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-rw-r--r--g4f/Provider/Nexra.py181
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diff --git a/g4f/Provider/Nexra.py b/g4f/Provider/Nexra.py
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+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