diff options
| -rw-r--r-- | g4f/Provider/nexra/NexraDalleMini.py | 66 | ||||
| -rw-r--r-- | g4f/Provider/nexra/NexraLLaMA31.py | 91 | ||||
| -rw-r--r-- | g4f/Provider/nexra/__init__.py | 2 |
3 files changed, 0 insertions, 159 deletions
diff --git a/g4f/Provider/nexra/NexraDalleMini.py b/g4f/Provider/nexra/NexraDalleMini.py deleted file mode 100644 index 92dd5343..00000000 --- a/g4f/Provider/nexra/NexraDalleMini.py +++ /dev/null @@ -1,66 +0,0 @@ -from __future__ import annotations - -from aiohttp import ClientSession -import json - -from ...typing import AsyncResult, Messages -from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin -from ...image import ImageResponse - - -class NexraDalleMini(AsyncGeneratorProvider, ProviderModelMixin): - label = "Nexra DALL-E Mini" - url = "https://nexra.aryahcr.cc/documentation/dall-e/en" - api_endpoint = "https://nexra.aryahcr.cc/api/image/complements" - working = False - - default_model = 'dalle-mini' - models = [default_model] - - @classmethod - def get_model(cls, model: str) -> str: - return cls.default_model - - @classmethod - async def create_async_generator( - cls, - model: str, - messages: Messages, - proxy: str = None, - response: str = "url", # base64 or url - **kwargs - ) -> AsyncResult: - # Retrieve the correct model to use - model = cls.get_model(model) - - # Format the prompt from the messages - prompt = messages[0]['content'] - - headers = { - "Content-Type": "application/json" - } - payload = { - "prompt": prompt, - "model": model, - "response": response - } - - async with ClientSession(headers=headers) as session: - async with session.post(cls.api_endpoint, json=payload, proxy=proxy) as response: - response.raise_for_status() - text_data = await response.text() - - try: - # Parse the JSON response - json_start = text_data.find('{') - json_data = text_data[json_start:] - data = json.loads(json_data) - - # Check if the response contains images - if 'images' in data and len(data['images']) > 0: - image_url = data['images'][0] - yield ImageResponse(image_url, prompt) - else: - yield ImageResponse("No images found in the response.", prompt) - except json.JSONDecodeError: - yield ImageResponse("Failed to parse JSON. Response might not be in JSON format.", prompt) diff --git a/g4f/Provider/nexra/NexraLLaMA31.py b/g4f/Provider/nexra/NexraLLaMA31.py deleted file mode 100644 index 53c30720..00000000 --- a/g4f/Provider/nexra/NexraLLaMA31.py +++ /dev/null @@ -1,91 +0,0 @@ -from __future__ import annotations - -from aiohttp import ClientSession -import json - -from ...typing import AsyncResult, Messages -from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin -from ..helper import format_prompt - - -class NexraLLaMA31(AsyncGeneratorProvider, ProviderModelMixin): - label = "Nexra LLaMA 3.1" - url = "https://nexra.aryahcr.cc/documentation/llama-3.1/en" - api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements" - working = False - supports_stream = True - - default_model = 'llama-3.1' - models = [default_model] - model_aliases = { - "llama-3.1-8b": "llama-3.1", - } - - @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.get(model, cls.default_model) - else: - return cls.default_model - - @classmethod - async def create_async_generator( - cls, - model: str, - messages: Messages, - proxy: str = None, - stream: bool = False, - markdown: bool = False, - **kwargs - ) -> AsyncResult: - model = cls.get_model(model) - - headers = { - "Content-Type": "application/json" - } - - async with ClientSession(headers=headers) as session: - prompt = format_prompt(messages) - data = { - "messages": [ - { - "role": "user", - "content": prompt - } - ], - "stream": stream, - "markdown": markdown, - "model": model - } - - async with session.post(f"{cls.api_endpoint}", json=data, proxy=proxy) as response: - response.raise_for_status() - - if stream: - # Streamed response handling - collected_message = "" - async for chunk in response.content.iter_any(): - if chunk: - decoded_chunk = chunk.decode().strip().split("\x1e") - for part in decoded_chunk: - if part: - message_data = json.loads(part) - - # Collect messages until 'finish': true - if 'message' in message_data and message_data['message']: - collected_message = message_data['message'] - - # When finish is true, yield the final collected message - if message_data.get('finish', False): - yield collected_message - return - else: - # Non-streamed response handling - response_data = await response.json(content_type=None) - - # Yield the message directly from the response - if 'message' in response_data and response_data['message']: - yield response_data['message'] - return diff --git a/g4f/Provider/nexra/__init__.py b/g4f/Provider/nexra/__init__.py index c2e6b2f6..32b159d1 100644 --- a/g4f/Provider/nexra/__init__.py +++ b/g4f/Provider/nexra/__init__.py @@ -6,11 +6,9 @@ from .NexraChatGptV2 import NexraChatGptV2 from .NexraChatGptWeb import NexraChatGptWeb from .NexraDallE import NexraDallE from .NexraDallE2 import NexraDallE2 -from .NexraDalleMini import NexraDalleMini from .NexraEmi import NexraEmi from .NexraFluxPro import NexraFluxPro from .NexraGeminiPro import NexraGeminiPro -from .NexraLLaMA31 import NexraLLaMA31 from .NexraMidjourney import NexraMidjourney from .NexraProdiaAI import NexraProdiaAI from .NexraQwen import NexraQwen |