from __future__ import annotations
import json
import base64
from aiohttp import ClientSession
from typing import AsyncGenerator
from ..typing import AsyncResult, Messages
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ..image import ImageResponse
from .helper import format_prompt
class Nexra(AsyncGeneratorProvider, ProviderModelMixin):
url = "https://nexra.aryahcr.cc"
api_endpoint_text = "https://nexra.aryahcr.cc/api/chat/gpt"
api_endpoint_image = "https://nexra.aryahcr.cc/api/image/complements"
working = True
supports_gpt_35_turbo = True
supports_gpt_4 = True
supports_stream = True
supports_system_message = True
supports_message_history = True
default_model = 'gpt-3.5-turbo'
models = [
# 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', 'dalle-mini', 'emi'
]
image_models = {"dalle", "dalle-mini", "emi"}
text_models = set(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",
}
@classmethod
def get_model(cls, model: str) -> str:
if model in cls.models:
return model
elif model in cls.model_aliases:
return cls.model_aliases[model]
else:
return cls.default_model
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
**kwargs
) -> AsyncGenerator[str | ImageResponse, None]:
model = cls.get_model(model)
if model in cls.image_models:
async for result in cls.create_image_async_generator(model, messages, proxy, **kwargs):
yield result
else:
async for result in cls.create_text_async_generator(model, messages, proxy, **kwargs):
yield result
@classmethod
async def create_text_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
**kwargs
) -> AsyncGenerator[str, None]:
headers = {
"Content-Type": "application/json",
}
async with ClientSession(headers=headers) as session:
data = {
"messages": messages,
"prompt": format_prompt(messages),
"model": model,
"markdown": False,
"stream": False,
}
async with session.post(cls.api_endpoint_text, json=data, proxy=proxy) as response:
response.raise_for_status()
result = await response.text()
json_result = json.loads(result)
yield json_result["gpt"]
@classmethod
async def create_image_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
**kwargs
) -> AsyncGenerator[ImageResponse | str, None]:
headers = {
"Content-Type": "application/json"
}
prompt = messages[-1]['content'] if messages else ""
data = {
"prompt": prompt,
"model": model
}
async def process_response(response_text: str) -> ImageResponse | None:
json_start = response_text.find('{')
if json_start != -1:
json_data = response_text[json_start:]
try:
response_data = json.loads(json_data)
image_data = response_data.get('images', [])[0]
if image_data.startswith('data:image/'):
return ImageResponse([image_data], "Generated image")
try:
base64.b64decode(image_data)
data_uri = f"data:image/jpeg;base64,{image_data}"
return ImageResponse([data_uri], "Generated image")
except:
print("Invalid base64 data")
return None
except json.JSONDecodeError:
print("Failed to parse JSON.")
else:
print("No JSON data found in the response.")
return None
async with ClientSession(headers=headers) as session:
async with session.post(cls.api_endpoint_image, json=data, proxy=proxy) as response:
response.raise_for_status()
response_text = await response.text()
image_response = await process_response(response_text)
if image_response:
yield image_response
else:
yield "Failed to process image data."
@classmethod
async def create_async(
cls,
model: str,
messages: Messages,
proxy: str = None,
**kwargs
) -> str:
async for response in cls.create_async_generator(model, messages, proxy, **kwargs):
if isinstance(response, ImageResponse):
return response.images[0]
return response
