] if messages else ""
data = {
"prompt": prompt,
"model": model,
"response": "url"
}
async with session.post(cls.image_api_endpoint, json=data, proxy=proxy) as response:
response.raise_for_status()
result = await response.text()
result_json = json.loads(result.strip('_'))
image_url = result_json['images'][0] if result_json['images'] else None
if image_url:
yield ImageResponse(images=image_url, alt=prompt)
else:
# Text completion
data = {
"messages": messages,
"prompt": format_prompt(messages),
"model": model,
"markdown": False
}
async with session.post(cls.chat_api_endpoint, json=data, proxy=proxy) as response:
response.raise_for_status()
result = await response.text()
try:
json_response = json.loads(result)
gpt_response = json_response.get('gpt', '')
yield gpt_response
except json.JSONDecodeError:
yield result
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